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  • Open Access

    ARTICLE

    IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

    Waleed T. Al-Sit1, Nidal A. Al-Dmour2, Taher M. Ghazal3,4,*, Ghassan F. Issa3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6867-6878, 2023, DOI:10.32604/cmc.2023.034952 - 28 December 2022

    Abstract In the age of universal computing, human life is becoming smarter owing to the recent developments in the Internet of Medical Things (IoMT), wearable sensors, and telecommunication innovations, which provide more effective and smarter healthcare facilities. IoMT has the potential to shape the future of clinical research in the healthcare sector. Wearable sensors, patients, healthcare providers, and caregivers can connect through an IoMT network using software, information, and communication technology. Ambient assisted living (AAL) allows the incorporation of emerging innovations into the routine life events of patients. Machine learning (ML) teaches machines to learn from More >

  • Open Access

    ARTICLE

    A More Efficient Approach for Remote Sensing Image Classification

    Huaxiang Song*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5741-5756, 2023, DOI:10.32604/cmc.2023.034921 - 28 December 2022

    Abstract Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC) of remote sensing images (RSI). However, the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms, e.g., automation and simplicity, partially lost. Traditional ML strategies (e.g., the handcrafted features or indicators) and accuracy-aimed strategies with a high trade-off (e.g., the multi-stage CNNs and ensemble of multi-CNNs) are widely used without any training efficiency optimization involved, which… More >

  • Open 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 - 28 December 2022

    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 Access

    ARTICLE

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

    Xiao Ya Ma1,2,*, Jin Tong1,2, Fei Jiang3, Min Xu4, Li Mei Sun1, Qiu Yan Chen1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6145-6159, 2023, DOI:10.32604/cmc.2023.034833 - 28 December 2022

    Abstract Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long… More >

  • Open 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 - 28 December 2022

    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 Access

    ARTICLE

    Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

    Mudiyala Aparna, Battula Srinivasa Rao*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796 - 28 December 2022

    Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify… More >

  • Open Access

    ARTICLE

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

    Aliaa El-Gawady1,*, BenBella S. Tawfik1, Mohamed A. Makhlouf1,2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5559-5572, 2023, DOI:10.32604/cmc.2023.034734 - 28 December 2022

    Abstract Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are… More >

  • Open Access

    ARTICLE

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

    Mohsen Sharifpur1,2, Mohamed Salem3, Yonis M Buswig4, Habib Forootan Fard5, Jaroon Rungamornrat6,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5679-5690, 2023, DOI:10.32604/cmc.2023.034726 - 28 December 2022

    Abstract Residential sector is one of the energy-consuming districts of countries that causes CO2 emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO2 and other greenhouse gases. In the present work, CO2 emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries… More >

  • Open 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 - 28 December 2022

    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 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 - 28 December 2022

    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 >

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