Daniil Kamakaev, Khaled Mahbub^*

Journal on Artificial Intelligence, Vol.8, pp. 183-202, 2026, DOI:10.32604/jai.2026.077823 - 15 April 2026

Abstract This paper presents an innovative system designed to automate the analysis of candidate interviews by integrating multiple analytical techniques into a single multimodal framework. This system combines text sentiment analysis, audio sentiment analysis, keyword extraction, and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction to evaluate candidate performance holistically. This system employs text sentiment analysis using VADER and transformer-based sentiment features (probability-based outputs), audio sentiment analysis with an SVM model trained on both IEMOCAP and MELD datasets, keyword extraction via KeyBERT, and audio feature extraction including MFCCs, delta MFCCs, pitch, and energy to evaluate candidate performance holistically. More >

Anandkumar Balasubramaniam, Seung Yeob Nam^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077582 - 09 April 2026

Abstract The increasing connectivity of modern vehicles exposes the in-vehicle controller area network (CAN) bus to various cyberattacks, including denial-of-service, fuzzy injection, and spoofing attacks. Existing machine learning and deep learning intrusion detection systems (IDS) often rely on labeled data, struggle with class imbalance, lack interpretability, and fail to generalize well across different datasets. This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization (NMF) to address these limitations. Our contributions include: (i) evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor (NMF-W) for classical, unsupervised, and deep… More >

Faten S. Alamri¹, Muhammad Amjad Raza^2,3, Abeer Rashad Mirdad⁴, Adil Ali Saleem², Tanzila Saba^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077084 - 09 April 2026

Abstract Rapid expansion of Industrial Internet of Things (IIoT) systems has heightened the vulnerability of critical infrastructure to sophisticated malware attacks. Traditional signature-based detection methods are ineffective against evolving threats, and many machine learning models fail to capture temporal behavior, offer interpretability, or operate efficiently in resource-constrained environments. This study proposes HMF-Net, a Hierarchical Multi-Feature Network, for accurate, interpretable, and efficient IIoT malware detection. HMF-Net combines hierarchical VT-Tag embedding (HVTE) to model semantic behavioral information, temporal detection ratio analysis (TDRA) to capture confidence variations for polymorphic malware, and static structural binary features. These features are fused… More >

Mete Han Kizilkaya¹, Boran Cakan^2,3,*, Mehmet Bicer⁴, Ender Odemis^1,5

Structural and Congenital Heart Disease, Vol.21, No.1, 2026, DOI:10.32604/schd.2026.074189 - 31 March 2026

Abstract Background: Transcatheter closure of perimembranous ventricular septal defects (pmVSDs) with Lifetech™ Konar-Multifunctional Occluder (MFO) has demonstrated high procedural success and safety. However, long-term complications due to valve–device interaction are rarely reported. We describe a pediatric patient who developed progressive right ventricular outflow tract (RVOT) obstruction and severe tricuspid regurgitation 20 months after MFO closure, highlighting mechanisms, management, and outcomes. Case Description: A 13-year-old girl underwent successful MFO closure of a 6-mm pmVSD. Early follow-up showed trivial tricuspid regurgitation and mild subpulmonic stenosis. Pre-procedural imaging revealed right ventricular hypertrophy caused by long-standing jet-related turbulence from the pmVSD, with… More >

Swetha K¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077293 - 30 March 2026

Abstract Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes, self-occlusions, and the complete loss of important structural and textural components, depressing recognition accuracy and visual fidelity. This paper introduces a new deep generative framework, Modified Multi-Scale Fused CycleGAN (MMF-CycleGAN), for robust and photo-realistic profile-to-frontal face synthesis. The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder. The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions, leading to sharper textures and improved More > Graphic Abstract

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026

Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

Yuechuan Zhang^1,2, Mingshu Zhang^1,2,*, Bin Wei^1,2, Hongyu Jin^1,2, Yaxuan Wang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073996 - 12 March 2026

Abstract Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise, inter-modal imbalance, computational bottlenecks, and semantic ambiguity. To address these issues, we propose SparseMoE-MFN, a novel unified framework that integrates sparse attention with a sparse-activated Mixture-of-Experts (MoE) architecture. This framework aims to enhance the efficiency, inferential depth, and interpretability of multimodal fake news detection. SparseMoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing. The sparse attention module adaptively filters irrelevant tokens and focuses on key regions, reducing computational costs and noise. The sparse MoE module dynamically… More >

Joon-ho Kim^*

Journal of Psychology in Africa, Vol.36, No.1, pp. 117-130, 2026, DOI:10.32604/jpa.2025.068219 - 26 February 2026

Abstract This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa (2008–2024, 17 years), using a Meta-Editorial Mapping Framework (MEMF) analysis. The MEMF integrates citation metrics, keyword novelty ratios, TF–IDF weighting, and cluster-based topic modeling to trace long-term thematic trends and editorial evolution. Findings reveal sustained attention to foundational domains such as mental health, education, and identity, alongside a gradual integration of emergent themes including digital well-being, organizational behavior, and post-pandemic adaptation. Articles with moderate topical novelty (40%–60% new keywords) achieved the highest citation and usage metrics, More >

Xiaolu Wang^1,2, Haoyu Sun¹, Aiguo Wang¹, Xin Xia^3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073991 - 27 January 2026

Abstract To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell (PEMFC) performance degradation prediction, this study proposes a data augmentation-based model to predict PEMFC performance degradation. Firstly, an improved generative adversarial network (IGAN) with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples. Then, the IGAN is used to generate data with a distribution analogous to real data, thereby mitigating the insufficiency and imbalance of original PEMFC samples and providing the prediction model with training data rich More >

Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >