CNN Based Deep Learning Modeling with Explainability Analysis for Detecting Fraudulent Blockchain Transactions by Mohammad Hasan, Mohammad Shahriar Rahman, Mohammad Jabed Morshed Chowdhury, Iqbal H. Sarker

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Real-time damage detection of bridges using adaptive time-frequency analysis and ANN by V. Ahmadian, S. B. Beheshti Aval, E. Darvishan

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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection. by Naif Almakayeel, E Laxmi Lydia, Oleg Razzhivin, S Rama Sree, Mohammed Altaf Ahmed, Bibhuti Bhusan Dash, S P Siddique Ibrahim

“…Various deep learning (DL) and artificial intelligence (AI) technologies have been successfully implemented for the analysis of medical images, namely grading of diabetic retinopathy (DR), breast cancer detection, skin cancer detection, and so on. …”
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Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection by Edeh Michael Onyema, B. Gunapriya, Balasubramanian Prabhu Kavin, Mueen Uddin, Priyan Malarvizhi Kumar, Tehseen Mazhar, Mamoon M. Saeed

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An Improved Man-Made Structure Detection Method for Multi-aspect Polarimetric SAR Data by Fabin Dong, Qiang Yin, Wen Hong

“…Consequently, relying solely on anisotropic analysis may not yield favorable man-made structure detection results. …”
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AI-Assisted MRI Analysis for Multiple Sclerosis: Lesion Detection and Brain Atrophy Assessment by Ioana-Andreea CÎRLIG, Ioana-Andreea GHEONEA

“…This study aimed to assess the performance and clinical relevance of AI-based MRI analysis for lesion detection and brain atrophy quantification in patients with MS. …”
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Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection by Kun Zheng, Haoshan Liang, Hongwei Zhao, Zhe Chen, Guohao Xie, Liguo Li, Jinghua Lu, Zhangda Long

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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. …”
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Quantitative Analysis of Sulfur Elements in Mars-like Rocks Based on Multimodal Data by Yuhang Dong, Zhengfeng Shi, Junsheng Yao, Li Zhang, Yongkang Chen, Junyan Jia

“…The Zhurong rover of the Tianwen-1 mission has detected sulfates in its landing area. The analysis of these sulfates provides scientific evidence for exploring past hydration conditions and atmospheric evolution on Mars. …”
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Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models by Ncedo S. Maseko, Bonginkosi A. Thango, Nkateko Mabunda

“…Advanced diagnostic techniques are essential for timely fault detection and predictive maintenance. This study investigates the application of machine learning (ML) techniques in transformer fault detection using Frequency Response Analysis (FRA) data. …”
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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

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Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation by Lemin Shi, Ping Gong, Mingye Li, Dianxin Song, Hao Zhang, Zhe Wang, Xin Feng

“…These improvements reduced false negatives and enhanced genomic abnormality detection accuracy. The proposed method significantly improves FISH signal clarity and stability, providing reliable support for cancer screening, genomic abnormality detection, molecular typing, prognosis evaluation, and targeted treatment planning.…”
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Detection and analysis of organic contaminants in potable water based on three-dimensional fluorescence spectroscopy by CHEN Fang, ZHANG Xiaoyan, HUANG Pingjie, HOU Dibo, ZHANG Guangxin, ZHAO Jiajia, HE Jiping

“…Support vector machine was applied to classify measured samples based on feature information of relative concentration value, which can distinguish normal water samples from the ones polluted by organics.Through the analysis of the experimental results, we could find that the method effectively detects the water samples contaminated by organics. …”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…ObjectiveParkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function and speech patterns. Early detection of PD through non-invasive methods, such as speech analysis, can improve treatment outcomes and quality of life for patients. …”
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Detecting Falls and Slips of Wheelchair Users Using Low-Resolution Thermal Image Analysis by Shisei Nakamura, Masaaki Yamauchi, Miwa Sugita, Yoshihiro Aso, Yuichi Ohsita, Hideyuki Shimonishi

“…To overcome this, we proposed a video analysis scheme using torso features and developed a system called “Fall Detection using CNN and Torso Features (FDCTF).…”
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An improved chilli pepper flower detection approach based on YOLOv8 by Zhi-Yong Wang, Cui-Ping Zhang

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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Extended color components, a novel sensitive dye screening method, and a feature screening method were integrated and applied to enhance pH detection. …”
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Effective classification of android malware families through dynamic features and neural networks by Gianni D'Angelo, Francesco Palmieri, Antonio Robustelli, Arcangelo Castiglione

“…Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. …”
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Deep Learning-Based Medical Object Detection: A Survey by Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee

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Facial and Neck Region Analysis for Deepfake Detection Using Remote Photoplethysmography Signal Similarity by Byeong Seon An, Hyeji Lim, Hyeon Ah Seong, Eui Chul Lee

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