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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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…After that, the most effective layer-adaptive magnitude-based pruning (LAMP) method is used to build away the redundant parameters to make the detection network more lightweight. …”
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Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy by Sonia Zouari, Farman Ali, Atef Masmoudi, Sarah Abu Ghazalah, Wajdi Alghamdi, Faris A. Kateb, Nouf Ibrahim

“…The CST‐PSSM‐based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. …”
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Trajectory Tracking Control Based on Visual Localization and Adaptive Radial Basis Function Sliding Mode Control by Hung-Yih Tsai, Shih-Min Liao, Chang-Hong Liu

“…Autonomous driving is a prominent research focus in the field of intelligent vehicles, with trajectory tracking serving as a fundamental control strategy. However, most existing tracking methods rely heavily on GPS signals, making them susceptible to signal loss due to environmental obstacles such as buildings. …”
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Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning by Krzysztof Szymoński, Natalia Janiszewska, Kamila Sofińska, Katarzyna Skirlińska-Nosek, Dawid Lupa, Michał Czaja, Marta Urbańska, Katarzyna Jurkowska, Kamila Konik, Marta Olszewska, Dariusz Adamek, Kamil Awsiuk, Ewelina Lipiec

“…In this study, we have developed a new promising methodology of spatial epigenomics that integrates multiplexed molecular imaging with convolutional neural networks. Then, we used it to map epigenetic modification levels in the six most prevalent PC subtypes. …”
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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

“…Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. …”
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Fish Detection Using Deep Learning by Suxia Cui, Yu Zhou, Yonghui Wang, Lujun Zhai

“…An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. …”
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Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images by Yi-Ching Cheng, Yi-Chieh Hung, Guan-Hua Huang, Tai-Been Chen, Nan-Han Lu, Kuo-Ying Liu, Kuo-Hsuan Lin

“…To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. …”
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Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets by Diogen BABUC, Alesia LOBONŢ, Alexandru FARCAŞ, Todor IVAŞCU, Sebastian-Aurelian ŞTEFĂNIGĂ

“…Preliminary results indicated that ZS-N2N and CycleGAN consistently achieve the lowest loss and highest accuracy, making them the most effective for denoising across different pathologies. …”
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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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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. …”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…Among these models, the LSTM model exhibited the most significant performance improvement and the best transferability. …”
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Diagnosing Gingiva Disease Using Artificial Intelligence Techniques by Rana Khalid Sabri, Lujain Younis Abdulkadir, AbdulSattar Mohammed Khidhir, Hiba Abdulkareem Saleh

“…MobileNet emerged as the most effective model, achieving a test accuracy of 92.73%; the suggested method relies mainly on its positive result. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. …”
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A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT by Ozgur Efiloglu, Muhammed Yildirim, Kadir Yildirim, Harun Bingol, Mustafa Kaan Akalin, Meftun Culpan, Bilal Alatas, Asif Yildirim

“…Extracorporeal shock wave lithotripsy (ESWL) is one of the most often employed therapy methods for managing kidney stones. …”
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A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas by Xiaoyong Tan, Min Deng, Kaiqi Chen, Yan Shi, Bingbing Zhao, Qinghao Liu

“…The results showed that the proposed SH-CA greatly improves the figure of merit and simulates the most real land-use patterns compared with other four sophisticated CA models. …”
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Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications by Minh Long Hoang, Guido Matrella, Dalila Giannetto, Paolo Craparo, Paolo Ciampolini

“…This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. …”
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Developing a Drowsiness Detection System for Safe Driving Using YOLOv9 by Fernando Candra Yulianto, Wiwit Agus Triyanto, Syafiul Muzid

“…Several drowsiness detection systems built using the eye aspect ratio (EAR), percentage of eyelid closure (PERCLOS), and convolutional neural network (CNN) methods still have limitations in terms of accuracy and response time. …”
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Spatial-Channel Multiscale Transformer Network for Hyperspectral Unmixing by Haixin Sun, Qiuguang Cao, Fanlei Meng, Jingwen Xu, Mengdi Cheng

“…In recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information, but limited in modeling long-range dependencies. …”
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Application of deep learning in malware detection: a review by Yafei Song, Dandan Zhang, Jian Wang, Yanan Wang, Yang Wang, Peng Ding

“…Taken together, this will help researchers at the current stage gain insight into the unresolved challenges or barriers faced by previous researchers. Among these, the most common problem is the lack of broader and consistent datasets, followed by the need for existing models for further improvement.…”
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