Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

“…Ten ML models, including Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), Gradient Boosting (GBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were compared for their performance in predicting the grid stability. …”
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Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System by Xiongwei Zhang, Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali

“…Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. …”
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Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Networks (CNNs), and Backpropagation Neural Networks (BPNNs) were used to establish both single-task and multi-task models for the prediction of milling quality attributes. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…The paper uses naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) as classifiers after careful investigation. …”
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Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights by Filippo Genuario, Giuseppe Santoro, Michele Giliberti, Stefania Bello, Elvira Zazzera, Donato Impedovo

“…The proposed work compares both shallow learning algorithms, such as decision trees, random forests, Naïve Bayes, logistic regression, XGBoost, and support vector machines, and deep learning algorithms, such as DNNs, CNNs, and LSTM, whose approach is relatively new in the literature. …”
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Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach by Dini Nurmalasari, Dini Hidayatul Qudsi, Nessa Chairani, Heri R Yuliantoro

“…Text preprocessing techniques, such as stemming, tokenizing, and filtering, are performed to ensure robust classification. Machine learning algorithms – Naïve Bayes, Support Vector Machine (SVM), and Random Forest – are then utilized to evaluate sentiment classification accuracy. …”
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Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Roberto Mantovani, José Antonio Marengo

“…We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This research addresses these challenges by employing advanced signal processing techniques and machine learning algorithms. The study investigates and optimizes fault diagnosis of rolling element bearings using various machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). …”
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Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning by Pengfei Gao, Yuanyuan Song, Jian Wang, Zhiyong Yang, Kai Wang, Yongyu Yuan

“…Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. …”
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Using weak signals to predict spontaneous breathing trial success: a machine learning approach by Romain Lombardi, Mathieu Jozwiak, Jean Dellamonica, Claude Pasquier

“…Results For the models tested, the best results were obtained with Support Vector Classifier model: AUC-PR 0.963 (0.936–0.970, p = 0.001), AUROC 0.922 (0.871–0.940, p < 0.001). …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Predictor variables included dam breed, body weight, age, litter size, lamb genotype, lambing season, time of lambing, parturition duration, and lambing assistance. Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. …”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…This study aims to build a multi-label classification model to classify the learning styles of elementary school students. The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis by Wen Wenjie, Li Rui, Zhuo Pengpeng, Deng Chao, Zhang Donglin

“…Data from SwissTargetPrediction, CTD databases, and GEO datasets were analyzed to identify potential targets. Three machine learning algorithms (Support Vector Machine, Random Forest, and LASSO regression) were applied for core target identification, followed by Molecular docking analyses. …”
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Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas, Athanasios I. Gelasakis

“…Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. …”
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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images by M. Shanmuga Eswari, S. Balamurali, Lakshmana Kumar Ramasamy

“…Methods We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…We trained ML-supervised algorithms like XG Boost, Logistic Regression, Random Forest Classifier, Ad- aBoost, and Support Vector Machine to help classify TB patients from large RNA-sequence count data. …”
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Machine learning predictive performance in road accident severity: A case study from Thailand by Ittirit Mohamad, Sajjakaj JomnonKwao, Vatanavongs Ratanavaraha

“…Eight algorithms were assessed, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (kNN), Neural Network (NN), Naïve Bayes (NB), Logistic Regression (LR), and Gradient Boosting (GB).A dataset comprising 112,837 road accidents over a five-year period in Thailand was analyzed, focusing exclusively on incidents where drivers were at fault. …”
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