Monica Casella¹, Raffaella Esposito^2,*, Michela Ponticorvo¹

International Journal of Mental Health Promotion, Vol.28, No.8, 2026, DOI:10.32604/ijmhp.2026.079327 - 31 August 2026

Abstract Background: Artificial Neural Networks (ANNs) are increasingly used in psychological assessment to score measures, classify clinical presentations, monitor symptoms, estimate risk, and tailor interventions. Methods: This scoping review maps ANN applications in psychological assessment and diagnosis and examines their relevance to psychiatric practice. A structured search of Scopus and Web of Science, followed by independent screening and data charting by three reviewers, identified 31 studies published between 2020 and 2023. Results: The literature demonstrates broad methodological versatility across psychometric, behavioral, textual, image, and sensor data, but clinical readiness remains limited by heterogeneous samples, architectures, metrics, and validation More >

Longqiao Hu¹, Yunjin Wang^1,*, Jia Liu¹, Jiacheng Yin¹, Siyu Zhang², Mengyu Li¹, Qi Wu¹, Jiawei Li¹, Fujian Zhou³

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.079317 - 30 August 2026

Abstract The flow mechanisms of shale oil are inherently complex, characterized by diverse occurrence states. Establishing a robust production forecasting model is essential for thoroughly evaluating well deliverability and the efficacy of reservoir stimulation. While conventional analytical techniques, numerical reservoir simulation, and production decline analysis constitute standard practice, they often struggle to balance computational efficiency with predictive fidelity. Intelligent algorithms offer superior precision in short-term forecasting following extensive data training; however, they frequently exhibit poor generalization and underfitting during long-term performance projections. Consequently, integrating domain-specific production decline theory as physical constraints represents a strategic pathway to… More >

Masood Karamoozian¹, Zarrin Mahdavipour², Mohammed Ameen³, Faisal Binzagr⁴, Abdolraheem Khader^2,*, Ahmed Hamza Osman³, Ali Ahmed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085421 - 28 August 2026

Abstract Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded… More >

Guangyu Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084644 - 28 August 2026

Abstract The fusion of GNNs and SSMs is creating a new era in the realm of dynamical learning with structures. Graph-Mamba is one of the most promising works in this fast-moving field. With its unique capability to incorporate graph topology and selective dynamics in a stable manner, Graph-Mamba has demonstrated its ability to model intricate relationships. However, the existing research on Graph-Mamba has not been compiled into a coherent form; the theoretical basis and practical implementation of the framework have not been synthesized systematically across different domains. First, we demonstrate the theoretical connection between graph propagation… More >

Hamza Rafik¹, Oussama Khouili², Mohamed Louzazni¹, Petru Adrian Cotfas³, Daniel Tudor Cotfas^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084256 - 28 August 2026

Abstract The rapid expansion of photovoltaic (PV) technologies has necessitated the enhancement of energy conversion efficiency by developing more and more sophisticated control and optimization techniques. In particular, novel MPPT methods combined with solar tracking systems and AI approaches emerge as a promising solution to surmount the barriers of the conventional PV systems. This research presents a critical assessment of the recent developments in the research area of PV systems with MPPT algorithms, solar tracking mechanisms, and AI-based techniques. Therefore, papers with publication years from 2021 to 2025 were selected using Web of Science Core Collection. More >

Hikmat Yar^1,2, Imran Ullah Khan³, Naqqash Dilshad⁴, Weiwei Jiang⁵, Heung Soo Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084202 - 28 August 2026

Abstract Transportation has become an essential component of modern daily life, with continuous advancements aimed at reducing travel time and improving mobility. However, this increased convenience has also contributed to a rise in road accidents, often caused by driver distraction, fatigue, and age-related cognitive decline. These concerns have driven growing interest in Artificial Intelligence (AI)-based real-time driver monitoring systems designed to enhance road safety. Despite recent progress, several challenges remain, including limitations in detection accuracy, inadequate temporal reasoning, and high computational complexity. To address these challenges, we utilize the EfficientNetV2-S model for backbone feature extraction due… More >

Eman Attallah H. Aljabarti, Mohd Yamani Idna Idris^*, Ainuddin Wahid Abdul Wahab

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083479 - 28 August 2026

Abstract Facial emotion recognition (FER) aims to recognize and classify human emotional expressions accurately. Although there has been significant progress in developing FER models with respectable accuracy, the accuracy still has substantial room for improvement. These claims are supported by several factors, including poor parameter tuning, class imbalance, dataset bias, generalization limitations, and inefficient preprocessing. These factors make it more difficult to capture hierarchical and high-level features in training data. To address these limitations, therefore, this work develops and fine-tunes a deep convolutional neural network-based model to effectively learn discriminative facial features. First, the data are… More > Graphic Abstract

Peter Kipngeno Langat^*, Michael Kimwele, Dennis Kaburu

Journal of Cyber Security, Vol.8, pp. 541-558, 2026, DOI:10.32604/jcs.2026.083592 - 21 August 2026

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying Zero-day attacks through a Recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of Recurrent Neural Networks (RNNs) to learn complex patterns and identify anomalies that… More >

Zehan Li¹, Yingyi Li^2,*, Zhiwei Tang³, Xuemeng Zhai³, Jiandong Liang¹, Guangmin Hu³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086118 - 13 August 2026

Abstract Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks… More >

David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026

Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >