Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
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A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Cla... by Mrinal Kanti Dhar, Mou Deb, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Avneet Kaur, Charmy Parikh, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. …”
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A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems by Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun, Md Al‐Amin Bhuiyan

“…Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. …”
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SP-IGAN: An Improved GAN Framework for Effective Utilization of Semantic Priors in Real-World Image Super-Resolution by Meng Wang, Zhengnan Li, Haipeng Liu, Zhaoyu Chen, Kewei Cai

“…The framework consists of two branches. The main branch introduces a Graph Convolutional Channel Attention (GCCA) module to transform channel dependencies into adjacency relationships between feature vertices, thereby enhancing pixel associations. …”
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PassAI: An Explainable Machine Learning Framework for Predicting Soccer Pass Outcomes Using Multimodal Match Data by Ryota Takamido, Jun Ota, Hiroki Nakamoto

“…Therefore, in this study, we introduce PassAI, a novel machine learning framework for classifying soccer passes success or failure using spatiotemporal tracking images and player-specific statistical profiles. …”
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A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes by Zhi Yang, Changzheng Liu, Ping Mei, Lei Wang

“…To address these challenges, this paper proposes a unified spatiotemporal sequence prediction framework based on spatiotemporal masking, which comprises two stages: self-supervised pre-training and task-oriented fine-tuning. …”
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Bridging Explainability and Security: An XAI-Enhanced Hybrid Deep Learning Framework for IoT Device Identification and Attack Detection by Prabhav Jain, Anshika Rathour, Aashima Sharma, Gurpal Singh Chhabra

“…To address these challenges, we propose a hybrid machine learning framework that combines deep feature extraction using Convolutional Neural Networks (CNNs) with the robust classification capabilities of XGBoost. …”
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Physics-Informed Learning Framework for Lower Limb Kinematic Prediction With Sparse Sensors and Its Application in Chronic Stroke by Yan Guo, Yusuke Sekiguchi, Wen Zeng, Satoru Ebihara, Dai Owaki, Mitsuhiro Hayashibe

“…This study proposes a physics-informed learning framework utilizing a temporal convolutional network (TCN) for lower-limb kinematics prediction, significantly reducing sensor count to only two IMUs. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…Methods To tackle these challenges, we present a prescription recommendation framework called PRDAGE, which is based on data augmentation and multi-graph embedding. …”
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Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

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A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors by Aymen M. Al-Hejri, Archana Harsing Sable, Riyadh M. Al-Tam, Mugahed A. Al-antari, Sultan S. Alshamrani, Kaled M. Alshmrany, Wedad Alatebi

“…This paper addresses this challenge by introducing a comprehensive explainable federated learning framework for breast cancer prediction. We evaluate three deep learning approaches in both centralized and federated scenario settings: (1) individual artificial intelligence (AI) models, (2) high-level feature space ensemble models, and (3) a hybrid model combining global Vision Transformer (ViT) and local convolutional neural network (CNN) features. …”
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Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI by Kriengsak Treeprapin, Rakdee Bandatang, Thitipong Panthum, Worapong Singchat, Jiraboon Prasanpan, Kornsorn Srikulnath, Suchin Trirongjitmoah

“…In this study, a deep learning-based framework is proposed to classify bighead catfish (Clarias macrocephalus), North African catfish (Clarias gariepinus), and their F1 hybrids using cranial morphological features. …”
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DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI by Saravanan Srinivasan, Lalitha Somasundharam, Sukumar Rajendran, Virendra Pal Singh, Sandeep Kumar Mathivanan, Usha Moorthy

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EvapoDeep: A Dual Deep Learning Framework Utilizing GNSS Data for Evapotranspiration Modeling and Predictive Analysis by Saeed Ebrahimi, Saeid Haji-Aghajany, Yazdan Amerian, Melika Tasan

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A Novel Framework for Saraiki Script Recognition Using Advanced Machine Learning Models (YOLOv8 and CNN) by Hafiz Muhammad Raza Ur Rehman, Syed Arfan Haider, Hiba Faisal, Kook-Yeol Yoo, M. Z. Jhandir, Gyu Sang Choi

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Masked and Noise-Masked Multimodal Brain Tumor Image Segmentation Using SegFormer and Shared Encoder Framework by K. Hemalatha, P. R. Vishnu Vardhan, Alfred Dharmaraj Aravindraj, S. Hari Hara Sudhan

“…Being a multimodal framework, MNMS can effectively work with and provide valuable segmentation results for any single modality which it has been trained for, ensuring robustness in real-world clinical scenarios where multimodal data may not always be available. …”
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A hybrid CNN-BILSTM deep learning framework for signal detection of a massive MIMONOMA system by Mohamed A. Abdelhamed, Mennatalla Samy, Bassem E. Elnaghi, Ahmed Magdy

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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. …”
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Exploring Gait Recognition in Wild Nighttime Scenes by Haotian Li, Wenjuan Gong, Yutong Li, Yikai Wu, Kechen Li, Jordi Gonzàlez

“…Furthermore, to tackle the challenges posed by low-light conditions and other influencing factors in outdoor nighttime gait recognition, we propose a novel pose-based gait recognition framework called GaitSAT. This framework models the intrinsic correlations of human joints by integrating self-attention and graph convolution modules. …”
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Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation by Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Eduardo P. Cavieres, Eduardo P. Cavieres, Eduardo P. Cavieres, Matías Salinas, Matías Salinas, Matías Salinas, Diego Mellado, Diego Mellado, Diego Mellado, Diego Mellado, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Steren Chabert, Steren Chabert, Steren Chabert, Marvin Querales, Marvin Querales, Julio Sotelo, Rodrigo Salas, Rodrigo Salas, Rodrigo Salas

“…Although Deep Learning (DL) models offer strong performance in tumor detection and segmentation using MRI, their black-box nature hinders clinical adoption due to a lack of interpretability.MethodsWe present a hybrid AI framework that integrates a 3D U-Net Convolutional Neural Network for MRI-based tumor segmentation with radiomic feature extraction. …”
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