Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…Among the various topographic factors, we found that elevation was the most important factor for discriminating all forest types. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…Seasonal wind speed analysis revealed distinct patterns, with winter emerging as the most efficient season for wind, featuring a median wind speed of 7.96 m/s. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…On the contrary, the algorithms applying YOLOv5, YOLOX, YOLOv7 and the paper's improved YOLOv5 achieved the recall rates from 95.26% to 96.28%, while algorithms applying DeepSORT, StrongSORT, Bot-SORT and CombineSORT achieved the MOTA values from 0.887 to 0.901. But most of them had the time cost exceeding 80ms, making them could not perform real-time calculations. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…[Objective]As one of China's most important agricultural products, apples hold a significant position in cultivation area and yield. …”
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Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning by Haopeng Wang, He Wang, Chenyun Dai, Xinming Huang, Edward A. Clancy

“…However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. …”
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Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN by Zhenbing ZHAO, Shuai ZHANG, Wei JIANG, Peng WU

“…Bolts are the mostly used fasteners on transmission lines, and their defect detection is an important content for transmission line inspection. …”
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Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis by Alexander Maniangat Luke, Nader Nabil Fouad Rezallah

“…The meta-analysis incorporates fourteen of the 21 articles included in this review. The research mostly uses convolutional neural networks (CNNs) for analyzing images, showing outstanding accuracy, sensitivity, and specificity in detecting caries. …”
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Lung volume assessment for mean dark-field coefficient calculation using different determination methods by Florian T. Gassert, Jule Heuchert, Rafael Schick, Henriette Bast, Theresa Urban, Tina Dorosti, Gregor S. Zimmermann, Sebastian Ziegelmayer, Alexander W. Marka, Markus Graf, Marcus R. Makowski, Daniela Pfeiffer, Franz Pfeiffer

“…Radiography-based methods simplify workflows and minimize biases, making them most suitable. Graphical Abstract…”
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NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning by Jianliang Wang, Chen Chen, Jiacheng Wang, Zhaosheng Yao, Ying Wang, Yuanyuan Zhao, Yi Sun, Fei Wu, Dongwei Han, Guanshuo Yang, Xinyu Liu, Chengming Sun, Tao Liu

“…Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R² increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. …”
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Early Detection and Classification of Diabetic Retinopathy: A Deep Learning Approach by Mustafa Youldash, Atta Rahman, Manar Alsayed, Abrar Sebiany, Joury Alzayat, Noor Aljishi, Ghaida Alshammari, Mona Alqahtani

“…Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. …”
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Enhancing head and neck cancer detection accuracy in digitized whole-slide histology with the HNSC-classifier: a deep learning approach by Haiyang Yu, Haiyang Yu, Wang Yu, Wang Yu, Yuan Enwu, Yuan Enwu, Yuan Enwu, Jun Ma, Jun Ma, Xin Zhao, Xin Zhao, Linlin Zhang, Linlin Zhang, Linlin Zhang, Fang Yang, Fang Yang, Fang Yang

“…Head and neck squamous cell carcinoma (HNSCC) represents the sixth most common cancer worldwide, with pathologists routinely analyzing histological slides to diagnose cancer by evaluating cellular heterogeneity, a process that remains time-consuming and labor-intensive. …”
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Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study by Li‐peng Xing, Gang Liu, Hao‐chen Zhang, Lei Wang, Shan Zhu, Man Du La Hua Bao, Yan‐ni Wang, Chao Chen, Zhi Wang, Xin‐yu Liu, Shuai Zhang, Qiang Yang

“…ABSTRACT Objective Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. …”
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Frailty prediction in patients with chronic digestive system diseases: based on multi-task learning model by Sihan Hu, Xiaochuan Guo, Xiaobao Wang, Zixiang Jin, Chenyang Zhou, Lang Tu, Zhoulong Shi, Weiyi Ao, Xin Zhang, Jay Zheng, Xuezhi Zhang, Hui Ye

“…Utilizing the Multi-Gate Mixture-of-Experts (MMoE) framework, we built and evaluated five models: Tab Transformer, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). …”
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Multimodal diagnosis of Alzheimer’s disease based on resting-state electroencephalography and structural magnetic resonance imaging by Junxiu Liu, Junxiu Liu, Shangxiao Wu, Shangxiao Wu, Shangxiao Wu, Qiang Fu, Qiang Fu, Xiwen Luo, Xiwen Luo, Yuling Luo, Yuling Luo, Sheng Qin, Sheng Qin, Yiting Huang, Yiting Huang, Zhaohui Chen, Zhaohui Chen

“…However, the inclusion of electroencephalography (EEG) in such multimodal studies has been relatively limited. Moreover, most multimodal studies on AD use convolutional neural networks (CNNs) to extract features from different modalities and perform fusion classification. …”
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Anomaly Detection Algorithms for Real-Time Log Data Analysis at Scale by Andras Horvath, Andras Olah, Attila Pinter, Balint Siklosi, Gergely Lukacs, Istvan Z. Reguly, Kalman Tornai, Tamas Zsedrovits, Zoltan Mathe

“…Our results underscore the importance of selecting the right balance between sophistication and simplicity, challenging the assumption that the most sophisticated methods are necessary for effective anomaly detection in real-world log data.…”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. …”
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Interpretable classification of Levantine ceramic thin sections via neural networks by Sara Capriotti, Alessio Devoto, Simone Scardapane, Silvano Mignardi, Laura Medeghini

“…A dataset of 1424 thin section images from 178 ceramic samples belonging to several archaeological sites across the Levantine area, mostly from the Bronze Age, with few samples dating to the Iron Age, was used to train and evaluate these models. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Its ability to generalize effectively from mostly normal samples makes it a practical and valuable solution for real-world industrial inspection systems. …”
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Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers by Elif Sertel, Can Michael Hucko, Mustafa Erdem Kabadayı

“…While transformer-based segmentation methods have been widely applied to image segmentation tasks, they have mostly focused on satellite images. There is a growing need to explore transformer-based approaches for geospatial object extraction from historical maps, given their superior performance over traditional convolutional neural network (CNN)-based architectures. …”
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