Home / Journals / CSSE / Vol.48, No.1, 2024
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  • Open AccessOpen Access

    REVIEW

    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866
    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this… More >

  • Open AccessOpen Access

    ARTICLE

    A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud

    Hayder Makki Shakir, Jaber Karimpour*, Jafar Razmara
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545
    Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this… More >

  • Open AccessOpen Access

    ARTICLE

    Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

    P. Nandakumar, R. Subhashini*
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 57-75, 2024, DOI:10.32604/csse.2023.042294
    Abstract Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was… More >

  • Open AccessOpen Access

    ARTICLE

    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer1,*, Ahmed H. Samak2, Ahmed I. Taloba3,4, Rasha M. Abd El-Aziz3,5
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

  • Open AccessOpen Access

    ARTICLE

    Computational Linguistics Based Arabic Poem Classification and Dictarization Model

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Najm Alotaibi3, Mohamed K. Nour4, Mahmoud Othman5, Gouse Pasha Mohammed1, Mohammed Rizwanullah1, Mohamed I. Eldesouki6
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 97-114, 2024, DOI:10.32604/csse.2023.034520
    Abstract Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model comprising deep belief network (DBN),… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Based Vehicle Detection and Counting System for Intelligent Transportation

    A. Vikram1, J. Akshya2, Sultan Ahmad3,4, L. Jerlin Rubini5, Seifedine Kadry6,7,8, Jungeun Kim9,*
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 115-130, 2024, DOI:10.32604/csse.2023.037928
    Abstract Traffic monitoring through remote sensing images (RSI) is considered an important research area in Intelligent Transportation Systems (ITSs). Vehicle counting systems must be simple enough to be implemented in real-time. With the fast expansion of road traffic, real-time vehicle counting becomes essential in constructing ITS. Compared with conventional technologies, the remote sensing-related technique for vehicle counting exhibits greater significance and considerable advantages in its flexibility, low cost, and high efficiency. But several techniques need help in balancing complexity and accuracy technique. Therefore, this article presents a deep learning-based vehicle detection and counting system for ITS (DLVDCS-ITS) in remote sensing images.… More >

  • Open AccessOpen Access

    ARTICLE

    Intrusion Detection and Prevention Model for Blockchain Based IoMT Applications

    Jameel Almalki*
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 131-152, 2024, DOI:10.32604/csse.2023.038085
    (This article belongs to this Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract The recent global pandemic has resulted in growth in the medical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare and medical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internet-based channels to be stored and analyzed in centralized servers. Patient diagnostics are performed based on the information retrieved… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Autoencoder-Based Hybrid Network for Building Energy Consumption Forecasting

    Noman Khan1,2, Samee Ullah Khan1,2, Sung Wook Baik1,2,*
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 153-173, 2024, DOI:10.32604/csse.2023.039407
    (This article belongs to this Special Issue: Metaheuristics Optimization for Real-World Applications)
    Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

  • Open AccessOpen Access

    ARTICLE

    Letter Recognition Reinvented: A Dual Approach with MLP Neural Network and Anomaly Detection

    Nesreen M. Alharbi*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 175-198, 2024, DOI:10.32604/csse.2023.041044
    Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… More >

  • Open AccessOpen Access

    ARTICLE

    Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction

    Ahmad Almadhor1, Chitapong Wechtaisong2,*, Usman Tariq3, Natalia Kryvinska4,*, Abdullah Al Hejaili5, Uzma Ghulam Mohammad6, Mohana Alanazi7
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 199-216, 2024, DOI:10.32604/csse.2023.039546
    Abstract Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfort model that makes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs the ASHRAE RP-884 standard dataset… More >

  • Open AccessOpen Access

    ARTICLE

    RLAT: Lightweight Transformer for High-Resolution Range Profile Sequence Recognition

    Xiaodan Wang*, Peng Wang, Yafei Song, Qian Xiang, Jingtai Li
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 217-246, 2024, DOI:10.32604/csse.2023.039846
    (This article belongs to this Special Issue: Pattern Recognition Based on Machine Learning)
    Abstract High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover, most existing methods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism

    Sanxiu Jiao1, Lecai Cai2,*, Jintao Meng3, Yue Zhao3, Kui Cheng2
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194
    (This article belongs to this Special Issue: Security and Privacy Challenges in Smart City)
    Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded… More >

  • Open AccessOpen Access

    CORRECTION

    Correction: Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

    L. Maria Michael Visuwasam1,*, D. Paul Raj2
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 267-267, 2024, DOI:10.32604/csse.2023.047461
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    CORRECTION

    Correction: Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 269-269, 2024, DOI:10.32604/csse.2023.047533
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    CORRECTION

    Correction: Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem

    Mariem Belhor1,2,3, Adnen El-Amraoui1,*, Abderrazak Jemai2, François Delmotte1
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 271-271, 2024, DOI:10.32604/csse.2023.048573
    Abstract This article has no abstract. More >

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