Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…This shows the importance of model optimization in real world clinical datasets which are imbalanced and noisy. …”
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. by Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”
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A New Hybrid Machine Learning Method for Stellar Parameter Inference by Sujay Shankar, Michael A. Gully-Santiago, Caroline V. Morley

“…The advent of machine learning (ML) is revolutionary to numerous scientific disciplines, with a growing number of examples in astronomical spectroscopic inference, as ML is more powerful than traditional techniques. …”
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Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations by Vjekoslav Tadić, Dorijan Radočaj, Mladen Jurišić

“…The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). …”
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Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction by Sujith Santhosh, Krishnaraj Chadaga, R.Vijaya Arjunan, Sandra D'Souza

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Computer Science Integrations with Laser Processing for Advanced Solutions by Serguei P. Murzin

“…It discusses key areas where computational methods enhance the precision, adaptability, and performance of laser operations. Through advanced modeling and simulation techniques, a deeper understanding of material behavior under laser irradiation was achieved, enabling the optimization of processing parameters and a reduction in defects. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

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High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators by K. Dautov, G. Tolebi, M. S. Hashmi, A. Jarndal, E. Almajali, G. Nauryzbayev

“…Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. …”
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Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data by Giambattista Albora, Andrea Zaccaria

“…Our results show that relatedness is scale dependent: the best assessments are obtained by using machine learning on the same typology of data one wants to predict. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…The SVR model was optimized to identify the best-fitting hyperplane in high-dimensional space, while the RF model utilized GridSearchCV for hyperparameter tuning and performed a “Feature Importance Analysis” to identify key factors influencing the outcomes. …”
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Optimal equivariant architectures from the symmetries of matrix-element likelihoods by Daniel Maître, Vishal S Ngairangbam, Michael Spannowsky

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A machine learning-powered energy consumption prediction system with API by Toyeeb Adekunle Abd’Azeez, Lanre Olatomiwa

“…In response, this study developed a machine learning model to predict household energy consumption in residential settings. …”
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Application of Machine Learning for Adaptive Trajectory Control of UAVs Under Uncertainty by Alexander S. Ermilov, Olga A. Saltykova

“…The article explores the potential of applying machine learning (ML) for adaptive trajectory control of unmanned aerial vehicles (UAVs) under uncertainty. …”
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An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance by Guodong Zhang, Gaofei Yin, Yi Zhang, Jiangchuan Hu, Zongyan Li, Changjing Wang, Dujuan Ma, Jiangliu Xie

“…This study developed a practical approach integrating radiative transfer (RT) modeling and machine learning to estimate 30-m FAPAR from Landsat surface reflectance. …”
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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

“…To select the optimal model, we apply fuzzy decision by opinion score method (FDOSM). …”
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Innovative Machining Strategies for Metal Matrix Composites: Trends and Future Prospects by Olugbenga Ogunbiyi, Tamba Jamiru, Samson Olaitan Jeje, Kazeem Oladiti Sanusi, Mxolisi Brendon Shongwe

“…The paper identifies current challenges in machining MMCs, such as tool wear, process instability, and the complexity of modeling MMC behavior. …”
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Enhanced hunger games search algorithm that incorporates the marine predator optimization algorithm for optimal extraction of parameters in PEM fuel cells by Mohamed Issa, Mohamed Abd Elaziz, Sameh I. Selem

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Analysis of soil suitability for agricultural needs using machine learning methods by Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly, Boyko Andrey, Volneikina Ekaterina

“…This research highlights the value of machine learning in optimizing agricultural practices by enabling data-driven soil assessment. …”
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Machine learning for grading prediction and survival analysis in high grade glioma by Xiangzhi Li, Xueqi Huang, Yi Shen, Sihui Yu, Lin Zheng, Yunxiang Cai, Yang Yang, Renyuan Zhang, Lingying Zhu, Enyu Wang

“…Abstract We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. …”
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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride, Eric Sénéchal

“…Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. …”
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