Deep ensemble learning for gastrointestinal diagnosis using endoscopic image classification by Samra Siddiqui, Junaid Ali Khan, Shabbab Algamdi

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Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting by Alireza Jafari, Geoffrey Fox, John B. Rundle, Andrea Donnellan, Lisa Grant Ludwig

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Classification of musculoskeletal pain using machine learning by Dalia Mohamed Fouad, Marwa Mahmoud Mahfouz, Mohammed Mostafa Mohamed, Mahmoud Yassin Elzanaty, Tarek Abd El-Hafeez

“…The proposed framework involves data preprocessing, definition of neural network architecture, implementation of PSO, and performance evaluation. …”
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Motor imagery EEG signal classification using novel deep learning algorithm by Sathish Mathiyazhagan, M. S. Geetha Devasena

“…For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. …”
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Microstructural integrity of autonomic central nervous tracts is linked to cardiovascular health by Janis M. Nolde, Alexander Rau, Elias Kellner, Horst Urbach, Cornelius Weiller, Fabian Bamberg, Jakob Weiss, Marco Reisert, Jonas A. Hosp

“…Introduction: The central autonomic network (CAN), which controls sympathetic and parasympathetic activity, plays a central role in the pathogenesis of cardiovascular disease and serves as a target for therapeutic approaches. …”
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Smart adaptive learning and optimized feature clustering for enhanced image retrieval by P. Umaeswari, Sujata Patil, Parameshachari Bidare Divakarachari, Przemysław Falkowski-Gilski

“…CBIR extracts key features related to texture, shape, and color using techniques such as Local Binary Pattern, Zernike Moments, and Color Moments. Additionally, an Entropy-based Divergence (ED) function is incorporated into a Convolutional Neural Network (CNN) named EDCNN to improve matching accuracy by reducing redundant activation in hidden layers. …”
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Generative AI in Intrusion Detection Systems for Internet of Things: A Systematic Literature Review by Zhe Deng, Ants Torim, Sadok Ben Yahia, Hayretdin Bahsi

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OXSeg: Multidimensional Attention UNet-Based Lip Segmentation Using Semi-Supervised Lip Contours by Hanie Moghaddasi, Christina Chambers, Sarah N. Mattson, Jeffrey R. Wozniak, Claire D. Coles, Raja Mukherjee, Michael Suttie

“…We unravel the micro-patterns in facial images using local binary patterns to build multidimensional inputs. …”
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NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification by Borra Vineetha, Munirathinam Nirmala

“…NeuroFuzDetect leverages advanced computational reasoning to analyze uncertainty and ambiguity in textual data by evaluating critical linguistic features, including credibility, emotional tone, and language patterns, ensuring interpretability by providing justifications for its predictions. …”
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The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter by Oktavia Citra Resmi Rachmawati, Ali Ridho Barakbah, Tita Karlita

“… Activation functions are a critical component in the feature extraction layer of deep learning models, influencing their ability to identify patterns and extract meaningful features from input data. …”
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A Global Deep Learning Perspective on Australia-Wide Monthly Precipitation Prediction by Luyi Shen, Guoqi Qian, Antoinette Tordesillas

“…Gaining a deep understanding of precipitation patterns is beneficial for enhancing Australia’s adaptability to climate change. …”
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GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence by Mohd Anjum, Ashit Kumar Dutta, Ali Elrashidi, Sana Shahab, Asma Aldrees, Zaffar Ahmed Shaikh, Abeer Aljohani

“…To address these limitations, this research proposes GraphFedAI, a novel framework that integrates adaptive session-based graph modeling, Pearson correlation-guided feature selection, interpolation-aware graph neural network (GNN) training, and federated learning to enable robust, scalable, and privacy-preserving DDoS detection in heterogeneous Internet of Things (IoT) networks.The framework represents the IoT network as dynamic graphs where communication patterns among devices are modeled as edges that evolve over time. …”
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Plasma state monitoring and disruption characterization using multimodal VAEs by Yoeri Poels, Alessandro Pau, Christian Donner, Giulio Romanelli, Olivier Sauter, Cristina Venturini, Vlado Menkovski, the TCV Team, the WPTE Team

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Identification of Candidate Genes for Soybean Storability via GWAS and WGCNA Approaches by Xu Wu, Yuhe Wang, Jiapei Xie, Zhenhong Yang, Haiyan Li, Yongguang Li, Weili Teng, Xue Zhao, Yuhang Zhan, Yingpeng Han

“…Through the analysis of expression patterns, two candidate genes (<i>Glyma.03G058300</i>, <i>Glyma.16G074600</i>) potentially involved in seed storage tolerance were ultimately identified. …”
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Drug Pair of Astragali Radix–Ligustri Lucidi Fructus Alleviates Acute Kidney Injury in Mice Induced by Ischemia–Reperfusion Through Inhibiting Ferroptosis by Xuanhe Liu, Dan Zhang, Yuting Xie, Mengdan Wang, Xiaochun Chen, Weijie Yu, Yuming Ma, Jia Zeng, Qixuan Long, Guangrui Huang, Jie Geng, Anlong Xu

“…LC-MS analyzed DAL’s chemical components, and network pharmacology predicted ferroptosis-related targets. …”
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Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

“…As a case study, we considered the binary classification of points belonging to a certain geometric pattern (the Two-Moons Classification problem) on a plane. …”
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Unveiling the molecular blueprint of SKP-SCs-mediated tissue engineering-enhanced neuroregeneration by Hui Zhu, Ying Wang, Siyuan Xu, Yunjian Song, Yifan Li, Yiting Wang, Qiuwen Sun, Muyuan Tong, Tianyi Huang, Yulin Pan, Hongkui Wang, Xi Xu, Chengbin Xue

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Hybrid AI for Predictive Cyber Risk Assessment: Federated Graph-Transformer Architecture With Explainability by Jaime Govea, Rommel Gutierrez, William Villegas-Ch, Alexandra Maldonado Navarro

“…The model was trained and evaluated using public datasets (CIC-IDS2017, UNSW-NB15, MITRE ATT&#x0026;CK, TON_IoT) and synthetically generated traffic from honeypots and real anonymized networks. …”
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Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and Methods by Aristeidis Mystakidis, Paraskevas Koukaras, Christos Tjortjis

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Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning by Chibuike Chiedozie Ibebuchi, Oluwaferanmi Akinyemi, Itohan‐Osa Abu

“…These results highlight the effectiveness of neural network techniques in evaluating and ranking GCMs for model intercomparison projects.…”
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