Home / Journals / CMC / Vol.74, No.3, 2023
Special lssues
Table of Content
  • Open AccessOpen Access

    ARTICLE

    Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

    Hameedur Rahman1, Junaid Tariq2,*, M. Ali Masood1, Ahmad F. Subahi3, Osamah Ibrahim Khalaf4, Youseef Alotaibi5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5527-5543, 2023, DOI:10.32604/cmc.2023.033190
    Abstract Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

    Mutiullah Jamil1, Hafeez ul Rehman1, SaleemUllah1, Imran Ashraf2,*, Saqib Ubaid1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5545-5557, 2023, DOI:10.32604/cmc.2023.033449
    Abstract Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular wheat species are identified by… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

    Aliaa El-Gawady1,*, BenBella S. Tawfik1, Mohamed A. Makhlouf1,2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5559-5572, 2023, DOI:10.32604/cmc.2023.034734
    Abstract Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS… More >

  • Open AccessOpen Access

    ARTICLE

    Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

    Jakkrit Suttanuruk1, Sajjakaj Jomnonkwao1,*, Vatanavong Ratanavaraha1, Sarunya Kanjanawattana2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5573-5588, 2023, DOI:10.32604/cmc.2023.033900
    Abstract Thailand has been on the World Health Organization (WHO)’s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Power Control for UAV Based on Trajectory and Game Theory

    Fadhil Mukhlif1,*, Ashraf Osman Ibrahim2, Norafida Ithnin1, Roobaea Alroobaea3, Majed Alsafyani3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5589-5606, 2023, DOI:10.32604/cmc.2023.034323
    Abstract Due to the fact that network space is becoming more limited, the implementation of ultra-dense networks (UDNs) has the potential to enhance not only network coverage but also network throughput. Unmanned Aerial Vehicle (UAV) communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes. A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article. In the IoT system, the UAV is utilised not only to determine how the resources should be… More >

  • Open AccessOpen Access

    ARTICLE

    Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

    Abdallah Elhigazi Abdallah1, Mosab Hamdan2, Shukor Abd Razak3, Fuad A. Ghalib3, Muzaffar Hamzah2,*, Suleman Khan4, Siddiq Ahmed Babikir Ali5, Mutaz H. H. Khairi1, Sayeed Salih6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5607-5623, 2023, DOI:10.32604/cmc.2023.031047
    Abstract IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classification-based methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to… More >

  • Open AccessOpen Access

    REVIEW

    A Review of Machine Learning Techniques in Cyberbullying Detection

    Daniyar Sultan1,2,*, Batyrkhan Omarov3, Zhazira Kozhamkulova4, Gulnur Kazbekova5, Laura Alimzhanova1, Aigul Dautbayeva6, Yernar Zholdassov1, Rustam Abdrakhmanov3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5625-5640, 2023, DOI:10.32604/cmc.2023.033682
    Abstract Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents’ lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review of 13 papers from four… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II

    Afia Zafar1, Muhammad Aamir2, Nazri Mohd Nawi1, Ali Arshad3, Saman Riaz3, Abdulrahman Alruban4,*, Ashit Kumar Dutta5, Badr Almutairi6, Sultan Almotairi7,8
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5641-5661, 2023, DOI:10.32604/cmc.2023.033733
    Abstract In computer vision, convolutional neural networks have a wide range of uses. Images represent most of today’s data, so it’s important to know how to handle these large amounts of data efficiently. Convolutional neural networks have been shown to solve image processing problems effectively. However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy. This technique is time consuming and requires a lot of work and domain knowledge. Designing a convolutional neural network architecture is a classic NP-hard optimization challenge. On the other hand, different datasets require different combinations of models… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling of Computer Virus Propagation with Fuzzy Parameters

    Reemah M. Alhebshi1, Nauman Ahmed2, Dumitru Baleanu3,4,5, Umbreen Fatima6,*, Fazal Dayan7, Muhammad Rafiq8,9, Ali Raza10, Muhammad Ozair Ahmad2, Emad E. Mahmoud11
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5663-5678, 2023, DOI:10.32604/cmc.2023.033319
    Abstract Typically, a computer has infectivity as soon as it is infected. It is a reality that no antivirus programming can identify and eliminate all kinds of viruses, suggesting that infections would persevere on the Internet. To understand the dynamics of the virus propagation in a better way, a computer virus spread model with fuzzy parameters is presented in this work. It is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity, which depends on the quantity of virus. Considering this, the parameters and being… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling CO2 Emission in Residential Sector of Three Countries in Southeast of Asia by Applying Intelligent Techniques

    Mohsen Sharifpur1,2, Mohamed Salem3, Yonis M Buswig4, Habib Forootan Fard5, Jaroon Rungamornrat6,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5679-5690, 2023, DOI:10.32604/cmc.2023.034726
    Abstract Residential sector is one of the energy-consuming districts of countries that causes CO2 emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO2 and other greenhouse gases. In the present work, CO2 emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed,… More >

  • Open AccessOpen Access

    ARTICLE

    Severity Based Light-Weight Encryption Model for Secure Medical Information System

    Firas Abedi1, Subhi R.M. Zeebaree2, Zainab Salih Ageed3, Hayder M.A. Ghanimi4, Ahmed Alkhayyat5,*, Mohammed A.M. Sadeeq6, Sarmad Nozad Mahmood7, Ali S. Abosinnee8, Zahraa H. Kareem9, Ali Hashim Abbas10, Waleed Khaild Al-Azzawi11, Mustafa Musa Jaber12,13, Mohammed Dauwed14
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5691-5704, 2023, DOI:10.32604/cmc.2023.034435
    Abstract As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren’t needed can… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194
    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

    Mrim M. Alnfiai*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5723-5740, 2023, DOI:10.32604/cmc.2023.033408
    Abstract In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal… More >

  • Open AccessOpen Access

    ARTICLE

    A More Efficient Approach for Remote Sensing Image Classification

    Huaxiang Song*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5741-5756, 2023, DOI:10.32604/cmc.2023.034921
    Abstract Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC) of remote sensing images (RSI). However, the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms, e.g., automation and simplicity, partially lost. Traditional ML strategies (e.g., the handcrafted features or indicators) and accuracy-aimed strategies with a high trade-off (e.g., the multi-stage CNNs and ensemble of multi-CNNs) are widely used without any training efficiency optimization involved, which may result in suboptimal performance.… More >

  • Open AccessOpen Access

    ARTICLE

    Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

    Nebras M. Sobahi1,*, Ahteshamul Haque2, V S Bharath Kurukuru2, Md. Mottahir Alam1, Asif Irshad Khan3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5757-5776, 2023, DOI:10.32604/cmc.2022.028340
    Abstract Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system.… More >

  • Open AccessOpen Access

    ARTICLE

    Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

    Mahdi Sahlabadi, Ravie Chandren Muniyandi, Zarina Shukur, Faizan Qamar*, Syed Hussain Ali Kazmi
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5777-5797, 2023, DOI:10.32604/cmc.2023.032504
    Abstract This research recognizes the limitation and challenges of adapting and applying Process Mining as a powerful tool and technique in the Hypothetical Software Architecture (SA) Evaluation Framework with the features and factors of lightweightness. Process mining deals with the large-scale complexity of security and performance analysis, which are the goals of SA evaluation frameworks. As a result of these conjectures, all Process Mining researches in the realm of SA are thoroughly reviewed, and nine challenges for Process Mining Adaption are recognized. Process mining is embedded in the framework and to boost the quality of the SA model for further analysis,… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

    Noureen Talpur1,*, Said Jadid Abdulkadir1, Mohd Hilmi Hasan1, Hitham Alhussian1, Ayed Alwadain2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5799-5820, 2023, DOI:10.32604/cmc.2023.034025
    Abstract Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. Since medical science is a sensitive field, the pre-processing of medical data requires careful handling to make quality clinical decisions. Generally, medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features. These factors indirectly upset the disease prediction and classification accuracy of any ML model. To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. However, the majority of such techniques frequently suffer from local minima issues… More >

  • Open AccessOpen Access

    ARTICLE

    An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

    Ala Saleh Alluhaidan1,*, Pushparaj2, Anitha Subbappa3, Ved Prakash Mishra4, P. V. Chandrika5, Anurika Vaish6, Sarthak Sengupta6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5821-5836, 2023, DOI:10.32604/cmc.2023.033995
    Abstract ESystems based on EHRs (Electronic health records) have been in use for many years and their amplified realizations have been felt recently. They still have been pioneering collections of massive volumes of health data. Duplicate detections involve discovering records referring to the same practical components, indicating tasks, which are generally dependent on several input parameters that experts yield. Record linkage specifies the issue of finding identical records across various data sources. The similarity existing between two records is characterized based on domain-based similarity functions over different features. De-duplication of one dataset or the linkage of multiple data sets has become… More >

  • Open AccessOpen Access

    ARTICLE

    Predicting and Curing Depression Using Long Short Term Memory and Global Vector

    Ayan Kumar1, Abdul Quadir Md1, J. Christy Jackson1,*, Celestine Iwendi2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.033431
    Abstract In today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which can share data with a… More >

  • Open AccessOpen Access

    ARTICLE

    Exploiting Human Pose and Scene Information for Interaction Detection

    Manahil Waheed1, Samia Allaoua Chelloug2,*, Mohammad Shorfuzzaman3, Abdulmajeed Alsufyani3, Ahmad Jalal1, Khaled Alnowaiser4, Jeongmin Park5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5853-5870, 2023, DOI:10.32604/cmc.2023.033769
    Abstract Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include critically examining the importance of… More >

  • Open AccessOpen Access

    ARTICLE

    Feature-Limited Prediction on the UCI Heart Disease Dataset

    Khadijah Mohammad Alfadli, Alaa Omran Almagrabi*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5871-5883, 2023, DOI:10.32604/cmc.2023.033603
    Abstract Heart diseases are the undisputed leading causes of death globally. Unfortunately, the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart issues. Several potentially indicative factors exist, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, etc. Manually analyzing these health signals’ interactions is challenging and requires years of medical training and experience. Therefore, this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. More specifically, this paper builds a hybrid model as a tool for data… More >

  • Open AccessOpen Access

    ARTICLE

    High-Bandwidth, Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

    Ameen Bin Obadi1, Alaa El-Din Hussein2, Samir Salem Al-Bawri3,4,*, Kabir Hossain5, Abdullah Abdulhameed4, Muzammil Jusoh1,6,7, Thennarasan Sabapathy1,6, Ahmed Jamal Abdullah Al-Gburi8, Mahmoud A. Albreem9
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5885-5900, 2023, DOI:10.32604/cmc.2023.033789
    Abstract This article presents an integrated current mode configurable analog block (CAB) system for field-programmable analog array (FPAA). The proposed architecture is based on the complementary metal-oxide semiconductor (CMOS) transistor level design where MOSFET transistors operating in the saturation region are adopted. The proposed CAB architecture is designed to implement six of the widely used current mode operations in analog processing systems: addition, subtraction, integration, multiplication, division, and pass operation. The functionality of the proposed CAB is demonstrated through these six operations, where each operation is chosen based on the user’s selection in the CAB interface system. The architecture of the… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

    R. Brindha1, S. Nandagopal2, H. Azath3, V. Sathana4, Gyanendra Prasad Joshi5, Sung Won Kim6,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5901-5914, 2023, DOI:10.32604/cmc.2023.030784
    Abstract Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for proficient classification of phishing emails,… More >

  • Open AccessOpen Access

    ARTICLE

    A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III

    Naret Ruttanaprommarin1, Zulqurnain Sabir2,3, Rafaél Artidoro Sandoval Núñez4, Emad Az-Zo’bi5, Wajaree Weera6, Thongchai Botmart6,*, Chantapish Zamart6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5915-5930, 2023, DOI:10.32604/cmc.2023.034362
    Abstract The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based… More >

  • Open AccessOpen Access

    ARTICLE

    Two-Stream Deep Learning Architecture-Based Human Action Recognition

    Faheem Shehzad1, Muhammad Attique Khan2, Muhammad Asfand E. Yar3, Muhammad Sharif1, Majed Alhaisoni4, Usman Tariq5, Arnab Majumdar6, Orawit Thinnukool7,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5931-5949, 2023, DOI:10.32604/cmc.2023.028743
    Abstract Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work, a deep learning and improved… More >

  • Open AccessOpen Access

    ARTICLE

    A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

    Wang Ning1,*, Siliang Chen2,*, Fu Qiang2, Haitao Tang2, Shen Jie2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5951-5965, 2023, DOI:10.32604/cmc.2023.035558
    Abstract With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that… More >

  • Open AccessOpen Access

    ARTICLE

    CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

    Abishek Mahesh1, Deeptimaan Banerjee1, Ahona Saha1, Manas Ranjan Prusty2,*, A. Balasundaram2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5967-5982, 2023, DOI:10.32604/cmc.2023.033920
    Abstract A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In… More >

  • Open AccessOpen Access

    ARTICLE

    Compact 5G Vivaldi Tapered Slot Filtering Antenna with Enhanced Bandwidth

    Sahar Saleh1,2, Mohd Haizal Jamaluddin1,*, Bader Alali3,4, Ayman A. Althuwayb4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5983-5999, 2023, DOI:10.32604/cmc.2023.035585
    Abstract Compact fifth-generation (5G) low-frequency band filtering antennas (filtennas) with stable directive radiation patterns, improved bandwidth (BW), and gain are designed, fabricated, and tested in this research. The proposed filtennas are achieved by combining the predesigned compact 5G (5.975 – 7.125 GHz) third-order uniform and non-uniform transmission line hairpin bandpass filters (UTL and NTL HPBFs) with the compact ultrawide band Vivaldi tapered slot antenna (UWB VTSA) in one module. The objective of this integration is to enhance the performance of 5.975 – 7.125 GHz filtennas which will be suitable for modern mobile communication applications by exploiting the benefits of UWB VTSA. Based on… More >

  • Open AccessOpen Access

    ARTICLE

    Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

    Idris Rabiu1,3,*, Naomie Salim2, Maged Nasser1,4, Aminu Da’u1, Taiseer Abdalla Elfadil Eisa5, Mhassen Elnour Elneel Dalam6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6001-6017, 2023, DOI:10.32604/cmc.2023.035221
    Abstract Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique,… More >

  • Open AccessOpen Access

    ARTICLE

    IoT Based Smart Framework Monitoring System for Power Station

    Arodh Lal Karn1, Panneer Selvam Manickam2, R. Saravanan3,*, Roobaea Alroobaea4, Jasem Almotiri4, Sudhakar Sengan5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6019-6037, 2023, DOI:10.32604/cmc.2023.032791
    Abstract Power Station (PS) monitoring systems are becoming critical, ensuring electrical safety through early warning, and in the event of a PS fault, the power supply is quickly disconnected. Traditional technologies are based on relays and don’t have a way to capture and store user data when there is a problem. The proposed framework is designed with the goal of providing smart environments for protecting electrical types of equipment. This paper proposes an Internet of Things (IoT)-based Smart Framework (SF) for monitoring the Power Devices (PD) which are being used in power substations. A Real-Time Monitoring (RTM) system is proposed, and… More >

  • Open AccessOpen Access

    ARTICLE

    Gait Image Classification Using Deep Learning Models for Medical Diagnosis

    Pavitra Vasudevan1, R. Faerie Mattins1, S. Srivarshan1, Ashvath Narayanan1, Gayatri Wadhwani1, R. Parvathi1, R. Maheswari2,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6039-6063, 2023, DOI:10.32604/cmc.2023.032331
    Abstract Gait refers to a person’s particular movements and stance while moving around. Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions, they all have common characteristics that help to define normalcy. Swiftly identifying such characteristics that are difficult to spot by the naked eye, can help in monitoring the elderly who require constant care and support. Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait. It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient… More >

  • Open AccessOpen Access

    ARTICLE

    Dataset of Large Gathering Images for Person Identification and Tracking

    Adnan Nadeem1,*, Amir Mehmood2, Kashif Rizwan3, Muhammad Ashraf4, Nauman Qadeer3, Ali Alzahrani1, Qammer H. Abbasi5, Fazal Noor1, Majed Alhaisoni6, Nadeem Mahmood7
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6065-6080, 2023, DOI:10.32604/cmc.2023.035012
    Abstract This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi, Madinah, Saudi Arabia. This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment. The methodology for building the dataset consists of four core phases; that include acquisition of videos, extraction of frames, localization of face regions, and cropping and resizing of detected face regions. The raw images in the dataset consist of a total of 4613 frames obtained from video sequences. The processed images in the dataset consist of the face… More >

  • Open AccessOpen Access

    ARTICLE

    Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

    K. Ishwarya, A. Alice Nithya*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6081-6099, 2023, DOI:10.32604/cmc.2023.034654
    Abstract Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.… More >

  • Open AccessOpen Access

    ARTICLE

    Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

    Mohammad Yamin1,*, Saleh Bajaba2, Zenah Mahmoud AlKubaisy1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.033677
    Abstract Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users’ information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment… More >

  • Open AccessOpen Access

    ARTICLE

    A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

    Chen Wei-wei1, He Wei1,2,*, Zhu Hai-long1, Zhou Guo-hui1, Mu Quan-qi1, Han Peng1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6119-6143, 2023, DOI:10.32604/cmc.2023.035743
    Abstract The prediction of processor performance has important reference significance for future processors. Both the accuracy and rationality of the prediction results are required. The hierarchical belief rule base (HBRB) can initially provide a solution to low prediction accuracy. However, the interpretability of the model and the traceability of the results still warrant further investigation. Therefore, a processor performance prediction method based on interpretable hierarchical belief rule base (HBRB-I) and global sensitivity analysis (GSA) is proposed. The method can yield more reliable prediction results. Evidence reasoning (ER) is firstly used to evaluate the historical data of the processor, followed by a… More >

  • Open AccessOpen Access

    ARTICLE

    Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

    Xiao Ya Ma1,2,*, Jin Tong1,2, Fei Jiang3, Min Xu4, Li Mei Sun1, Qiu Yan Chen1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6145-6159, 2023, DOI:10.32604/cmc.2023.034833
    Abstract Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

    Mousa Alhajlah*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6161-6175, 2023, DOI:10.32604/cmc.2023.032505
    Abstract Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these… More >

  • Open AccessOpen Access

    ARTICLE

    Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

    Moeen Tayyab1, Ayyaz Hussain2,*, Usama Mir3, M. Aqeel Iqbal4, Muhammad Haneef5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6177-6193, 2023, DOI:10.32604/cmc.2023.034669
    Abstract The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneous-shaped classes. This incorporates linguistic taxonomy in… More >

  • Open AccessOpen Access

    ARTICLE

    Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

    T. Gopalakrishnan1, Mohamed Yacin Sikkandar2, Raed Abdullah Alharbi3, P. Selvaraj4, Zahraa H. Kareem5, Ahmed Alkhayyat6,*, Ali Hashim Abbas7
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6195-6212, 2023, DOI:10.32604/cmc.2023.033252
    Abstract The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of… More >

  • Open AccessOpen Access

    ARTICLE

    3D Face Reconstruction from a Single Image Using a Combined PCA-LPP Method

    Jee-Sic Hur1, Hyeong-Geun Lee1, Shinjin Kang2, Yeo Chan Yoon3, Soo Kyun Kim1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6213-6227, 2023, DOI:10.32604/cmc.2023.035344
    Abstract In this paper, we proposed a combined PCA-LPP algorithm to improve 3D face reconstruction performance. Principal component analysis (PCA) is commonly used to compress images and extract features. One disadvantage of PCA is local feature loss. To address this, various studies have proposed combining a PCA-LPP-based algorithm with a locality preserving projection (LPP). However, the existing PCA-LPP method is unsuitable for 3D face reconstruction because it focuses on data classification and clustering. In the existing PCA-LPP, the adjacency graph, which primarily shows the connection relationships between data, is composed of the e-or k-nearest neighbor techniques. By contrast, in this study,… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Gait Analysis Using Deep Learning Techniques

    K. M. Monica, R. Parvathi*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6229-6249, 2023, DOI:10.32604/cmc.2023.032273
    Abstract Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used… More >

  • Open AccessOpen Access

    ARTICLE

    Design of Ka-Band Phased Array Antenna with Calibration Function

    Xiao Liu1,2, Xingyao Zeng1,*, Chengxiang Hao1, Haibo Zhang1, Zhongjun Yu1,2, Ting Lv3, Meng Li4, Zhen Zhang5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6251-6261, 2023, DOI:10.32604/cmc.2023.027114
    Abstract In this paper, we have proposed a novel structure of Ka-band based phased array antenna with calibration function. In the design of Ka-band antenna, the active phased array system is adopted and the antenna would work in the dual polarization separation mode. We have given out the schematic diagram for the proposed Ka-band antenna, where the Ka-band antenna is in the form of waveguide slot array antenna, with 96 units in azimuth and 1 unit in distance. Each group of units is driven by a single-channel Transmitter/Receiver (T/R) component, and the whole array contains 192 T/R components in total. The… More >

  • Open AccessOpen Access

    ARTICLE

    Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

    Arjmand Khaliq1, Syed Abdul Rahman Kashif1, Fahad Ahmad2, Muhammad Anwar3,*, Qaisar Shaheen4, Rizwan Akhtar5, Muhammad Arif Shah5, Abdelzahir Abdelmaboud6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6263-6287, 2023, DOI:10.32604/cmc.2023.033027
    Abstract Vector control schemes have recently been used to drive linear induction motors (LIM) in high-performance applications. This trend promotes the development of precise and efficient control schemes for individual motors. This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation (SVPWM) inverters. The framework under consideration is developed in four stages. To begin, MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamic model. The research presents a modified SVPWM inverter control scheme. By tuning the proportional-integral (PI) controller with a transfer function, optimized values for… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Framework for Virtual Machine Migration in Cloud Computing

    Tahir Alyas1, Taher M. Ghazal2,3, Badria Sulaiman Alfurhood4, Munir Ahmad5, Ossma Ali Thawabeh6, Khalid Alissa7, Qaiser Abbas8,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6289-6305, 2023, DOI:10.32604/cmc.2023.035161
    Abstract In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network… More >

  • Open AccessOpen Access

    ARTICLE

    Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

    Ibrahim M. Almanjahie1,2,*, Omar Fetitah3, Mohammed Kadi Attouch3, Tawfik Benchikh3,4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6307-6319, 2023, DOI:10.32604/cmc.2023.033441
    Abstract Functional statistics is a new technique for dealing with data that can be viewed as curves or images. Parallel to this approach, the Near-Infrared Reflectance (NIR) spectroscopy methodology has been used in modern chemistry as a rapid, low-cost, and exact means of assessing an object’s chemical properties. In this research, we investigate the quality of corn and cookie dough by analyzing the spectroscopic technique using certain cutting-edge statistical models. By analyzing spectral data and applying functional models to it, we could predict the chemical components of corn and cookie dough. Kernel Functional Classical Estimation (KFCE), Kernel Functional Quantile Estimation (KFQE),… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimized Offloaded Task Execution for Smart Cities Applications

    Ahmad Naseem Alvi1, Muhammad Awais Javed1,*, Mozaherul Hoque Abul Hasanat2, Muhammad Badruddin Khan2, Abdul Khader Jilani Saudagar2, Mohammed Alkhathami2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6321-6334, 2023, DOI:10.32604/cmc.2023.029913
    Abstract Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes… More >

  • Open AccessOpen Access

    ARTICLE

    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860
    Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results.… More >

  • Open AccessOpen Access

    ARTICLE

    Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

    Gengyu Ge1,2,*, Zhong Qin1, Xin Chen1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6351-6369, 2023, DOI:10.32604/cmc.2023.035832
    Abstract Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses… More >

  • Open AccessOpen Access

    ARTICLE

    New Trends in Fuzzy Modeling Through Numerical Techniques

    M. M. Alqarni1, Muhammad Rafiq2, Fazal Dayan3,*, Jan Awrejcewicz4, Nauman Ahmed5, Ali Raza6, Muhammad Ozair Ahmad5, Witold Pawłowski7, Emad E. Mahmoud8
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6371-6388, 2023, DOI:10.32604/cmc.2023.033553
    Abstract Amoebiasis is a parasitic intestinal infection caused by the highly pathogenic amoeba Entamoeba histolytica. It is spread through person-to-person contact or by eating or drinking food or water contaminated with feces. Its transmission rate depends on the number of cysts present in the environment. The traditional models assumed a homogeneous and contradictory transmission with reality. The heterogeneity of its transmission rate is a significant factor when modeling disease dynamics. The heterogeneity of disease transmission can be described mathematically by introducing fuzzy theory. In this context, a fuzzy SEIR Amoebiasis disease model is considered in this study. The equilibrium analysis and… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

    Y. C. A. Padmanabha Reddy1, Shyam Sunder Reddy Kasireddy2, Nageswara Rao Sirisala3, Ramu Kuchipudi4, Purnachand Kollapudi5,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6389-6409, 2023, DOI:10.32604/cmc.2023.034072
    Abstract The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual cross-language processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov… More >

Per Page:

Share Link