Possibilities of statistical methodology in the study of economic security of the region by Dmitry V. Dianov, Elena A. Radugina

“…The use of the designated statistical tools allowed obtaining such results as justification of analytical capabilities and approbation of statistical methods in the study of economic security of the Moscow region; formation of clusters, consisting of homogeneous municipal districts of the Moscow region, designated as clustering units; calculation of relative analytical indicators; construction and interpretation of regression models.Conclusion: the complex of the obtained results and conclusions, as well as the scientifically based algorithm of statistical tools application, tested in the work, are undoubtedly an important information component in the management of economic and financial security not only in the Moscow region, but also in any region, both in the Central Federal district and in the whole central Russia.…”
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Global Aerosol Climatology from ICESat-2 Lidar Observations by Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka

“…This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models.…”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…However, traditional detection models suffer significant performance degradation due to challenges including substantial scale variations, high proportions of small targets, and dense occlusions in UAV-captured images. …”
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Advanced GPU Techniques for Dynamic Remeshing and Self-Collision Handling in Real-Time Cloth Tearing by Jong-Hyun Kim, Jung Lee

“…In this study, we propose a GPU-optimized dynamic remeshing algorithm that enables real-time processing of high-resolution cloth tearing. …”
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Trajectories of Cognitive Complaints in Patients With Breast Cancer and Their Association With Psychosocial and Neurobiological Factors by Rob Colaes, Gwen Schroyen, Rebeca Alejandra Gavrila Laic, Jeroen Blommaert, Sigrid Hatse, Ann Smeets, Stefan Sunaert, Sabine Deprez

“…Using the partition around medoids (PAM) algorithm on the difference scores of cognitive complaints, patients were clustered into distinct groups. …”
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Grapes leaf disease dataset for precision agricultureMendeley Data by Madhuri Dharrao, Nilima Zade, R. Kamatchi, Rakesh Sonawane, Rabinder Henry, Deepak Dharrao

“…This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. …”
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Accelerating Ray Tracing-Based Wireless Channels Generation for Real-Time Network Digital Twins by Claudio Modesto, Lucas Mozart, Pedro Batista, Andre Cavalcante, Aldebaro Klautau

“…Ray tracing (RT) simulation is a widely used approach to enable modeling wireless channels in applications such as network digital twins. …”
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Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights by Filippo Genuario, Giuseppe Santoro, Michele Giliberti, Stefania Bello, Elvira Zazzera, Donato Impedovo

“…The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and reducing waste. …”
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Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement by Rafi Mohammad Ariful Islam, Islam Tahidul

“…Results revealed varying stress levels, from minimal to extreme, reflecting widespread distress. Machine learning models were employed to classify the stress levels, with the Decision Tree model achieving 55% accuracy, the Random Forest model improving to 67%, and XGBoost performing better than both with over 94% accuracy. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles by Prot Piyakawanich, Pattarapong Phasukkit

“…By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation 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 Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…Results: Development of the first prototype of the device is nearing completion, and lab data has been collected for training the device's drug-detecting predictive model. Current experiments with established supervised-learning algorithms show favourable results in distinguishing Synthetic Cannabinoids. …”
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Deep Reinforcement Learning-Based Attention Decision Network for Agile Earth Observation Satellite Scheduling by Dongning Liu, Guanghui Zhou

“…Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. …”
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Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends by Chen Boshan

“…Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…By using Euclidean distance along skeletal paths, the algorithm minimized error rates in length and width estimation. …”
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Slip and tractive efficiency of an electric tractor with a 4WID E-axle system by SeungYun Baek, HyeonHo Jeon, CheolGyu Park, YongJoo Kim

“…Abstract This study aims to verify the performance of an electric tractor equipped with a four-wheel independent driving (4WID) electric axle (E-axle) system through simulation analysis using a MATLAB/Simulink model. The simulation model was validated through bench testing to ensure accuracy and reliability. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Once the model fine-tunes the eGFR estimations, it feeds them into various algorithms for CKD stage classification, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…Among the evaluated algorithms, XGBoost model performed best, achieving an R2 of 0.933 and RMSE of 0.023 cm3/cm3. …”
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Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning by Feng Lu, Yingying Liu, Zhen Chen, Shuning Chen, Weidong Liang, Fuzhou Hua, Maolin Zhong, Lifeng Wang

“…A total of 113 diagnostic models were developed through 12 machine learning algorithms, with the optimal model, “Lasso + Stepglm[both],” featuring six genes: Abca1, Cdh1, Glipr1, Glt8d2, Il10ra, and Pde5a. …”
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