Estimation for Reduction Potential Evaluation of CO₂ Emissions from Individual Private Passenger Cars Using Telematics by Masahiro Mae, Ziyang Wang, Shoma Nishimura, Ryuji Matsuhashi

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Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…Abstract We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data. …”
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Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections by Ahmad Almatroudi

“…This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. …”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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Efficient Machine Learning Models for Solar Radiation Prediction Using Ensemble Techniques: A Case Study in Low-Rainfall Arid Climates by Jimmy Aurelio Rosales Huamani, Uwe Rojas Villanueva, Christian Leonardo Rosales Ventocilla, Jose Luis Castillo Sequera, Jose Manuel Gomez Pulido

“…From this, dimensionality reduction was carried out using the Principal Component Analysis (PCA) technique, obtaining the 8 most appropriate representative variables for the prediction of solar radiation using different Machine Learning (ML) models. …”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC). …”
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Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma by Ting Ji, Ting Ji, Juanli Jiang, Juanli Jiang, Xin Wang, Xin Wang, Kai Yang, Kai Yang, Shaojin Wang, Shaojin Wang, Bin Pan, Bin Pan

“…Using the GeneCards ferroptosis gene set (1515 genes), ferroptosis-related differentially expressed genes in macrophages were screened. Eight machine learning algorithms (LASSO, SVM, XGBoost, etc.) were leveraged to identify prognostic genes and build a Cox regression risk model. …”
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Conciliating accuracy and efficiency to empower engineering based on performance: a short journey by Chinesta, Francisco, Cueto, Elias

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Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India by Varun Menon, Sreevalsa Kolathayar

“…Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. …”
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Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies by Ayad M. Dalloo, Amjad J. Humaidi

“…This paper investigates the application of approximate computing techniques as a viable solution to reduce computational complexity and optimize machine learning models, focusing on two widely used supervised machine learning models: k-nearest neighbors (KNN) and support vector machines (SVM). …”
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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models by RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das

“…Machine learning models were able to give accurate early yield predictions. …”
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Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning by Liu Liu, Rongyu Sun, He Zhao

“…Here an automatic identification pipeline based on machine learning model ShuffleNet V2 is developed, after trained with large amount of raw data, high efficiency is achieved. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

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Genomic prediction of plant traits by popular machine learning methods by K. N. Kozlov, M. P. Bankin, E. A. Semenova, M. G. Samsonova

“…Among hybrid approaches, the prospect of combining machine learning models and models of plant development based on biophysical and biochemical processes is emphasized. …”
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Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach by Rashmita Khilar, G. Merlin Suba, T. Sathesh Kumar, J. Samson Isaac, Santaji Krishna Shinde, S. Ramya, V. Prabhu, Kuma Gowwomsa Erko

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Dynamic Risk Thresholds for SIEM Alerting Based on Machine Learning by Artur Kapera, Marcin Niemiec

“…In the article, a theoretical concept of a Dynamic Risk-Based Alerting model for SIEM based on machine learning has been presented. …”
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Applications of machine learning in decision analysis for dose management for dofetilide. by Andrew E Levy, Minakshi Biswas, Rachel Weber, Khaldoun Tarakji, Mina Chung, Peter A Noseworthy, Christopher Newton-Cheh, Michael A Rosenberg

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Surface Ice Detection Using Hyperspectral Imaging and Machine Learning by Steve Vanlanduit, Arnaud De Vooght, Thomas De Kerf

“…This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. …”
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Machine learning and optimization in hybrid energy storage systems: Integrating MPC, PSO, MILP, and ANN for grid stability and efficiency by Rajesh M. Bhagat, Dilendra B. Jasutkar, Amitkumar B. Ranit, Vikrant S. Vairagade, Balram Yelamasetti, Sagar D. Shelare, Shubham Sharma, Abinash Mahapatro, V.K. Bupesh Raja, Quadri Noorulhasan Naveed, Naoufel Kraiem, Saiful Islam, Jasmina Lozanovic

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Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

“…This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. …”
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