Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…We perform intensive hyper-parameter tuning for each model using grid search and randomized search to fine-tune the performance of these models. …”
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Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning by Hasballah Zakaria, Elsa Sari Hayunah Nurdiniyah, Astri Maria Kurniawati, Dziban Naufal, Nana Sutisna

“…This work also details the method covering data set preparation, algorithm and design parameters exploration for pre-processing ECG signals, features extraction and selection, and hyper parameter tuning for employed machine learning methods. …”
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Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation by Sudha Sakthivel, Muhammad Mansoor Alam, Aznida Abu Bakar Sajak, Mazliham Mohd Su'ud, Mohammad Riyaz Belgaum

“…This work discusses knowledge-driven DWDM design, utilizing machine learning to improve flexibility, identify FWM parameters, and predict transmission quality. …”
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Comparative study of ultrasonic and laser assisted machining for sustainable leather cutting in greener industry practices by Samir Mekid, Vasanth Swaminathan, Ismail Chekalil

“…The proposed study investigates the comparative analysis of ultrasonic assisted machining and CO2 laser assisted machining for leather cutting focusing on sustainable leather processing. …”
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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability by Sathvik Sharath Chandra, Rakesh Kumar, Archudha Arjunasamy, Sakshi Galagali, Adithya Tantri, Sujay Raghavendra Naganna

“…The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. …”
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Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features by Yuchen Wang, Jianliang Wang, Jiayue Li, Jiacheng Wang, Hanzeyu Xu, Tao Liu, Juan Wang

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Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning by Zhanjie Liu, Yixuan Huo, Qionghai Chen, Siqi Zhan, Qian Li, Qingsong Zhao, Lihong Cui, Jun Liu

“…The TPOT is then applied to automatically find the best model and parameter combinations, creating an optimal predictive model for the mixed dataset. …”
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Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning by Mariam Farouk, Anwer S. Abd El-Hameed, Angie R. Eldamak, Dalia N. Elsheakh

“…Glucose concentrations can be evaluated by measuring the changes in the dielectric properties of blood by sending microwave waves through the body and assessing the collected S-parameter signals. The measurement parameters encompass the reflection, phase, magnitude, as well as transmission parameters. …”
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MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification by Haonan Li, Vijay Govindarajan, Tan Fong Ang, Zaffar Ahmed Shaikh, Amel Ksibi, Yen-Lin Chen, Chin Soon Ku, Ming Chern Leong, Fatiha Hana Shabaruddin, Wan Zamaniah Wan Ishak, Lip Yee Por

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Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment by Onaira Zahoor, Sher Jamal Khan, Muhammad Usama, Henry J. Tanudjaja, Noreddine Ghaffour, Muhammad Saqib Nawaz

“…Accurate TMP predictions through machine learning can help in maintaining key parameters within optimal ranges, thereby reducing the likelihood of membrane fouling and enhancing system efficiency.…”
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Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization by Wenli Liu, Yang Chen, Tianxiang Liu, Wen Liu, Jue Li, Yangyang Chen

“…Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. …”
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Plant-based protein extrusion optimization: Comparison between machine learning and conventional experimental design by Yingfen Jiang, Noor Irsyad Bin Noor Azlee, Wing Shan Ko, Kaiqi Chen, Bee Gim Lim, Arif Z. Nelson

“…In contrast, Bayesian Optimization (BO), a machine learning technique, uses probabilistic surrogate models to efficiently explore parameter spaces and optimize black-box functions with fewer experiments. …”
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Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods by Yuandong MO, Yazhi WANG, Shuqi HUANG, Jiajun ZHONG

“…The findings contribute to the theoretical understanding of micro-EDM processes while also providing practical insights for optimizing machining parameters in real-world applications. The recommended optimal combination of parameters provides a clear guide for achieving high precision in micro-hole machining, addressing a critical need in industries where micro-EDM is employed. …”
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A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar by Saurabh Kumar, Uruya Weesakul, Divesh Ranjan Kumar, Pradeep Thangavel, Warit Wipulanusat, Jirapon Sunkpho

“…Machine learning analyses, including multivariate adaptive regression splines (MARS), the group method of data handling (GMDH), and multivariate polynomial regression (MPMR), consider 13 features, including pH, temperature, conductivity, total dissolved solids, alkalinity, hardness, calcium hardness, magnesium hardness, chloride, sulfate, nitrate, dissolved oxygen, and time, as input parameters, with the corrosion rate as the output parameter. …”
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Explainable machine learning and online calculators to predict heart failure mortality in intensive care units by An‐Tian Chen, Yuhui Zhang, Jian Zhang

“…Abstract Aims This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF). …”
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The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis by Lan Xu, Zian Chen, Dan Zhu, Yingjun Wang

“…Furthermore, challenges such as data heterogeneity and interpretability caused by segmentation and imaging parameter variations require further optimization and refinement. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. …”
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Simultaneous Design of PSS Parameters and SVC Control System by VURPSO Algorithm in Order to Increase Stability of the Power System by Esmaeel Ghaedi, Ghazanfar Shahgholian, Rahmat Allah Hooshmand

“…In this paper, the multi-machine power system is simulated and in addition, VURPSO optimization algorithm is used to optimize the parameters of simultaneous controllers of Static Var Compensator (SVC) and power system stabilizer (PSS) in order that the power system stability increases. …”
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