3D Human Motion Capture Method Based on Computer Vision by A. D. Obukhov, D. L. Dedov, E. O. Surkova, I. L. Korobova

“…Based on the skeletal points obtained from the neural network, a three-dimensional reconstruction of the human body model was carried out using various triangulation algorithms.Results. …”
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Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest by Hindayati Mustafidah, Suwarsito Suwarsito, Rahmat Setiawan, Abdul Karim

“…Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. …”
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Bidirectional Relationship Between Hypoalbuminemia and Postoperative Pneumonia in Elderly Hip Fracture Patients: A Retrospective Cohort Study by Wang J, Yu H, Xu X, Guo J

“…After predefined participants selection inclusion and exclusion criteria, 1661 surgically treated HF patients were included and analyzed utilizing multiple statistical models, including univariate logistic regression, Lasso regression, and Boruta algorithm for variable selection, while multivariate logistic regression and propensity score matching (PSM) for assess the bidirectional relationship between hypoalbuminemia and postoperative pneumonia. …”
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A Statistical Analysis Based Probabilistic Routing for Resource-Constrained Delay Tolerant Networks by Jixing Xu, Jianbo Li, Shan Jiang, Chenqu Dai, Lei You

“…In this paper, we propose an improved probabilistic routing algorithm that fully takes into account message's time-to-live when predicting the delivery probability. …”
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Simulation of Minefield Installation in a Video Game Engine by Maksym Maksymov, Oleksii Neizhpapa, Oleksandr Toshev, Maksym Kiriakidi

“…The first objective of this research is to improve damage prediction algorithms, enabling the simulation to more accurately estimate the consequences of ships passing through a minefield. …”
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Re-Estimating GEDI Ground Elevation Using Deep Learning: Impacts on Canopy Height and Aboveground Biomass by Rei Mitsuhashi, Yoshito Sawada, Ken Tsutsui, Hidetake Hirayama, Tadashi Imai, Taishi Sumita, Koji Kajiwara, Yoshiaki Honda

“…Optimizing models for each region could further enhance accuracy. …”
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Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN by Padmaja Ramachandra, Santhi Vaithiyanathan

“…This paper uses federated learning to create synthetic data to improve loan defaulter prediction. By allowing multiple institutions to collaboratively train a shared model without directly exchanging sensitive loan data, this privacy-preserving approach allows for the utilization of more extensive and diverse datasets, potentially leading to significant improvements in prediction accuracy compared to traditional, siloed approaches. …”
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A lightweight YOLO network using temporal features for high-resolution sonar segmentation by Sen Gao, Sen Gao, Wei Guo, Wei Guo, Gaofei Xu, Gaofei Xu, Ben Liu, Ben Liu, Yu Sun, Bo Yuan

“…Future work will focus on optimizing the keyframe selection algorithm and expanding the dataset to include more complex underwater scenarios.…”
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Enhancing credit card fraud detection: the impact of oversampling rates and ensemble methods with diverse feature selection by Mohamed Akouhar, Abdallah Abarda, Mohamed El Fatini, Mohamed Ouhssini

“…The results show that the application of SMOTE significantly improves the performance of the machine learning models, with an optimal oversampling rate of 20% identified. …”
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Fourier Analysis of CMFD Method in Cylindrical Geometry by WEN Yuchen, HAO Chen, WANG Yizhen

“…The coarse mesh finite difference (CMFD) method has been widely adopted as an acceleration algorithm for neutron transport calculations, effectively reducing computational costs and improving convergence. …”

Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. …”
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Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis by Iyad Almadani, Aaron L. Robinson, Mohammed Abuhussein

“…Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. …”
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A data driven approach to urban area delineation using multi source geospatial data by Chenyu Fang, Lin Zhou, Xinyue Gu, Xing Liu, Martin Werner

“…Abstract This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which represents a significant improvement in precision and methodology compared to traditional approaches that rely on simplistic OpenStreetMap (OSM) road node data aggregations. …”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…This framework holds transformative potential for pediatric endocrinology and orthopedics by standardizing BAA, improving diagnostic equity, and optimizing resource use. …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…This study aims to use classification algorithms with minimally invasive electrical impedance spectroscopy to improve neoplastic lung tissue identification during biopsies. …”
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MATE-ViT: A multi-channel contrast-limited adaptive time-frequency enhancement and vision transformer framework for bearing fault diagnosis by Jiangdong Zhao, Jiao Yu, Wenming Wang, Ji Huang, Xiaolu Ma

“…First, an improved CLAHE algorithm is used to independently enhance the multi-channel time-frequency images, effectively improving the local contrast and detail expression of the images, thereby enhancing the recognizability of fault features. …”
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Cost-Effective Multitask Active Learning in Wearable Sensor Systems by Asiful Arefeen, Hassan Ghasemzadeh

“…Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. …”
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Machine learning based adaptive traffic prediction and control using edge impulse platform by Manoj Tolani, G. E. Saathwik, Ayush Roy, L. A. Ameeth, Dhanush Bharadwaj Rao, Ambar Bajpai, Arun Balodi

“…Using machine learning algorithms, the system can forecast future traffic conditions and optimize real-time traffic control by significantly reducing congestion and delays. …”
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Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range by Yu Miao, Yang Gao, Xinyue Liu, Yuan Liang, Lin Liu

“…Future research should focus on developing high-precision, temperature-adaptive models and lightweight real-time algorithms. Additionally, exploring the deep coupling of physical models and data-driven methods with multi-source heterogeneous data fusion technology can further improve the accuracy and robustness of SOC estimation. …”
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Integrating artificial intelligence into thermodynamics: A new paradigm for sustainable future by Marwan Al-Raeei

“…In addition, we examine the application of AI-driven optimization techniques, such as genetic algorithms and reinforcement learning, which have proven essential for improving energy efficiency and reliability across various industries. …”
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