A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats by Dheyaaldin Alsalman

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The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES by Xinghong Guo, Mingze Ma, Lipei Zhao, Jian Wu, Yan Lin, Fengyi Fei, Clifford Silver Tarimo, Saiyi Wang, Jingyi Zhang, Xinya Cheng, Beizhu Ye

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Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm by Jiannan Yu, Jinlou Zhao

“…Cascading defaults are also generated in the simulation according to different crisis-triggering (targeted defaults) methods. We also use machine learning techniques to identify the synthetic features of the network. …”
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Developing a Machine Learning–Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study by Pooja Guhan, Naman Awasthi, Kathryn McDonald, Kristin Bussell, Gloria Reeves, Dinesh Manocha, Aniket Bera

“…ObjectiveThis study aimed to examine the ability of machine learning models to estimate patient engagement levels during a tele–mental health session and understand whether the machine learning approach could support therapeutic engagement between the client and psychotherapist. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

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A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things. by Umhara Rasool Khan, Javaid A Sheikh, Aqib Junaid, Shazia Ashraf, Altaf A Balkhi

“…In this article, regression-based Machine learning (ML) techniques are used to facilitate the design of Horse shoe shaped patch antenna to predict the frequency of operation, radiation efficiency and Specific Absorption Rate (SAR) values to accelerate its design process for on-body applications. …”
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A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 by S. Lakshmana Kumar, V. Jacintha, A. Mahendran, R. M. Bommi, M. Nagaraj, Umamahesawari Kandasamy

“…In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. …”
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A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities by Hang Chen, Sulaiman Khan, Bo Kou, Shah Nazir, Wei Liu, Anwar Hussain

“…To reduce the death rates and propose accurate treatment, this paper presents a smart IoT-based application using machine learning algorithms for the human brain hemorrhage diagnosis. …”
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Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE/GRACE-FO Gravimetric Data and Machine Learning by Mohamed Hamdi, Anas El Alem, Kalifa Goita

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A machine learning model using the snapshot ensemble approach for soil respiration prediction in an experimental Oak Forest by S.N. Ferdous, J.P. Ahire, R. Bergman, L. Xin, E. Blanc-Betes, Z. Zhang, J. Wang

“…To mitigate this requirement for high computational power for the assimilation of high-throughput data to identify patterns and trends in soil respiration and guide in deploying effective strategies to reduce Greenhouse gas (GHG) emission, the application of machine-learning (ML) models can help reduce the requirement of intensive computational capacity and improve the accuracy of predictions. …”
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A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease by Abdelaaziz Hessane, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane, Fatima Amounas

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Radiomics and machine learning model can improve the differentiation between ocular adnexal lymphoma and idiopathic orbital inflammation by Guorong Wang, Xiaoxia Qu, Jian Guo, Yongheng Luo, Junfang Xian, Rongman Jia

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Penerapan Machine Learning untuk Mengendalikan Parameter Budidaya Tanaman Hidroponik Berbasis Edge dan Cloud Computing by Helmy Helmy, Arif Nursyahid, Thomas Agung Setyawan

“…Dalam penelitian ini, pengendalian pH dan TDS menggunakan machine learning dengan metode regresi linear berganda serta teknologi edge computing dan cloud computing. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid–Liquid Phase Separation and Ferroptosis-Related Genes by Wenchao Chen, Ting Zhu, Xiaofan Pu, Linlin Zhao, Senhao Zhou, Xin Zhong, Suihan Wang, Tianyu Lin

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Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis by Haofuzi Zhang, Peng Zou, Peng Luo, Xiaofan Jiang

“…Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice. …”
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Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses by Ruiqi Ma, Qian Cheng, Jing Yao, Zhiyu Peng, Mingxu Yan, Jie Lu, Jingjing Liao, Lejin Tian, Wenjun Shu, Yunqiu Zhang, Jinghan Wang, Pengfei Jiang, Weiyi Xia, Xiaofeng Li, Lu Gan, Yue Zhao, Jiang Zhu, Bing Qin, Qin Jiang, Xiawei Wang, Xintong Lin, Haifeng Chen, Weifang Zhu, Dehui Xiang, Baoqing Nie, Jingtao Wang, Jie Guo, Kang Xue, Hongguang Cui, Jinwei Cheng, Xiangjia Zhu, Jiaxu Hong, Fei Shi, Rui Zhang, Xinjian Chen, Chen Zhao

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Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin by Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen

“…However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. …”
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Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach by Tirunagaru V. Sarathkumar, Arup Kumar Goswami, Baseem Khan, Kamel A. Shoush, Sherif S. M. Ghoneim, Ramy N. R. Ghaly

“…On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue’s superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. …”
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