Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive–Motor Rehabilitation by Kathya P. Acuña Luna, Edgar Rafael Hernandez-Rios, Victor Valencia, Carlos Trenado, Christian Peñaloza

“…Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. …”
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CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography by Erdem Yanar, Fırat Hardalaç, Kubilay Ayturan

“…This study investigates the application of deep learning techniques for the automated diagnosis of cardiomegaly from chest X-ray (CXR) images, utilizing both convolutional neural networks (CNNs) and Vision Transformers (ViTs). …”
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Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest by Majdi Fanous, Jonathan M. Eden, Juntao Yang, Simon See, Vasile Palade, Alireza Daneshkhah

“…Compared to the FE model, the proposed framework achieved a five-fold reduction in training time (24 h vs. 5 days) and supports inference within seconds, enabling near real-time predictions. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). …”
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Transformer-based tokenization for IoT traffic classification across diverse network environments by Firdaus Afifi, Faiz Zaki, Hazim Hanif, Nik Aqil, Nor Badrul Anuar

“…To address these challenges, this study introduces MIND-IoT, a novel and scalable framework for classifying generalized IoT traffic. …”
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Precision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy by Fatma Hilal Yagin, Yasin Gormez, Cemil Colak, Abdulmohsen Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. …”
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Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea by Hyeongmok Lee, Go-Eun Kim, Woo-Jin Shin, Yuyoung Lee, Sanghee Park, Kwang-Sik Lee, Jina Jeong, Seung-Ik Park, Sungwook Choung

“…To address this, we propose a hybrid framework for 87Sr/86Sr isoscape mapping that integrates a kriging-based data augmentation method with a deep learning (DL) classifier. …”
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Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection by Mohd Shukri Ab Yajid, Nilesh Bhosle, Gadug Sudhamsu, Ali Khatibi, Sahil Sharma, Rubal Jeet, R. Sivaranjani, A. Bhowmik, A. Johnson Santhosh

“…The efficacy of the proposed framework is accessed through experiments conducted on the ECC (European Credit Cardholders) dataset. …”
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XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection by Gia-Huy Luu, Minh-Khang Duong, Trong-Phuc Pham-Ngo, Thanh-Sang Ngo, Dat-Thinh Nguyen, Xuan-Ha Nguyen, Kim-Hung Le

“…Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. …”
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A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance by Ye Mu, Jinghuan Hu, Zhipeng Li, Heyang Wang, He Gong, Yu Sun, Tianli Hu

“…To assess scalability, the framework is extended to pig facial recognition and cattle individual detection tasks, demonstrating cross-species generalization capabilities. …”
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Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Nithin Raj, J. Pooja, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…This study introduces a novel deep learning framework that leverages patch-level significance analysis for precise identification of ischemic strokes in Computed Tomography (CT) images. …”
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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. …”
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Multi‑feature geological hazard susceptibility assessment by integrating improved ResNet and transfer learning: A case study of the Loess Plateau in Northern Shaanxi by Hao Cheng, Chong Xu, Rong Guo, Hai-kun Jing, Zeng-lin Hong, Feng-chen Fu, Ruo-shu Li

“…A lightweight deep network framework was then developed by simplifying the ResNet-18 backbone and embedding a Self-Attention mechanism and a convolutional block attention module. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…[Methods] The WM-CTA model primarily consists of two frameworks: a data preprocessing module (Wavelet Transform and Maximum Information Coefficient) and a prediction module (Convolutional Neural Network and Attention Mechanism). …”
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An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks by Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana, Tarequl Islam, Mohammad Motiur Rahman, Hani Alshahrani, Adel Sulaiman, Mana Saleh Al Reshan

“…Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.…”
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An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management by Jian Dong, Yuan Xu, Rigeng Wu, Chengwang Xiao

“…This paper proposes an innovative network model based on the improved snow ablation optimizer algorithm. It employs convolutional neural network, structured within a bidirectional gated recurrent unit framework, combined with a multi-head attention mechanism, for spatial target trajectory prediction. …”
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An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images by Mamuna Fatima, Muhammad Attique Khan, Anwar M. Mirza, Jungpil Shin, Areej Alasiry, Mehrez Marzougui, Jaehyuk Cha, Byoungchol Chang

“…Abstract Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. …”
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A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles by Carolina Tripp-Barba, José Alfonso Aguilar-Calderón, Luis Urquiza-Aguiar, Aníbal Zaldívar-Colado, Alan Ramírez-Noriega

“…The findings disclose various methods that boost the accuracy and reliability of SoC, including enhanced variants of the Kalman filter, machine learning models like long short-term memory (LSTM) and convolutional neural networks (CNNs), as well as hybrid optimization frameworks that combine Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). …”
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Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention by Ziyang Liu, Emmanuel Agu, Peder Pedersen, Clifford Lindsay, Bengisu Tulu, Diane Strong

“…We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. …”
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