J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >

Abdulaziz Alshahrani^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4067-4090, 2025, DOI:10.32604/cmc.2025.065211 - 23 September 2025

Abstract High dropout rates in short-term job skills training programs hinder workforce development. This study applies machine learning to predict program completion while addressing class imbalance challenges. A dataset of 6548 records with 24 demographic, educational, program-specific, and employment-related features was analyzed. Data preprocessing involved cleaning, encoding categorical variables, and balancing the dataset using the Synthetic Minority Oversampling Technique (SMOTE), as only 15.9% of participants were dropouts. six machine learning models—Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and XGBoost—were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split. Performance More >

Saleh Albahli^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1095-1128, 2025, DOI:10.32604/cmes.2025.058821 - 11 April 2025

Abstract Predicting hospital readmission and length of stay (LOS) for diabetic patients is critical for improving healthcare quality, optimizing resource utilization, and reducing costs. This study leverages machine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade. A comprehensive preprocessing pipeline, including feature selection, data transformation, and class balancing, was implemented to ensure data quality and enhance model performance. Exploratory analysis revealed key patterns, such as the influence of age and the number of diagnoses on readmission rates, guiding the More >

Lei Shen^1,2, Chutong Zhang², Yuwei Ge¹, Shanyun Gu¹, Qiang Gao¹, Wei Li¹, Jie Ji^2,*

Energy Engineering, Vol.122, No.4, pp. 1581-1602, 2025, DOI:10.32604/ee.2025.061010 - 31 March 2025

Abstract The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis. This paper presents an innovative framework, the Intelligent Power Stability and Scheduling (IPSS) System, which is designed to enhance the safety, stability, and economic efficiency of power systems, particularly those integrated with green energy sources. The IPSS System is distinguished by its integration of a CNN-Transformer predictive model, which leverages the strengths of Convolutional Neural Networks (CNN) for local feature extraction and Transformer architecture for global dependency modeling, offering significant… More > Graphic Abstract

Avi Deb Raha¹, Fatema Jannat Dihan², Mrityunjoy Gain¹, Saydul Akbar Murad³, Apurba Adhikary², Md. Bipul Hossain², Md. Mehedi Hassan¹, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Mohammed Kadrie⁴, Anupam Kumar Bairagi^1,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024

Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >

Soha Rawas¹, Cerine Tafran¹, Duaa AlSaeed², Nadia Al-Ghreimil^2,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4575-4601, 2024, DOI:10.32604/cmc.2024.055307 - 19 December 2024

Abstract In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the… More >

Man Liu¹, Zhuoheng Lv^1,#, Hongjing Wang^2,*, Lu Liu^1,*

Psycho-Oncologie, Vol.18, No.4, pp. 305-316, 2024, DOI:10.32604/po.2024.054098 - 04 December 2024

Abstract Objective: Non-small cell lung cancer (NSCLC) patients often experience significant fear of recurrence. To facilitate precise identification and appropriate management of this fear, this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network (BPNN) against logistic regression in modeling fear of cancer recurrence prediction. Methods: Data from 596 NSCLC patients, collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences, were analyzed. Nine clinically and statistically significant variables, identified via univariate logistic regression, were inputted into both BPNN and logistic regression models developed… More >

Mitra Madanchian^1,*, Hamed Taherdoost^1,2,3,4

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

Gagan Deep^*, Jyoti Verma

Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329 - 22 September 2023

Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information… More >

Muhammad Hammad Waseem¹, Malik Sajjad Ahmed Nadeem^1,*, Ishtiaq Rasool Khan², Seong-O-Shim³, Wajid Aziz¹, Usman Habib⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710 - 31 March 2023

Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the… More >