Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025

Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >

Samira Marhraoui^1,2,*, Basma Saad³, Hassan Silkan¹, Said Laasri², Asmaa El Hannani³

Energy Engineering, Vol.122, No.7, pp. 2653-2672, 2025, DOI:10.32604/ee.2025.064498 - 27 June 2025

Abstract Photovoltaic (PV) power forecasting is essential for balancing energy supply and demand in renewable energy systems. However, the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation. This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies: Hybrid-Si, Mono-Si, and Poly-Si, across three forecasting horizons: 1-step, 12-step, and 24-step. Among the tested models, the Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture exhibited superior performance, particularly for the 24-step horizon, achieving R² = 0.9793 and MAE = 0.0162 for More >

Saadia Tabassum^1,2, Fazal Muhammad², Muhammad Ayaz Khan³, Muhammad Uzair Khan^2,4, Dawar Awan⁴, Neelam Gohar⁵, Shahid Khan⁶, Amal Al-Rasheed^7,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1253-1278, 2025, DOI:10.32604/cmc.2025.065423 - 09 June 2025

Abstract Early and accurate detection of Heart Disease (HD) is critical for improving patient outcomes, as HD remains a leading cause of mortality worldwide. Timely and precise prediction can aid in preventive interventions, reducing fatal risks associated with misdiagnosis. Machine learning (ML) models have gained significant attention in healthcare for their ability to assist professionals in diagnosing diseases with high accuracy. This study utilizes 918 instances from publicly available UCI and Kaggle datasets to develop and compare the performance of various ML models, including Adaptive Boosting (AB), Naïve Bayes (NB), Extreme Gradient Boosting (XGB), Bagging, and… More >

Zhenfu Zhang¹, Haiyan Yin², Liudong Zuo³, Pan Lai^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 919-936, 2025, DOI:10.32604/cmc.2025.062980 - 09 June 2025

Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >

Ganesh Wakte^1,*, Mukesh Kumar², Mohammad Aljaidi³, Ramesh Kumar⁴, Manish Kumar Singla⁴

Energy Engineering, Vol.122, No.6, pp. 2519-2537, 2025, DOI:10.32604/ee.2025.060658 - 29 May 2025

Abstract Nodal pricing is a critical mechanism in electricity markets, utilized to determine the cost of power transmission to various nodes within a distribution network. As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations, traditional nodal pricing models often fall short to meet these new challenges. This research introduces a novel enhanced nodal pricing mechanism for distribution networks, integrating advanced optimization techniques and hybrid models to overcome these limitations. The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and… More > Graphic Abstract

Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Ahmed El Youssefi¹, Abdelaaziz Hessane^1,2, Imad Zeroual¹, Yousef Farhaoui^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3411-3433, 2025, DOI:10.32604/cmc.2025.063218 - 16 April 2025

Abstract This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators. In this work, over 130 technical indicators—covering momentum, volatility, volume, and trend-related technical indicators—are subjected to three distinct feature selection approaches. Specifically, mutual information (MI), recursive feature elimination (RFE), and random forest importance (RFI). By extracting an optimal set of 20 predictors, the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability. These feature subsets are integrated into support vector regression (SVR), Huber regressors, and k-nearest neighbors (KNN) models to forecast the… More >

Abdullah A. Asiri¹, Toufique A. Soomro^2,3,*, Ahmed Ali⁴, Faisal Bin Ubaid⁵, Muhammad Irfan^6,*, Khlood M. Mehdar⁷, Magbool Alelyani⁸, Mohammed S. Alshuhri⁹, Ahmad Joman Alghamdi¹⁰, Sultan Alamri¹⁰

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 255-287, 2025, DOI:10.32604/cmes.2025.061683 - 11 April 2025

Abstract Global mortality rates are greatly impacted by malignancies of the brain and nervous system. Although, Magnetic Resonance Imaging (MRI) plays a pivotal role in detecting brain tumors; however, manual assessment is time-consuming and susceptible to human error. To address this, we introduce ICA2-SVM, an advanced computational framework integrating Independent Component Analysis Architecture-2 (ICA2) and Support Vector Machine (SVM) for automated tumor segmentation and classification. ICA2 is utilized for image preprocessing and optimization, enhancing MRI consistency and contrast. The Fast-Marching Method (FMM) is employed to delineate tumor regions, followed by SVM for precise classification. Validation on More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Sofia Yousuf¹, Muhammad Bilal Kadri^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1193-1210, 2025, DOI:10.32604/cmc.2024.054587 - 03 January 2025

Abstract Terrain Aided Navigation (TAN) technology has become increasingly important due to its effectiveness in environments where Global Positioning System (GPS) is unavailable. In recent years, TAN systems have been extensively researched for both aerial and underwater navigation applications. However, many TAN systems that rely on recursive Unmanned Aerial Vehicle (UAV) position estimation methods, such as Extended Kalman Filters (EKF), often face challenges with divergence and instability, particularly in highly non-linear systems. To address these issues, this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter. To enhance the… More >