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  • Open Access

    ARTICLE

    Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

    Irfan Khan1, Xianchao Zhang1,*, Ramesh Kumar Ayyasamy2,*, Saadat M. Alhashmi3, Azizur Rahim4

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1611-1636, 2025, DOI:10.32604/cmes.2025.058566 - 27 January 2025

    Abstract The rapid growth of machine learning (ML) across fields has intensified the challenge of selecting the right algorithm for specific tasks, known as the Algorithm Selection Problem (ASP). Traditional trial-and-error methods have become impractical due to their resource demands. Automated Machine Learning (AutoML) systems automate this process, but often neglect the group structures and sparsity in meta-features, leading to inefficiencies in algorithm recommendations for classification tasks. This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso (MSGL) to address these limitations. Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional More >

  • Open Access

    REVIEW

    Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

    Syed Ijaz Ur Rahman1, Naveed Abbas1, Sikandar Ali2, Muhammad Salman1, Ahmed Alkhayat3, Jawad Khan4,*, Dildar Hussain5, Yeong Hyeon Gu5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1199-1231, 2025, DOI:10.32604/cmes.2025.057462 - 27 January 2025

    Abstract Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system. Analysis of white blood cells (WBCs) in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts. For Acute Lymphocytic Leukemia (ALL), the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse. The researchers have done a lot of work in this field, to demonstrate… More >

  • Open Access

    ARTICLE

    Enhancing Network Security: Leveraging Machine Learning for Integrated Protection and Intrusion Detection

    Nada Mohammed Murad1, Adnan Yousif Dawod2, Saadaldeen Rashid Ahmed3,4,*, Ravi Sekhar5, Pritesh Shah5

    Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624 - 10 January 2025

    Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

  • Open Access

    ARTICLE

    Energy-Efficient Internet of Things-Based Wireless Sensor Network for Autonomous Data Validation for Environmental Monitoring

    Tabassum Kanwal1, Saif Ur Rehman1,*, Azhar Imran2, Haitham A. Mahmoud3

    Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

    Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >

  • Open Access

    REVIEW

    The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications

    Jinlong Wang1,2,*, Zhenyu Liu1, Xingtao Yang1, Min Li1, Zhihan Lyu3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1-39, 2025, DOI:10.32604/cmc.2024.058926 - 03 January 2025

    Abstract With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from More >

  • Open Access

    ARTICLE

    Machine Learning for QoS Optimization and Energy-Efficient in Routing Clustering Wireless Sensors

    Rahma Gantassi, Zaki Masood, Yonghoon Choi*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 327-343, 2025, DOI:10.32604/cmc.2024.058143 - 03 January 2025

    Abstract Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem (TSP) to locate the optimal path traversed by the Mobile Data Collector (MDC), in terms of energy and QoS efficiency. To be more specific, to minimize energy consumption in the CH election stage, we have developed the M-T protocol using the K-Means and the grid clustering algorithms. In addition, to improve More >

  • Open Access

    REVIEW

    Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

    Amjad Rehman1, Muhammad Mujahid1, Alex Elyassih1, Bayan AlGhofaily1, Saeed Ali Omer Bahaj2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 41-72, 2025, DOI:10.32604/cmc.2024.058036 - 03 January 2025

    Abstract In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a… More >

  • Open Access

    ARTICLE

    Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

    Adel Binbusayyis*, Mohemmed Sha

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 909-931, 2025, DOI:10.32604/cmes.2024.058202 - 17 December 2024

    Abstract Prediction of stability in SG (Smart Grid) is essential in maintaining consistency and reliability of power supply in grid infrastructure. Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid. It also possesses a better impact on averting overloading and permitting effective energy storage. Even though many traditional techniques have predicted the consumption rate for preserving stability, enhancement is required in prediction measures with minimized loss. To overcome the complications in existing studies, this paper intends to predict stability from the smart grid… More >

  • Open Access

    ARTICLE

    Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

    Mohd Anjum1, Naoufel Kraiem2, Hong Min3,*, Ashit Kumar Dutta4, Yousef Ibrahim Daradkeh5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 357-384, 2025, DOI:10.32604/cmes.2024.057889 - 17 December 2024

    Abstract Machine learning (ML) is increasingly applied for medical image processing with appropriate learning paradigms. These applications include analyzing images of various organs, such as the brain, lung, eye, etc., to identify specific flaws/diseases for diagnosis. The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification. Most of the extracted image features are irrelevant and lead to an increase in computation time. Therefore, this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features. This process… More >

  • Open Access

    REVIEW

    Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models

    Shanmugasundaram Hariharan1, D. Anandan2, Murugaperumal Krishnamoorthy3, Vinay Kukreja4, Nitin Goyal5, Shih-Yu Chen6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 91-122, 2025, DOI:10.32604/cmes.2024.057214 - 17 December 2024

    Abstract Liver cancer remains a leading cause of mortality worldwide, and precise diagnostic tools are essential for effective treatment planning. Liver Tumors (LTs) vary significantly in size, shape, and location, and can present with tissues of similar intensities, making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging. This review examines recent advancements in Liver Segmentation (LS) and Tumor Segmentation (TS) algorithms, highlighting their strengths and limitations regarding precision, automation, and resilience. Performance metrics are utilized to assess key detection algorithms and analytical methods, emphasizing their effectiveness and relevance in clinical contexts. The More >

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