Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. …”
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Reversible image steganography based on residual structure and attention mechanism by Lianshan Liu, Shanshan Tong, Qianwen Xue

“…This paper introduces dilated convolution to design a multi-scale convolution attention module that combines feature information from different scales, highlights essential features, and precisely locates the ideal embedding position. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…In this approach, the basic framework of DeepSORT is adopted, and the Bot net with ResNet50 as the backbone is taken to extract the appearance features. …”
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Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions by Thais de Castro Moraes, Jiancheng Qin, Xue-Ming Yuan, Ek Peng Chew

“…<i>Methods:</i> Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. …”
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Estimating Bandwidth and Analyzing Worst-Case Delay Bounds Using Network Calculus by Md Amirul Islam, Giovanni Stea

“…This paper proposes a bandwidth estimation framework for Network-on-Chip systems using network calculus, addressing the challenges due to nonlinear behavior and dynamic traffic. …”
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Next Generation Human Action Recognition: A Comprehensive Review of State-of-the-Art Signal Processing Techniques by Misha Karim, Shah Khalid, Sungyoung Lee, Sulaiman Almutairi, Abdallah Namoun, Mohammed Abohashrh

“…This review bridges the gap between classical techniques, such as Fourier Transform (FT), Wavelet Transform (WT), and Principal Component Analysis (PCA), and advanced Deep Learning (DL) approaches, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and emerging Transformer-based architectures. …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…The TMVM framework leverages Theory of Mind AI to analyze students’ facial emotions via smartphone cameras while watching videos. …”
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Adopting Artificial Intelligence to Strengthen Legal Safeguards in Blockchain Smart Contracts: A Strategy to Mitigate Fraud and Enhance Digital Transaction Security by Hassen Louati, Ali Louati, Abdulla Almekhlafi, Maha ElSaka, Meshal Alharbi, Elham Kariri, Youssef N. Altherwy

“…This approach not only enhances the security of digital transactions on blockchain platforms but also informs the development of legal standards and regulatory frameworks necessary for governing these technologies. …”
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Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care by Rahul Kumar, Kyle Sporn, Akshay Khanna, Phani Paladugu, Chirag Gowda, Alex Ngo, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

“…AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. …”
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Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization by Xiongwei Liang, Shaopeng Yu, Bo Meng, Yongfu Ju, Shuai Wang, Yingning Wang

“…Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. …”
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Multi-Feature Facial Complexion Classification Algorithms Based on CNN by Xiyuan Cao, Delong Zhang, Chunyang Jin, Zhidong Zhang, Chenyang Xue

“…Innovative frameworks of the three algorithms can more effectively exploit facial features, improving the utilization rate of feature information and classification performance. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…Surrogate modeling techniques aim to decrease computational expense while retaining dominant solution features and characteristics. Existing frameworks based on convolutional neural networks and snapshot-matrix decomposition often rely on lossy pixelization and data-preprocessing, limiting their effectiveness in realistic engineering scenarios. …”
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Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback by Hafiz Muhammad Qadir, M. Taseer Suleman, Rafaqat Alam Khan, Muhammad Sohaib, Md Junayed Hasan, Syed Abid Hussain

“…Abstract Accurate prediction of student performance is essential for the creation of adaptive learning frameworks and the best utilization of educational strategies. …”
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Research on the Automatic Multi-Label Classification of Flight Instructor Comments Based on Transformer and Graph Neural Networks by Zejian Liang, Yunxiang Zhao, Mengyuan Wang, Hong Huang, Haiwen Xu

“…Traditional written evaluations conducted by flight instructors are often marred by subjectivity and inefficiency, rendering them inadequate to satisfy the stringent demands of Competency-Based Training and Assessment (CBTA) frameworks. To address this challenge, this study presents a novel multi-label classification model that seamlessly integrates RoBERTa, a robust language model, with Graph Convolutional Networks (GCNs). …”
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Study of line spectra emitted by hydrogen isotopes in tokamaks through Deep-Learning algorithms by N. Saura, M. Koubiti, S. Benkadda

“…The paper focuses on the application of the convolutional neural network (CNN) algorithm to emission spectroscopy from the divertor regions of magnetic fusion devices. …”
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Scalable and Efficient Multi-Class Brain Tumor Classification with a Compact Hybrid Deep Learning Model for Real-Time Applications by Sohaib R. Awad, Amar I. Daood, Akram A. Dawood

“…A new hybrid deep learning solution merges convolutional neural networks (CNNs) with support vector machines (SVMs) to improve classification results as this paper describes. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…The integration of KAN-based models into existing cybersecurity frameworks can enhance the detection and mitigation of sophisticated botnet threats, thus contributing to more resilient and secure IoT ecosystems.…”
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Advancements in artificial intelligence and machine learning for poultry farming: Applications, challenges, and future prospects by Muhammad Halim Natsir, Wayan Firdaus Mahmudy, Mochamad Tono, Yuli Frita Nuningtyas

“…Future directions include developing lightweight, energy-efficient models, enhancing user-friendly interfaces, and promoting policy frameworks that support ethical and inclusive AI adoption. …”
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Research on graph-based heterogeneous data integration method by HUANG Yuezhen, YANG Fen, TIAN Feng, ZHANG Chengye, LI Yuchan

“…Through verification on real data within the enterprise, the experimental results show that the framework proposed in the paper can improve the development efficiency of data integration, and the model is not limited by business fields and has strong portability.…”
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