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  • Open Access

    ARTICLE

    Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

    Hairani Hairani1,2, Triyanna Widiyaningtyas1,*, Didik Dwi Prasetya1, Afrig Aminuddin3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2931-2949, 2025, DOI:10.32604/cmc.2024.060837 - 17 February 2025

    Abstract An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of… More >

  • Open Access

    REVIEW

    Particle Swarm Optimization: Advances, Applications, and Experimental Insights

    Laith Abualigah*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025

    Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

  • Open Access

    ARTICLE

    Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

    Tanzila Saba1, Muhammad Mujahid1, Shaha Al-Otaibi2, Noor Ayesha3, Amjad Rehman Khan1,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3337-3353, 2025, DOI:10.32604/cmc.2025.060163 - 17 February 2025

    Abstract Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with… More >

  • Open Access

    ARTICLE

    Diagnosing Retinal Eye Diseases: A Novel Transfer Learning Approach

    Mohammed Salih Ahmed1, Atta Rahman2,*, Yahya Alhabboub1, Khalid Alzahrani1, Hassan Baragbah1, Basel Altaha1, Hussein Alkatout1, Sardar Asad Ali Biabani3,4, Rashad Ahmed5, Aghiad Bakry2

    Intelligent Automation & Soft Computing, Vol.40, pp. 149-175, 2025, DOI:10.32604/iasc.2025.059080 - 12 February 2025

    Abstract This study rigorously evaluates the potential of transfer learning in diagnosing retinal eye diseases using advanced models such as YOLOv8, Xception, ConvNeXtTiny, and VGG16. All models were trained on the esteemed RFMiD dataset, which includes images classified into six critical categories: Diabetic Retinopathy (DR), Macular Hole (MH), Diabetic Neuropathy (DN), Optic Disc Changes (ODC), Tesselated Fundus (TSLN), and normal cases. The research emphasizes enhancing model performance by prioritizing recall metrics, a crucial strategy aimed at minimizing false negatives in medical diagnostics. To address the challenge of imbalanced data, we implemented effective preprocessing techniques, including cropping,… More >

  • Open Access

    ARTICLE

    Cultural Adaptation of the Mental Health Literacy Scale

    Anwar Khatib1,2,*, Avital Laufer3, Michal Finkelstein2, Marc Gelkopf1

    International Journal of Mental Health Promotion, Vol.27, No.1, pp. 19-28, 2025, DOI:10.32604/ijmhp.2024.057925 - 31 January 2025

    Abstract Background: Mental health literacy (MHL) refers to one’s knowledge and understanding of mental health disorders and their treatments. This literacy may be influenced by cultural norms and values that shape individuals’ experiences, beliefs, attitudes, and behaviors regarding mental health. This study focuses on adapting the Mental health literacy scale (MHLS) for use in the multicultural context of Israel. Objectives include validating its construct, assessing its accuracy in measuring MHL in this diverse setting and examining and comparing levels of MHL across different cultural groups. Methods: The data collection included 1057 participants, representing all the ethnic… More >

  • Open Access

    REVIEW

    Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

    Rakesh Kumar1,*, Ajay Singh2, Ahmed Subahi Ahmed Kassar3, Mohammed Ismail Humaida3, Sudhanshu Joshi4, Manu Sharma5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1289-1349, 2025, DOI:10.32604/cmes.2025.059152 - 27 January 2025

    Abstract This review aims to analyze the development and impact of Artificial Intelligence (AI) in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives. It is extensively devoted to AI technology deployment relevant to disease management, healthcare delivery, epidemiology, and policy-making. However, its AI is culturally sensitive and ethically grounded in Islam. Based on the PRISMA framework, an SLR evaluated primary academic literature, cases, and practices of Saudi Arabia’s AI implementation in the public healthcare sector. Instead, it categorizes prior research based on how AI can work, the issues it poses, and… More >

  • Open Access

    ARTICLE

    A Blockchain-Based Access Management System for Enhanced Patient Privacy and Secure Telehealth and Telemedicine Data

    Ayoub Ghani1,*, Ahmed Zinedine1, Mohammed El Mohajir2

    Intelligent Automation & Soft Computing, Vol.40, pp. 75-98, 2025, DOI:10.32604/iasc.2025.060143 - 23 January 2025

    Abstract The Internet of Things (IoT) advances allow healthcare providers to distantly gather and immediately analyze patient health data for diagnostic purposes via connected health devices. In a COVID-19-like pandemic, connected devices can mitigate virus spread and make essential information, such as respiratory patterns, available to healthcare professionals. However, these devices generate vast amounts of data, rendering them susceptible to privacy breaches, and data leaks. Blockchain technology is a robust solution to address these issues in telemedicine systems. This paper proposes a blockchain-based access management solution to enhance patient privacy and secure telehealth and telemedicine data.… More >

  • Open Access

    ARTICLE

    Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network

    Yongfeng Tai1, Xingyu Yan2, Xiangyi Geng3, Lin Mu4, Mingshun Jiang2, Faye Zhang2,*

    Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

    Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

  • Open Access

    ARTICLE

    Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

    Liyao Yang1, Hongyan Ma1,2,3,*, Yingda Zhang1, Wei He1

    Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

    Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

  • Open Access

    ARTICLE

    Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

    Yu Zhang, Daoyu Zhang*, Tiezhou Wu

    Energy Engineering, Vol.122, No.1, pp. 203-220, 2025, DOI:10.32604/ee.2024.056244 - 27 December 2024

    Abstract Precisely estimating the state of health (SOH) of lithium-ion batteries is essential for battery management systems (BMS), as it plays a key role in ensuring the safe and reliable operation of battery systems. However, current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation. Additionally, the Elman neural network, which is commonly employed for SOH estimation, exhibits several drawbacks, including slow training speed, a tendency to become trapped in local minima, and the initialization of weights and thresholds using pseudo-random numbers, leading to unstable model performance.… More >

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