Home / Journals / CMC / Vol.72, No.1, 2022
Special lssues
Table of Content
  • Open AccessOpen Access

    ARTICLE

    Dimensionality and Angular Disparity Influence Mental Rotation in Computer Gaming

    Akanksha Tiwari1,*, Ram Bilas Pachori1,2, Premjit Khanganba Sanjram1,3,4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 887-905, 2022, DOI:10.32604/cmc.2022.023886
    (This article belongs to this Special Issue: Emergent Computer-Based Methods and Internet of Things Technologies for Physical Therapy, Dentistry, Medicine, and Engineering)
    Abstract Computer gaming is one of the most common activities that individuals are indulged in their usual activities concerning interactive system-based entertainment. Visuospatial processing is an essential aspect of mental rotation (MR) in playing computer-games. Previous studies have explored how objects’ features affect the MR process; however, non-isomorphic 2D and 3D objects lack a fair comparison. In addition, the effects of these features on brain activation during the MR in computer-games have been less investigated. This study investigates how dimensionality and angular disparity affect brain activation during MR in computer-games. EEG (electroencephalogram) data were recorded from sixty healthy adults while playing… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

    Amani Abdulrahman Albraikan1, Siwar Ben Haj Hassine2, Suliman Mohamed Fati3, Fahd N. Al-Wesabi2,4, Anwer Mustafa Hilal5,*, Abdelwahed Motwakel5, Manar Ahmed Hamza5, Mesfer Al Duhayyim6
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 907-923, 2022, DOI:10.32604/cmc.2022.024488
    Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN model is carried out with… More >

  • Open AccessOpen Access

    ARTICLE

    Compact Multibeam Array with Miniaturized Butler Matrix for 5G Applications

    Suleiman A. Babale1, Muhammad K. Ishfaq2,*, Ali Raza2, Jamal Nasir3, Ahmad Fayyaz3, Umer Ijaz2
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 925-937, 2022, DOI:10.32604/cmc.2022.024711
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract This paper presents the design and implementation of a miniaturized beam steering network that produces broadside beams when it is fed with a compact antenna array. Butler Matrix (BM) was used as the beam steering network. It was completely built from a miniaturized 3 dB hybrid-couplers in planar microstrip technology. It was configured by feeding the BM with a Planar Inverted-E Antenna (PIEA) array separated at 0.3 λ as against the 0.5 λ separation. This makes the BM produce a major radiation pattern at the broadside. Apart from the miniaturization, no modification was made from the BM side. However, employing effective… More >

  • Open AccessOpen Access

    ARTICLE

    Fusion-Based Deep Learning Model for Hyperspectral Images Classification

    Kriti1, Mohd Anul Haq2, Urvashi Garg1, Mohd Abdul Rahim Khan2,*, V. Rajinikanth3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 939-957, 2022, DOI:10.32604/cmc.2022.023169
    (This article belongs to this Special Issue: Applications of Intelligent Systems in Computer Vision)
    Abstract A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation (SR) has likewise been presented… More >

  • Open AccessOpen Access

    ARTICLE

    A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

    Milan Tair1, Nebojsa Bacanin1, Miodrag Zivkovic1, K. Venkatachalam2,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 959-982, 2022, DOI:10.32604/cmc.2022.024989
    Abstract There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and… More >

  • Open AccessOpen Access

    ARTICLE

    Directional Wideband Wearable Antenna with Circular Parasitic Element for Microwave Imaging Applications

    N. A. Koma'rudin1, Z. Zakaria1,*, A. A. Althuwayb2, H. Lago3, H. Alsariera1, H. Nornikman1, A. J. A. Al-Gburi1, P. J. Soh4,5
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 983-998, 2022, DOI:10.32604/cmc.2022.024782
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract This work proposes a wideband and unidirectional antenna consisting of dual layer of coplanar waveguide based on the circular parasitic element technique. The design procedure is implemented in three stages: Design A, which operates at 3.94 GHz with a bandwidth of 3.83 GHz and a fractional bandwidth (FBW) of 97.2%; Design B, which operates at 3.98 GHz with a bandwidth of 0.66 GHz (FBW of 56.53%); and Design C as the final antenna. The final Design C is designed to resonate at several frequencies between 2.89 and 7.0 GHz for microwave imaging applications with a bandwidth of 4.11 GHz (79.8%)… More >

  • Open AccessOpen Access

    ARTICLE

    Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

    Anita Gehlot1, Rajesh Singh1, Sweety Siwach2, Shaik Vaseem Akram1, Khalid Alsubhi3, Aman Singh4,*, Irene Delgado Noya4,5, Sushabhan Choudhury2
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 999-1015, 2022, DOI:10.32604/cmc.2022.023861
    Abstract Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino… More >

  • Open AccessOpen Access

    ARTICLE

    Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

    Noman Shabbir1, Lauri Kütt1, Muhammad Jawad2, Oleksandr Husev1, Ateeq Ur Rehman3, Akber Abid Gardezi4, Muhammad Shafiq5, Jin-Ghoo Choi5,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1017-1033, 2022, DOI:10.32604/cmc.2022.024576
    Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in… More >

  • Open AccessOpen Access

    ARTICLE

    Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

    Shuguang Wei, Jiaqi Li*, Zixu Zhao, Dong Yuan
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1035-1052, 2022, DOI:10.32604/cmc.2022.024201
    Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal Scheme for WSN Based on Compressed Sensing

    Firas Ibrahim AlZobi1, Ahmad Ali AlZubi2,*, Kulakov Yurii3, Abdullah Alharbi2, Jazem Mutared Alanazi2, Sami Smadi1
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1053-1069, 2022, DOI:10.32604/cmc.2022.025555
    (This article belongs to this Special Issue: Big Data and Smart Cities Challenges)
    Abstract Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the cluster heads are selected in… More >

  • Open AccessOpen Access

    ARTICLE

    Triple-Band Metamaterial Inspired Antenna for Future Terahertz (THz) Applications

    Adel Y. I. Ashyap1, S. Alamri2, S. H. Dahlan1,*, Z. Z. Abidin3, M. Inam Abbasi4, Huda A. Majid2, M. R. Kamarudin1, Y. A. Al-Gumaei5, M. Hashim Dahri6
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1071-1087, 2022, DOI:10.32604/cmc.2022.025636
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract For future healthcare in the terahertz (THz) band, a triple-band microstrip planar antenna integrated with metamaterial (MTM) based on a polyimide substrate is presented. The frequencies of operation are 500, 600, and 880 GHz. The triple-band capability is accomplished by etching metamaterial on the patch without affecting the overall antenna size. Instead of a partial ground plane, a full ground plane is used as a buffer to shield the body from back radiation emitted by the antenna. The overall dimension of the proposed antenna is 484 × 484 μm2. The antenna's performance is investigated based on different crucial factors, and excellent results are… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Multi-Cost Routing Protocol to Enhance Lifetime for Wireless Body Area Network

    Muhammad Mateen Yaqoob1, Waqar Khurshid1, Leo Liu2, Syed Zulqarnain Arif1, Imran Ali Khan1, Osman Khalid1,*, Raheel Nawaz2
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1089-1103, 2022, DOI:10.32604/cmc.2022.024798
    Abstract Wireless Body Area Network (WBAN) technologies are emerging with extensive applications in several domains. Health is a fascinating domain of WBAN for smart monitoring of a patient's condition. An important factor to consider in WBAN is a node's lifetime. Improving the lifetime of nodes is critical to address many issues, such as utility and reliability. Existing routing protocols have addressed the energy conservation problem but considered only a few parameters, thus affecting their performance. Moreover, most of the existing schemes did not consider traffic prioritization which is critical in WBANs. In this paper, an adaptive multi-cost routing protocol is proposed… More >

  • Open AccessOpen Access

    ARTICLE

    Constructing Collective Signature Schemes Using Problem of Finding Roots Modulo

    Tuan Nguyen Kim1,*, Duy Ho Ngoc2, Nikolay A. Moldovyan3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1105-1122, 2022, DOI:10.32604/cmc.2022.025653
    (This article belongs to this Special Issue: Soft Computing and Machine Learning for Predictive Data Analytics)
    Abstract Digital signature schemes are often built based on the difficulty of the discrete logarithm problems, of the problem of factor analysis, of the problem of finding the roots modulo of large primes or a combination of the difficult problems mentioned above. In this paper, we use the new difficult problem, which is to find the root in the finite ground field to build representative collective signature schemes, but the chosen modulo p has a special structure distinct , where is an even number and are prime numbers of equal magnitude, about . The characteristics of the proposed scheme are: i)… More >

  • Open AccessOpen Access

    ARTICLE

    A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

    Xiaorui Zhang1,2,3,*, Jie Zhou2, Wei Sun3,4, Sunil Kumar Jha5
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589
    Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More >

  • Open AccessOpen Access

    ARTICLE

    Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

    Norah Saleh Alghamdi1, Sami Bourouis2,*, Nizar Bouguila3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1139-1156, 2022, DOI:10.32604/cmc.2022.022959
    (This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
    Abstract Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some real-world applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

    Ashit Kumar Dutta1, Jenyfal Sampson2, Sultan Ahmad3, T. Avudaiappan4, Kanagaraj Narayanasamy5,*, Irina V. Pustokhina6, Denis A. Pustokhin7
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1157-1172, 2022, DOI:10.32604/cmc.2022.024109
    Abstract Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

    Daniyal M. Alghazzawi1, Osama Bassam J. Rabie1, Surbhi Bhatia2, Syed Hamid Hasan1,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1173-1193, 2022, DOI:10.32604/cmc.2022.022748
    (This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
    Abstract The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model… More >

  • Open AccessOpen Access

    ARTICLE

    Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

    Ibrahim Abunadi1, Maha M. Althobaiti2, Fahd N. Al-Wesabi3,4, Anwer Mustafa Hilal5, Mohammad Medani6, Manar Ahmed Hamza5,*, Mohammed Rizwanullah5, Abu Serwar Zamani5
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1195-1212, 2022, DOI:10.32604/cmc.2022.025473
    Abstract The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly… More >

  • Open AccessOpen Access

    ARTICLE

    User Recognition System Based on Spectrogram Image Conversion Using EMG Signals

    Jae Myung Kim1,2, Gyu Ho Choi2, Min-Gu Kim2, Sung Bum Pan1,2,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1213-1227, 2022, DOI:10.32604/cmc.2022.025213
    (This article belongs to this Special Issue: Security and Privacy Issues in Systems and Networks Beyond 5G)
    Abstract Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over… More >

  • Open AccessOpen Access

    ARTICLE

    Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

    Qamar Abbas1, Khalid Mahmood Malik2, Abdul Khader Jilani Saudagar3,*, Muhammad Badruddin Khan3, Mozaherul Hoque Abul Hasanat3, Abdullah AlTameem3, Mohammed AlKhathami3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211
    (This article belongs to this Special Issue: Data Science in Ubiquitous Computing: Data Analytics, Data Mining and Data Security)
    Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

  • Open AccessOpen Access

    ARTICLE

    Windows 10's Browser Forensic Analysis for Tracing P2P Networks’ Anonymous Attacks

    Saima Kauser1, Tauqeer Safdar Malik1,*, Mohd Hilmi Hasan2, Emelia Akashah P. Akhir2, Syed Muhammad Husnain Kazmi3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1251-1273, 2022, DOI:10.32604/cmc.2022.022475
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract A web browser is the most basic tool for accessing the internet from any of the machines/equipment. Recently, data breaches have been reported frequently from users who are concerned about their personal information, as well as threats from criminal actors. Giving loss of data and information to an innocent user comes under the jurisdiction of cyber-attack. These kinds of cyber-attacks are far more dangerous when it comes to the many types of devices employed in an internet of things (IoT) environment. Continuous surveillance of IoT devices and forensic tools are required to overcome the issues pertaining to secure data and… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

    Richu Mary Thomas, Malarvizhi Subramani*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1275-1291, 2022, DOI:10.32604/cmc.2022.024507
    (This article belongs to this Special Issue: Radio Networks for new Disruptive Digital Services in Fourth Industrial Revolution)
    Abstract The recent aggrandizement of radio frequency (RF) signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service (QoS). In addition, it does not require any unnecessary alterations on the transmission hardware side. A hybridized global optimization technique uniting Global best and Local best (GL) based particle swarm optimization (PSO) and ant colony optimization (ACO) is proposed in this paper to optimally allocate resources in wireless powered communication networks (WPCN) through coordinated operation of communication… More >

  • Open AccessOpen Access

    ARTICLE

    Locomotion of Bioinspired Underwater Snake Robots Using Metaheuristic Algorithm

    Souad Larabi-Marie-Sainte1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Amani Abdulrahman Albraikan5, Manar Ahmed Hamza6,*, Abdelwahed Motwakel6, Ishfaq Yaseen6, Mesfer Al Duhayyim7
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1293-1308, 2022, DOI:10.32604/cmc.2022.024585
    Abstract Snake Robots (SR) have been successfully deployed and proved to attain bio-inspired solutions owing to its capability to move in harsh environments, a characteristic not found in other kinds of robots (like wheeled or legged robots). Underwater Snake Robots (USR) establish a bioinspired solution in the domain of underwater robotics. It is a key challenge to increase the motion efficiency in underwater robots, with respect to forwarding speed, by enhancing the locomotion method. At the same time, energy efficiency is also considered as a crucial issue for long-term automation of the systems. In this aspect, the current research paper concentrates… More >

  • Open AccessOpen Access

    ARTICLE

    Design of Low Power Transmission Gate Based 9T SRAM Cell

    S. Rooban1, Moru Leela1, Md. Zia Ur Rahman1,*, N. Subbulakshmi2, R. Manimegalai3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1309-1321, 2022, DOI:10.32604/cmc.2022.023934
    Abstract Considerable research has considered the design of low-power and high-speed devices. Designing integrated circuits with low-power consumption is an important issue due to the rapid growth of high-speed devices. Embedded static random-access memory (SRAM) units are necessary components in fast mobile computing. Traditional SRAM cells are more energy-consuming and with lower performances. The major constraints in SRAM cells are their reliability and low power. The objectives of the proposed method are to provide a high read stability, low energy consumption, and better writing abilities. A transmission gate-based multi-threshold single-ended Schmitt trigger (ST) 9T SRAM cell in a bit-interleaving structure without… More >

  • Open AccessOpen Access

    ARTICLE

    Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

    Mahmoud Ragab1,2,3,*, Samah Alshehri4, Hani A. Alhadrami5,6,7, Faris Kateb1, Ehab Bahaudien Ashary8, S. Abdel-khalek9,10
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1323-1338, 2022, DOI:10.32604/cmc.2022.024775
    Abstract Rapid advancements of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) pose serious security issues by revealing secret data. Therefore, security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time. Practically, AI techniques can be utilized to design image steganographic techniques in IIoT. In addition, encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access. In order to accomplish secure data transmission in IIoT environment, this study presents novel encryption with image steganography based data hiding technique (EIS-DHT) for… More >

  • Open AccessOpen Access

    ARTICLE

    A New Method for Scene Classification from the Remote Sensing Images

    Purnachand Kollapudi1, Saleh Alghamdi2, Neenavath Veeraiah3,*, Youseef Alotaibi4, Sushma Thotakura5, Abdulmajeed Alsufyani6
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1339-1355, 2022, DOI:10.32604/cmc.2022.025118
    Abstract The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

    Nguyen A. Tuan1, D. Akila2, Souvik Pal3, Bikramjit Sarkar4, Thien Khai Tran1, G. Mothilal Nehru2, Dac-Nhuong Le5,6,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1357-1372, 2022, DOI:10.32604/cmc.2022.024096
    (This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
    Abstract This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

    Chandrashekhar Meshram1,*, Agbotiname Lucky Imoize2,3, Sajjad Shaukat Jamal4, Parkash Tambare5, Adel R. Alharbi6, Iqtadar Hussain7
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1373-1389, 2022, DOI:10.32604/cmc.2022.024996
    Abstract The Human-Centered Internet of Things (HC-IoT) is fast becoming a hotbed of security and privacy concerns. Two users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key agreement. Most of the early authenticated key agreement systems relied on pairing, hashing, or modular exponentiation processes that are computationally intensive and cost-prohibitive. In order to address this problem, this paper offers a new three-party authenticated key agreement technique based on fractional chaotic maps. The new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network… More >

  • Open AccessOpen Access

    ARTICLE

    Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

    Ahmed S. Almasoud1, Abdelzahir Abdelmaboud2, Taiseer Abdalla Elfadil Eisa3, Mesfer Al Duhayyim4, Asma Abbas Hassan Elnour5, Manar Ahmed Hamza6,*, Abdelwahed Motwakel6, Abu Sarwar Zamani6
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1391-1407, 2022, DOI:10.32604/cmc.2022.024618
    Abstract In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner. In addition,… More >

  • Open AccessOpen Access

    ARTICLE

    Optimized Load Balancing Technique for Software Defined Network

    Aashish Kumar1, Darpan Anand1, Sudan Jha2, Gyanendra Prasad Joshi3, Woong Cho4,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1409-1426, 2022, DOI:10.32604/cmc.2022.024970
    Abstract Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning Empowered Electricity Consumption Prediction

    Maissa A. Al Metrik*, Dhiaa A. Musleh
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1427-1444, 2022, DOI:10.32604/cmc.2022.025722
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes… More >

  • Open AccessOpen Access

    ARTICLE

    Detection of Lung Nodules on X-ray Using Transfer Learning and Manual Features

    Imran Arshad Choudhry*, Adnan N. Qureshi
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1445-1463, 2022, DOI:10.32604/cmc.2022.025208
    Abstract The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support Vectors Machines (SVM) are used.… More >

  • Open AccessOpen Access

    ARTICLE

    Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

    Ali E. Takieldeen1, El-Sayed M. El-kenawy1,2, Mohammed Hadwan3,4,5,*, Rokaia M. Zaki6,7
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1465-1481, 2022, DOI:10.32604/cmc.2022.026026
    Abstract Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex… More >

  • Open AccessOpen Access

    ARTICLE

    A Perfect Knob to Scale Thread Pool on Runtime

    Faisal Bahadur1,*, Arif Iqbal Umar1, Insaf Ullah2, Fahad Algarni3, Muhammad Asghar Khan2, Samih M. Mostafa4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1483-1493, 2022, DOI:10.32604/cmc.2022.024936
    (This article belongs to this Special Issue: Social Networks Analysis and Knowledge Management)
    Abstract Scalability is one of the utmost nonfunctional requirement of server applications, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) has been used extensively as a middleware service in server applications. The size of thread pool is the most significant factor, that affects the overall performance of servers. Determining the optimal size of thread pool dynamically on runtime is a challenging problem. The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request… More >

  • Open AccessOpen Access

    ARTICLE

    Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

    Kazem Nouri1,*, Milad Fahimi1, Leila Torkzadeh1, Dumitru Baleanu2,3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1495-1514, 2022, DOI:10.32604/cmc.2022.024406
    Abstract The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancement of Biomass Material Characterization Images Using an Improved U-Net

    Zuozheng Lian1, Hong Zhao2,*, Qianjun Zhang1, Haizhen Wang1, E. Erdun3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1515-1528, 2022, DOI:10.32604/cmc.2022.024779
    Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of… More >

  • Open AccessOpen Access

    ARTICLE

    Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds

    Mazen Farid1,3,*, Rohaya Latip1,2, Masnida Hussin1, Nor Asilah Wati Abdul Hamid1
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1529-1560, 2022, DOI:10.32604/cmc.2022.021410
    Abstract One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation. The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines. Most optimization methods for solving non-deterministic polynomial-time hardness (NP-hard) problems deploy multi-objective algorithms. As such, Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front. However, the main drawback of this method is that it requires a reasonably long time to provide an optimum solution. In this paper, a new multi-objective minimum… More >

  • Open AccessOpen Access

    ARTICLE

    Game Theory-Based IoT Efficient Power Control in Cognitive UAV

    Fadhil Mukhlif1,*, Norafida Ithnin1, Omar B. Abdulghafoor2, Faiz Alotaibi3, Nourah Saad Alotaibi4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1561-1578, 2022, DOI:10.32604/cmc.2022.026074
    Abstract With the help of network densification, network coverage as well as the throughput can be improved via ultra-dense networks (UDNs). In tandem, Unmanned Aerial Vehicle (UAV) communications have recently garnered much attention because of their high agility as well as widespread applications. In this paper, a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal. Further, the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation. The quality of service (QoS) related to the cognitive node was considered as a utility function… More >

  • Open AccessOpen Access

    ARTICLE

    A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

    Mahmoud Ragab1,2,3,*, Ali Altalbe1
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1579-1592, 2022, DOI:10.32604/cmc.2022.025828
    Abstract Due to the drastic increase in the number of critical infrastructures like nuclear plants, industrial control systems (ICS), transportation, it becomes highly vulnerable to several attacks. They become the major targets of cyberattacks due to the increase in number of interconnections with other networks. Several research works have focused on the design of intrusion detection systems (IDS) using machine learning (ML) and deep learning (DL) models. At the same time, Blockchain (BC) technology can be applied to improve the security level. In order to resolve the security issues that exist in the critical infrastructures and ICS, this study designs a… More >

  • Open AccessOpen Access

    ARTICLE

    Urdnet: A Cryo-EM Particle Automatic Picking Method

    Jianquan Ouyang1, Yue Zhang1, Kun Fang1,2,*, Tianming Liu3, Xiangyu Pan2
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1593-1610, 2022, DOI:10.32604/cmc.2022.025072
    Abstract Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and pixel-level tags, used to accurately… More >

  • Open AccessOpen Access

    ARTICLE

    Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction

    Huma Hasan Rizvi1,2, Sadiq Ali Khan1, Rabia Noor Enam2, Kashif Nisar3,*, Muhammad Reazul Haque4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1611-1626, 2022, DOI:10.32604/cmc.2022.023081
    Abstract In an Underwater Wireless Sensor Network (UWSN), extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system. Systems based on clustering strategies, instead of each node sending information by itself, utilize cluster heads to collect information inside the clusters for forwarding collective information to sink. This can effectively minimize the total energy loss during transmission. The environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission bandwidth. Round base… More >

  • Open AccessOpen Access

    ARTICLE

    Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

    Zulqurnain Sabir1, Manoj Gupta2, Muhammad Asif Zahoor Raja3, N. Seshagiri Rao4, Muhammad Mubashar Hussain5, Faisal Alanazi6, Orawit Thinnukool7, Pattaraporn Khuwuthyakorn7,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1627-1644, 2022, DOI:10.32604/cmc.2022.021462
    (This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)
    Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form… More >

  • Open AccessOpen Access

    ARTICLE

    Hyperchaos and MD5 Based Efficient Color Image Cipher

    Muhammad Samiullah1, Waqar Aslam1, Saima Sadiq2, Arif Mehmood1, Gyu Sang Choi3,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1645-1670, 2022, DOI:10.32604/cmc.2022.021019
    Abstract While designing and developing encryption algorithms for text and images, the main focus has remained on security. This has led to insufficient attention on the improvement of encryption efficiency, enhancement of hyperchaotic sequence randomness, and dynamic DNA-based S-box. In this regard, a new symmetric block cipher scheme has been proposed. It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images. Our proposed scheme supports various size color images. It generates three DNA based S-boxes for substitution namely DNA_1_s-box, DNA_2_s-box and DNA_3_s-box, each of size . Next, the 4D hyperchaotic… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Frequency Estimation Under Additive Mixture Noise

    Yuan Chen1, Yulu Tian1, Dingfan Zhang2, Longting Huang3,*, Jingxin Xu4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1671-1684, 2022, DOI:10.32604/cmc.2022.022371
    Abstract In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because… More >

  • Open AccessOpen Access

    ARTICLE

    COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

    Nebojsa Budimirovic1, E. Prabhu2, Milos Antonijevic1, Miodrag Zivkovic1, Nebojsa Bacanin1,*, Ivana Strumberger1, K. Venkatachalam3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1685-1698, 2022, DOI:10.32604/cmc.2022.023418
    Abstract Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature… More >

  • Open AccessOpen Access

    ARTICLE

    DAVS: Dockerfile Analysis for Container Image Vulnerability Scanning

    Thien-Phuc Doan, Souhwan Jung*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1699-1711, 2022, DOI:10.32604/cmc.2022.025096
    (This article belongs to this Special Issue: Security and Privacy Issues in Systems and Networks Beyond 5G)
    Abstract Container technology plays an essential role in many Information and Communications Technology (ICT) systems. However, containers face a diversity of threats caused by vulnerable packages within container images. Previous vulnerability scanning solutions for container images are inadequate. These solutions entirely depend on the information extracted from package managers. As a result, packages installed directly from the source code compilation, or packages downloaded from the repository, etc., are ignored. We introduce DAVS–A Dockerfile analysis-based vulnerability scanning framework for OCI-based container images to deal with the limitations of existing solutions. DAVS performs static analysis using file extraction based on Dockerfile information to… More >

  • Open AccessOpen Access

    ARTICLE

    Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

    Elias Hossain1, Mohammed Alshehri2, Sultan Almakdi2,*, Hanan Halawani2, Md. Mizanur Rahman3, Wahidur Rahman4, Sabila Al Jannat5, Nadim Kaysar6, Shishir Mia4
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822
    (This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
    Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural… More >

  • Open AccessOpen Access

    ARTICLE

    Self-Care Assessment for Daily Living Using Machine Learning Mechanism

    Mouazma Batool1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1747-1764, 2022, DOI:10.32604/cmc.2022.025112
    Abstract Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.… More >

  • Open AccessOpen Access

    ARTICLE

    Cyber Security Analysis and Evaluation for Intrusion Detection Systems

    Yoosef B. Abushark1, Asif Irshad Khan1,*, Fawaz Alsolami1, Abdulmohsen Almalawi1, Md Mottahir Alam2, Alka Agrawal3, Rajeev Kumar4, Raees Ahmad Khan3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1765-1783, 2022, DOI:10.32604/cmc.2022.025604
    Abstract Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching information security. Anomaly detection, software… More >

  • Open AccessOpen Access

    ARTICLE

    Classification of Images Based on a System of Hierarchical Features

    Yousef Ibrahim Daradkeh1, Volodymyr Gorokhovatskyi2, Iryna Tvoroshenko2,*, Mujahed Al-Dhaifallah3
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1785-1797, 2022, DOI:10.32604/cmc.2022.025499
    Abstract The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets of descriptors are compared. The… More >

Per Page:

Share Link