Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia by Sir E, Aydogan S, Batur Sir GD, Celenlioglu AE

“…Ender Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@gmail.comBackground: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia.Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…The RF technique achieves the largest area under the curve, and the RF technique is the most effective of all ML algorithms, according to the results of the applied ML algorithms. …”
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Predictive model for initial response to first-line treatment in children with infantile epileptic spasms syndrome by Wenrong Ge, Lin Wan, Zong Wang, Lijun Fu, Guang Yang

“…Methods Using a dataset from our previously published research, we constructed and tested a predictive model for the initial response to first-line treatment in children with IESS. …”
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Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer by Jie Lv, Chun-yang Yu, Yao-zu Xiong, Ting-ting Dai, Xiao-chu Hu, Peng Pan, Shun Yue, Chang-hua Yu

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Predictive value of machine learning model based on CT values for urinary tract infection stones by Jiaxin Li, Yao Du, Gaoming Huang, Chiyu Zhang, Zhenfeng Ye, Jinghui Zhong, Xiaoqing Xi, Yawei Huang

“…Seven machine learning algorithms along with eleven preoperative variables were used to construct the prediction model. …”
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Strategies and Challenges for Unmanned Aerial Vehicle-Based Continuous Inspection and Predictive Maintenance of Solar Modules by Ghulam E. Mustafa Abro, Amjad Ali, Sufyan Ali Memon, Tayab Din Memon, Faheem Khan

“…Our review paper emphasizes instant functionality and practical insights for predictive maintenance, without necessitating complex computing resources or optimization tools. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…The SlogP_VSA2 descriptor is the primary factor influencing predictions of N-dealkylation metabolism. Then an ensemble model was generated that uses a consensus strategy to integrate three different algorithms, whose performance is generally better than any single algorithm, with an accuracy rate of 86.2%. …”
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Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer by Yulu Sun, Yinan Guan, Hao Yu, Yin Zhang, Jinqiu Tao, Weijie Zhang, Yongzhong Yao

“…Survival analysis was performed using the Kaplan-Meier method and log-rank test. A predictive model for pCR was constructed using machine learning algorithms.ResultsAmong the 209 breast cancer patients, 29 achieved pCR. …”
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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). …”
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A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics by Mehdi Soltani Tehrani, Siamak Noori, Ehsan Dehghani

“…This approach combines the performance assessment strengths of data envelopment analysis with the predictive capabilities of neural networks, enabling a data-informed site selection process. …”
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

“…The BN’s unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. …”
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Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…ConclusionsConsidering the dynamic nature of suicidal behavior posts, we proposed a fused architecture that captures both localized and generalized contextual information that is important for understanding the language patterns and predict the evolution of suicidal ideation over time. …”
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A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems by Badreddine Kanouni, Abd Essalam Badoud, Saad Mekhilef, Ahmed Elsanabary, Mohit Bajaj, Ievgen Zaitsev

“…Abstract This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). …”
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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

“…This study employs machine learning techniques to analyze CHD-related pathogenic factors and proposes an efficient diagnostic and predictive framework. To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms-Decision Trees, KNN, SVM, XGBoost, and Random Forest-are applied for classification tasks. …”
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Algorithmic literacy and media practices among young people in Brussels by Sylvain Malcorps, Arnaud Claes, Thibault Philippette, Marie Dufrasne

“…This article concludes with an exploration of media education approaches which take these observations into account.…”
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Implementation of Fisher-Yates Shuffle and Fuzzy Tsukamoto in Nusantara Educational Game by Khairul Insan Karim, Chairunnisa Lamasitudju, Ryfial Azhar, Fizar Syafaat, Anisa Yulandari

Subjects: “…education game…”
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Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R² = 0.81) than in temperature-dominated cases (R² = 0.65–0.73), reflecting greater complexity under extreme warming. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. …”
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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…By introducing AI-driven systems into agricultural practices, this study aims to revolutionize disease identification, prediction, and management. The overarching objective is to minimize crop losses and enhance agricultural productivity. …”
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Support Vector Machine and Granular Computing Based Time Series Volatility Prediction by Yuan Yang, Xu Ma

“…With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. …”
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