Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution by Turki Al Hagbani, Jawaher Abdullah Alamoudi, Majed A. Bajaber, Huda Ibrahim Alsayed, Halah Jawad Al-fanhrawi

“…The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML). The ML models explored include the Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Fully Connected Neural Network (FCNN), and Deep Neural Network (DNN). …”
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Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning by Yingbin Wang, Yulong Huang, Xiaobao Liu, Chishan Kang, Wenjie Wu

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Machine-learning approach to understanding ultrafast carrier dynamics in the three-dimensional Brillouin zone of PtBi_{2} by Paulina Majchrzak, Charlotte Sanders, Yu Zhang, Andrii Kuibarov, Oleksandr Suvorov, Emma Springate, Iryna Kovalchuk, Saicharan Aswartham, Grigory Shipunov, Bernd Büchner, Alexander Yaresko, Sergey Borisenko, Philip Hofmann

“…We characterize the electron dynamics close to the Weyl points and in other parts of three-dimensional Brillouin zone using k-means, an unsupervised machine-learning technique. This reveals distinct differences—in particular, that the electron dynamics close to the Weyl points are slower than in Brillouin zone regions close to the bulk Fermi surface.…”
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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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Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management by Maryam Bagheri, Mohsen Bagheritabar, Sohila Alizadeh, Mohammad (Sam) Salemizadeh Parizi, Parisa Matoufinia, Yang Luo

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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…Comparison of ROC curve AUC between logistic regression and RF model for predicting MACE risk showed a statistically significant difference (Z=2.278, P=0.023).Conclusion: The RF model showed good performance in predicting the risk of MACE in perimenopausal women providing a reference for the early identification of high-risk patients and the development of targeted intervention strategies.Keywords: major adverse cardiovascular events, machine learning, perimenopause, risk factor…”
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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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Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes by Fanqi Liang, Man Zheng, Jingjiu Lu, Peng Liu, Xinyu Chen

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Noninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models by Chuan Jiang, Zhenyu Xu, Jinqing Liu, Ronghua Li, Keyu Chen, Wenting Peng, Yueming Xiao, Da Cheng, Lei Fu, Shifang Peng

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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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