Pikkili Gopala Krishna^1,*, Jalari Somasekar²

Intelligent Automation & Soft Computing, Vol.40, pp. 235-259, 2025, DOI:10.32604/iasc.2025.063657 - 28 April 2025

Abstract Stress is mental tension caused by difficult situations, often experienced by hospital workers and IT professionals who work long hours. It is essential to detect the stress in shift workers to improve their health. However, existing models measure stress with physiological signals such as PPG, EDA, and blink data, which could not identify the stress level accurately. Additionally, the works face challenges with limited data, inefficient spatial relationships, security issues with health data, and long-range temporal dependencies. In this paper, we have developed a federated learning-based stress detection system for IT and hospital workers, integrating… More >

Muhammad Izhar^1,*, Khadija Parwez², Saman Iftikhar³, Adeel Ahmad⁴, Shaikhan Bawazeer³, Saima Abdullah⁴

Journal on Artificial Intelligence, Vol.7, pp. 55-83, 2025, DOI:10.32604/jai.2025.062479 - 25 April 2025

Abstract The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a… More >

Alaa Mahmood, İsa Avcı^*

Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

Ridha Djebali^1,*, Bernard Pateyron², Mokhtar Ferhi¹, Mohamed Ouerhani³, Karim Khemiri¹, Montassar Najari¹, M. Ammar Abbassi⁴, Chohdi Amri⁵, Ridha Ennetta⁶, Zied Driss⁷

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 441-461, 2025, DOI:10.32604/fhmt.2025.063375 - 25 April 2025

Abstract This paper investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) as a versatile thermal spray technique for the application of coatings with tailored properties to various substrates. The process uses a high-speed, high-temperature plasma jet to melt and propel the feedstock powder particles, making it particularly useful for improving the performance and durability of components in renewable energy systems such as solar cells, wind turbines, and fuel cells. The integration of nanostructured alumina (Al₂O₃) thin films into multilayer coatings is considered a promising advancement that improves mechanical strength, thermal stability, and environmental resistance. The More >

Shervin Fateh Khanshir¹, Saeed Dinarvand^2,*, Ramtin Fateh Khanshir³

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 663-684, 2025, DOI:10.32604/fhmt.2025.060559 - 25 April 2025

Abstract This article aims to model and analyze the heat and fluid flow characteristics of a carboxymethyl cellulose (CMC) nanofluid within a convergent-divergent shaped microchannel (Two-dimensional). The base fluid, water + CMC (0.5%), is mixed with CuO and Al₂O₃ nanoparticles at volume fractions of 0.5% and 1.5%, respectively. The research is conducted through the conjugate usage of experimental and theoretical models to represent more realistic properties of the non-Newtonian nanofluid. Three types of microchannels including straight, divergent, and convergent are considered, all having the same length and identical inlet cross-sectional area. Using ANSYS FLUENT software, Navier-Stokes equations… More >

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

Energy Engineering, Vol.122, No.5, pp. 2077-2097, 2025, DOI:10.32604/ee.2025.064175 - 25 April 2025

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 >

Yongfu Liu¹, Haitao Zhao¹, Xingliang Deng¹, Baozhu Guan¹, Jing Li^2,*, Chengqiang Yang², Guipeng Huang²

Energy Engineering, Vol.122, No.5, pp. 1735-1754, 2025, DOI:10.32604/ee.2025.063706 - 25 April 2025

Abstract The seepage characteristics of shale reservoirs are influenced not only by multi-field coupling effects such as stress field, temperature field, and seepage field but also exhibit evident creep characteristics during oil and gas exploitation. The complex fluid flow in such reservoirs is analyzed using a combination of theoretical modeling and numerical simulation. This study develops a comprehensive mathematical model that integrates the impact of creep on the seepage process, with consideration of factors including stress, strain, and time-dependent deformation. The model is validated through a series of numerical experiments, which demonstrate the significant influence of… More >

Michela Costa^1,*, Adolfo Palombo², Andrea Ricci², Ugo Sorge³

Energy Engineering, Vol.122, No.5, pp. 1629-1643, 2025, DOI:10.32604/ee.2025.062613 - 25 April 2025

Abstract Effective thermal management is paramount for successfully deploying lithium-ion batteries in residential settings as storage systems for the exploitation of renewable sources. Uncontrolled temperature increases within battery packs can lead to critical issues such as cell overheating, potentially culminating in thermal runaway events and, in extreme cases, leading to fire or explosions. This work presents a comprehensive numerical thermal model of a battery pack made of prototype pouch cells based on lithium ferrophosphate (LFP) chemistry. The multi-physical model is specifically developed to investigate real-world operating scenarios and to assess safety considerations. The considered energy storage… More >

Chenyang Fu^*, Xinghua Wang, Zilv Li, Xixian Liu, Xiongfei Zhang, Zhuoli Zhao

Energy Engineering, Vol.122, No.5, pp. 1805-1821, 2025, DOI:10.32604/ee.2025.061172 - 25 April 2025

Abstract With the large-scale promotion of distributed photovoltaics, new challenges have emerged in the photovoltaic consumption within distribution networks. Traditional photovoltaic consumption schemes have primarily focused on static analysis. However, as the scale of photovoltaic power generation devices grows and the methods of integration diversify, a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks. Therefore, this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity, aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in… More > Graphic Abstract

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >