Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks by Shaho Hassen, Ahmed Abdlrazaq

“… Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. …”
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World model-based end-to-end scene generation for accident anticipation in autonomous driving by Yanchen Guan, Haicheng Liao, Chengyue Wang, Xingcheng Liu, Jiaxun Zhang, Zhenning Li

“…To tackle these issues, we propose a comprehensive framework combining generative scene augmentation with adaptive temporal reasoning. …”
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Enhancing the trustworthiness of chaos and synchronization of chaotic satellite model: a practice of discrete fractional-order approaches by Saima Rashid, Sher Zaman Hamidi, Saima Akram, Moataz Alosaimi, Yu-Ming Chu

“…The study highlights the technique’s vulnerability to fractional-order factors, resulting in exclusive, changing trends and equilibrium frameworks. Because of its diverse and convoluted behaviour, the satellite chaotic model is an intriguing and crucial subject for research.…”
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Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU by Sio-Kei Im, Ka-Hou Chan

“…In this context, our study aims to develop a robust predictive framework that can accurately forecast digit trajectories while preserving structural integrity. …”
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Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks by Ramesh Vatambeti, Hari Prasad Gandikota, D. Siri, G. Satyanarayana, Narasimhula Balayesu, M. Ganesh Karthik, Koteswararao Ch

“…This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique—Self-Inspected Adaptive SMOTE (SASMOTE). …”
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EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys by Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni

“…To address this challenge and improve prediction accuracy in small data sets, we develop a generative network framework: Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks (EFTGAN). …”
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Computer Assisted Objective Assessment of Micro-Neurosurgical Skills From Intraoperative Videos by Pon Deepika, Kalahasti V. V. Deepesh, Phani Sriram Vadali, Madhav Rao, Vikas Vazhayil, Alok Mohan Uppar

“…Hence, the aim of this study is to construct a framework for automated objective assessment of micro-neurosurgical skill. …”
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Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning by Ke Wang, Yuqian Mao, Qi Qiao

“…This framework incorporates a novel causal intervention method based on multi-scale random convolution to eliminate spurious correlations caused by imaging variations, thereby enhancing feature stability. …”
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Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites by Yue Yu, Shuang Xiang, Minghao Wu

“…Following this, M6A-SAI employs a self-correlation fusion graph convolution framework to merge information from both the similarity and awareness graphs, thus producing enriched sequence representations. …”
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Drone Detection Using YOLOv5 With GhostNet Backbone and Adaptive Fourier Neural Operator (AFNO 2D) by K. Kavin Akash, Uma Kuppusamy

“…This study proposes a lightweight yet high-accuracy drone detection framework based on the YOLOv5 object detection model, enhanced with a GhostNet backbone and Adaptive Fourier Neural Operator (AFNO 2D). …”
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Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications by Tao Song, Shijie Yuan, Rui Zhong

“…This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. …”
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Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction by Tingting Zhao, Yongyong Ren, Hui Lu, Yan Kong

“…Here, we presented a decision level multimodal data fusion framework for integrating multiomics and pathological tissue slide images for prognosis prediction. …”
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Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning by Jinling Ren, Misheng Cai, Dapai Shi

“…The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. …”
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Artificial intelligence−assisted radiation imaging pathways for distinguishing uterine fibroids and malignant lesions in patients presenting with cancer pain: a literature review by Chengfeng Cai, Wenhui Hu, Haimei Zhou, Xian Zhang, Rongfei Ren, Yilin Liu, Facui Ye

“…AI-assisted imaging, encompassing techniques like radiomics, convolutional neural networks (CNNs), and multimodal fusion, has demonstrated substantial improvements in distinguishing between uterine fibroids and malignant smooth-muscle tumours. …”
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Physically Meaningful Surrogate Data for COPD by Harry J. Davies, Ghena Hammour, Hongjian Xiao, Patrik Bachtiger, Alexander Larionov, Philip L. Molyneaux, Nicholas S. Peters, Danilo P. Mandic

“…As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. …”
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A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and Crow-Search optimized k-NN algorithm by M. Chandralekha, N. Priyadharshini Jayadurga, Thomas M. Chen, Mithileysh Sathiyanarayanan, Kasif Saleem, Mehmet A. Orgun

“…It becomes evident after a detailed evaluation that conventional machine learning algorithms if implemented with correct feature extraction and fine-tuning surpass the deep learning approaches including the frameworks composed of Convolutional Neural Network and Principal Component Analysis and empirical mode decomposition by approximately 96% across all datasets. …”
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Downhole Coal–Rock Recognition Based on Joint Migration and Enhanced Multidimensional Full-Scale Visual Features by Bin Jiao, Chuanmeng Sun, Sichao Qin, Wenbo Wang, Yu Wang, Zhibo Wu, Yong Li, Dawei Shen

“…To tackle these issues, this work introduces a recognition framework specifically designed for underground environments, leveraging joint migration and enhancement of multidimensional and full-scale visual representations. …”
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Emission-line Stacking of 21 cm Intensity Maps with MeerKLASS: Inference Pipeline and Application to the L-band Deep-field Data by Zhaoting Chen, Steven Cunnington, Alkistis Pourtsidou, Laura Wolz, Marta Spinelli, José Luis Bernal, Matilde Barberi-Squarotti, Stefano Camera, Isabella P. Carucci, José Fonseca, Keith Grainge, Melis O. Irfan, Mario G. Santos, Jingying Wang, (MeerKLASS Collaboration)

“…An extensive simulation framework is built to study the viability of the stacking detection, the covariance estimation, and the model inference, which are then applied to the data. …”
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A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

“…The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). …”
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Graph Learning-Based Power System Health Assessment Model by Koji Yamashita, Nanpeng Yu, Evangelos Farantatos, Lin Zhu

“…The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. …”
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