Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm by Zhifang Bi, Fumin Ma, Jiaxiong Guan, Jie Wu, Juxia Li, Fuzhong Li, Yanwen Li, Zhanli Liu

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Integrating behavioral and neurophysiological insights: High trait anxiety enhances observational fear learning by Xianchao Ming, Ganzhong Luo, Jinxia Wang, Haoran Dou, Hong Li, Yi Lei

“…Observational fear learning delineates the process by which individuals learn about potential threats through observing others’ reactions. …”
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Metric-Based Few-Shot Learning With Triplet Selection for Adaptive GNSS Interference Classification by Felix Ott, Lucas Heublein, Tobias Feigl, Alexander Rugamer, Christopher Mutschler

“…We employ pairwise learning techniques, including triplet and quadruplet loss functions, during the training process to enhance the latent representation. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. …”
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Unleashing the Potential of Learning Agility: A Catalyst for Innovative Work Behavior and Employee Performance by Christian Wiradendi Wolor, Usep Suhud, Ahmad Nurkhin, Wong Chee Hoo, Mahmoud Ali Rababah

“…Conclusion: These results emphasize the need for companies to not only prioritize learning agility during recruitment but also to foster a work environment that supports continuous learning through structured training programs, cross-functional project opportunities, and mentorship initiatives, ensuring employees remain agile and innovative in response to technological advancements. …”
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Forecasting of Islamic Stock Index in Turkey with Deep Learning Using Index-Based Features by Dilşad Tülgen Çetin, Sedat Metlek

“…With the designed model, the Participation index was forecasted with 0.06, 0.08, 0.02, and 0.994 values in MAE, RMSE, MAPE, and R2 error functions, respectively. The main contribution of the study to the literature is that it is the first study in Turkey to use the LSTM model as a deep learning method in forecasting the Islamic stock index. …”
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Comparative Analysis of Distance Learning Systems in the United Arab Emirates and the United States of America by Oleksii Nalyvaiko, Albina Khomenko, Daria Vereshchak, Danilo Poliakov

“… The article is devoted to the current problem of distance learning. In the theoretical field, various aspects of the functioning of distance education are considered on the example of the analysis of the works of leading scientists in this field. …”
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The digital pulse of preparedness: staff perceptions on learning management systems in health emergency operations by Mohammed Abdullah Alamri, Hisham Hassan Ali Dinar, Sualiman Sultan Alasmari

“…Abstract Background: The learning management system (LMSs) are online platforms that are used to conduct and support teaching and training at various institutions worldwide. …”
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The Effect of Asymmetric Transistor Aging on Systolic Arrays for Mission Critical Machine Learning Applications by Firas Ramadan, Gil Shomron, Freddy Gabbay

“…In response, advancements in Very Large Scale Integration (VLSI) process nodes have significantly intensified the development of machine learning (ML) accelerators, offering enhanced transistor miniaturization and power efficiency. …”
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling by Chaoyi Li, Hongxin Xiang, Wenjie Du, Tengfei Ma, Haowen Chen, Xiangxiang Zeng, Lei Xu

“…GraphGIM is pre-trained on 2 million 2D graphs and multi-view 3D geometry images through contrastive learning. Furthermore, we find that as the convolutional layers process the image becomes deeper, the information of feature maps gradually changes from global molecular-level information (molecular scaffolds) to local atomic-level information (molecular atoms and functional groups), which provides chemical information at different scales. …”
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Deep learning approach to the estimation of the ratio of reproductive modes in a partially clonal population by T. A. Nikolaeva, A. A. Poroshina, D. Yu. Sherbakov

“…Genetic diversity among biological entities, including populations, species, and communities, serves as a fundamental source of information for understanding their structure and functioning. However, many ecological and evolutionary problems arise from limited and complex datasets, complicating traditional analytical approaches. …”
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A comprehensive review on safe reinforcement learning for autonomous vehicle control in dynamic environments by Rohan Inamdar, S. Kavin Sundarr, Deepen Khandelwal, Varun Dev Sahu, Nitish Katal

“…These algorithms utilize methods like uncertainty and risk estimation along with penalty functions, to avoid excessively risky actions and have the potential to significantly reduce accidents and build public trust in autonomous driving. …”
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FAViTNet: An Effective Deep Learning Framework for Non-Invasive Fetal Arrhythmia Diagnosis by Bipin Samuel, Malaya Kumar Hota

“…In this work, authors aim to develop a three-stage fetal arrhythmia detection framework comprised of 1) fECG extraction from abdominal Electrocardiogram (aECG) signal based on functional link neural network adaptive filter, 2) denoising of extracted fECG exploiting a generative adversarial network where the generator and the discriminator follow robust architectures, and 3) fetal arrhythmia detection based on FAViTNet architecture. …”
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Toward a physics-guided machine learning approach for predicting chaotic systems dynamics by Liu Feng, Yang Liu, Benyun Shi, Jiming Liu

“…Specifically, our method consists of three key elements: a data-driven component (DDC) that captures dynamic patterns and mapping functions from historical data; a physics-guided component (PGC) that leverages the governing principles of the system to inform and constrain the learning process; and a nonlinear learning component (NLC) that effectively synthesizes the outputs of both the data-driven and physics-guided components. …”
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Health Workers’ Perspectives on Mobile Health Care Learning Stickiness: Mixed Methods Study by Sabila Nurwardani, Putu Wuri Handayani

“…ResultsOn the basis of the web-based questionnaire (quantitative) results, we found that cognitive (P=.01) and emotional (P=.004) app relationship quality affected health workers’ stickiness in mobile learning. On the other hand, factors related to the functionality of the app and health workers’ experience were proven to affect cognitive and emotional app relationship quality (P<.005). …”
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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…Patients were categorized into favorable [modified Rankin Scale (mRS) 0–2] and poor outcome (mRS 3–6) groups based on their 90-day functional independence. Preoperative clinical and radiological data, including a quantitative assessment of IAC, were systematically collected. …”
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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. …”
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Stacked ensemble model for accurate crop yield prediction using machine learning techniques by Ramesh V, Kumaresan P

“…The proposed model employs a stacked ensemble learning approach, with a Decision Tree Regressor functioning as the meta-model to amalgamate predictions from six distinct base learner models: Linear Regression (LR), Elastic Net, XGBoost Regressor, K-Neighbors Regressor (KNR), AdaBoost Regressor, and Random Forest Regressor (RFR). …”
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UAV data and deep learning: efficient tools to map ant mounds and their ecological impact by Jérémy Monsimet, Sofie Sjögersten, Nathan J. Sanders, Micael Jonsson, Johan Olofsson, Matthias Siewert

“…Abstract High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. …”
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Single-cell sequencing combined with machine learning to identify glioma biomarkers and therapeutic targets by Yu Yan, Zhengmin Chu, Qi Zhong, Genghuan Wang

“…Additionally, machine learning identified four biomarkers with potential for aiding in the diagnosis and treatment of GBM.…”
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