Cong Zhang^1,2, Chutong Zhang², Lei Shen¹, Renwei Guo², Wan Chen¹, Hui Huang², Jie Ji^2,*

Energy Engineering, DOI:10.32604/ee.2025.064175

Abstract This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method. The solution involves a hybrid prediction framework based on an improved grey regression neural network (IGRNN), which combines grey prediction, an improved BP neural network, and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy. Additionally, an improved cuckoo search (ICS) algorithm is designed to empower the neural network model, incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation, achieving… More >

Yajie Bai^1,*, Jian Hou^2,3, Yongge Liu^2,3

Energy Engineering, DOI:10.32604/ee.2025.063198

Abstract Natural gas hydrate widely exists in the South China Sea as clean energy. A three-phase transition layer widely exists in low permeability Class I hydrates in the Shenhu offshore area. Therefore, taking into account the low-permeability characteristics with an average permeability of 5.5 mD and moderate heterogeneity, a 3-D geological model of heterogeneous Class I hydrate reservoirs with three-phase transition layers is established by Kriging interpolation and stochastic modeling method, and a numerical simulation model is used to describe the depressurization production performance of the reservoir. With the development of depressurization, a specific range of… More >

Perry Yang Tchie Hunn¹, Hadi Nabipour Afrouzi^2,*

Energy Engineering, DOI:10.32604/ee.2025.060124

Abstract With growing interest in its potential applications across both stationary and transportation sectors, hydrogen has emerged as a promising alternative for environmentally responsible power generation. By replacing traditional fuels, hydrogen can significantly reduce greenhouse gas emissions in the transportation sector. This study focuses on the design and downsizing of a green hydrogen fuel cell car, aiming to scale the concept for larger vehicles. Key components, including fuel cells, electrolysers, and solar panels, were evaluated through extensive laboratory testing. The findings reveal that variations in sunlight impact the solar panel’s hydrogen production rate, with differences of… More > Graphic Abstract

Chiara Innocente^1,*, Matteo Boemio², Gianmarco Lorenzetti², Ilaria Pulito², Diego Romagnoli², Valeria Saponaro², Giorgia Marullo¹, Luca Ulrich¹, Enrico Vezzetti¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063186

Abstract Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To… More >

Veena Dillshad¹, Muhammad Attique Khan^2,*, Muhammad Nazir¹, Jawad Ahmad², Dina Abdulaziz AlHammadi³, Taha Houda², Hee-Chan Cho⁴, Byoungchol Chang^5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063851

Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >

Ahmet Emre Ergün, Aytuğ Onan^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063826

Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >

Tehreem Fatima¹, Kewen Xia^1,*, Wenbiao Yang², Qurat Ul Ain¹, Poornima Lankani Perera¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063686

Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062867

Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

Praveen Kumar Sekharamantry^1,2,*, Farid Melgani¹, Roberto Delfiore³, Stefano Lusardi³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062686

Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062340

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >