Mehmet Balci^1,*, Emrah Dokur², Ugur Yuzgec³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 945-968, 2025, DOI:10.32604/cmes.2025.067851 - 31 July 2025

Abstract Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems. Nevertheless, the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting. To address these difficulties, this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory (LSTM) network with a Single Candidate Optimizer (SCO) algorithm. In contrast to conventional techniques that rely on random parameter initialization, the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work More > Graphic Abstract

Hussain Dawood¹, Marriam Nawaz², Tahira Nazir³, Ali Javed², Abdul Khader Jilani Saudagar^4,*, Hatoon S. AlSagri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025

Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >

Harendra Pratap Singh^1,2, Ishfaq Hussain Rather³, Sushil Kumar¹, Mohammad Aljaidi⁴, Omprakash Kaiwartya^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5577-5595, 2025, DOI:10.32604/cmc.2025.065741 - 30 July 2025

Abstract Electric Vehicles (EVs) have emerged as a cleaner, low-carbon, and environmentally friendly alternative to traditional internal combustion engine (ICE) vehicles. With the increasing adoption of EVs, they are expected to eventually replace ICE vehicles entirely. However, the rapid growth of EVs has significantly increased energy demand, posing challenges for power grids and infrastructure. This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road. To address these challenges, various deep… More >

Sanjay P. Pande¹, Sarika Khandelwal², Ganesh K. Yenurkar^3,*, Rakhi D. Wajgi³, Vincent O. Nyangaresi^4,5,*, Pratik R. Hajare⁶, Poonam T. Agarkar⁷

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5937-5975, 2025, DOI:10.32604/cmc.2025.064505 - 30 July 2025

Abstract Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems, particularly in dynamic and unstructured environments. While recent advancements in deep learning have significantly enhanced road scene classification, simultaneously achieving high accuracy, computational efficiency, and adaptability across diverse conditions continues to be difficult. To address these challenges, this study proposes HybridLSTM, a novel and efficient framework that integrates deep learning-based, object-based, and handcrafted feature extraction methods within a unified architecture. HybridLSTM is designed to classify four distinct road scene categories—crosswalk (CW), highway (HW), overpass/tunnel (OP/T), and parking (P)—by… More >

Ruiheng Pan^*, Shuyan Wang, Yihan Huang, Gang Ma

Energy Engineering, Vol.122, No.8, pp. 3079-3098, 2025, DOI:10.32604/ee.2025.065124 - 24 July 2025

Abstract Contemporary power network planning faces critical challenges from intensifying climate variability, including greenhouse effect amplification, extreme precipitation anomalies, and persistent thermal extremes. These meteorological disruptions compromise the reliability of renewable energy generation forecasts, particularly in photovoltaic (PV) systems. However, current predictive methodologies exhibit notable deficiencies in extreme weather monitoring, systematic transient phenomena analysis, and preemptive operational strategies, especially for cold-wave weather. In order to address these limitations, we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks (TimeGAN) for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data… More >

Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Sabina Cristiana Necula, Vasile-Daniel Păvăloaia^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025

Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman³, Satoshi Nishimura¹, Sultan Alfarhood⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025

Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More > Graphic Abstract

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 >

Chun Xiao^1,2,*

Energy Engineering, Vol.122, No.7, pp. 2719-2750, 2025, DOI:10.32604/ee.2025.063782 - 27 June 2025

Abstract In contemporary power systems, delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids. This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration. In the initial stage, using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads, including temperature-controlled loads, electric vehicles, and energy storage devices, a novel calculation method for their maximum adjustable capacities is devised. Distinct from conventional methods, this newly developed approach enables more precise and adaptable quantification… More >