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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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…In responding to these, this paper proposes a framework based on a novel lightweight Slice-Diff self attention, which consists of several key components. …”
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Enhancing Real-Time Aerial Image Object Detection with High-Frequency Feature Learning and Context-Aware Fusion by Xin Ge, Liping Qi, Qingsen Yan, Jinqiu Sun, Yu Zhu, Yanning Zhang

“…To address these limitations, we propose a novel VMC-DETR framework designed to enhance the extraction and utilization of high-frequency texture features in aerial images. …”
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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling by Ayush Saurabh, Peter T. Brown, J. Shepard Bryan IV, Zachary R. Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P. Shepherd, Steve Pressé

“…Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. …”
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Slim-sugarcane: a lightweight and high-precision method for sugarcane node detection and edge deployment in natural environments by Lijiao Wei, Lijiao Wei, Shuo Wang, Xinwei Liang, Dongjie Du, Xinyi Huang, Ming Li, Yuangang Hua, Weihua Huang, Zhenhui Zheng, Zhenhui Zheng

“…Built upon YOLOv8, our model integrates GSConv, a hybrid convolution module combining group and spatial convolutions, to significantly reduce computational overhead while maintaining detection accuracy. …”
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The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review by Radwan Qasrawi, Ghada Issa, Suliman Thwib, Razan AbuGhoush, Malak Amro, Raghad Ayyad, Stephanny Vicuna, Eman Badran, Yousef Khader, Raeda Al Qutob, Faris Al Bakri, Hana Trigui, Elie Sokhn, Emmanuel Musa, Jude Dzevela Kong

“…Future research directions should prioritize multi-center validation studies, standardized reporting frameworks, and integration of diverse data modalities to enhance model robustness and clinical applicability.…”
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Bessel statistical convergence: New concepts and applications in sequence theory. by Ibrahim S Ibrahim, Majeed A Yousif, Pshtiwan Othman Mohammed, Dumitru Baleanu, Ahmad Zeeshan, Mohamed Abdelwahed

“…Additionally, we extend the first and second Korovkin-type approximation theorems by incorporating Bessel statistical convergence, providing a more robust and comprehensive framework than existing results. The practical implications of these theorems are demonstrated through examples involving the classical Bernstein operator and Fejér convolution operators. …”
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A study on land use change simulation based on PLUS model and the U-net structure: A case study of Jilin Province by Jiafu Liu, Xiangli Kong, Yue Zhu, Baihao Zhang

“…This study investigates land use changes in Jilin Province from 2000 to 2020, independently constructing land cover change simulation frameworks using the PLUS model and U-Net neural network, and simulates land use for the year 2030. …”
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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

“…This article introduces FraudGNN-RL, an innovative framework that combines Graph Neural Networks (GNNs) with Reinforcement Learning (RL) for adaptive and context-aware financial fraud detection. …”
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A Rotated Object Detection Model With Feature Redundancy Optimization for Coronary Athero-Sclerotic Plaque Detection by Xue Hao, Haza Nuzly Abdull Hamed, Qichen Su, Xin Dai, Linqiang Deng

“…Based on RFDS-SCConv, a lightweight plaque detection framework, YOLO-Plaque, is proposed. RFDS-SCConv and FasterBlock are integrated into the framework to substantially reduce the interference of redundant features during feature extraction and fusion. …”
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MultiScaleFusion-Net and ResRNN-Net: Proposed Deep Learning Architectures for Accurate and Interpretable Pregnancy Risk Prediction by Amna Asad, Madiha Sarwar, Muhammad Aslam, Edore Akpokodje, Syeda Fizzah Jilani

“…This research proposes a deep learning framework for personalized pregnancy risk prediction using the NFHS-5 dataset, and class imbalance is addressed through a hybrid NearMiss-SMOTE approach. …”
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Knowledge graph reasoning: modern methods and applications by Wenguang WANG

“…Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced, including the methods based on embedding into Euclidean space and hyperbolic space, and based on deep learning methods such as convolution neural network, capsule network, graph neural network, etc.Simultaneously, the applications of knowledge reasoning in various technical fields and industries were presented, and the existing challenges and opportunities were pointed out as well.…”
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A Survey on Anti-Money Laundering Techniques in Blockchain Systems by Leyuan Liu, Xiangye Li, Tian Lan, Yakun Cheng, Wei Chen, Zhixin Li, Sheng Cao, Weili Han, Xiaosong Zhang, Hongfeng Chai

“…This study provides a comprehensive review of AML research in blockchain systems, examining the foundational AML frameworks, including traditional AML models and blockchain-based money laundering methodologies. …”
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