The urban heat Island effect: A review on predictive approaches using artificial intelligence models by Ali Najah Ahmed, Nouar AlDahoul, Nurhanani A. Aziz, Y.F. Huang, Mohsen Sherif, Ahmed El-Shafie

“…While conventional ML algorithms remain widely used, DL and hybrid models have shown superior performance in predictive accuracy. …”
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Machine learning predictive performance in road accident severity: A case study from Thailand by Ittirit Mohamad, Sajjakaj JomnonKwao, Vatanavongs Ratanavaraha

“…This study evaluates the predictive performance of multiple supervised machine learning algorithms in classifying accident severity, addressing the gap in prior research that lacks a comparative analysis of multiple models trained on large-scale crash data. …”
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Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach by Abdulmohsen Algarni, Iqrar Shah, Ali Imran Jehangiri, Mohammed Alaa Ala'Anzy, Zulfiqar Ahmad

“…In this study, we employ three time series analysis algorithms—AR, ARIMA, and ETS—to predict the energy usage of Docker containers over the next hour. …”
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Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person by Mustufa Haider Abidi

Subjects: “…Human motion intention prediction…”
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Employing Machine Learning-Based Predictive Analytical Approaches to Classify Autism Spectrum Disorder Types by Muhammad Kashif Hanif, Naba Ashraf, Muhammad Umer Sarwar, Deleli Mesay Adinew, Reehan Yaqoob

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Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0 by Özlem Sabuncu, Bülent Bilgehan

“…The model was further compared with the PSO algorithm, demonstrating its superiority with a 7.5% reduction in total maintenance cost and a 6.3% decrease in total downtime. …”
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Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning by Tiantian He, Jie Geng, Chuandong Hou, Hongyi Li, Hong Zhang, Peng Zhao, Peifeng He, Xuechun Lu

“…Additionally, 12 machine learning algorithms were employed to construct predictive models for DLBCL, with performance evaluated using AUC and F-score metrics. …”
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Classifying Divorce Cases in Iranian Judiciary Courts Using Machine Learning: A Predictive Perspective by Elham Tabrizi, Mohadeseh Farzammehr

“…Using data collected between 2011 and 2018 and various machine learning algorithms, the study evaluates the performance of predictive models through a rigorous 10-fold cross-validation process. …”
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Increment of Academic Performance Prediction of At-Risk Student by Dealing With Data Imbalance Problem by Nguyen Giap Cu, Thi Lich Nghiem, Thi Hoai Ngo, Manh Tuong Lam Nguyen, Hong Quan Phung

“…Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. …”
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Enhanced Model Predictive Control Using State Variable Feedback for Steady-State Error Cancellation by Marcos Andreu, Jaime Rohten, José Espinoza, José Silva, Esteban Pulido, Lesyani Leon

“…The rapid dynamic responses of predictive control algorithms are widely acknowledged. …”
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Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean by Jiang Wagner Mamani Lopez, Antonio Víctor Morales Gonzales, Pedro Pablo Chambi Condori

“…This study aims to develop machine learning models to predict the adoption of digital payments in Latin America and the Caribbean using statistical data from the World Bank's Global Findex Database for 2021. …”
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Predictive Analytics for Sucker Rod Pump Failures in Kazakhstani Oil Wells Using Machine Learning by Laura Utemissova, Timur Merembayev, Bakbergen Bekbau, Sagyn Omirbekov

“…This article presents a novel methodology for predicting downhole pumping equipment failures. A detailed analysis was conducted on historical data regarding downhole pumping equipment failures, which were then incorporated into algorithms to calculate the operation of downhole equipment. …”
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Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling by K. Rajammal, M. Chinnadurai

“…The proposed SNN model identifies the short-term workload fluctuations and long-term trends whereas TGNN represents the cloud environment as a dynamic graph to predict future resource availability. Additionally, reinforcement learning is incorporated in the proposed work to optimize SNN decisions based on feedback from the TGNN’s state predictions. …”
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Proactive spectrum handoff in distributed cognitive radio networks by Hua JIANG, Li-chuan MA, Ding-yu YAN, Nan PAN

Subjects: “…distributed cognitive radio network;spectrum handoff;spectrum prediction;task allocation algorithm of ant colony…”
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Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization by Atul B. Kathole, Viomesh Kumar Singh, Ankur Goyal, Shiv Kant, Amit Sadanand Savyanavar, Swapnaja Amol Ubale, Prince Jain, Mohammad Tariqul Islam

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Predictive optimization in automotive supply chains: a BiLSTM-Attention and reinforcement learning approach by Asmae Amellal, Issam Amellal, Mohammed Rida Ech-charrat, Hamid Seghiouer

“…To further our analysis, we incorporated reinforcement learning, evaluating three algorithms: Q-learning, Deep Q-Networks (DQN), and SARSA. …”
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The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model by Shenping Liu, Zhi Wei, Huiqing Ding

“…Objective To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric model for predicting prognosis and enhancing personalized treatment. …”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…EEF values were calculated based on T2WI fat suppression (T2WI-FS) sequences, and radiomics features were extracted. Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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Long-term survival prediction in patients with acute brain lesions using ensemble machine learning algorithms: a cohort study with combined national health insurance service and its self-run hospital database by Dougho Park, Daeyoung Hong, Suntak Jin, Jong Hun Kim, Hyoung Seop Kim

“…Random forest (RF), extreme gradient boosting (XGB), and algorithm stacking methods were used for prediction. …”
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Predictive Performance of Machine Learning with Evoked Potentials for SCI and MS Prognosis: A Meta-Analysis by Constantinos Koutsojannis, Dionysia Chrysanthakopoulou

“…Pooled results showed high predictive accuracy of 77.7% (95% CI, 75.1–80.3%; I² = 57%) and AUC 0.82 (95% CI, 0.79–0.85; I² = 55%). …”
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