Home / Journals / CMC / Vol.72, No.3, 2022
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  • Open AccessOpen Access

    ARTICLE

    Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

    Maqsood Ahmad1,2, Noorhaniza Wahid1, Rahayu A Hamid1, Saima Sadiq2, Arif Mehmood3, Gyu Sang Choi4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5041-5058, 2022, DOI:10.32604/cmc.2022.026077
    Abstract Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Grey DEMATEL and PLS-SEM Model to Investigate COVID-19 Vaccination Intention

    Phi-Hung Nguyen1,2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5059-5078, 2022, DOI:10.32604/cmc.2022.027630
    Abstract The main objective of this study is to comprehensively investigate individuals’ vaccination intention against COVID-19 during the second wave of COVID-19 spread in Vietnam using a novel hybrid approach. First, the Decision-Making Trial and Evaluation Laboratory based on Grey Theory (DEMATEL-G) was employed to explore the critical factors of vaccination intention among individuals. Second, Partial Least Squares-Structural Equation Modeling (PLS-SEM) was applied to test the hypotheses of individual behavioral intention to get the vaccine to prevent the outbreak of COVID-19. A panel of 661 valid respondents was collected from June 2021 to July 2021, and… More >

  • Open AccessOpen Access

    ARTICLE

    CWoT-Share: Context-Based Web of Things Resource Sharing in Blockchain Environment

    Yangqun Li1,2,*, Jin Qi1,2, Lijuan Min1,2, Hongzhi Yang1,2, Chenyang Zhou1,2, Bonan Jin3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5079-5098, 2022, DOI:10.32604/cmc.2022.027281
    Abstract Web of Things (WoT) resources are not only numerous, but also have a wide range of applications and deployments. The centralized WoT resource sharing mechanism lacks flexibility and scalability, and hence cannot satisfy requirement of distributed resource sharing in large-scale environment. In response to this problem, a trusted and secure mechanism for WoT resources sharing based on context and blockchain (CWoT-Share) was proposed. Firstly, the mechanism can respond quickly to the changes of the application environment by dynamically determining resource access control rules according to the context. Then, the flexible resource charging strategies, which reduced More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

    Bareera Zafar1, Syed Abbas Zilqurnain Naqvi1, Muhammad Ahsan1, Allah Ditta2,*, Ummul Baneen1, Muhammad Adnan Khan3,4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.027874
    Abstract This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions. CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space. CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit… More >

  • Open AccessOpen Access

    ARTICLE

    Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

    Hiam Alquran1,2, Wan Azani Mustafa3,4,*, Isam Abu Qasmieh2, Yasmeen Mohd Yacob3,4, Mohammed Alsalatie5, Yazan Al-Issa6, Ali Mohammad Alqudah2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5117-5134, 2022, DOI:10.32604/cmc.2022.025692
    Abstract Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The… More >

  • Open AccessOpen Access

    ARTICLE

    Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

    Xingjian Xue1,*, Linjuan Ge2, Longxin Zeng2, Weiran Li2, Rui Song2, Neal N. Xiong3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5135-5148, 2022, DOI:10.32604/cmc.2022.027356
    Abstract To study riding safety at intersection entrance, video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method. It is analyzed the relationship among the width of non-motorized lanes at the entrance lane of the intersection, the vehicle-bicycle soft isolation form of the entrance lane of intersection, the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles, the speed of right-turning motor vehicles, and straight-going non-motor vehicles, and the conflict between right-turning motor vehicles and straight-going non-motor vehicles. Due to the traditional statistical methods, to overcome the discreteness of vehicle-bicycle… More >

  • Open AccessOpen Access

    ARTICLE

    Handling Big Data in Relational Database Management Systems

    Kamal ElDahshan1, Eman Selim2, Ahmed Ismail Ebada2, Mohamed Abouhawwash3,4, Yunyoung Nam5,*, Gamal Behery2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5149-5164, 2022, DOI:10.32604/cmc.2022.028326
    Abstract Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model. The migration from relational databases to NoSQL databases is challenging due to the data complexity. This study aims to enhance the storage performance of RDBMSs in handling a variety of data. The paper presents two approaches. The first approach proposes a convenient representation of unstructured data storage. Several extensive experiments were More >

  • Open AccessOpen Access

    ARTICLE

    A Truck Scheduling Problem for Multi-Crossdocking System with Metaheuristics

    Phan Nguyen Ky Phuc1, Nguyen Van Thanh2,*, Duong Bao Tram1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5165-5178, 2022, DOI:10.32604/cmc.2022.027967
    Abstract The cross-docking is a very important subject in logistics and supply chain managements. According to the definition, cross-docking is a process dealing with transhipping inventory, in which goods and products are unloaded from an inbound truck and process through a flow-center to be directly loaded onto an outbound truck. Cross-docking is favored due to its advantages in reducing the material handing cost, the needs to store the product in warehouse, as well decreasing the labor cost by eliminating packaging, storing, pick-location and order picking. In cross-docking, products can be consolidated and transported as a full… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

    Md. Maniruzzaman1, Jungpil Shin1,*, Md. Al Mehedi Hasan1, Akira Yasumura2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5179-5195, 2022, DOI:10.32604/cmc.2022.028339
    Abstract Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7–12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The… More >

  • Open AccessOpen Access

    ARTICLE

    Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

    Rashmi Sharma1, Nitin Nitin2, Deepak Dahiya3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5197-5213, 2022, DOI:10.32604/cmc.2022.025059
    Abstract Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with… More >

  • Open AccessOpen Access

    ARTICLE

    Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

    Abdulraqeb Alhammadi1,*, Wan Haslina Hassan1, Ayman A. El-Saleh2, Ibraheem Shayea3, Hafizal Mohamad4, Yousef Ibrahim Daradkeh5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5215-5232, 2022, DOI:10.32604/cmc.2022.024713
    Abstract Fifth-generation (5G) cellular networks offer high transmission rates in dense urban environments. However, a massive deployment of small cells will be required to provide wide-area coverage, which leads to an increase in the number of handovers (HOs). Mobility management is an important issue that requires considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with current fourth-generation (4G) networks. Although mobility robustness optimization (MRO) and load balancing optimization (LBO) functions have been introduced in the 3GPP standard to address HO problems, non-robust and nonoptimal algorithms for selecting appropriate HO control parameters (HCPs) still… More >

  • Open AccessOpen Access

    ARTICLE

    Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

    Nawaf Alhebaishi1, Abdulrhman M. Alshareef1, Tawfiq Hasanin1, Raed Alsini1, Gyanendra Prasad Joshi2, Seongsoo Cho3, Doo Ill Chul4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5233-5250, 2022, DOI:10.32604/cmc.2022.025596
    Abstract In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this… More >

  • Open AccessOpen Access

    ARTICLE

    Compiler IR-Based Program Encoding Method for Software Defect Prediction

    Yong Chen1, Chao Xu1,*, Jing Selena He2, Sheng Xiao3, Fanfan Shen1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5251-5272, 2022, DOI:10.32604/cmc.2022.026750
    Abstract With the continuous expansion of software applications, people's requirements for software quality are increasing. Software defect prediction is an important technology to improve software quality. It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy. However, the current encoding methods are mainly based on the traditional manual features or the AST of source code. Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST,… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image

    Ahmad Alassaf, Mohamed Yacin Sikkandar*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577
    Abstract Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Analysis of Multilayer Coil Based MI Waveguide Communication System

    Sandeep N. Dandu1, Vinay Kumar2, Joydeep Sengupta1, Neeraj D. Bokde3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5287-5300, 2022, DOI:10.32604/cmc.2022.026390
    Abstract In the non-conventional media like underwater and underground, the Radio Frequency (RF) communication technique does not perform well due to large antenna size requirement and high path loss. In such media, magnetic induction (MI) communication technique is very promising due to small coil size and constant channel behavior. Unlike the RF technique, the communication range in MI technique is relatively less. To enhance this range, a waveguide technique is already brought in practice. This technique employs single layer coils to enhance the performance of MI waveguide. To further enhance the system functioning, in this paper,… More >

  • Open AccessOpen Access

    ARTICLE

    Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

    Muhamad Erza Aminanto1,2,*, Ibnu Rifqi Purbomukti3, Harry Chandra2, Kwangjo Kim4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5301-5314, 2022, DOI:10.32604/cmc.2022.026749
    Abstract Internet of Things (IoT) networks leverage wireless communication protocols, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into an image. This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

    Tao Zhang1,*, Yi Cao2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083
    Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Sign Language Recognition System for E-Learning Context

    Muhammad Jamil Hussain1, Ahmad Shaoor1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5327-5343, 2022, DOI:10.32604/cmc.2022.025953
    Abstract In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASL-alphabet More >

  • Open AccessOpen Access

    ARTICLE

    XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation

    Shweta Paliwal1, Amit Kumar Mishra2,*, Ram Krishn Mishra3, Nishad Nawaz4, M. Senthilkumar5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.025858
    Abstract Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare. Disease diagnosis, personalized medicine, and Recommendation system (RS) are among the promising applications that are using Machine Learning (ML) at a higher level. A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly. Today people share their experiences through reviews and hence designing of recommendation system based on users’ sentiments is a challenge. The recommendation system has gained significant attention in different fields but considering More >

  • Open AccessOpen Access

    ARTICLE

    A Steganography Model Data Protection Method Based on Scrambling Encryption

    Xintao Duan1,*, Zhiqiang Shao1, Wenxin Wang1, En Zhang1, Dongli Yue1, Chuan Qin2, Haewoon Nam3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5363-5375, 2022, DOI:10.32604/cmc.2022.027807
    Abstract At present, the image steganography method based on CNN has achieved good results. The trained model and its parameters are of great value. Once leaked, the secret image will be exposed. To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper. Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public… More >

  • Open AccessOpen Access

    ARTICLE

    Improving Method of Anomaly Detection Performance for Industrial IoT Environment

    Junwon Kim1, Jiho Shin2, Ki-Woong Park3, Jung Taek Seo4,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5377-5394, 2022, DOI:10.32604/cmc.2022.026619
    Abstract Industrial Control System (ICS), which is based on Industrial IoT (IIoT), has an intelligent mobile environment that supports various mobility, but there is a limit to relying only on the physical security of the ICS environment. Due to various threat factors that can disrupt the workflow of the IIoT, machine learning-based anomaly detection technologies are being presented; it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation. In this paper, we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by… More >

  • Open AccessOpen Access

    ARTICLE

    A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

    Huanrong Tang1, Changlin Hu1, Tianming Liu2, Jianquan Ouyang1,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5395-5413, 2022, DOI:10.32604/cmc.2022.028284
    Abstract In response to problems in the centralized storage of personal resumes on third-party recruitment platforms, such as inadequate privacy protection, inability of individuals to accurately authorize downloads, and inability to determine who downloaded the resume and when, this study proposes a blockchain-based framework for secure storage and sharing of resumes. Users can employ an authorized access mechanism to protect their privacy rights. The proposed framework uses smart contracts, interplanetary file system, symmetric encryption, and digital signatures to protect, verify, and share resumes. Encryption keys are split and stored in multiple depositories through secret-sharing technology to… More >

  • Open AccessOpen Access

    ARTICLE

    Incremental Learning Model for Load Forecasting without Training Sample

    Charnon Chupong, Boonyang Plangklang*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5415-5427, 2022, DOI:10.32604/cmc.2022.028416
    Abstract This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data More >

  • Open AccessOpen Access

    ARTICLE

    A Searchable Encryption Scheme Based on Lattice for Log Systems in Blockchain

    Gang Xu1, Yibo Cao1, Shiyuan Xu1, Xin Liu2,*, Xiu-Bo Chen3, Yiying Yu1, Xiaojun Wang4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5429-5441, 2022, DOI:10.32604/cmc.2022.028562
    Abstract With the increasing popularity of cloud storage, data security on the cloud has become increasingly visible. Searchable encryption has the ability to realize the privacy protection and security of data in the cloud. However, with the continuous development of quantum computing, the standard Public-key Encryption with Keyword Search (PEKS) scheme cannot resist quantum-based keyword guessing attacks. Further, the credibility of the server also poses a significant threat to the security of the retrieval process. This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems. Firstly, we design More >

  • Open AccessOpen Access

    ARTICLE

    Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

    Laiali Almazaydeh1,*, Saleh Atiewi2, Arar Al Tawil3, Khaled Elleithy4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5443-5458, 2022, DOI:10.32604/cmc.2022.025526
    Abstract Genres are one of the key features that categorize music based on specific series of patterns. However, the Arabic music content on the web is poorly defined into its genres, making the automatic classification of Arabic audio genres challenging. For this reason, in this research, our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres, which are: Eastern Takht, Rai, Muwashshah, the poem, and Mawwal, and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks (CNNs) architectures on Arabic music genres classification. In this work, More >

  • Open AccessOpen Access

    ARTICLE

    ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

    Khudran M. Alzhrani*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5459-5479, 2022, DOI:10.32604/cmc.2022.028512
    Abstract Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

    Yanhua Lu1, Xuehui Gong2,*, Andrew Byron Kipnis3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5481-5497, 2022, DOI:10.32604/cmc.2022.027089
    Abstract Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving… More >

  • Open AccessOpen Access

    ARTICLE

    Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

    Masoud Alajmi1, Mohamed K. Nour2, Siwar Ben Haj Hassine3, Mimouna Abdullah Alkhonaini4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5499-5513, 2022, DOI:10.32604/cmc.2022.026187
    Abstract Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms… More >

  • Open AccessOpen Access

    ARTICLE

    Power Allocation in NOMA-CR for 5G Enabled IoT Networks

    Mohammed Basheri1, Mohammad Haseeb Zafar1,2,3,*, Imran Khan3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5515-5530, 2022, DOI:10.32604/cmc.2022.027532
    Abstract In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With… More >

  • Open AccessOpen Access

    ARTICLE

    STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems

    Juan Fang1,*, Yunfei Mao1, Min Cai1, Li’ang Zhao1, Huijie Chen1, Wei Xiang2,3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5531-5545, 2022, DOI:10.32604/cmc.2022.027177
    Abstract Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts… More >

  • Open AccessOpen Access

    ARTICLE

    An Innovative Approach Utilizing Binary-View Transformer for Speech Recognition Task

    Muhammad Babar Kamal1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Malik Muhammad Ali Shahid4, Chitapong Wechtaisong2,*, Muhammad Daud Kamal5, Muhammad Junaid Ali6, Peerapong Uthansakul2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5547-5562, 2022, DOI:10.32604/cmc.2022.024590
    Abstract The deep learning advancements have greatly improved the performance of speech recognition systems, and most recent systems are based on the Recurrent Neural Network (RNN). Overall, the RNN works fine with the small sequence data, but suffers from the gradient vanishing problem in case of large sequence. The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data. Generally, in speech recognition, the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities. The image is classified by the machine learning mechanism to generate a… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

    Ashit Kumar Dutta1, S. Srinivasan2, S. N. Kumar3, T. S. Balaji4,5, Won Il Lee6, Gyanendra Prasad Joshi7, Sung Won Kim8,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5563-5576, 2022, DOI:10.32604/cmc.2022.027707
    Abstract Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

    Mahmoud Ragab1,2,3,*, Ashwag Albukhari2,4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5577-5591, 2022, DOI:10.32604/cmc.2022.026715
    Abstract Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Malek Z. Alksasbeh3, Bassam A. Y. Alqaralleh2,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666
    Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved… More >

  • Open AccessOpen Access

    ARTICLE

    Iterative Semi-Supervised Learning Using Softmax Probability

    Heewon Chung, Jinseok Lee*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5607-5628, 2022, DOI:10.32604/cmc.2022.028154
    Abstract For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the More >

  • Open AccessOpen Access

    ARTICLE

    An Adaptive Real-Time Third Order Sliding Mode Control for Nonlinear Systems

    Ahmed M. Elmogy1,2,*, Amany Sarhan2, Wael M. Elawady2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5629-5641, 2022, DOI:10.32604/cmc.2022.025247
    Abstract As most real world systems are significantly nonlinear in nature, developing robust controllers have attracted many researchers for decades. Robust controllers are the controllers that are able to cope with the inherent uncertainties of the nonlinear systems. Many control methods have been developed for this purpose. Sliding mode control (SMC) is one of the most commonly used methods in developing robust controllers. This paper presents a higher order SMC (HOSMC) approach to mitigate the chattering problem of the traditional SMC techniques. The developed approach combines a third order SMC with an adaptive PID (proportional, integral, More >

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    ARTICLE

    Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

    Hajer Faris1, Musaria Karim Mahmood2, Osama Ahmad Alomari3,*, Ashraf Elnagar4
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5643-5661, 2022, DOI:10.32604/cmc.2022.026228
    Abstract Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Cluster-based hierarchical routing algorithms are known for their good performance in energy conservation… More >

  • Open AccessOpen Access

    ARTICLE

    Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

    S. Vinson Joshua1, A. Selwin Mich Priyadharson1, Raju Kannadasan2, Arfat Ahmad Khan3, Worawat Lawanont3,*, Faizan Ahmed Khan4, Ateeq Ur Rehman5, Muhammad Junaid Ali6
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178
    Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data… More >

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    ARTICLE

    Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

    Mujeeb Ur Rehman1,*, Maha Driss2,3, Abdukodir Khakimov4, Sohail Khalid1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840
    Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence,… More >

  • Open AccessOpen Access

    ARTICLE

    Experimental Analysis of Methods Used to Solve Linear Regression Models

    Mua’ad Abu-Faraj1,*, Abeer Al-Hyari2, Ziad Alqadi3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5699-5712, 2022, DOI:10.32604/cmc.2022.027364
    Abstract Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes. Regression is one of the most important types of supervised machine learning, in which labeled data is used to build a prediction model, regression can be classified into three different categories: linear, polynomial, and logistic. In this research paper, different methods will be implemented to solve the linear regression problem, where there is a linear relationship between the… More >

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    ARTICLE

    Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

    Md Zia Ur Rahman1,*, S. Rooban1, P. Rohini2, M. V. S. Ramprasad3, Pradeep Vinaik Kodavanti3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5713-5726, 2022, DOI:10.32604/cmc.2022.027672
    Abstract The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and… More >

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    ARTICLE

    Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

    Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Bassam A.Y. Alqaralleh2,*, Tamer AbuKhalil3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5727-5740, 2022, DOI:10.32604/cmc.2022.026780
    Abstract Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for… More >

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    ARTICLE

    Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

    Fawad Salam Khan1,4, Mohd Norzali Haji Mohd1,*, Saiful Azrin B. M. Zulkifli2, Ghulam E Mustafa Abro2, Suhail Kazi3, Dur Muhammad Soomro1
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5741-5759, 2022, DOI:10.32604/cmc.2022.024927
    Abstract The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive… More >

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    ARTICLE

    Simulation and Modelling of Water Injection for Reservoir Pressure Maintenance

    Rishi Dewan1, Adarsh Kumar2, Mohammad Khalid Imam Rahmani3, Surbhi Bhatia4, Md Ezaz Ahmed3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5761-5776, 2022, DOI:10.32604/cmc.2022.024762
    Abstract Water injection has shown to be one of the most successful, efficient, and cost-effective reservoir management strategies. By re-injecting treated and filtered water into reservoirs, this approach can help maintain reservoir pressure, increase hydrocarbon output, and reduce the environmental effect. The goal of this project is to create a water injection model utilizing Eclipse reservoir simulation software to better understand water injection methods for reservoir pressure maintenance. A basic reservoir model is utilized in this investigation. For simulation designs, the reservoir length, breadth, and thickness may be changed to different levels. The water-oil contact was More >

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    ARTICLE

    3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

    Siddiqui Muhammad Yasir1, Amin Muhammad Sadiq2, Hyunsik Ahn3,*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5777-5791, 2022, DOI:10.32604/cmc.2022.025909
    Abstract 3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis. The computer vision, graphics, and machine learning fields have all given it a lot of attention. Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale data. Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success… More >

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    ARTICLE

    Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

    Noreen Mushtaq1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Muhammad Junaid Ali4, Malik Muhammad Ali Shahid5, Chitapong Wechtaisong2,*, Peerapong Uthansakul2
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5793-5806, 2022, DOI:10.32604/cmc.2022.024316
    Abstract Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors. Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate. Various imaging modalities have been used for diagnosing by expert radiologists, and Medical Resonance Image (MRI) is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain region. One of the challenging issues is to identify the tumorous region from the MRI scans correctly. Manual segmentation is performed by medical experts, which is… More >

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    ARTICLE

    Subcarrier BD with Cooperative Communication for MIMO-NOMA System

    Jung-In Baik, Ji-Hwan Kim, Beom-Sik Shin, Ji-Hye Oh, Hyoung-Kyu Song*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5807-5821, 2022, DOI:10.32604/cmc.2022.028434
    Abstract With the rapid evolution of Internet of things (IoT), many edge devices require simultaneous connection in 5G communication era. To afford massive data of IoT devices, multiple input multiple output non-orthogonal multiple access (MIMO-NOMA) method has been considered as a promising technology. However, there are numerous drawbacks due to error propagation and inter-user interferences. Therefore, proposed scheme aims to improve the reliability of the MIMO-NOMA system with digital beamforming and intra-cluster cooperative multi point (CoMP) to efficiently support IoT system. In the conventional MIMO-NOMA system, user entities are grouped into clusters. Block diagonalization (BD) is… More >

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    ARTICLE

    An Advanced Stochastic Numerical Approach for Host-Vector-Predator Nonlinear Model

    Prem Junswang1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Soheil Salahshour4, Thongchai Botmart5,*, Wajaree Weera5
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5823-5843, 2022, DOI:10.32604/cmc.2022.027629
    Abstract A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations. The host-vector-predator nonlinear model depends upon five groups or classes, host plant susceptible and infected populations, vectors population of susceptible and infected individuals and the predator population. An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms. For solving the host-vector-predator nonlinear model, a merit function is constructed using the differential model and its associated boundary conditions.… More >

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    ARTICLE

    A Mutual Authentication and Cross Verification Protocol for Securing Internet-of-Drones (IoD)

    Saeed Ullah Jan1, Irshad Ahmed Abbasi2,*, Fahad Algarni3
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5845-5869, 2022, DOI:10.32604/cmc.2022.026179
    Abstract With the rapid miniaturization in sensor technology, Internet-of-Drones (IoD) has delighted researchers towards information transmission security among drones with the control station server (CSS). In IoD, the drone is different in shapes, sizes, characteristics, and configurations. It can be classified on the purpose of its deployment, either in the civilian or military domain. Drone’s manufacturing, equipment installation, power supply, multi-rotor system, and embedded sensors are not issues for researchers. The main thing is to utilize a drone for a complex and sensitive task using an infrastructure-less/self-organization/resource-less network type called Flying Ad Hoc Network (FANET). Monitoring… More >

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    ARTICLE

    Automated Service Search Model for the Social Internet of Things

    Farhan Amin, Seong Oun Hwang*
    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5871-5888, 2022, DOI:10.32604/cmc.2022.028342
    Abstract The social internet of things (SIoT) is one of the emerging paradigms that was proposed to solve the problems of network service discovery, navigability, and service composition. The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT, an object can offer multiple services and different objects can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service customization and service filtering. This problem is known as service explosion. The selection of suitable… More >

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