Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making by Zixuan Liu, Dehua Wei, Jiangning Wang, Lei Gao

“…Subsequently, risk prediction models were independently developed by using these feature variables based on six machine learning algorithms: logistic regression, random forest, support vector machine, K-nearest neighbors, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost). …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…Additionally, machine learning algorithms recommend tailored exercise, diet type, Basal Metabolic Rate (BMR), and daily caloric intake based on individual member data. …”
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Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach by Adeel Iqbal, Tahir Khurshaid, Yazdan Ahmad Qadri

“…This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. …”
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A Promising Methodological Approach to Identifying Areas with the Greatest Potential for the Development of the City Cycling Infrastructure by O. А. Grishina, A. I. Grishin, I. A. Stroganov

“…The methodology is presented in the form of an algorithmic scheme, as well as a detailed description of its constituent stages. …”
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Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. by Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond S Lun, Brian Weiner, Maha R Farhat, Tan-Yun Cheng, D Branch Moody, Megan Murray, James E Galagan

“…E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.…”
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Optimizing UAV performance with IoT and fuzzy linear fractional transportation models by Samaher Al-Janabi, Nawras G. Seyhood

“…The UAV system not only minimizes human involvement but also reduces the response time by transmitting real-time video, audio, and GPS data to responders over long distances. …”
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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Six machine learning algorithms were trained and evaluated using sensitivity (SN), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC) and AUC.ResultsThe eXtreme gradient boosting (XGBoost) model demonstrated superior performance across all metrics (SN = 0.78, SP = 0.84, ACC = 0.81, MCC = 0.62, AUC = 0.90), and was selected for subsequent interpretation. …”
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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…Harvesting Damask roses by hand is especially challenging because of the thorns on their stems, which not only complicate the process but also pose a risk of injury to workers. This problem highlights the need for automation solutions to facilitate the harvesting process. …”
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Analysis and synthesis of intelligent automatic control systems with type-1 fuzzy regulator by Yu. A. Bykovtsev, V. M. Lokhin

“…The effectiveness of the proposed technique, which guarantees the absolute stability not only of the equilibrium position but also of the processes, is confirmed by the results of model experiments.Conclusions. …”
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A novel approach for solving the triangular fuzzy neutrosophic assignment problem by Srinivas Subadhra, Prabakaran K.

“…This study develops and refines algorithms tailored to address these complexities, ensuring originality through a stepwise procedure that simplifies computations in a neutrosophic manner. …”
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THE INFLUENCE OF SOIL DENSITY AND THE CHARACTER OF RADIOACTIVE 134Cs And 137Cs POLLUTION’S DISTRIBUTION ON in situ MEASUREMENTS by A. I. Zhukouski, O. M. Anshakou, A. O. Nichyparchuk, P. M. Marozik, S. A. Kuten

“…Periodic radiation monitoring of soils today is a priority task not only for Belarus, but also for Japan, suffered by Fukushima nuclear power plant accident. …”
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Automated Control System for Drum-Type Biofermenter by A. Yu. Briukhanov, E. V. Vasilev, S. A. Egorov

“…This capability facilitates the identification of optimal operating modes and the development of adjustment algorithms tailored to different types of organic mixtures, ultimately contributing to the production of a high-quality end product.…”
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A review of the use of technology in sport coaching: current trends and future directions by Fadli Ihsan, Ahmad Nasrulloh, Sigit Nugroho, Rina Yuniana

“…Recent trends include the integration of artificial intelligence and machine learning algorithms, utilizing big data analytics, and the development of mobile applications for remote training and athlete monitoring. …”
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Electroencephalographic monitoring in detection and prediction of delayed cerebral ischemia due to non-traumatic subarachnoid hemorrhage by I. V. Okuneva, M. V. Sinkin, A. I. Evdokimov, A. V. Prirodov

“…Future studies should be aimed at clarifying and validating the most informative DCI biomarkers including while recording EEG with intracranial electrodes that may contribute to development of automated algorithms for DCI detection.…”
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Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques by Sameer Salasakar, Sabyasachi Prakash, Ganesh Thakur

“…Previous review papers have focused on analyzing various optimization algorithms and techniques for well placement. This article explores various types of proxy models that are the most suitable for well placement optimization due their discrete and nonlinear natures and focuses on recent advances in the area. …”
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The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare by Paulo Santos, Paulo Santos, Isabel Nazaré, Isabel Nazaré

“…This paper addresses the critical gap in AI integration in preventive healthcare, highlighting statistical evidence of its impact. It also explores the intersection of AI, preventive medicine, and ethical challenges in future healthcare, envisioning the evolving roles of physicians and patients in an AI-integrated ecosystem. …”
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Particle Swarm Optimization-Based Content Delivery Protocol for UAV VAENTs by Yongje Shin, Hyun-Seok Choi, Youngju Nam, Euisin Lee

“…Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. …”
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Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods by Mürsel Kahveci

“…The ANN classifier achieved 88.6% accuracy for all cancer types.The use of machine learning algorithms may provide a more sensitive and objective way to evaluate patients' response to treatment. …”
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Classification and Time-Frequency Localization of Arbitrary LPWAN Signals With Radial Deformable DETR by Chun Ho Kong, Haibo Hu

“…Therefore, it is important not only to classify but also to localize the frequency, bandwidth, and time of these LPWAN signals on the air for management, security, or band planning purposes. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…In addition, we established predictive models for lung doses utilizing the Least Absolute Shrinkage and Selection Operator, ridge, and linear regression algorithms while also evaluating dosimetric parameters for both the PTV and the organs at risk (OARs). …”
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