What Business Managers Should Know About Quantum Computing? by Kelvin Leong, Anna Sung

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Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers by Hamidreza Amirhassankhani, Abbas Toloie Eshlaghy, Reza Radfar, Alireza pourebrahimi

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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

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A Crime Data Analysis of Prediction Based on Classification Approaches by Fatima Shaker Hussain, Abbas Fadhil Aljuboori

“…The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. …”
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Multidimensional routing with the increased navigation accuracy with the maintenance of flight notifications of the flight vehicles by V. I. Goncharenko, G. N. Lebedev, V. B. Malygin

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Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

“…This study aimed to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 using machine learning algorithms. Data from Demographic and Health Surveys covering 147,466 men were analyzed. …”
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Landslides in the Himalayas: The Role of Conditioning Factors and Their Resolution in Susceptibility Mapping by Lalit Pathak, Badri Baral, Kamana Joshi, Dipak Raj Basnet, Danilo Godone

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Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life’s essential 8: a cross-sectional study by Haolin Shi, Yangyi Fang, Xiuhua Ma

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The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI). by David J McLernon, John F Dillon, Frank M Sullivan, Paul Roderick, William M Rosenberg, Stephen D Ryder, Peter T Donnan

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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

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Integrated Guidance and Control for Strap-Down Flight Vehicle: A Deep Reinforcement Learning Approach by Qinglong Zhang, Bin Zhao, Yifu Jiang, Jingyan Zhang, Jiale Zhang

“…Firstly, according to different states and actions, the pitch and yaw channels of the six-degree-of-freedom (6-DOF) IGC model are modeled as Markov decision processes (MDPs). Secondly, a channel-by-channel progressive training method based on the twin delayed deep deterministic policy gradient (TD3) algorithm is proposed. …”
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Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections by S.M. Rowe, E. Zhang, S.M. Godden, A.K. Vasquez, D.V. Nydam

“…Machine learning (ML) algorithms evaluated were logistic regression, decision tree, random forest, light gradient-boosting machine, naive Bayes, and neural networks. …”
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Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort by Zhe Xu, Ruguo Zhang, Qiuhan Chen, Guoxuan Peng, Shanpeng Luo, Chen Liu, Ling Zeng, Jin Deng

“…The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms. Methods A total of 697 patients who were older than 65 years of age were selected; 590 patients were divided into training and test sets (at a ratio of 7:3), and 107 individuals were used for external validation. …”
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Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa by Tirualem Zeleke Yehuala, Nebebe Demis Baykemagn, Bewuketu Terefe

“…The most significant determinants of increasing feeding practice were richest household, faculty delivery, use of modern contraception method, the number of children 3–5, women's employment status, maternal age is 25–34, having media exposure, and health-seeking decisions made by mothers were associated positively, whereas not using contraception, home delivery, the total number of children is large, and the sex of the household was male, which was associated negatively with feeding practice during diarrhea in East Africa.ConclusionMachine learning (ML) algorithms have provided valuable insights into the complex factors influencing feeding practices during diarrheal disease in under-five children in East Africa. …”
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Epidemiological Assessment and Inference in Race-Based Clinical Algorithms: A Narrative Review and Health Policy Analysis Focused on Living Kidney Donation by Sienna E. Schaeffer, Carolina Gonzalez Bravo, Christopher D. Ahlers, Alaina N. Elliott-Wherry, Hannah Zadeh, Precious-Junia de-Winton Cummings, Kimberly C. Dukes, Nasrien E. Ibrahim, DeShauna Jones, Patrick T. Zamba, Aloha D. Wilks, Martha L. Carvour

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Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients by Sarah A. Teele, MD, MSHPEd, Avihu Z. Gazit, MD, Craig Futterman, MD, William G. La Cava, PhD, David S. Cooper, MD, MPH, MBA, Steven M. Schwartz, MD, MS, Joshua W. Salvin, MD, MPH

“…The predictive analytics algorithm, inadequate delivery of oxygen (IDo2) index, was developed to noninvasively estimate the probability of a patient’s Svo2 to fall below a preselected threshold. …”
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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…The supervised machine learning algorithms used include K-Nearest Neighbor (KNN), decision tree classifier (CART), Naïve Bayes (NB) and Support vector machine (SVM). …”
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A hybrid optimization algorithm based on cascaded (1 + PI)-PI-PID controller for load frequency control in interconnected power systems by Md. Shahid Iqbal, Md. Faiyaj Ahmed Limon, Md. Monirul Kabir, Md. Zakir Hossain, Md. Fahad Jubayer, Md. Janibul Alam Soeb

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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…What sets this study apart is its consideration of factors like three types of grinding wheels, four distinct cooling solutions, and seven varied depths of cut. …”
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