Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review by Suli Li, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, Xinwei Qi

“…Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. …”
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EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning by Gabriella Miles, Melvyn Smith, Nancy Zook, Wenhao Zhang

“…Alzheimer’s Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. …”
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Contrasted Trends in Chlorophyll‐a Satellite Products by Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, Maïlys Roux

“…This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. …”
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Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait by Wenhao Qi, Shiying Shen, Chaoqun dong, Mengjiao Zhao, Shuaiqi Zang, Xiaohong Zhu, Jiaqi Li, Bin Wang, Yankai Shi, Yongze Dong, Huajuan Shen, Junling Kang, Xiaodong Lu, Guowei Jiang, Jingsong Du, Eryi Shu, Qingbo Zhou, Jinghua Wang, Shihua Cao

“…In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures. …”
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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis by Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

“…ResultsA total of 41 studies met the inclusion criteria. The most common classification task was binary detection of abnormal lung sounds, particularly wheezing. …”
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Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis by Hisaki Makimoto, Takayuki Okatani, Masanori Suganuma, Tomoyuki Kabutoya, Takahide Kohro, Yukiko Agata, Yukiyo Ogata, Kenji Harada, Redi Llubani, Alexandru Bejinariu, Obaida R. Rana, Asuka Makimoto, Elisabetha Gharib, Anita Meissner, Malte Kelm, Kazuomi Kario

“…Single-beat models revealed limb leads, particularly I and aVR, as most indicative of dysfunction. An analysis indicated segments from QRS to T-wave were most revealing, with P segments enhancing model performance. …”
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Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms by Raka Thoriq Araaf, Arkar Minn, Tofael Ahamed

“…Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust. …”
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Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review by Catarina Sousa Santos, Mário Amorim-Lopes

“…Results From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018–2022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. …”
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Medical Image Hybrid Watermark Algorithm Based on Frequency Domain Processing and Inception v3 by Yu Fan, Jingbing Li, Uzair Aslam Bhatti, Saqib Ali Nawaz, Yenwei Chen

“…Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…Particularly, an EfficientNetB0 convolutional neural networks achieved the most impressive results, demonstrating a high sensitivity of 98.85, a specificity of 99.22, and a Matthews correlation coefficient of 0.98. …”
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Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…Some models even reached an accuracy of over 95% in GC detection. Convolutional neural networks (CNN) are the most commonly used DL models. …”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. However, most high-precision vehicle detection algorithms suffer from high computational effort and slow detection speeds, resulting in the challenging task of deploying these algorithms on mobile devices. …”
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EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…Salient object detection in remote sensing images (RSI-SOD) aims to identify visually prominent objects by mimicking human visual perception. While convolutional neural networks (CNNs) have significantly improved detection accuracy, most RSI-SOD methods suffer from high computational costs and large model sizes, limiting their applicability in resource-constrained environments. …”
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Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

“…RAADA dynamically adapts decisions based on real-time field responses, ensuring efficiency and sustainability.ResultsThe experimental findings obtained from large-scale agricultural datasets show that our framework far exceeds the existing most advanced methods in terms of the accuracy of yield prediction, resource optimization, and environmental impact mitigation.DiscussionThis research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.…”
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Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure by Duc-Thuan Nguyen, Tuan-Khai Nguyen, Zahoor Ahmad, Jong-Myon Kim

“…Pressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. …”
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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impairments during the initial stages of the illness. …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Historical Blurry Video-Based Face Recognition by Lujun Zhai, Suxia Cui, Yonghui Wang, Song Wang, Jun Zhou, Greg Wilsbacher

“…Face recognition is a widely used computer vision, which plays an increasingly important role in user authentication systems, security systems, and consumer electronics. The models for most current applications are based on high-definition digital cameras. …”
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Managing Timing Uncertainties in Worst-Case Design of Machine Learning Applications by Robin Hapka, Rolf Ernst

“…., robot-human collaboration using convolutional neural networks, timing must be considered to operate safely. …”
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Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models by Muhammad Waqas, Usa Wannasingha Humphries

“…MPI-ESM1-2-LR and CanESM5 were found to be the most reliable CMIP6 GCMs for reproducing historical precipitation patterns in the Thai region. …”
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