Home / Journals / CMC / Vol.74, No.1, 2023
Table of Content
  • Open Access

    ARTICLE

    Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

    Mohamed A. Meselhi*, Saber M. Elsayed, Daryl L. Essam, Ruhul A. Sarker
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.027448
    Abstract Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers.… More >

  • Open Access

    ARTICLE

    Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2,3, Miguel Rio1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 19-36, 2023, DOI:10.32604/cmc.2023.032499
    Abstract The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to… More >

  • Open Access

    ARTICLE

    Privacy Data Management Mechanism Based on Blockchain and Federated Learning

    Mingsen Mo1, Shan Ji2, Xiaowan Wang3,*, Ghulam Mohiuddin4, Yongjun Ren1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 37-53, 2023, DOI:10.32604/cmc.2023.028843
    Abstract Due to the extensive use of various intelligent terminals and the popularity of network social tools, a large amount of data in the field of medical emerged. How to manage these massive data safely and reliably has become an important challenge for the medical network community. This paper proposes a data management framework of medical network community based on Consortium Blockchain (CB) and Federated learning (FL), which realizes the data security sharing between medical institutions and research institutions. Under this framework, the data security sharing mechanism of medical network community based on smart contract and the data privacy protection mechanism… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Composite Electrolyte with GQD for All-Solid-State Lithium Batteries

    Sung Won Hwang, Dae-Ki Hong*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 55-66, 2023, DOI:10.32604/cmc.2023.028845
    Abstract The use a stabilized lithium structure as cathode material for batteries could be a fundamental alternative in the development of next-generation energy storage devices. However, the lithium structure severely limits battery life causes safety concerns due to the growth of lithium (Li) dendrites during rapid charge/discharge cycles. Solid electrolytes, which are used in high-density energy storage devices and avoid the instability of liquid electrolytes, can be a promising alternative for next-generation batteries. Nevertheless, poor lithium ion conductivity and structural defects at room temperature have been pointed out as limitations. In this study, through the application of a low-dimensional graphene quantum… More >

  • Open Access

    ARTICLE

    Application of Time Serial Model in Water Quality Predicting

    Jiang Wu1, Jianjun Zhang1, Wenwu Tan1, Hao Lan1,*, Sirao Zhang1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 67-82, 2023, DOI:10.32604/cmc.2023.030703
    Abstract Water resources are an indispensable and valuable resource for human survival and development. Water quality predicting plays an important role in the protection and development of water resources. It is difficult to predict water quality due to its random and trend changes. Therefore, a method of predicting water quality which combines Auto Regressive Integrated Moving Average (ARIMA) and clustering model was proposed in this paper. By taking the water quality monitoring data of a certain river basin as a sample, the water quality Total Phosphorus (TP) index was selected as the prediction object. Firstly, the sample data was cleaned, stationary… More >

  • Open Access

    ARTICLE

    Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

    Chih-Ta Yen1,*, Tz-Yun Chen2, Un-Hung Chen3, Guo-Chang Wang3, Zong-Xian Chen3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 83-99, 2023, DOI:10.32604/cmc.2023.032739
    Abstract A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their… More >

  • Open Access

    ARTICLE

    RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

    Ziquan Zhu1, Muhammad Attique Khan2, Shui-Hua Wang1, Yu-Dong Zhang1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 101-111, 2023, DOI:10.32604/cmc.2023.030790
    Abstract Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN.… More >

  • Open Access

    ARTICLE

    Estimating Construction Material Indices with ARIMA and Optimized NARNETs

    Ümit Işıkdağ1, Aycan Hepsağ2, Süreyya İmre Bıyıklı3, Derya Öz4, Gebrail Bekdaş5, Zong Woo Geem6,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 113-129, 2023, DOI:10.32604/cmc.2023.032502
    Abstract Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to… More >

  • Open Access

    ARTICLE

    Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm

    Xianhui Gu1,*, Xiaokan Wang1, Shuang Liang2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 131-139, 2023, DOI:10.32604/cmc.2023.028756
    Abstract In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality… More >

  • Open Access

    ARTICLE

    Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

    Nidhi Agarwal1,2, M. Gokilavani3, S. Nagarajan4, S. Saranya5, Hadeel Alsolai6, Sami Dhahbi7,*, Amira Sayed Abdelaziz8
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 141-152, 2023, DOI:10.32604/cmc.2023.030074
    Abstract In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to… More >

  • Open Access

    ARTICLE

    Impact of Portable Executable Header Features on Malware Detection Accuracy

    Hasan H. Al-Khshali1,*, Muhammad Ilyas2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 153-178, 2023, DOI:10.32604/cmc.2023.032182
    Abstract One aspect of cybersecurity, incorporates the study of Portable Executables (PE) files maleficence. Artificial Intelligence (AI) can be employed in such studies, since AI has the ability to discriminate benign from malicious files. In this study, an exclusive set of 29 features was collected from trusted implementations, this set was used as a baseline to analyze the presented work in this research. A Decision Tree (DT) and Neural Network Multi-Layer Perceptron (NN-MLPC) algorithms were utilized during this work. Both algorithms were chosen after testing a few diverse procedures. This work implements a method of subgrouping features to answer questions such… More >

  • Open Access

    ARTICLE

    Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

    Hadi Givi1, Marie Hubalovska2,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 179-202, 2023, DOI:10.32604/cmc.2023.030379
    Abstract Metaheuristic algorithms are widely used in solving optimization problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal… More >

  • Open Access

    ARTICLE

    Image-Based Automatic Energy Meter Reading Using Deep Learning

    Muhammad Imran1,*, Hafeez Anwar2, Muhammad Tufail1, Abdullah Khan1, Murad Khan3, Dzati Athiar Ramli4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 203-216, 2023, DOI:10.32604/cmc.2023.029834
    Abstract We propose to perform an image-based framework for electrical energy meter reading. Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units. Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup. However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and… More >

  • Open Access

    ARTICLE

    Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

    Mazen Mushabab Alqahtani1, Ashit Kumar Dutta2, Sultan Almotairi3, M. Ilayaraja4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6,7,*, Mesfer Al Duhayyim8
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280
    Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

  • Open Access

    ARTICLE

    Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

    Binhui Tang1,3, Junfeng Wang2,*, Huanran Qiu3, Jian Yu2, Zhongkun Yu2, Shijia Liu2,4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 235-252, 2023, DOI:10.32604/cmc.2023.029135
    Abstract The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors’ profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this… More >

  • Open Access

    ARTICLE

    Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

    Salaheddine Kably1,2,*, Tajeddine Benbarrad1, Nabih Alaoui2, Mounir Arioua1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 253-278, 2023, DOI:10.32604/cmc.2023.032220
    Abstract Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion… More >

  • Open Access

    ARTICLE

    A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

    Iftikhar Ahmad1,*, Ambreen Shahnaz2, Muhammad Asfand-e-Yar3, Wajeeha Khalil1, Yasmin Bano1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 279-291, 2023, DOI:10.32604/cmc.2023.031344
    Abstract The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be… More >

  • Open Access

    ARTICLE

    Fast Verification of Network Configuration Updates

    Jiangyuan Yao1, Zheng Jiang2, Kaiwen Zou2, Shuhua Weng1, Yaxin Li3, Deshun Li1, Yahui Li4,*, Xingcan Cao5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 293-311, 2023, DOI:10.32604/cmc.2023.031282
    Abstract With the expansion of network services, large-scale networks have progressively become common. The network status changes rapidly in response to customer needs and configuration changes, so network configuration changes are also very frequent. However, no matter what changes, the network must ensure the correct conditions, such as isolating tenants from each other or guaranteeing essential services. Once changes occur, it is necessary to verify the after-changed network. Whereas, for the verification of large-scale network configuration changes, many current verifiers show poor efficiency. In order to solve the problem of multiple global verifications caused by frequent updates of local configurations in… More >

  • Open Access

    ARTICLE

    Triple-Band Circularly Polarized Dielectric Resonator Antenna (DRA) for Wireless Applications

    Azuwa Ali1, Mohd Najib Mohd Yasin2,*, Ismahayati Adam2, Ali H. Rambe3, Mohd Haizal Jamaludin4, Hasliza A Rahim2, Corhan Cengiz5, Mohd Ibrahim Shapiai Abd. Razak6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 313-325, 2023, DOI:10.32604/cmc.2023.031706
    Abstract This paper proposes a new dielectric resonator antenna (DRA) design that can generate circularly polarized (CP) triple-band signals. A triple-band CP DRA antenna fed by a probe feed system is achieved with metal strips structure on side of DRA structure. The design start with conventional rectangular DRA with F shaped metal strips on DRA structure alongside the feed. Then, the F metal strip is enhanced by extending the length of the metal strip to obtain wider impedance bandwidth. Further improvement on the antenna performance is observed by improvised the conventional DRA structure. The method of removing part of DRA bottom… More >

  • Open Access

    ARTICLE

    Real and Altered Fingerprint Classification Based on Various Features and Classifiers

    Saif Saad Hameed, Ismail Taha Ahmed*, Omar Munthir Al Okashi
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 327-340, 2023, DOI:10.32604/cmc.2023.031622
    Abstract Biometric recognition refers to the identification of individuals through their unique behavioral features (e.g., fingerprint, face, and iris). We need distinguishing characteristics to identify people, such as fingerprints, which are world-renowned as the most reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints… More >

  • Open Access

    ARTICLE

    Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

    Ankush Kapoor1, Pradeep Kumar2,*, Ranjan Mishra3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 341-361, 2023, DOI:10.32604/cmc.2023.028874
    Abstract Frequency selective surfaces (FSSs) play an important role in wireless systems as these can be used as filters, in isolating the unwanted radiation, in microstrip patch antennas for improving the performance of these antennas and in other 5G applications. The analysis and design of the double concentric ring frequency selective surface (DCRFSS) is presented in this research. In the sub-6 GHz 5G FR1 spectrum, a computational synthesis technique for creating DCRFSS based spatial filters is proposed. The analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial filters.… More >

  • Open Access

    ARTICLE

    Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

    Khalid Hamid1, Muhammad Waseem Iqbal2,*, Muhammad Usman Ashraf3, Ahmed Mohammed Alghamdi4, Adel A. Bahaddad5, Khalid Ali Almarhabi6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 363-378, 2023, DOI:10.32604/cmc.2023.032271
    Abstract Due to a tremendous increase in mobile traffic, mobile operators have started to restructure their networks to offload their traffic. New research directions will lead to fundamental changes in the design of future Fifth-generation (5G) cellular networks. For the formal reason, the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory. Any number that can be uniquely calculated by a graph is known as a graph invariant. During the last two decades, innumerable numerical graph invariants have been portrayed and used… More >

  • Open Access

    ARTICLE

    An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

    Edgar Scavino1,*, Mohd Amiruddin Abd Rahman1, Zahid Farid2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824
    Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received… More >

  • Open Access

    ARTICLE

    Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

    Nora Abdullah Alkhaldi1,*, Hanan T. Halawani2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 399-414, 2023, DOI:10.32604/cmc.2023.030872
    Abstract Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated… More >

  • Open Access

    ARTICLE

    Replication Strategy with Comprehensive Data Center Selection Method in Cloud Environments

    M. A. Fazlina, Rohaya Latip*, Hamidah Ibrahim, Azizol Abdullah
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 415-433, 2023, DOI:10.32604/cmc.2023.020764
    Abstract As the amount of data continues to grow rapidly, the variety of data produced by applications is becoming more affluent than ever. Cloud computing is the best technology evolving today to provide multi-services for the mass and variety of data. The cloud computing features are capable of processing, managing, and storing all sorts of data. Although data is stored in many high-end nodes, either in the same data centers or across many data centers in cloud, performance issues are still inevitable. The cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud environment.… More >

  • Open Access

    ARTICLE

    Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

    Ahyoung Lee1, Jui Mhatre1, Rupak Kumar Das2, Min Hong3,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 435-452, 2023, DOI:10.32604/cmc.2023.029340
    Abstract Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we… More >

  • Open Access

    ARTICLE

    Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

    Mohamed Amine Ouamri1,2, Reem Alkanhel3,*, Cedric Gueguen1, Manal Abdullah Alohali4, Sherif S. M. Ghoneim5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 453-467, 2023, DOI:10.32604/cmc.2023.031450
    Abstract With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed… More >

  • Open Access

    ARTICLE

    Internet of Cultural Things: Current Research, Challenges and Opportunities

    Xiaoting Liang1, Fang Liu1,*, Linqi Wang1, Baoying Zheng1, Yiyuan Sun2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 469-488, 2023, DOI:10.32604/cmc.2023.029641
    Abstract Driven by the visions of the Internet of Things (IoT), Artificial Intelligence (AI), and 5G communications, the Internet of Cultural Things (IoCT) realize the comprehensive interconnection among cultural products, cultural services, cultural resources, and cultural platforms, bringing individuals with richer humanistic experience, increasing economic benefits for the cultural sector, and promoting the development of cultural heritage protection and education. At present, IoCT has received widespread attention in both industry and academia. To explore new research opportunities and assist users in constructing suitable IoCT systems for specific applications, this survey provides a comprehensive overview of the IoCT components and key technologies.… More >

  • Open Access

    ARTICLE

    Big Data Analytics Using Graph Signal Processing

    Farhan Amin1, Omar M. Barukab2, Gyu Sang Choi1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 489-502, 2023, DOI:10.32604/cmc.2023.030615
    Abstract The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal processing (DSP). We then summarize… More >

  • Open Access

    ARTICLE

    Artificial Magnetic Conductor as Planar Antenna for 5G Evolution

    Komsan Kanjanasit1, Pracha Osklang2,*, Terapass Jariyanorawiss3, Akkarat Boonpoonga4, Chuwong Phongcharoenpanich5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 503-522, 2023, DOI:10.32604/cmc.2023.032427
    Abstract A 5G wireless system requests a high-performance compact antenna device. This research work aims to report the characterization and verification of the artificial magnetic conductor (AMC) metamaterial for a high-gain planar antenna. The configuration is formed by a double-side structure on an intrinsic dielectric slab. The 2-D periodic pattern as an impedance surface is mounted on the top surface, whereas at the bottom surface the ground plane with an inductive narrow aperture source is embedded. The characteristic of the resonant transmission is illustrated based on the electromagnetic virtual object of the AMC resonant structure to reveal the unique property of… More >

  • Open Access

    ARTICLE

    A Novel Action Transformer Network for Hybrid Multimodal Sign Language Recognition

    Sameena Javaid*, Safdar Rizvi
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 523-537, 2023, DOI:10.32604/cmc.2023.031924
    Abstract Sign language fills the communication gap for people with hearing and speaking ailments. It includes both visual modalities, manual gestures consisting of movements of hands, and non-manual gestures incorporating body movements including head, facial expressions, eyes, shoulder shrugging, etc. Previously both gestures have been detected; identifying separately may have better accuracy, but much communicational information is lost. A proper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others. Our novel proposed system contributes as Sign Language Action Transformer Network (SLATN), localizing hand, body, and facial gestures in video sequences. Here… More >

  • Open Access

    ARTICLE

    An Algorithm to Reduce Compression Ratio in Multimedia Applications

    Dur-e-Jabeen1,*, Tahmina Khan2, Rumaisa Iftikhar1, Ali Akbar Siddique1, Samiya Asghar1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 539-557, 2023, DOI:10.32604/cmc.2023.032393
    Abstract In recent years, it has been evident that internet is the most effective means of transmitting information in the form of documents, photographs, or videos around the world. The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual quality. During transmission, this massive data necessitates a lot of channel space. In order to overcome this problem, an effective visual compression approach is required to resize this large amount of data. This work is based on lossy image compression and is offered for static color images. The quantization procedure determines… More >

  • Open Access

    ARTICLE

    Stochastic Investigations for the Fractional Vector-Host Diseased Based Saturated Function of Treatment Model

    Thongchai Botmart1, Qusain Hiader2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Wajaree Weera1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 559-573, 2023, DOI:10.32604/cmc.2023.031871
    Abstract The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system (VHDNS) along with the numerical treatment of artificial neural networks (ANNs) techniques supported by Levenberg-Marquardt backpropagation (LMQBP), known as ANNs-LMQBP. This mechanism is physically appropriate, where the number of infected people is increasing along with the limited health services. Furthermore, the biological effects have fading memories and exhibit transition behavior. Initially, the model is developed by considering the two and three categories for the humans and the vector species. The VHDNS is constructed with five classes, susceptible humans , infected humans , recovered… More >

  • Open Access

    ARTICLE

    Analyzing the ZnO and CH3NH3PbI3 as Emitter Layer for Silicon Based Heterojunction Solar Cells

    Jasurbek Gulomov1,*, Oussama Accouche2, Rayimjon Aliev1, Marc AZAB2, Irodakhon Gulomova1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 575-590, 2023, DOI:10.32604/cmc.2023.031289
    Abstract Today, it has become an important task to modify existing traditional silicon-based solar cell factory to produce high-efficiency silicon-based heterojunction solar cells, at a lower cost. Therefore, the aim of this paper is to analyze CH3NH3PbI3 and ZnO materials as an emitter layer for p-type silicon wafer-based heterojunction solar cells. CH3NH3PbI3 and ZnO can be synthesized using the cheap Sol-Gel method and can form n-type semiconductor. We propose to combine these two materials since CH3NH3PbI3 is a great light absorber and ZnO has an optimal complex refractive index which can be used as antireflection material. The photoelectric parameters of n-CH3NH3PbI3/p-Si,… More >

  • Open Access

    ARTICLE

    Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework

    Mushtaq Niazi1, Sagheer Abbas1, Abdel-Hamid Soliman2, Tahir Alyas3, Shazia Asif4, Tauqeer Faiz5,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 591-606, 2023, DOI:10.32604/cmc.2023.032474
    Abstract Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion… More >

  • Open Access

    ARTICLE

    Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

    Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Mohamed K. Nour4, Mashael M. Asiri5, Ali M. Al-Sharafi6, Mahmoud Othman7, Abdelwahed Motwakel2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 607-621, 2023, DOI:10.32604/cmc.2023.031371
    Abstract Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation,… More >

  • Open Access

    ARTICLE

    Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

    Farah Mohammad*, Saad Al-Ahmadi
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552
    Abstract

    There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >

  • Open Access

    ARTICLE

    Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

    Kun Ding1, Lu Xu2, Ming Liu1, Xiaoxiong Zhang1, Liu Liu1, Daojian Zeng2,*, Yuting Liu1,3, Chen Jin4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 641-654, 2023, DOI:10.32604/cmc.2023.031052
    Abstract Event detection (ED) is aimed at detecting event occurrences and categorizing them. This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence. Thus, current approaches require both annotated triggers as well as event types in training data. Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the “most clearly” word from a sentence, particularly in longer sentences. To decrease manual effort, we evaluate event detection without triggers. We propose a novel framework that combines Type-aware Attention and Graph… More >

  • Open Access

    ARTICLE

    Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

    Divya Sharma1,*, Sanjay Jain2, Vivek Maik3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 655-669, 2023, DOI:10.32604/cmc.2023.032207
    Abstract Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only… More >

  • Open Access

    ARTICLE

    Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

    Ahmed Abdulhakim Al-Absi1, Dae-Ki Kang2,*, Mohammed Abdulhakim Al-Absi3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 671-691, 2023, DOI:10.32604/cmc.2023.030262
    Abstract Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then… More >

  • Open Access

    ARTICLE

    BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

    Khlood Shinan1,2, Khalid Alsubhi2, M. Usman Ashraf3,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 693-714, 2023, DOI:10.32604/cmc.2023.031641
    Abstract The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study is to build a botnet… More >

  • Open Access

    ARTICLE

    Wind Power Prediction Based on Machine Learning and Deep Learning Models

    Zahraa Tarek1, Mahmoud Y. Shams2,*, Ahmed M. Elshewey3, El-Sayed M. El-kenawy4,5, Abdelhameed Ibrahim6, Abdelaziz A. Abdelhamid7,8, Mohamed A. El-dosuky1,9
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 715-732, 2023, DOI:10.32604/cmc.2023.032533
    Abstract Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and… More >

  • Open Access

    ARTICLE

    Cancellable Multi-Biometric Feature Veins Template Generation Based on SHA-3 Hashing

    Salwa M. Serag Eldin1,*, Ahmed Sedik2, Sultan S. Alshamrani3, Ahmed M. Ayoup4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 733-749, 2023, DOI:10.32604/cmc.2023.030789
    Abstract In this paper, a novel cancellable biometrics technique called Multi-Biometric-Feature-Hashing (MBFH) is proposed. The MBFH strategy is utilized to actualize a single direction (non-invertibility) biometric shape. MBFH is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies (retina, palm print, Hand Dorsum, fingerprint). A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical sources. This may raise worries about their utilization and security when these spread out designs are subverted as… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

    Fatemeh Sadeghi1, Omid Rostami2, Myung-Kyu Yi3, Seong Oun Hwang3,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 751-768, 2023, DOI:10.32604/cmc.2023.031519
    Abstract Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system… More >

  • Open Access

    ARTICLE

    Analysis of LDPC Code in Hybrid Communication Systems

    Hasnain Kashif1,*, Muhammad Nasir Khan1, Zubair Nawaz2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 769-781, 2023, DOI:10.32604/cmc.2023.032565
    Abstract Free-space optical (FSO) communication is of supreme importance for designing next-generation networks. Over the past decades, the radio frequency (RF) spectrum has been the main topic of interest for wireless technology. The RF spectrum is becoming denser and more employed, making its availability tough for additional channels. Optical communication, exploited for messages or indications in historical times, is now becoming famous and useful in combination with error-correcting codes (ECC) to mitigate the effects of fading caused by atmospheric turbulence. A free-space communication system (FSCS) in which the hybrid technology is based on FSO and RF. FSCS is a capable solution… More >

  • Open Access

    ARTICLE

    Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

    Muhammad Hasnain1, Imran Ghani2, Seung Ryul Jeong3,*, Muhammad Fermi Pasha4, Sardar Usman5, Anjum Abbas6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 783-799, 2023, DOI:10.32604/cmc.2023.030162
    Abstract Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to conduct this empirical analysis a… More >

  • Open Access

    ARTICLE

    Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

    Yongjie Yang1, Shanshan Tu1, Raja Hashim Ali2, Hisham Alasmary3,4, Muhammad Waqas5,6,*, Muhammad Nouman Amjad7
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 801-815, 2023, DOI:10.32604/cmc.2023.031907
    Abstract With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed.… More >

  • Open Access

    ARTICLE

    Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

    Kamal A. ElDahshan, AbdAllah A. AlHabshy, Luay Thamer Mohammed*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 817-839, 2023, DOI:10.32604/cmc.2023.032287
    Abstract This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into a classification process using the… More >

  • Open Access

    ARTICLE

    Tracking and Analysis of Pedestrian’s Behavior in Public Places

    Mahwish Pervaiz1, Mohammad Shorfuzzaman2, Abdulmajeed Alsufyani2, Ahmad Jalal3, Suliman A. Alsuhibany4, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 841-853, 2023, DOI:10.32604/cmc.2023.029629
    Abstract Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded… More >

  • Open Access

    ARTICLE

    Towards Fully Secure 5G Ultra-Low Latency Communications: A Cost-Security Functions Analysis

    Borja Bordel1,*, Ramón Alcarria1, Joaquin Chung2, Rajkumar Kettimuthu2, Tomás Robles1, Iván Armuelles3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 855-880, 2023, DOI:10.32604/cmc.2023.026787
    Abstract Future components to enhance the basic, native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered, or lightweight solutions adapted to ultra-reliable low-latency communications (URLLC) but whose security properties remain under discussion. Although different 5G network slices may have different requirements, in general, both visions seem to fall short at provisioning secure URLLC in the future. In this work we address this challenge, by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks. We categorize those… More >

  • Open Access

    ARTICLE

    Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

    Nitin Sharma1, Mohd Anul Haq2, Pawan Kumar Dahiya3, B. R. Marwah4, Reema Lalit5, Nitin Mittal6, Ismail Keshta7,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 881-895, 2023, DOI:10.32604/cmc.2023.027899
    Abstract Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices… More >

  • Open Access

    ARTICLE

    Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

    Sandeep Kumar1, Muhammad Badruddin Khan2, Mozaherul Hoque Abul Hasanat2, Abdul Khader Jilani Saudagar2,*, Abdullah AlTameem2, Mohammed AlKhathami2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 897-914, 2023, DOI:10.32604/cmc.2023.031867
    Abstract Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed… More >

  • Open Access

    ARTICLE

    Quantum Oblivious Transfer with Reusable Bell State

    Shu-Yu Kuo1, Kuo-Chun Tseng2, Yao-Hsin Chou3, Fan-Hsun Tseng4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 915-932, 2023, DOI:10.32604/cmc.2023.032320
    Abstract In cryptography, oblivious transfer (OT) is an important multi-party cryptographic primitive and protocol, that is suitable for many upper-layer applications, such as secure computation, remote coin-flipping, electrical contract signing and exchanging secrets simultaneously. However, some no-go theorems have been established, indicating that one-out-of-two quantum oblivious transfer (QOT) protocols with unconditional security are impossible. Fortunately, some one-out-of-two QOT protocols using the concept of Crépeau’s reduction have been demonstrated not to conform to Lo’s no-go theorem, but these protocols require more quantum resources to generate classical keys using all-or-nothing QOT to construct one-out-of-two QOT. This paper proposes a novel and efficient one-out-of-two… More >

  • Open Access

    ARTICLE

    Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

    M. V. Rajesh1, T. Archana Acharya2, Hafis Hajiyev3, E. Laxmi Lydia4, Haya Mesfer Alshahrani5, Mohamed K Nour6, Abdullah Mohamed7, Mesfer Al Duhayyim8,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 933-949, 2023, DOI:10.32604/cmc.2023.032549
    Abstract Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures… More >

  • Open Access

    ARTICLE

    A New Framework for Employing Responsive End-Users Using FAHP and PSO Algorithm

    Reza Etemad1, Mohammad Sadegh Ghazizadeh1, Mehrdad Ahmadi Kamarposhti2,*, Ilhami Colak3, Kei Eguchi4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 951-964, 2023, DOI:10.32604/cmc.2023.032631
    Abstract The capacitor bank and synchronous condenser have been the only available sources of reactive power. Nowadays, most of the appliances use a power electronic interface for their connection. Applying a power electronic interface adds many features to these appliances. One of the promising features is their capability to interact with Volt-VAR programs. In this paper was investigated the reactive power interaction of the end-user appliances. For this purpose, the distribution network buses are ranked based on their effectiveness, followed by studying their interaction in the Volt-VAR program. To be able to consider the uncertainties, Probability Density Function (PDF) curve was… More >

  • Open Access

    ARTICLE

    Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

    Hamayun Khan1,*, Irfan Ud din2, Arshad Ali3, Sami Alshmrany3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 965-981, 2023, DOI:10.32604/cmc.2023.031223
    Abstract Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%–15% because excessive on-chip temperature shortens the chip’s life cycle. In this paper, we address the scheduling… More >

  • Open Access

    ARTICLE

    Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

    Chang Zhang1, Ruiwen Ni1, Ye Mu1,2,3,4, Yu Sun1,2,3,4,*, Thobela Louis Tyasi5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 983-994, 2023, DOI:10.32604/cmc.2023.027269
    Abstract In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective. This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network (CNN) model and proposes a recognition model that combines a multi-scale convolution module with… More >

  • Open Access

    ARTICLE

    SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

    Khadem Ullah1,*, Nasru Minallah1, Durre Nayab1, Ishtiaque Ahmed2, Jaroslav Frnda3,4, Jan Nedoma4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 995-1010, 2023, DOI:10.32604/cmc.2023.030531
    Abstract With the ever growth of Internet users, video applications, and massive data traffic across the network, there is a higher need for reliable bandwidth-efficient multimedia communication. Versatile Video Coding (VVC/H.266) is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding (HEVC) while providing versatile effective use for Ultra-High Definition (HD) videos. This article analyzes the quality performance of convolutional codes, turbo codes and self-concatenated convolutional (SCC) codes based on performance metrics for reliable future video communication. The advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Program-Wide Binary Code Similarity for Smart Contracts

    Yuan Zhuang1, Baobao Wang1, Jianguo Sun2,*, Haoyang Liu1, Shuqi Yang1, Qingan Da3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1011-1024, 2023, DOI:10.32604/cmc.2023.028058
    Abstract Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks. There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts. Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis. However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor… More >

  • Open Access

    ARTICLE

    Characteristic of Line-of-Sight in Infrastructure-to-Vehicle Visible Light Communication Using MIMO Technique

    Adisorn Kaewpukdee, Peerapong Uthansakul*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1025-1048, 2023, DOI:10.32604/cmc.2023.032569
    Abstract Visible Light Communication (VLC) technology is aggressive research for the next generation of communication. Currently, Radio Frequency (RF) communication has crowed spectrum. An Intelligent Transportation System (ITS) has been improved in the communication network for Vehicle-to-Vehicle (V2 V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Vehicle (I2 V) by using the visible light spectrum instead of the RF spectrum. This article studies the characterization of Line-of-Sight (LOS) optical performance in an Outdoor Wireless Visible Light Communication (OWVLC) system employing a Multiple-Input Multiple-Output (MIMO) technique for I2 V communications in ITS regulations. We design the new configuration of the OWVLC-I2 V system, which is an alternative approach to communication… More >

  • Open Access

    ARTICLE

    Liver Ailment Prediction Using Random Forest Model

    Fazal Muhammad1,*, Bilal Khan2, Rashid Naseem3, Abdullah A Asiri4, Hassan A Alshamrani4, Khalaf A Alshamrani4, Samar M Alqhtani5, Muhammad Irfan6, Khlood M Mehdar7, Hanan Talal Halawani8
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1049-1067, 2023, DOI:10.32604/cmc.2023.032698
    Abstract Today, liver disease, or any deterioration in one’s ability to survive, is extremely common all around the world. Previous research has indicated that liver disease is more frequent in younger people than in older ones. When the liver’s capability begins to deteriorate, life can be shortened to one or two days, and early prediction of such diseases is difficult. Using several machine learning (ML) approaches, researchers analyzed a variety of models for predicting liver disorders in their early stages. As a result, this research looks at using the Random Forest (RF) classifier to diagnose the liver disease early on. The… More >

  • Open Access

    ARTICLE

    Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

    Rashid Jahangir1,*, Muhammad Asif Nauman2, Roobaea Alroobaea3, Jasem Almotiri3, Muhammad Mohsin Malik1, Sabah M. Alzahrani3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1069-1091, 2023, DOI:10.32604/cmc.2023.032719
    Abstract Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta… More >

  • Open Access

    ARTICLE

    Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

    Rupo Ma1,2,*, Siping Gao3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1093-1105, 2023, DOI:10.32604/cmc.2023.030979
    Abstract Electromagnetic pulse (EMP) is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum, which usually disrupts communications and damages electronic equipment and system. It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum. Therefore, analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results. Waveform fidelity is usually employed to evaluate the distortion of an antenna. However, this metric depends on specific signal waveforms, thus is unsuitable for evaluating and analyzing the distortion of… More >

  • Open Access

    ARTICLE

    Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

    P. Muthukrishnan*, P. Muthu Kannan
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1107-1122, 2023, DOI:10.32604/cmc.2023.031962
    Abstract Recently, vehicular ad hoc networks (VANETs) finds applicability in different domains such as security, rescue operations, intelligent transportation systems (ITS), etc. VANET has unique features like high mobility, limited mobility patterns, adequate topology modifications, and wireless communication. Despite the benefits of VANET, scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques. It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes. The main drawback of VANET network is the network unsteadiness that results in minimum lifetime. In order to avoid reduced network lifetime in VANET, this paper presents an… More >

  • Open Access

    ARTICLE

    Deep Learned Singular Residual Network for Super Resolution Reconstruction

    Gunnam Suryanarayana1,*, D. Bhavana2, P. E. S. N. Krishna Prasad3, M. M. K. Narasimha Reddy1, Md Zia Ur Rahman2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1123-1137, 2023, DOI:10.32604/cmc.2023.031227
    Abstract Single image super resolution (SISR) techniques produce images of high resolution (HR) as output from input images of low resolution (LR). Motivated by the effectiveness of deep learning methods, we provide a framework based on deep learning to achieve super resolution (SR) by utilizing deep singular-residual neural network (DSRNN) in training phase. Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs. Singular value decomposition (SVD) is applied to each LR-residual image pair to decompose into subbands of low and high frequency components. Later, DSRNN is trained on these subbands through input and output… More >

  • Open Access

    ARTICLE

    Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

    Shafaq Abbas1, Muhammad Attique Khan1, Majed Alhaisoni2, Usman Tariq3, Ammar Armghan4, Fayadh Alenezi4, Arnab Majumdar5, Orawit Thinnukool6,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1139-1159, 2023, DOI:10.32604/cmc.2023.028824
    Abstract Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained… More >

  • Open Access

    ARTICLE

    A Systematic Review of Blockchain Technology for Government Information Sharing

    Lu Zhang1, Jiarong Mao1, Yuting An1, Tianshuo Zhang1, Jixin Ma2, Chen Feng3, Xiaoyi Zhou1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1161-1181, 2023, DOI:10.32604/cmc.2023.032452
    Abstract Government information sharing (GIS) refers to that act of required or provided for duty government information, commercial information and public welfare information, and it is a basic issue of government services. However, the existing GIS has low transparency and is lack of flexibility between different departments. Aiming at such problems, this paper takes blockchain as a solusion, and systematically summarizes the development of digital GIS, the advantages and challenges of blockchain and its theoretical research and practical applications. Specifically, it reviews e-government interactive structure, big data and other solutions, analyses their imperfections, and puts forward blockchain-based solutions. The blockchain improves… More >

  • Open Access

    ARTICLE

    Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

    Atif Ishaq Khan*, Syed Asad Raza Kazmi, Awais Qasim
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1183-1197, 2023, DOI:10.32604/cmc.2023.032691
    Abstract A self-adaptive resource provisioning on demand is a critical factor in cloud computing. The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests. Therefore, a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload. In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy (CARSS) Framework that formally addresses these issues and is more expressive than traditional approaches. The decision making in CARSS is based on more than one factors. The MAPE-K based framework determines the state of the… More >

  • Open Access

    ARTICLE

    Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs

    Vinod Kumar Menaria1, Anand Nayyar2, Sandeep Kumar3, Ketan Kotecha4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1199-1216, 2023, DOI:10.32604/cmc.2023.032162
    Abstract In today’s information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters… More >

  • Open Access

    ARTICLE

    Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

    Rajesh Singh1, Anita Gehlot1, Ritika Saxena2, Khalid Alsubhi3, Divya Anand1,*, Irene Delgado Noya4,5, Shaik Vaseem Akram1, Sushabhan Choudhury2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1217-1233, 2023, DOI:10.32604/cmc.2023.023894
    Abstract Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is… More >

  • Open Access

    ARTICLE

    Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

    Javed Rashid1,2, Imran Khan1, Ghulam Ali3, Shafiq ur Rehman4, Fahad Alturise5, Tamim Alkhalifah5,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1235-1257, 2023, DOI:10.32604/cmc.2023.032005
    Abstract The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a… More >

  • Open Access

    ARTICLE

    Speech Enhancement via Mask-Mapping Based Residual Dense Network

    Lin Zhou1,*, Xijin Chen1, Chaoyan Wu1, Qiuyue Zhong1, Xu Cheng2, Yibin Tang3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1259-1277, 2023, DOI:10.32604/cmc.2023.027379
    Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal ratio masking (IRM) matrix of… More >

  • Open Access

    ARTICLE

    A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

    Yu Fan1,6, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Chunyan Shao1, Cheng Gong1, Jieren Cheng3,5, Yenwei Chen4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1279-1302, 2023, DOI:10.32604/cmc.2023.031445
    Abstract Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes… More >

  • Open Access

    ARTICLE

    A Two-Phase Paradigm for Joint Entity-Relation Extraction

    Bin Ji1, Hao Xu1, Jie Yu1, Shasha Li1, Jun Ma1, Yuke Ji2,*, Huijun Liu1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1303-1318, 2023, DOI:10.32604/cmc.2023.032168
    Abstract An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables… More >

  • Open Access

    ARTICLE

    CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

    Syeda Naila Batool, Ghulam Gilanie*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1319-1332, 2023, DOI:10.32604/cmc.2023.032121
    Abstract Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional Neural Network (CNN)-based model named… More >

  • Open Access

    ARTICLE

    A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

    Rehana Ghulam1, Sammar Fatima1, Tariq Ali1, Nazir Ahmad Zafar1, Abdullah A. Asiri2, Hassan A. Alshamrani2,*, Samar M. Alqhtani3, Khlood M. Mehdar4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1333-1349, 2023, DOI:10.32604/cmc.2023.031695
    Abstract Human brain consists of millions of cells to control the overall structure of the human body. When these cells start behaving abnormally, then brain tumors occurred. Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts. To handle this issue, various deep learning techniques for brain tumor detection and segmentation techniques have been developed, which worked on different datasets to obtain fruitful results, but the problem still exists for the initial stage of detection of brain tumors to save human lives. For this purpose, we proposed a novel U-Net-based Convolutional… More >

  • Open Access

    ARTICLE

    Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

    Nasir Sayed1, Muhammad Shoaib2,*, Waqas Ahmed3, Sultan Noman Qasem4, Abdullah M. Albarrak4, Faisal Saeed5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1351-1374, 2023, DOI:10.32604/cmc.2023.030831
    Abstract Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion… More >

  • Open Access

    ARTICLE

    Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

    Amir Yasseen Mahdi1,2,*, Siti Sophiayati Yuhaniz1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1375-1392, 2023, DOI:10.32604/cmc.2023.032671
    Abstract The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text… More >

  • Open Access

    ARTICLE

    A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

    Muhammad Attique Khan1, Majed Alhaisoni2, Muhammad Nazir1, Abdullah Alqahtani3, Adel Binbusayyis3, Shtwai Alsubai3, Yunyoung Nam4, Byeong-Gwon Kang4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1393-1412, 2023, DOI:10.32604/cmc.2023.032064
    Abstract The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 in Wuhan, China. After that, it quickly spread throughout the world and became a disease. It has significantly impacted our everyday lives, the national and international economies, and public health. However, early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system. Clinical radiologists primarily use chest X-rays, and computerized tomography (CT) scans to test for pneumonia infection. We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study. We proposed a joint framework for prediction based on classical… More >

  • Open Access

    ARTICLE

    Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

    Aqsa Aqdus1, Rashid Amin1,*, Sadia Ramzan1, Sultan S. Alshamrani2, Abdullah Alshehri3, El-Sayed M. El-kenawy4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719
    Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

  • Open Access

    ARTICLE

    Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

    S. Prabhakaran1,*, R. Annie Uthra1, J. Preetharoselyn2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1437-1455, 2023, DOI:10.32604/cmc.2023.032300
    Abstract Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the… More >

  • Open Access

    ARTICLE

    A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

    Zahid Ullah1,*, Mona Jamjoom2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752
    Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such… More >

  • Open Access

    ARTICLE

    Constructing Representative Collective Signature Protocols Using The GOST R34.10-1994 Standard

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1475-1491, 2023, DOI:10.32604/cmc.2023.029253
    Abstract The representative collective digital signature, which was suggested by us, is built based on combining the advantages of group digital signature and collective digital signature. This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers. The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis, the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United… More >

  • Open Access

    ARTICLE

    Calf Posture Recognition Using Convolutional Neural Network

    Tan Chen Tung1, Uswah Khairuddin1, Mohd Ibrahim Shapiai1, Norhariani Md Nor2,*, Mark Wen Han Hiew2, Nurul Aisyah Mohd Suhaimie3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1493-1508, 2023, DOI:10.32604/cmc.2023.029277
    Abstract Dairy farm management is crucial to maintain the longevity of the farm, and poor dairy youngstock or calf management could lead to gradually deteriorating calf health, which often causes premature death. This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years. Calf posture recognition is one of the effective methods to monitor calf behaviour and health state, which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf, and the latter, passive calf. Calf posture recognition module is… More >

  • Open Access

    ARTICLE

    The Fusion Model of Catalytic Combustion and Thermal Conductivity

    Bin Lin1, Zhengyu Li2,*, Dong Wen2, Jianchao Liu2, Shan Yang3, Yong Zhou2, Chao Lu4, Qian Qiu4,5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1509-1521, 2023, DOI:10.32604/cmc.2023.032557
    Abstract The further development of catalytic elements has been plagued by activation and binary problems. The automatic shift model that has emerged in recent years helps components achieve full range. However, the detection data still remains unstable in the shift area (7%~13%). This paper proposes a Catalytic Combustion and Thermal Conductivity (CCTC) model for the specified range, which can be explained from two aspects based on the existing methods. On the one hand, it uses iterative location search to process heterogeneous data, judges the prediction position of data points, and then gives weight evaluation. On the other hand, it corrects the… More >

  • Open Access

    ARTICLE

    Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

    Sandeep Kumar1, MohdAnul Haq2, Arpit Jain3, C. Andy Jason4, Nageswara Rao Moparthi1, Nitin Mittal5, Zamil S. Alzamil2,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1523-1540, 2023, DOI:10.32604/cmc.2023.028631
    Abstract Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate… More >

  • Open Access

    ARTICLE

    Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

    Sandeep Kaur*, Bharat Bhushan Sagar
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1541-1559, 2023, DOI:10.32604/cmc.2023.032857
    Abstract Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars, stones, or leaves. Optical recognition systems can help in preserving, sharing, and accelerate the study of the ancient scripts, but lack of standard dataset for such scripts is a major constraint. Although many scholars and researchers have captured and uploaded inscription images on various websites, manual searching, downloading and extraction of these images is tedious and error prone. Web search queries return a vast number of irrelevant results, and manually extracting images for a specific script is not scalable. This paper proposes a… More >

  • Open Access

    ARTICLE

    COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

    Tahir Sher1, Abdul Rehman2, Dongsun Kim2,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1561-1574, 2023, DOI:10.32604/cmc.2023.032020
    Abstract COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five… More >

  • Open Access

    ARTICLE

    Genetic Crossover Operators for the Capacitated Vehicle Routing Problem

    Zakir Hussain Ahmed1,*, Naif Al-Otaibi1, Abdullah Al-Tameem2, Abdul Khader Jilani Saudagar2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1575-1605, 2023, DOI:10.32604/cmc.2023.031325
    Abstract We study the capacitated vehicle routing problem (CVRP) which is a well-known NP-hard combinatorial optimization problem (COP). The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized. Since the problem is very complicated, solving the problem using exact methods is almost impossible. So, one has to go for the heuristic/metaheuristic methods and genetic algorithm (GA) is broadly applied metaheuristic method to obtain near optimal solution to such COPs. So, this paper studies GAs to find solution to the… More >

  • Open Access

    ARTICLE

    Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

    Samia Allaoua Chelloug1,*, Hamid Ashfaq2, Suliman A. Alsuhibany3, Mohammad Shorfuzzaman4, Abdulmajeed Alsufyani4, Ahmad Jalal2, Jeongmin Park5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1607-1624, 2023, DOI:10.32604/cmc.2023.032245
    Abstract In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. The major research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard… More >

  • Open Access

    ARTICLE

    Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

    Mehdi Jamei1, Nadjem Bailek2,*, Kada Bouchouicha3, Muhammed A. Hassan4, Ahmed Elbeltagi5, Alban Kuriqi6, Nadhir Al-Ansar7, Javier Almorox8, El-Sayed M. El-kenawy9,10
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406
    Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR),… More >

  • Open Access

    ARTICLE

    Profiling Astronomical Objects Using Unsupervised Learning Approach

    Theerapat Sangpetch1, Tossapon Boongoen1,*, Natthakan Iam-On2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1641-1655, 2023, DOI:10.32604/cmc.2023.026739
    Abstract Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined… More >

  • Open Access

    ARTICLE

    Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

    Dahyeon Kim, Junho Ahn*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1657-1671, 2023, DOI:10.32604/cmc.2023.029216
    Abstract When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and two-dimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location… More >

  • Open Access

    ARTICLE

    Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

    Sukhendra Singh1, Sur Singh Rawat2, Manoj Gupta3, B. K. Tripathi4, Faisal Alanzi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364
    Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training… More >

  • Open Access

    ARTICLE

    Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

    V. Sridhar1, K.V. Ranga Rao2, Saddam Hussain3,*, Syed Sajid Ullah4, Roobaea Alroobaea5, Maha Abdelhaq6, Raed Alsaqour7
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1693-1708, 2023, DOI:10.32604/cmc.2023.028129
    Abstract Nonorthogonal Multiple Access (NOMA) is incorporated into the wireless network systems to achieve better connectivity, spectral and energy effectiveness, higher data transfer rate, and also obtain the high quality of services (QoS). In order to improve throughput and minimum latency, a Multivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access (MRRWPBA-NOMA) technique is introduced for network communication. In the downlink transmission, each mobile device's resources and their characteristics like energy, bandwidth, and trust are measured. Followed by, the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different… More >

  • Open Access

    ARTICLE

    The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

    Mohammad Amaz Uddin1, Mohammad Salah Uddin Chowdury1, Mayeen Uddin Khandaker2,*, Nissren Tamam3, Abdelmoneim Sulieman4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1709-1722, 2023, DOI:10.32604/cmc.2023.031177
    Abstract Human speech indirectly represents the mental state or emotion of others. The use of Artificial Intelligence (AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech. In this study, we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). About 2800 audio files were extracted from the Toronto emotional speech set (TESS) database for this study. A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as… More >

  • Open Access

    ARTICLE

    Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

    Awny Sayed1,2, Maha Mahrous3, Enas Elgeldawi1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1723-1734, 2023, DOI:10.32604/cmc.2023.032838
    Abstract Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern. A robust data security system should protect the information assets of any organization against cybercriminal activities. The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence. But when it comes to security, it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security. Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard (AES) algorithm… More >

  • Open Access

    ARTICLE

    Fractional Order Environmental and Economic Model Investigations Using Artificial Neural Network

    Wajaree Weera1, Chantapish Zamart1, Zulqurnain Sabir2,3, Muhammad Asif Zahoor Raja4, Afaf S. Alwabli5, S. R. Mahmoud6, Supreecha Wongaree7, Thongchai Botmart1,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1735-1748, 2023, DOI:10.32604/cmc.2023.032950
    Abstract The motive of these investigations is to provide the importance and significance of the fractional order (FO) derivatives in the nonlinear environmental and economic (NEE) model, i.e., FO-NEE model. The dynamics of the NEE model achieves more precise by using the form of the FO derivative. The investigations through the non-integer and nonlinear mathematical form to define the FO-NEE model are also provided in this study. The composition of the FO-NEE model is classified into three classes, execution cost of control, system competence of industrial elements and a new diagnostics technical exclusion cost. The mathematical FO-NEE system is numerically studied… More >

  • Open Access

    ARTICLE

    GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control Systems

    Hyo-Seok Kim1, Chang-Gyoon Lim2, Sang-Joon Lee3, Yong-Min Kim4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1749-1763, 2023, DOI:10.32604/cmc.2023.026708
    Abstract Recently, Industrial Control Systems (ICSs) have been changing from a closed environment to an open environment because of the expansion of digital transformation, smart factories, and Industrial Internet of Things (IIoT). Since security accidents that occur in ICSs can cause national confusion and human casualties, research on detecting abnormalities by using normal operation data learning is being actively conducted. The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results. In this paper, we propose a GRU-based Buzzer Ensemble for Abnormal Detection (GBE-AD) model for detecting anomalies in industrial control systems to ensure rapid response… More >

  • Open Access

    ARTICLE

    Multi-Band Metamaterial Antenna for Terahertz Applications

    Adel Y. I. Ashyap1, M. Inam2, M. R. Kamarudin1, M. H. Dahri3, Z. A. Shamsan4,*, K. Almuhanna4, F. Alorifi4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1765-1782, 2023, DOI:10.32604/cmc.2023.030618
    Abstract A multi-band metamaterial antenna is proposed to operate at the terahertz (THz) band for medical applications. The proposed structure is designed on a polyimide as a support layer, and its radiating elements are made of graphene. Initially, the design is started with a conventional shape showing a single operating frequency at 1.1 THz. To achieve a multi-band operating frequency, the conventional shape was replaced with the proposed metamaterial as a radiating patch that has properties not exist in nature. The multi-band frequencies are obtained without compromising the overall size of the design. The overall size is 600 × 600 × 25 μm3. The operating… More >

  • Open Access

    ARTICLE

    Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

    Poonam*, Suman Sangwan
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1783-1799, 2023, DOI:10.32604/cmc.2023.031729
    Abstract This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification… More >

  • Open Access

    ARTICLE

    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710
    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open Access

    ARTICLE

    Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

    Zakarya Farea Shaaf1,*, Muhammad Mahadi Abdul Jamil1, Radzi Ambar1, Ahmed Abdu Alattab2,3, Anwar Ali Yahya3,4, Yousef Asiri4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1819-1835, 2023, DOI:10.32604/cmc.2023.031900
    Abstract The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from… More >

  • Open Access

    ARTICLE

    A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

    Nagwa Elaraby*, Sherif Barakat, Amira Rezk
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288
    Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly… More >

  • Open Access

    ARTICLE

    Optimization of the Placement and Size of Photovoltaic Source

    Maawiya Ould Sidi1,*, Mustafa Mosbah2, Rabie Zine3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1855-1870, 2023, DOI:10.32604/cmc.2023.030032
    Abstract This paper presents a new optimization study of the placement and size of a photovoltaic source (PVS) in a distribution grid, based on annual records of meteorological parameters (irradiance, temperature). Based on the recorded data, the production output as well as the daily average power (24-h vector) of the PVS is extracted over the year. When a power vector is available, it can be used as an input when searching for the optimal size of the PVS. This allows to take into account the constraint of the variation of the power generated by this source considering the variation of the… More >

  • Open Access

    ARTICLE

    Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

    Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Saqib Raza6, Ahmad Salman Khan3, Yasir Mahmood3,4, Nazri Kama4, Azri Azmi4, Assad Alzayed5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818
    Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in… More >

  • Open Access

    ARTICLE

    Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

    Lei Hong1, Yiji Qian1,*, Chaofan Gong2, Yurui Zhang1, Xin Zhou3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1887-1903, 2023, DOI:10.32604/cmc.2023.029180
    Abstract The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture, and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand. There are key node users in social networks. Compared with ordinary users, their influence is greater, their radiation range is wider, and their information transmission capabilities are better. The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites. In order to solve the problems of incomplete evaluation factors, poor… More >

  • Open Access

    ARTICLE

    Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

    Reem Alkanhel1, El-Sayed M. El-kenawy2,3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Mostafa Abotaleb7, Doaa Sami Khafaga8,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1905-1921, 2023, DOI:10.32604/cmc.2023.032157
    Abstract In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However, MANET’s lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which… More >

  • Open Access

    ARTICLE

    GrCol-PPFL: User-Based Group Collaborative Federated Learning Privacy Protection Framework

    Jieren Cheng1, Zhenhao Liu1,*, Yiming Shi1, Ping Luo1,2, Victor S. Sheng3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758
    Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of communication. When there are a… More >

  • Open Access

    ARTICLE

    A Survey on Image Semantic Segmentation Using Deep Learning Techniques

    Jieren Cheng1,3, Hua Li2,*, Dengbo Li3, Shuai Hua2, Victor S. Sheng4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757
    Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

  • Open Access

    ARTICLE

    Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

    Mubarak Almutairi*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1959-1983, 2023, DOI:10.32604/cmc.2023.031155
    Abstract The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular features can be classified into… More >

  • Open Access

    ARTICLE

    Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

    Joo Woo1, Jonghyun Kim1, Jong Hwan Park2,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1985-2003, 2023, DOI:10.32604/cmc.2023.033102
    Abstract A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time to increase significantly. In 2016,… More >

  • Open Access

    ARTICLE

    Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

    Swaleha Zubair1, Anjani Kumar Singha1, Nitish Pathak2, Neelam Sharma3, Shabana Urooj4,*, Samia Rabeh Larguech4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2005-2019, 2023, DOI:10.32604/cmc.2023.031481
    Abstract Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural networks at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period. These techniques are monotonically… More >

  • Open Access

    ARTICLE

    Hybrid Global Optimization Algorithm for Feature Selection

    Ahmad Taher Azar1,2,*, Zafar Iqbal Khan2, Syed Umar Amin2, Khaled M. Fouad1,3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2021-2037, 2023, DOI:10.32604/cmc.2023.032183
    Abstract This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA),… More >

  • Open Access

    ARTICLE

    Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

    Iftikhar Naseer1,*, Tehreem Masood1, Sheeraz Akram1, Arfan Jaffar1, Muhammad Rashid2, Muhammad Amjad Iqbal3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2039-2054, 2023, DOI:10.32604/cmc.2023.032927
    Abstract Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT… More >

  • Open Access

    ARTICLE

    Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

    Xiaojuan Wang1, Zikui Lu1,*, Siyuan Sun2, Jingyue Wang1, Luona Song3, Merveille Nicolas4
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750
    Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain… More >

  • Open Access

    ARTICLE

    Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

    Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566
    Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >

  • Open Access

    ARTICLE

    An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

    Hamayun Khan1,*, Irfan Ud Din2, Arshad Ali3, Mohammad Husain3
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999
    Abstract Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper… More >

  • Open Access

    ARTICLE

    Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

    Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2115-2131, 2023, DOI:10.32604/cmc.2023.032993
    Abstract Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying… More >

  • Open Access

    ARTICLE

    Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

    Ahmed Y. Hamed1, M. Kh. Elnahary1,*, Faisal S. Alsubaei2, Hamdy H. El-Sayed1
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2133-2148, 2023, DOI:10.32604/cmc.2023.032215
    Abstract Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing… More >

  • Open Access

    ARTICLE

    Solar Energy Harvesting Using a Timer-Based Relay Selection

    Raed Alhamad1,*, Hatem Boujemaa2
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2149-2159, 2023, DOI:10.32604/cmc.2023.033222
    Abstract In this paper, the throughput and delay of cooperative communications are derived when solar energy is used and relay node is selected using a timer. The source and relays harvest energy from sun using a photo voltaic system. The harvested power is used by the source to transmit data to the relays. Then, a selected relay amplifies the signal to the destination. Opportunistic, partial and reactive relay selection are used. The relay transmits when its timer elapses. The timer is set to a value proportional to the inverse of its Signal to Noise Ratio (SNR). Therefore, the relay with largest… More >

  • Open Access

    ARTICLE

    Power Prediction of VLSI Circuits Using Machine Learning

    E. Poovannan*, S. Karthik
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2161-2177, 2023, DOI:10.32604/cmc.2023.032512
    Abstract The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective… More >

  • Open Access

    ARTICLE

    Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

    R. Poonguzhali1, Sultan Ahmad2, P. Thiruvannamalai Sivasankar3, S. Anantha Babu3, Pranav Joshi4, Gyanendra Prasad Joshi5, Sung Won Kim6,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816
    Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

  • Open Access

    ARTICLE

    Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

    S. Rama Sree1, Inderjeet Kaur2, Alexey Tikhonov3, E. Laxmi Lydia4, Ahmed A. Thabit5, Zahraa H. Kareem6, Yousif Kerrar Yousif7, Ahmed Alkhayyat8,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2195-2209, 2023, DOI:10.32604/cmc.2023.032586
    Abstract Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and… More >

  • Open Access

    ARTICLE

    Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

    Farrukh Shahzad Ahmed, Awais Majeed, Tamim Ahmed Khan*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2211-2238, 2023, DOI:10.32604/cmc.2023.032664
    Abstract Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP… More >

  • Open Access

    ARTICLE

    Generation of OAM Waves and Analysis of Mode Purity for 5G Sub-6 GHz Applications

    Shehab Khan Noor1, Arif Mawardi Ismail1, Mohd Najib Mohd Yasin1,*, Mohamed Nasrun Osman1, Nurulazlina Ramli2, Ali H. Rambe3, J. Iqbal4,5
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2239-2259, 2023, DOI:10.32604/cmc.2023.031170
    Abstract This article presents the generation of Orbital Angular Momentum (OAM) vortex waves with mode 1 using Uniform Circular Array (UCA) antenna. Two different designs, namely, UCA-1 (4-element array antenna) and UCA-2 (8-element array antenna), were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz (5G mid-band). The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed technique. The elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift value. The generated OAM waves were confirmed by measuring the null in… More >

  • Open Access

    ARTICLE

    Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

    Hanadi AlZaabi1, Khaled Shaalan1, Taher M. Ghazal2,3,*, Muhammad A. Khan4,5, Sagheer Abbas6, Beenu Mago7, Mohsen A. A. Tomh6, Munir Ahmad6
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2261-2278, 2023, DOI:10.32604/cmc.2023.031834
    Abstract Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as… More >

  • Open Access

    ARTICLE

    An Integrated and Comprehensive Fuzzy Multi-Criteria Model for Electronic Wallet Selection

    Phuoc Van Nguyen*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2279-2301, 2023, DOI:10.32604/cmc.2023.030019
    Abstract Electronic wallet (E-wallet), which combines software, hardware, and human interaction, is a modern platform for electronic payment. Financial services managers can create more effective strategies for advancing existing positions by evaluating the success of e-wallet services. The objective of this research is to create an integrated model for assessing E-wallet services using the Fuzzy Analytic Hierarchy Process (FAHP) in conjunction with a technique for extent analysis and the ARAS approach (Additive Ratio Assessment). In this study, E-wallet service providers are ranked and evaluated using the ARAS approach and the FAHP priority weights. In addition, a case study from Vietnam illustrates… More >

  • Open Access

    ARTICLE

    Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

    K. Kalyani1, Velmurugan Subbiah Parvathy2, Hikmat A. M. Abdeljaber3, T. Satyanarayana Murthy4, Srijana Acharya5, Gyanendra Prasad Joshi6, Sung Won Kim7,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2303-2316, 2023, DOI:10.32604/cmc.2023.033162
    Abstract In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning… More >

  • Open Access

    ARTICLE

    Query Optimization Framework for Graph Database in Cloud Dew Environment

    Tahir Alyas1, Ali Alzahrani2, Yazed Alsaawy2, Khalid Alissa3, Qaiser Abbas2, Nadia Tabassum4,*
    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2317-2330, 2023, DOI:10.32604/cmc.2023.032454
    Abstract The query optimizer uses cost-based optimization to create an execution plan with the least cost, which also consumes the least amount of resources. The challenge of query optimization for relational database systems is a combinatorial optimization problem, which renders exhaustive search impossible as query sizes rise. Increases in CPU performance have surpassed main memory, and disk access speeds in recent decades, allowing data compression to be used—strategies for improving database performance systems. For performance enhancement, compression and query optimization are the two most factors. Compression reduces the volume of data, whereas query optimization minimizes execution time. Compressing the database reduces… More >

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