Cuiping Li¹, Ziyun Zong¹, Xingxu Zhu¹, Zheng Fang², Caiqi Jia³, Wenbo Si⁴, Gangui Yan¹, Junhui Li^1,*

Energy Engineering, Vol.122, No.9, pp. 3459-3485, 2025, DOI:10.32604/ee.2025.066629 - 26 August 2025

Abstract In response to the issue of determining the appropriate capacity when hybrid energy storage systems (HESS) collaborate with thermal power units (TPU) in the system’s secondary frequency regulation, a configuration method for HESS based on the analysis of frequency regulation demand analysis is proposed. And a corresponding simulation platform is developed. Firstly, a frequency modulation demand method for reducing the frequency modulation losses of TPU is proposed. Secondly, taking into comprehensive consideration that flywheel energy storage features rapid power response and battery energy storage has the characteristic of high energy density, a coordinated control strategy… More > Graphic Abstract

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

Osama M. Alshehri¹, Ahmad Shaf^2,*, Muhammad Irfan^3,*, Mohammed M. Jalal⁴, Malik A. Altayar⁴, Mohammed H. Abu-Alghayth⁵, Humood Al Shmrany⁶, Tariq Ali⁷, Toufique A. Soomro⁸, Ali G. Alkhathami⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025

Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

Qamar H. Naith^1,*, H. Mancy^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 387-405, 2025, DOI:10.32604/cmes.2025.066917 - 31 July 2025

Abstract Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time… More >

Surender Singh^1,2,*, Naveen Bilandi^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 489-509, 2025, DOI:10.32604/cmes.2025.065101 - 31 July 2025

Abstract Wireless Body Area Network (WBAN) is essential for continuous health monitoring. However, they face energy efficiency challenges due to the low power consumption of sensor nodes. Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters. Efficient network traffic management remains a challenge, with existing approaches often resulting in increased delay and reduced throughput. Additionally, insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios. Several routing strategies and procedures have been developed to… More >

Jingrui Liu^1,2,*, Zhiwen Hou^1,2, Boyu Wang^1,2, Tianxiang Yin^3,4

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4729-4754, 2025, DOI:10.32604/cmc.2025.066138 - 30 July 2025

Abstract In response to the increasing global energy demand and environmental pollution, microgrids have emerged as an innovative solution by integrating distributed energy resources (DERs), energy storage systems, and loads to improve energy efficiency and reliability. This study proposes a novel hybrid optimization algorithm, DE-HHO, combining differential evolution (DE) and Harris Hawks optimization (HHO) to address microgrid scheduling issues. The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts. The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind, solar, micro-gas turbine, More >

Bandar Alotaibi^1,*, Aljawhara Almutarie², Shuaa Alotaibi³, Munif Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4451-4467, 2025, DOI:10.32604/cmc.2025.066050 - 30 July 2025

Abstract X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid… More >

Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025

Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601 - 30 July 2025

Abstract Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using… 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 >