Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Furthermore, we evaluated different performance indicators, discussed possible reasons for errors in regional ETa prediction, and conducted sensitivity analysis of the model characteristics. …”
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Computer-vision based automatic rider helmet violation detection and vehicle identification in Indian smart city scenarios using NVIDIA TAO toolkit and YOLOv8 by Uttam U. Deshpande, Goh Kah Ong Michael, Sufola Das Chagas Silva Araujo, Vaidehi Deshpande, Rudragoud Patil, Ramchandra Alias Ameet Chate, Varun R. Tandur, Supreet S. Goudar, Shreya Ingale, Vaishnavi Charantimath

“…Two-wheeler traffic violations can take many different forms, such as overloading, triple riding, and helmetless riding. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. …”
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Practical guidelines for cell segmentation models under optical aberrations in microscopy by Boyuan Peng, Jiaju Chen, P. Bilha Githinji, Ijaz Gul, Qihui Ye, Minjiang Chen, Peiwu Qin, Xingru Huang, Chenggang Yan, Dongmei Yu, Jiansong Ji, Zhenglin Chen

“…We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG, Swin Transformer), under aberrated conditions. …”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…Abstract The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter‐laboratory differences in image contrast and resolution quality and non‐void artifacts. …”
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Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids by Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

“…Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.…”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…Abstract In the research of non-intrusive load monitoring (NILM), the temporal characteristics of V–I trajectories are often overlooked, and using a single feature for identification may lead to insignificant differences between similar loads. Based on this, this paper proposes a non-intrusive load monitoring method based on time-enhanced multidimensional feature visualization. …”
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A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer by Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G. Thomas Brown, Kimberly M. Greenfield, John D. McGeeney, Sally Elsamanoudi, Shyh-Han Tan, Cara Schafer, Jiji Jiang, Gyorgy Petrovics, Albert Dobi, Francisco J. Rentas, Peter A. Pinto, Gregory T. Chesnut, Peter Choyke, Baris Turkbey, Joel T. Moncur

“…This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. …”
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Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. Additionally, models trained on such data tend to struggle with recognizing rare or unseen characters, often leading to recognition errors. …”
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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…However, traditional approaches to optimizing PE schedules may not adequately account for individual differences in demographics and activity patterns.MethodsThis study proposes an efficient method for optimizing PE schedules using deep learning (DL) techniques. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…This paper proposes a methodology for leveraging convolutional neural networks (CNNs) in conjunction with advanced data preprocessing to facilitate optimal quality control decision-making in high pressure casting (HPDC) processes. …”
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Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models by Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao

“…Sample sizes varied significantly, ranging from 28 to 900 patients. MRI protocols also differed across studies, with variations in field strengths, slice thicknesses, and sequences used. …”
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Machine Learning for Fire Safety in the Built Environment: A Bibliometric Insight into Research Trends and Key Methods by Mehmet Akif Yıldız

“…In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. …”
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Comparative Deep Learning–Based Facial Image Analysis for Early Autism Prediction in School-Aged Children by Sarah Sabeeh

“…All models significantly outperformed the chance baseline (p < 0.05), though pairwise differences in accuracy did not reach statistical significance at the α = 0.05 level. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…Statistical validation was also performed using the Diebold-Mariano test to establish significant differences in performance. The most important results reveal that the CNN GRU model outperformed the other models, achieving a MAE of 0.2104 MW, an MSE of 0.1028 MW, an RMSE of 0.3206 MW, and an R² of 0.9768. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. …”
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Real-Time Player Engagement Measurement Using Nonintrusive Game Telemetry by Ammar Rashed, Shervin Shirmohammadi, Mohamed Hefeeda

“…Further cross-domain validation of the framework, as is and without transfer learning, with the games FIFA&#x2019;23 and Street Fighter V, leads to 66% accuracy, demonstrating the model&#x2019;s stable performance despite the significant differences in the test domains. Interestingly, our results suggest that objective gameplay metrics may better reflect engagement than subjective player assessments, with skill estimates showing significant correlation with self-reports.…”
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Motion Classification With Embroidery Bend Sensors Using Multiple Zigzag-Stitch for Loose-Fitting Garments by Kaisei Minami, Yasuhiro Akiyama, Takuya Umedachi

“…The model successfully learned the differences in signal amplitude and frequency as distinguishing features of each activity, resulting in an average classification accuracy of 99.02% across the ten types of activities.…”
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Developing an Algorithm for Robotic Precision Application of Crop Protection Products by M. A. Mirzaev

“…The image parameters tend to differ significantly in applied solutions. (Research purpose) To develop an algorithm for crop plant recognition by a robotic device using a state-of-the-art convolutional neural network (R-CNN) and deep learning technology. …”
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CF-YOLO for small target detection in drone imagery based on YOLOv11 algorithm by Chengcheng Wang, Yuqi Han, Chenggui Yang, Mingjie Wu, Zaiqing Chen, Lijun Yun, Xuesong Jin

“…Abstract Images captured from a drone’s perspective are significantly impacted in terms of target detection algorithm performance due to the notable differences in target scales and the presence of numerous small target objects lacking detailed information. …”
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