A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning by Rogelio Reyes-Reyes, Yeredith G. Mora-Martinez, Beatriz P. Garcia-Salgado, Volodymyr Ponomaryov, Jose A. Almaraz-Damian, Clara Cruz-Ramos, Sergiy Sadovnychiy

“…Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. …”
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Enhancing anomaly detection in plant disease recognition with knowledge ensemble by Jiuqing Dong, Jiuqing Dong, Jiuqing Dong, Heng Zhou, Alvaro Fuentes, Alvaro Fuentes, Sook Yoon, Dong Sun Park, Dong Sun Park

“…We first benchmark the anomaly detection performance of three major visual frameworks—convolutional neural networks (CNNs), vision transformers (ViTs), and vision-language models (VLMs)—under varying fine-tuning strategies. …”
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AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms by Chuanyan Wu, Bentao Lin, Jialin Zhang, Rui Gao, Rui Song, Zhi-Ping Liu

“…This method leverages both cross-attention and self-attention frameworks, focusing on enhancing prediction accuracy through the integration of features across diverse scales. …”
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Opinion Mining and Analysis Using Hybrid Deep Neural Networks by Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri, Minyar Sassi Hidri

“…The introduced hybrid BGRU-LSTM (HBGRU-LSTM) architecture attained a testing accuracy of 95%, exceeding the performance of traditional DL frameworks such as LSTM (93.06%), CNN+LSTM (93.31%), and GRU+LSTM (92.20%). …”
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Estimating Gait Speed in the Real World With a Head-Worn Inertial Sensor by Paolo Tasca, Francesca Salis, Samanta Rosati, Gabriella Balestra, Claudia Mazza, Andrea Cereatti

“…These findings lay the technical and analytical groundwork for future clinical validation and application of gait analysis frameworks that integrate inertial sensors with head-worn devices.…”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management.…”
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Deep Learning Techniques for Lung Cancer Diagnosis with Computed Tomography Imaging: A Systematic Review for Detection, Segmentation, and Classification by Kabiru Abdullahi, Kannan Ramakrishnan, Aziah Binti Ali

“…Hybrid architectures, such as convolutional neural networks (CNNs) and transformers, show promise in improving nodule localization. …”
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A Cross-Mamba Interaction Network for UAV-to-Satallite Geolocalization by Lingyun Tian, Qiang Shen, Yang Gao, Simiao Wang, Yunan Liu, Zilong Deng

“…Recent advancements in this area have been largely driven by learning-based frameworks that utilize convolutional neural networks (CNNs) and Transformers. …”
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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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