GLClick: Interactive Segmentation Combining Global and Local Features by Jiaying Tang, Hongyuan Wang, Zongyuan Ding, Zihao Xin

“…Convolutional neural networks (CNNs) are the backbone of most modern interactive segmentation algorithms. …”
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Spatiotemporal evaluation of irrigation groundwater quality in Hungarian agricultural sites using hydrochemical and machine learning approaches by Musaab A. A. Mohammed, Norbert P. Szabó, Viktória Mikita, Péter Szűcs

“…HCA indicated low to moderate mineralization in most samples, while SOMs revealed notable spatial and temporal shifts, including gradual degradation due to natural and anthropogenic factors. …”
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Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging by Rui Zhang PhD, Chao Cheng PhD, Xuehua Zhao PhD, Xuechen Li PhD

“…Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. …”
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High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image by Hongcui Wang, Yihong Zheng, Ouxiang Chen

“…., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.…”
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Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning by Gang Xiang, Kun Tian

“…Experiments on two open datasets demonstrate that our proposed WDATL model outperforms most of the state-of-the-art approaches on transfer diagnosis tasks under diverse working circumstances.…”
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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 systematic review seeks to address the gaps by answering three questions: (1) What AI methodologies are most commonly applied in CS projects? (2) How does AI integration impact the efficiency and scalability of CS initiatives? …”
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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

“…Among 13 categories, we employ six core categories, which are the most frequent occurrences in the collected dataset. …”
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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

“…Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. …”
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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

“…Among the weighting strategies, IPTW yields the most substantial improvements in model performance and bias reduction. …”
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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

“…<p>Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. …”
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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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