Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. …”
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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

“…This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. …”
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Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning by Jiandong Liu, Wei Luo, Guoqing Zhang, Ruihao Li

“…Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. …”
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NOVEL MICROWAVE SENSOR FOR ENHANCED BIOCHEMICAL DETECTION AND PREDICTION THROUGH MACHINE LEARNING FOR INDUSTRIAL APPLICATIONS by G. Challa Ram, M. Venkata Subbarao, K. Padma Satya Sri, Naveen Kumar Maurya, D. Ramesh Varma, M. Prema Kumar

“…This design proposes the sensor's potential to function as a highly sensitive sensor by utilizing changes in the dielectric constant of biological samples. …”
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Machine learning-based prediction of CO2 solubility in methyldiethanolamine solutions: A comparative study by Sajjad Fazeli, Mohammad Amin Moradkhani, Behrouz Bayati

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A novel neuroimaging based early detection framework for alzheimer disease using deep learning by Areej Alasiry, Khlood Shinan, Abeer Abdullah Alsadhan, Hanan E. Alhazmi, Fatmah Alanazi, M. Usman Ashraf, Taseer Muhammad

“…By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer’s disease detection.…”
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Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture by Bekir Eray Katı, Ecir Uğur Küçüksille, Güncel Sarıman

“…In addition to existing preprocessing methods in the literature, a novel preprocessing function tailored to the requirements of deep learning models was developed for the dataset. …”
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Physical Education and Sport Essential as transversality and body integration in the Learning Process: A Systematic Review by Alimuddin, Syahida Binti Mohd Nazri, Liza Liza, Dewi Pebriyani, Arif Padli Muchlis

“… Superior cognitive function and the sensorimotor system are interconnected in interesting ways, according to recent research primarily related to neuroscience. …”
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Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors by Abdulrahman Alkurdi, Jean Clore, Richard Sowers, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez

“…The resilience of machine learning models for anxiety detection through wearable technology was explored. …”
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Tca4rec: contrastive learning with popularity-aware asymmetric augmentation for robust sequential recommendation by Yanan Bai, Liji Xiao, Chongjun Xia, Kexiang Zeng, Xiaoyu Shi

“…To mitigate popularity bias, we derive an Asymmetric Multi-instance Noise Contrastive Estimation (AMINCE) loss function that supplies rich, bias-aware training signals, while our two-stage training strategy significantly reduces the over-dominance of popular items during optimization. …”
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Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation by CHENG Jialin, YUAN Deyu, SUN Zeyu, CHEN Ziyan

“…Secondly, the deep neural network is used to construct the mapping function to obtain the accurate expression of user nodes. …”
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Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds by Lama Shaheen, Bader Rasheed, Manuel Mazzara

“…The framework operates in two stages: a pretext task applies geometric transformations—rotation (from –45° to +45°), translation (between –1 and 1 units), and scaling (between 0.5 and 2.0)—to learn robust features, while an unsupervised segmentation step leverages an energy function that combines height, density, and slope attributes to cluster points corresponding to individual trees. …”
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Machine learning algorithms to predict depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

“…ObjectiveThe 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.MethodsThis research selected 7,880 older adult people by utilizing data from the 2020 China Health and Retirement Longitudinal Study. …”
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Enhancing satellite image compositing with temporal proximity weighting for deep learning–based cropland segmentation by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Gongliu Yang

“…This study proposes a compositing method that improves temporal coherence for tracking phenological stages in deep learning–based cropland segmentation. The compositing method integrates the near–infrared to blue band reflectance ratio with a Gaussian weighting function to prioritize pixel selection based on temporal proximity to the center of the target month. …”
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Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

“…Data scaling and encoding techniques, including StandardScaler and MinMaxScaler, are employed to enhance the accuracy of these machine learning models. Additionally, preprocessing steps, such as Numerical Variable Correlation, Categorical Variables Analysis, Continuous Variables Analysis, Bivariate Analysis, Balancing Classes (oversampling function) are applied to enhance the model’s performance. …”
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Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment by Shu-Ping Yeh, Sonia Bhattacharya, Rashika Sharma, Hassnaa Moustafa

“…This paper focuses on the RAN intelligence ecosystem and presents an intelligent network application (xApp) for network slicing for the RAN using AI and Deep Learning techniques. We evaluated the xApp with a near Real-Time RAN Intelligent Controller (near-RT RIC) and showed the network slicing functionality in an automated and intelligent fashion. …”
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A Knowledge-Driven Smart System Based on Reinforcement Learning for Pork Supply-Demand Regulation by Haohao Song, Jiquan Wang

“…By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). …”
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Autonomous Dogfight Decision-Making for Air Combat Based on Reinforcement Learning with Automatic Opponent Sampling by Can Chen, Tao Song, Li Mo, Maolong Lv, Defu Lin

“…This approach encompasses three pivotal components: a phased opponent policy pool with simulated annealing (SA)-inspired curriculum learning, an SA-inspired Boltzmann Meta-Solver, and a Gate Function based on the sliding window. …”
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Intelligent diagnosis of gearbox in data heterogeneous environments based on federated supervised contrastive learning framework by Ruoyang Bai, Hongwei Wang, Wenlei Sun, Li He, Yuxin Shi, Qingang Xu, Yunhang Wang, Xicong Chen

“…To tackle these issues, FSCL integrates the federated learning paradigm with a supervised contrastive mechanism: firstly, it overcomes the limitations of data silos through distributed collaborative training, enabling multiple participants to jointly develop diagnostic models without disclosing raw data; secondly, to address the feature space mismatch induced by heterogeneous data, a hybrid contrastive loss function is designed, which constrains the similarity between local models and the global model through supervised loss, thereby enhancing the feature representation capability of the global model. …”
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Complex Environmental Geomagnetic Matching-Assisted Navigation Algorithm Based on Improved Extreme Learning Machine by Jian Huang, Zhe Hu, Wenjun Yi

“…The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. …”
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