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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Emotion recognition with multiple physiological parameters based on ensemble learning by Yilong Liao, Yuan Gao, Fang Wang, Li Zhang, Zhenrong Xu, Yifan Wu

“…We proposed a hybrid model framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, trained and optimized using a random seed initialization strategy and a cosine annealing warm restart strategy. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…To address this issue, this paper introduces a deep learning-based multiscale patch residual network (MPR) for the automatic detection and localization of subtle pediatric supracondylar fractures. The MPR framework combines a CNN for automatic feature extraction with a multiscale generative adversarial network (GAN) to model skeletal integrity using healthy samples. …”
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Non-Contact Blood Pressure Monitoring Using Radar Signals: A Dual-Stage Deep Learning Network by Pengfei Wang, Minghao Yang, Xiaoxue Zhang, Jianqi Wang, Cong Wang, Hongbo Jia

“…We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. …”
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Astronomical Pointlike Source Detection via Deep Feature Matching by Ma Long, Xin Jiarong, Du Jiangbin, Zhao Jiayao, Wang Xiaotian, Zhang Yu

“…The feature extraction module is built on residual blocks and adopts an encoder–decoder framework to distill features from images robustly. …”
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DSIT UNet a dual stream iterative transformer based UNet architecture for segmenting brain tumors from FLAIR MRI images by Shakib Al Hasan, S. M. Mahim, Md Emamul Hossen, Md Olid Hasan, Md Khairul Islam, Patrizia Livreri, Salah Uddin Khan, Mohammad Alibakhshikenari, Md Sipon Miah

“…We propose Dual-Stream Iterative Transformer UNet (DSIT-UNet), a novel framework that combines Iterative Transformer (IT) modules with a dual-stream encoder–decoder architecture. …”
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Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms by Jianmin Zhang, Yuliang Li, Mingxing Zhou, Sixuan Chu

“…This research provides a robust framework for cross-category industrial product style evaluation, enhancing design efficiency and shortening development cycles.…”
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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.…”
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Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

“…Firstly, the backbone network integrates depthwise separable convolutions and a Convolutional Block Attention Module (CBAM) to suppress background clutter and extract effective features. …”
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