Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…Machine Learning (ML) has a big impact on smart farming, especially rice productivity. …”
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Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach by Mohammad Safaei, Jane Southworth, Cerian Gibbes, Hannah V. Herrero, Mashoukur Rahaman, Bewuket B. Tefera, Jason K. Blackburn

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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

“…These features were used to train a machine learning (ML) classifier using logistic regression. We evaluated the performance of our classifier by assessing the accuracy and precision of predictions. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…These strategies can use machine learning (ML) and deep learning (DL) classifiers, but they should be able to lead with tight computational requirements. …”
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The urban heat Island effect: A review on predictive approaches using artificial intelligence models by Ali Najah Ahmed, Nouar AlDahoul, Nurhanani A. Aziz, Y.F. Huang, Mohsen Sherif, Ahmed El-Shafie

“…While conventional ML algorithms remain widely used, DL and hybrid models have shown superior performance in predictive accuracy. …”
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Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

“…Computer design has become widespread, which or repurposing "in silico", where information about the drug is used: targets, chemical structures, metabolic pathways, side effects, followed by the construction of appropriate models. Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. …”
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Urban buzz or urban bust? Beekeeping challenges, suitability, and survival insights in Montreal, Canada by Navid Mahdizadeh Gharakhanlou, Julien Vadnais, Liliana Perez, Nico Coallier

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Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy by Kausalya Neelavara Makkithaya, Wei-Chung Chen, Chun-Chieh Wu, Ming-Chi Chen, Wei-Hsun Wang, Jackson Rodrigues, Ming-Tsang Wu, Nirmal Mazumder, I-Chen Wu, Guan-Yu Zhuo

“…Unlike previous studies on cancer diagnosis using two-photon microscopy, quantitative analysis or machine learning (ML) algorithms need to be used to determine the subtle structural changes in images and the structural features that are statistically meaningful in cancer development. …”
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A Social Media Sentiment Analysis Using Machine Learning Approaches by Noor Salah Irzooqi Al-Agele, Didem KIVANÇ TÜRELİ

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Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models by Edgar Omar Molina Molina, Victor H. Diaz-Ramirez

“…Experimental results using a training/testing ratio of 70/30 on 15,506 mammography images from three datasets produced an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>86.28</mn><mo>%</mo></mrow></semantics></math></inline-formula>, a precision of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78.75</mn><mo>%</mo></mrow></semantics></math></inline-formula>, a recall of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>86.14</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and an F1-score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>80.09</mn><mo>%</mo></mrow></semantics></math></inline-formula> with the modified EfficientNetV2 model and stacking classifier. …”
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Machine learning opportunities to predict obstetric haemorrhages by Yu. S. Boldina, A. A. Ivshin

“…One of the particular AI variants is presented by machine learning (ML), which develops accurate predictive models using computer analysis. …”
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An upscaling framework for estimating regional-scale fractional cover of Chinese fir (FCCF) by integrating UAV and satellite images by Houxi Zhang, Jianwei Cai, Minghui Cai, Xunlong Chen, Yiming Sun, Kun Chen, Shijiang Cao, Jianling Ren

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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. …”
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Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review by Ping Zhang, Qing Zhang, Xinya Hu, Wei Chi, Weihua Yang

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A Systematic Literature Review on Machine Learning Algorithms for the Detection of Social Media Fake News in Africa by Joshua Ebere Chukwuere, Tlhalitshi Volition Montshiwa

“…The study identified 14 effective ML classifiers to manage fake news on social media platforms, including Random Forest, Naive Bayes, and others. …”
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Windows Malware Detection Under the Machine Learning Models and Neutrosophic Numbers by Alber S. Aziz, Mohamed eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ashraf. M. Hussein, Nariman A. Khalil

“…Supervised machine learning classifiers have shown great promise in the field of malware detection. …”
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An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity by Kamran Aziz, Feng Chen, Mahmood Ahmad, Muhammad Salman Khan, Mohanad Muayad Sabri Sabri, Hamad Almujibah

“…To accurately estimate the multi-class accident injuries and comprehend their severity we proposed a novel method called Bayesian Optimized Dynamic Ensemble Selection for Multi-Class Imbalance (DES-MI) with Ensemble Imbalance Learning (EIL), which involves; generating a pool of base classifiers with EIL methods and utilizing DES-MI to choose suitable classifiers. …”
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Predicting Students’ Performance Using a Hybrid Machine Learning Approach by Ropafadzo Duwati, Tawanda Mudawarima

“…This presents an opportunity for learning institutions to predict students’ performance using Machine Learning (ML) techniques. This paper explores the application of a hybrid ML approach to students’ performance. …”
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A Generalized and Robust Nonlinear Approach based on Machine Learning for Intrusion Detection by Jakiur Rahman, Jaskaran Singh, Soumen Nayak, Biswajit Jena, Lopamudra Mohanty, Narpinder Singh, John R. Laird, Rajesh Singh, Deepak Garg, Narendra N. Khanna, Mostafa M. Fouda, Luca Saba, Jasjit S. Suri

“…We employed ten machine learning (ML) classifiers, consisting of five LC and five NLC. …”
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A data-driven machine learning approach toward an improved maize crop production by Tosin Comfort Olayinka, Adebayo Olusola Adetunmbi, Olayinka Olumide Obe, Emmanuel Onwuka Ibam, Akinola Samson Olayinka

“…In the performance evaluation of these classifiers, the hybridized ANN-KNN shows an accuracy of 99.45 % building the performance of the ANN classifiers with an accuracy of 98.83 %. …”
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