Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning by Gang Xiang, Kun Tian

“…Moreover, an improved one-dimensional convolutional neural network- (CNN-) based feature extractor which utilizes exponential linear units (ELU) as activation functions and wide kernels is designed to automatically extract the latent features of raw time-series input data. …”
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Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification by Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein

“… Lung cancer is the most common dangerous disease that, if treated late, can lead to death. …”
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A systematic literature review on the role of artificial intelligence in citizen science by Germain Abdul-Rahman, Andrej Zwitter, Noman Haleem

“…Our findings illustrate ML techniques, including deep learning, clustering algorithms, and convolutional neural networks, boost data annotation, classification, and validation in applications across various disciplines. …”
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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology by Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

“…ResultsOut of 94 reviewed articles, 10 met the inclusion criteria. Most studies employed convolutional neural networks (CNN) for image analysis, with accuracy rates ranging from 90.1% to 99.5%. …”
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Computer Vision-Based Concrete Crack Identification Using MobileNetV2 Neural Network and Adaptive Thresholding by Li Hui, Ahmed Ibrahim, Riyadh Hindi

“…To address this issue, computer vision is one of the most innovative solutions for concrete cracking evaluation, and its application has been an area of research interest in the past few years. …”
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CRxK dataset: a multi-view surveillance video dataset for re-enacted crimes in Korea by Chaehee An, Minyoung Lee, Eunil Park

“…Our training and validation involved four convolutional neural network (CNN) models and a single transformer model. …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

“…Abstract Early Childhood Caries (ECC) is one of the most prevalent non-communicable diseases. It includes a range of environmental and genetic risk factors due to its multifaceted nature. …”
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Image Augmentation Approaches for Building Dimension Estimation in Street View Images Using Object Detection and Instance Segmentation Based on Deep Learning by Dongjin Hwang, Jae-Jun Kim, Sungkon Moon, Seunghyeon Wang

“…Using all augmentations at once rarely outperformed the single most effective method, and sometimes degraded the accuracy; shearing augmentation ranked as the second-best approach. …”
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Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…We employ a combination of deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, to achieve accurate and timely predictions of BTS power failures. …”
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Rain removal method for single image of dual-branch joint network based on sparse transformer by Fangfang Qin, Zongpu Jia, Xiaoyan Pang, Shan Zhao

“…Additionally, since tokens with low relevance in the Transformer may influence image recovery, this study introduces a residual sparse Transformer branch (RSTB) to overcome the limitations of the Convolutional Neural Network’s (CNN’s) receptive field. …”
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Improving Cardiovascular Disease Prediction With Deep Learning and Correlation-Aware SMOTE by Maria Trigka, Elias Dritsas

“…In this direction, this study investigates the performance of five well-established deep learning (DL) models, namely Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Autoencoder in predicting CVD using a diverse patient dataset. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…Convolutional neural networks (CNNs) are increasingly applied in crop disease identification, yet most existing techniques are optimized solely for laboratory environments. …”
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Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. …”
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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…This review aims to analyze research trends in applying DIP for N management over the past 5 years, summarize the most recent studies, and identify challenges and opportunities. …”
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Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area by B. Peng, X. Wu

“…Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. …”
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Artificial Intelligence Approaches for the Detection of Normal Pressure Hydrocephalus: A Systematic Review by Luis R. Mercado-Diaz, Neha Prakash, Gary X. Gong, Hugo F. Posada-Quintero

“…We found that traditional ML methods like Support Vector Machines, Random Forest, and Logistic Regression were commonly used, while DL methods, particularly Deep Convolutional Neural Networks, were also widely employed. …”
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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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A secured accreditation and equivalency certification using Merkle mountain range and transformer based deep learning model for the education ecosystem by Sumathy Krishnan, Surendran Rajendran, Mohammad Zakariah

“…TCRN employs Bi-GRU to retain long-term academic trends, Depth-wise separable convolutions (DSC) to concentrate on course-specific information, and BERT to capture global semantic context. …”
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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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A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.ResultsOur review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. …”
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