Home / Journals / CMC / Vol.74, No.1, 2023
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  • Open AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 AccessOpen 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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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    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 >

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