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

    ARTICLE

    A GDPR Compliant Approach to Assign Risk Levels to Privacy Policies

    Abdullah R. Alshamsan1, Shafique A. Chaudhry1,2,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4631-4647, 2023, DOI:10.32604/cmc.2023.034039
    Abstract Data privacy laws require service providers to inform their customers on how user data is gathered, used, protected, and shared. The General Data Protection Regulation (GDPR) is a legal framework that provides guidelines for collecting and processing personal information from individuals. Service providers use privacy policies to outline the ways an organization captures, retains, analyzes, and shares customers’ data with other parties. These policies are complex and written using legal jargon; therefore, users rarely read them before accepting them. There exist a number of approaches to automating the task of summarizing privacy policies and assigning… More >

  • Open AccessOpen Access

    ARTICLE

    MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

    Rukiye Karakis1,2,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881
    Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a… More >

  • Open AccessOpen Access

    ARTICLE

    CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

    Shengchun Wang1, Baoxuan Huang1, Tsz Ho Wong2, Jingui Huang1,*, Hong Deng1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4667-4684, 2023, DOI:10.32604/cmc.2023.031444
    Abstract In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features. A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality. In order… More >

  • Open AccessOpen Access

    ARTICLE

    DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

    Adil Khan1,*, Jinling Zhang1, Shabeer Ahmad1, Saifullah Memon2, Babar Hayat1, Ahsan Rafiq3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4685-4702, 2023, DOI:10.32604/cmc.2023.034892
    Abstract The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access… More >

  • Open AccessOpen Access

    ARTICLE

    Smart Contract to Traceability of Food Social Selling

    Cristian Valencia-Payan*, José Fernando Grass-Ramírez, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4703-4728, 2023, DOI:10.32604/cmc.2023.031554
    Abstract Traditionally, food sustainability has been considered solely in the stage of agricultural production. However, globalization, the expansion of the food production industry, and the emergence of supermarket chains that control the retail food market require specific significant changes in supply chains in the food sector and, therefore, we need to address the economic, social, and environmental impacts of these events. On the other hand, social selling has increased rapidly in recent years, with a further boom, following current events related to the coronavirus disease (COVID-19). This explosion of social sales, where there are usually no… More >

  • Open AccessOpen Access

    ARTICLE

    Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

    Mohanapriya Marimuthu1, Santhosh Rajendran2, Reshma Radhakrishnan2, Kalpana Rengarajan3, Shahzada Khurram4, Shafiq Ahmad5, Abdelaty Edrees Sayed5, Muhammad Shafiq6,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4729-4745, 2023, DOI:10.32604/cmc.2023.033020
    Abstract Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

    S. Sandhiyaa*, C. Gomathy
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4747-4768, 2023, DOI:10.32604/cmc.2023.032298
    Abstract The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization… More >

  • Open AccessOpen Access

    ARTICLE

    Probe Attack Detection Using an Improved Intrusion Detection System

    Abdulaziz Almazyad, Laila Halman, Alaa Alsaeed*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4769-4784, 2023, DOI:10.32604/cmc.2023.033382
    Abstract The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

    Manish Kumar1,2,*, Nitai Pal1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4785-4799, 2023, DOI:10.32604/cmc.2022.032971
    Abstract Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid… More >

  • Open AccessOpen Access

    ARTICLE

    Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

    Vivian Lee Lay Shan, Gan Keng Hoon*, Tan Tien Ping, Rosni Abdullah
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4801-4818, 2023, DOI:10.32604/cmc.2023.033752
    Abstract Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heavily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally… More >

  • Open AccessOpen Access

    ARTICLE

    Throughput Analysis of HARQ Scheme Based on Full-Duplex Two-Way AF SWIPT Relay

    Xiaoye Shi1,*, Fei Ding1,2, Haiting Zhu1, Zhaowei Zhang1, Lei Zhang1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4819-4830, 2023, DOI:10.32604/cmc.2023.018637
    Abstract The simultaneous wireless information and power transfer (SWIPT) relay system is one of the emerging technologies. Xiaomi Corporation and Motorola Inc. recently launched indoor wireless power transfer equipment is one of the most promising applications. To tap the potential of the system, hybrid automatic repeat request (HARQ) is introduced into the SWIPT relay system. Firstly, the time slot structure of HARQ scheme based on full duplex two-way amplify and forward (AF) SWIPT relay is given, and its retransmission status is analyzed. Secondly, the equivalent signal-to-noise ratio and outage probability of various states are calculated by More >

  • Open AccessOpen Access

    ARTICLE

    MCRO-PUF: A Novel Modified Crossover RO-PUF with an Ultra-Expanded CRP Space

    Hassan Rabiei1, Masoud Kaveh2, Mohammad Reza Mosavi1, Diego Martín3,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4831-4845, 2023, DOI:10.32604/cmc.2023.034981
    Abstract With the expanding use of the Internet of Things (IoT) devices and the connection of humans and devices to the Internet, the need to provide security in this field is constantly growing. The conventional cryptographic solutions need the IoT device to store secret keys in its non-volatile memory (NVM) leading the system to be vulnerable to physical attacks. In addition, they are not appropriate for IoT applications due to their complex calculations. Thus, physically unclonable functions (PUFs) have been introduced to simultaneously address these issues. PUFs are lightweight and easy-to-access hardware security primitives which employ… More >

  • Open AccessOpen Access

    ARTICLE

    Resource Management in UAV Enabled MEC Networks

    Muhammad Abrar1, Ziyad M. Almohaimeed2,*, Ushna Ajmal1, Rizwan Akram2, Rooha Masroor3, Muhammad Majid Hussain4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4847-4860, 2023, DOI:10.32604/cmc.2023.030242
    Abstract Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and More >

  • Open AccessOpen Access

    ARTICLE

    Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

    Gul Freen1, Sajida Kousar1, Nasreen Kausar2, Dragan Pamucar3, Georgia Irina Oros4,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4861-4879, 2023, DOI:10.32604/cmc.2023.032985
    Abstract The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Body-Transfer Wheelchair for Assisting Functionally Impaired People

    Chyi-Yeu Lin1,2,3, Bahrudin1, Salman Masroor1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4881-4900, 2023, DOI:10.32604/cmc.2023.032837
    Abstract Functionally impaired people always have difficulty accomplishing activities of daily living. In this regard, tasks including toileting and bathing have a higher prevalence rate of injuries and greater risk of falling. In this study, a body-transfer wheelchair was developed to assist people in transferring from bed to wheelchair for bathing, and toileting. The body-transfer wheelchair is a semi-automatic wheelchair that has features such as a controlled leg and backrest, linkage commode slot, and height adjustment. The wheelchair consists of a seat and a main frame that can be detached to enable bathtub transfer. This mechanism… More >

  • Open AccessOpen Access

    ARTICLE

    Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

    Yeonjee Choi1, Hyun Suk Hwang2, Chang Soo Kim1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4901-4916, 2023, DOI:10.32604/cmc.2023.035219
    Abstract A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs… More >

  • Open AccessOpen Access

    ARTICLE

    Social Engineering Attack Classifications on Social Media Using Deep Learning

    Yichiet Aun1,*, Ming-Lee Gan1, Nur Haliza Binti Abdul Wahab2, Goh Hock Guan1
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4917-4931, 2023, DOI:10.32604/cmc.2023.032373
    Abstract In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom… More >

  • Open AccessOpen Access

    ARTICLE

    Data Security Storage Mechanism Based on Blockchain Network

    Jin Wang1, Wei Ou1, Wenhai Wang2, R. Simon Sherratt3, Yongjun Ren4, Xiaofeng Yu5,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4933-4950, 2023, DOI:10.32604/cmc.2023.034148
    Abstract With the rapid development of information technology, the development of blockchain technology has also been deeply impacted. When performing block verification in the blockchain network, if all transactions are verified on the chain, this will cause the accumulation of data on the chain, resulting in data storage problems. At the same time, the security of data is also challenged, which will put enormous pressure on the block, resulting in extremely low communication efficiency of the block. The traditional blockchain system uses the Merkle Tree method to store data. While verifying the integrity and correctness of… More >

  • Open AccessOpen Access

    ARTICLE

    Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

    Allam Abumwais*, Mahmoud Obaid
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4951-4963, 2023, DOI:10.32604/cmc.2023.032822
    Abstract Modern shared-memory multi-core processors typically have shared Level 2 (L2) or Level 3 (L3) caches. Cache bottlenecks and replacement strategies are the main problems of such architectures, where multiple cores try to access the shared cache simultaneously. The main problem in improving memory performance is the shared cache architecture and cache replacement. This paper documents the implementation of a Dual-Port Content Addressable Memory (DPCAM) and a modified Near-Far Access Replacement Algorithm (NFRA), which was previously proposed as a shared L2 cache layer in a multi-core processor. Standard Performance Evaluation Corporation (SPEC) Central Processing Unit (CPU)… More >

  • Open AccessOpen Access

    ARTICLE

    Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

    Mohammad Yamin1,*, Mishaal Mofleh Almutairi2, Saeed Badghish3, Saleh Bajaba4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4965-4981, 2023, DOI:10.32604/cmc.2023.033705
    Abstract Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C… More >

  • Open AccessOpen Access

    ARTICLE

    Transparent Access to Heterogeneous IoT Based on Virtual Resources

    Wenquan Jin1, Sunhwan Lim2, Young-Ho Suh2, Chanwon Park2, Dohyeun Kim3,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4983-4999, 2023, DOI:10.32604/cmc.2023.020851
    Abstract The Internet of Things (IoT) inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily life. Edge computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected devices. According to the purpose of the environment including privacy level, domain functionality, network scale and service quality, various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge computing. However, for providing user-friendly service scenarios based on the transparent… More >

  • Open AccessOpen Access

    ARTICLE

    Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

    Ha Manh Toan1, Lam Thanh Hien2, Ngo Duc Vinh3, Do Nang Toan1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5001-5016, 2023, DOI:10.32604/cmc.2023.033429
    Abstract Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so… More >

  • Open AccessOpen Access

    ARTICLE

    Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

    Umair Muneer Butt1,2,*, Hadiqa Aman Ullah2, Sukumar Letchmunan1, Iqra Tariq2, Fadratul Hafinaz Hassan1, Tieng Wei Koh3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512
    Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification… More >

  • Open AccessOpen Access

    ARTICLE

    Offshore Software Maintenance Outsourcing Process Model Validation: A Case Study Approach

    Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Adel Sulaiman3, Muhammad Akram3, Ahmad Salman Khan4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5035-5048, 2023, DOI:10.32604/cmc.2023.034692
    Abstract The successful execution and management of Offshore Software Maintenance Outsourcing (OSMO) can be very beneficial for OSMO vendors and the OSMO client. Although a lot of research on software outsourcing is going on, most of the existing literature on offshore outsourcing deals with the outsourcing of software development only. Several frameworks have been developed focusing on guiding software system managers concerning offshore software outsourcing. However, none of these studies delivered comprehensive guidelines for managing the whole process of OSMO. There is a considerable lack of research working on managing OSMO from a vendor’s perspective. Therefore,… More >

  • Open AccessOpen Access

    ARTICLE

    Improving Brain Tumor Classification with Deep Learning Using Synthetic Data

    Muhammed Mutlu Yapici1, Rukiye Karakis2,*, Kali Gurkahraman3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5049-5067, 2023, DOI:10.32604/cmc.2023.035584
    Abstract Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay… More >

  • Open AccessOpen Access

    ARTICLE

    Research on Federated Learning Data Sharing Scheme Based on Differential Privacy

    Lihong Guo*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5069-5085, 2023, DOI:10.32604/cmc.2023.034571
    Abstract To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode. This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters. Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible… More >

  • Open AccessOpen Access

    ARTICLE

    A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

    Al-Hussien Seddik1, Mohammed Salah2, Gamal Behery2, Ahmed El-harby2, Ahmed Ismail Ebada2, Sokea Teng3, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5087-5103, 2023, DOI:10.32604/cmc.2023.035364
    Abstract The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency. Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image. This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation. This system consists of two components. The first component contains two processes, encryption process,… More >

  • Open AccessOpen Access

    REVIEW

    Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

    Batyrkhan Omarov1,*, Sergazi Narynov2, Zhandos Zhumanov2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5105-5122, 2023, DOI:10.32604/cmc.2023.034655
    Abstract Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of… More >

  • Open AccessOpen Access

    ARTICLE

    Impact of Artificial Compressibility on the Numerical Solution of Incompressible Nanofluid Flow

    Tohid Adibi1, Shams Forruque Ahmed2,*, Seyed Esmail Razavi3, Omid Adibi4, Irfan Anjum Badruddin5, Syed Javed5
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5123-5139, 2023, DOI:10.32604/cmc.2023.034008
    Abstract The numerical solution of compressible flows has become more prevalent than that of incompressible flows. With the help of the artificial compressibility approach, incompressible flows can be solved numerically using the same methods as compressible ones. The artificial compressibility scheme is thus widely used to numerically solve incompressible Navier-Stokes equations. Any numerical method highly depends on its accuracy and speed of convergence. Although the artificial compressibility approach is utilized in several numerical simulations, the effect of the compressibility factor on the accuracy of results and convergence speed has not been investigated for nanofluid flows in… More >

  • Open AccessOpen Access

    ARTICLE

    A Defect Detection Method for the Primary Stage of Software Development

    Qiang Zhi1, Wanxu Pu1, Jianguo Ren1, Zhengshu Zhou2,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5141-5155, 2023, DOI:10.32604/cmc.2023.035846
    Abstract In the early stage of software development, a software requirements specification (SRS) is essential, and whether the requirements are clear and explicit is the key. However, due to various reasons, there may be a large number of misunderstandings. To generate high-quality software requirements specifications, numerous researchers have developed a variety of ways to improve the quality of SRS. In this paper, we propose a questions extraction method based on SRS elements decomposition, which evaluates the quality of SRS in the form of numerical indicators. The proposed method not only evaluates the quality of SRSs but… More >

  • Open AccessOpen Access

    ARTICLE

    Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

    Mousa Alhajlah*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5157-5172, 2023, DOI:10.32604/cmc.2023.033339
    Abstract Underwater images degraded due to low contrast and visibility issues. Therefore, it is important to enhance the images and videos taken in the underwater environment before processing. Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images. The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera. Various researchers have proposed different solutions to overcome these problems. Dark channel prior… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

    Hengfu Yang1,2,*, Mingfang Jiang1,2, Zhichen Gao3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5173-5189, 2023, DOI:10.32604/cmc.2023.034819
    Abstract Few previous Reversible Visible Watermarking (RVW) schemes have both good transparency and watermark visibility. An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding (BTC) compressed domain, called TVB-RVW is proposed in this paper. A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation. Then, a visual perception factor computation model is devised by fusing texture and luminance characteristics. An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC More >

  • Open AccessOpen Access

    ARTICLE

    Data Augmentation and Random Multi-Model Deep Learning for Data Classification

    Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420
    Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of More >

  • Open AccessOpen Access

    ARTICLE

    Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

    Noor Gul1,2, Saeed Ahmed1,3, Su Min Kim1, Muhammad Sajjad Khan4, Junsu Kim1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5209-5227, 2023, DOI:10.32604/cmc.2023.026945
    Abstract A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing More >

  • Open AccessOpen Access

    ARTICLE

    Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

    Mohamed Elnagi1, Salah Kamel2, Abdelhady Ramadan2, Mohamed F. Elnaggar3,4,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5229-5250, 2023, DOI:10.32604/cmc.2023.032469
    Abstract Renewable energy sources are gaining popularity, particularly photovoltaic energy as a clean energy source. This is evident in the advancement of scientific research aimed at improving solar cell performance. Due to the non-linear nature of the photovoltaic cell, modeling solar cells and extracting their parameters is one of the most important challenges in this discipline. As a result, the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate. In this paper, a weIghted meaN oF vectOrs algorithm (INFO) that calculates the weighted mean for a set of vectors… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

    Hanan Abdullah Mengash1, Hamed Alqahtani2, Mohammed Maray3, Mohamed K. Nour4, Radwa Marzouk1, Mohammed Abdullah Al-Hagery5, Heba Mohsen6, Mesfer Al Duhayyim7,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5251-5265, 2023, DOI:10.32604/cmc.2023.032729
    Abstract Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the More >

  • Open AccessOpen Access

    ARTICLE

    Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

    Muhammad Javaid Iqbal1, Muhammad Waseem Iqbal2, Muhammad Anwar3,*, Muhammad Murad Khan4, Abd Jabar Nazimi5, Mohammad Nazir Ahmad6
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024
    Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images… More >

  • Open AccessOpen Access

    ARTICLE

    Billiards Optimization Algorithm: A New Game-Based Metaheuristic Approach

    Hadi Givi1,*, Marie Hubálovská2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5283-5300, 2023, DOI:10.32604/cmc.2023.034695
    Abstract Metaheuristic algorithms are one of the most widely used stochastic approaches in solving optimization problems. In this paper, a new metaheuristic algorithm entitled Billiards Optimization Algorithm (BOA) is proposed and designed to be used in optimization applications. The fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards game. Various steps of BOA are described and then its mathematical model is thoroughly explained. The efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal, high-dimensional multimodal, and More >

  • Open AccessOpen Access

    ARTICLE

    Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

    Muhammad Ibrahim1, Imran Sarwar Bajwa1, Nadeem Sarwar2,*, Haroon Abdul Waheed3, Muhammad Zulkifl Hasan4, Muhammad Zunnurain Hussain4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5301-5317, 2023, DOI:10.32604/cmc.2023.032856
    Abstract Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To overcome the cold start problem, the new overall rating is generated… More >

  • Open AccessOpen Access

    ARTICLE

    A Transaction Frequency Based Trust for E-Commerce

    Dong Huang1,*, Sean Xu2
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5319-5329, 2023, DOI:10.32604/cmc.2023.033798
    Abstract Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration, which makes it easy for one party of the transaction to obtain a high trust value in a short time, and brings many disadvantages, uncertainties and even attacks. To solve this problem, a transaction frequency based trust is proposed in this study. The proposed method is composed of two parts. The first part is built on the classic Bayes analysis based trust models which are ease of computing for the E-commerce system. The second part is the More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Numerical Scheme for Solving Large System of Nonlinear Equations

    Mudassir Shams1, Nasreen Kausar2,*, Shams Forruque Ahmed3, Irfan Anjum Badruddin4, Syed Javed4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5331-5347, 2023, DOI:10.32604/cmc.2023.033528
    Abstract A fifth-order family of an iterative method for solving systems of nonlinear equations and highly nonlinear boundary value problems has been developed in this paper. Convergence analysis demonstrates that the local order of convergence of the numerical method is five. The computer algebra system CAS-Maple, Mathematica, or MATLAB was the primary tool for dealing with difficult problems since it allows for the handling and manipulation of complex mathematical equations and other mathematical objects. Several numerical examples are provided to demonstrate the properties of the proposed rapidly convergent algorithms. A dynamic evaluation of the presented methods More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

    Khalid A. Alissa1, Mohammed Maray2, Areej A. Malibari3, Sana Alazwari4, Hamed Alqahtani5, Mohamed K. Nour6, Marwa Obbaya7, Mohamed A. Shamseldin8, Mesfer Al Duhayyim9,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5349-5367, 2023, DOI:10.32604/cmc.2023.033532
    Abstract Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such… More >

  • Open AccessOpen Access

    ARTICLE

    Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

    Atulya Gupta*, Rajendra Prasad Mahapatra
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5369-5387, 2023, DOI:10.32604/cmc.2023.031261
    Abstract Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and… More >

  • Open AccessOpen Access

    ARTICLE

    A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

    Samir M. Umran1,2, Songfeng Lu1,3, Zaid Ameen Abduljabbar1,4, Xueming Tang1,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5389-5416, 2023, DOI:10.32604/cmc.2023.034331
    Abstract There are numerous internet-connected devices attached to the industrial process through recent communication technologies, which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things (IIoTs). Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services, external trusted authorities, and centralized architectures; they have high computation and communication costs, low performance, and are exposed to a single authority of failure and bottleneck. Blockchain technology (BC) is widely adopted in the industrial sector for its valuable features in… More >

  • Open AccessOpen Access

    ARTICLE

    Identification of Anomaly Scenes in Videos Using Graph Neural Networks

    Khalid Masood1, Mahmoud M. Al-Sakhnini2,3, Waqas Nawaz4,*, Tauqeer Faiz5,6, Abdul Salam Mohammad7, Hamza Kashif8
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5417-5430, 2023, DOI:10.32604/cmc.2023.033590
    Abstract Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language… More >

  • Open AccessOpen Access

    ARTICLE

    Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

    Muhammad Umair1,*, Zafar Saeed1, Faisal Saeed2, Hiba Ishtiaq1, Muhammad Zubair1, Hala Abdel Hameed3,4
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5431-5446, 2023, DOI:10.32604/cmc.2023.033884
    Abstract As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure,… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

    Badriyya B. Al-onazi1, Saud S. Alotaib2, Saeed Masoud Alshahrani3,*, Najm Alotaibi4, Mrim M. Alnfiai5, Ahmed S. Salama6, Manar Ahmed Hamza7
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564
    Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality… More >

  • Open AccessOpen Access

    ARTICLE

    Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

    Mohammed Maray1, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Saeed Masoud Alshahrani4,*, Najm Alotaibi5, Sana Alazwari6, Mahmoud Othman7, Manar Ahmed Hamza8
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5467-5482, 2023, DOI:10.32604/cmc.2023.033534
    Abstract The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The… More >

  • Open AccessOpen Access

    ARTICLE

    Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

    Meshal Alharbi, Shabana R. Ziyad*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5483-5505, 2023, DOI:10.32604/cmc.2023.032341
    Abstract Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature… More >

  • Open AccessOpen Access

    ARTICLE

    Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

    Subhajit Chatterjee1, Yung-Cheol Byun2,*
    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5507-5525, 2023, DOI:10.32604/cmc.2023.032843
    Abstract The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with… More >

Per Page:

Share Link