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 research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input application. …”
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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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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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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques by Ireneusz Czarnowski, Piotr Jędrzejowicz

“…In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. …”
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Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning by Houchao Wang, Fengyao Lv, Zhenfei Zhan, Hailong Zhao, Jie Li, Kangte Yang

“…In this study, a dataset was constructed by collecting data from high-speed tensile experiments on 65 automotive steels. Five machine learning models, including ridge regression, support vector machine regression, gradient boosted regression tree, random forest, and adaptive boosting regression, were developed to predict the yield strength (YS), ultimate tensile strength (UTS), and fracture elongation (FE) of automotive steels at 100/s using the composition, sample size, and quasi-static mechanical properties of automotive steels as input variables. …”
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Machine Learning Approaches for the Prediction of Displaced Abomasum in Dairy Cows Using a Highly Imbalanced Dataset by Zeinab Asgari, Ali Sadeghi-Sefidmazgi, Abbas Pakdel, Saleh Shahinfar

“…For this purpose, in this study, the ability of five machine learning algorithms, namely Logistic Regression (LR), Naïve Bayes (NB), Decision Tree, Random Forest (RF) and Gradient Boosting Machines (GBM), to predict cases of DA was investigated. …”
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Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets by Ali Keyvandarian, Ahmed Saif, Ronald Pelot

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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

“…However, accurate SOC prediction remains a challenging task due to the complex, high-dimensional, and nonlinear nature of soil data. Recent advances in machine learning (ML) have improved SOC estimation, yet these models often suffer from overfitting and computational inefficiency when effective feature selection and hyperparameter tuning are not applied. …”
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Hybrid FRP strengthening of reinforced concrete deep beams: Experimental, theoretical and machine learning-based study by Phromphat Thansirichaisree, Qudeer Hussain, Mingliang Zhou, Ali Ejaz, Shabbir Ali Talpur, Panumas Saingam

“…To overcome this issue, machine learning approaches were utilized, employing gradient boosting regression and random forest methods. …”
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A machine learning-based clinical predictive tool to identify patients at high risk of medication errors by Ammar Abdo, Lyse Gallay, Thibault Vallecillo, Justine Clarenne, Pauline Quillet, Vincent Vuiblet, Rudy Merieux

“…The data from 7200 patients were used to train four machine learning-based models based on 52 variables in the development dataset. …”
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Building and explaining data-driven energy demand models for Indian states by Kieran M R Hunt, Hannah C Bloomfield

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Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis, Evangelos Diamantis

“…Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). …”
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Machine Learning-Based Predictive Maintenance for Photovoltaic Systems by Ali Al-Humairi, Enmar Khalis, Zuhair A. Al-Hemyari, Peter Jung

“…A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. …”
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Sensing using SERS-substrate and machine learning approaches by Bakal, Victoria Alexandrovna, Gusliakova, Olga Igorevna, Prikhozhdenko, Ekaterina Sergeevna

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Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance by Leonel Patrício, Leonilde Varela, Zilda Silveira

“…This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. …”
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A Stochastic Learning Algorithm for Machine Fault Diagnosis by Zhipeng Dong, Yucheng Liu, Jianshe Kang, Shaohui Zhang

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Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact by Supriya Raghav, Santosh Kumar, Hamid Ashraf, Poonam Khanna

“…BackgroundThis study investigated the cost-effectiveness and clinical impact of the 3E model (education, empowerment, and economy) in diabetes management using advanced machine learning techniques.MethodsWe conducted an observational longitudinal descriptive analysis involving 320 patients, who were grouped into intervention and control groups over a 24-month period.ResultsThe 3E model demonstrated significant cost reductions, with the intervention group achieving a 74.3% decrease in total costs compared to 41.8% in the control group while maintaining the same level of glycemic control. …”
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Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model by Yu Xia, Ao Shen, Tianci Che, Wenbo Liu, Jie Kang, Wei Tang

“…In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. …”
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Zooming into Berlin: tracking street-scale CO2 emissions based on high-resolution traffic modeling using machine learning by Max Anjos, Fred Meier

“…Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. …”
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