Mean-Variance optimal portfolio selection integrated with support vector and fuzzy support vector machines by Simrandeep Kaur, Arti Singh, Abha Aggarwal

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A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images With Various Machine Learning Classifiers by Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, Son Vu Truong Dao

“…The final subset feature has been used for recognizing fruits using several machine learning classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP). …”
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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“…Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. …”
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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA by Felice Elena, Robyn Irawan, Benny Yong

“…This paper will use the Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and hybrid Adaptive Boosting-SVM (AdaBoost-SVM) model. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. …”
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Accounting Support Using Artificial Intelligence for Bank Statement Classification by Marco Lecci, Thomas Hanne

“…The study employs Feedforward Neural Networks and Support Vector Machines with various settings and compares their performance with that of previous models embedded in similar predictive tasks. …”
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Multi-objective portfolio optimization using real coded genetic algorithm based support vector machines by B. Surja, L. Chin, F. Kusnadi

“…There are three classes of stocks that accommodate those criteria: Liquid, high-yield, and less-risky. Classifying stocks help investors build portfolios that align with their risk profiles and investment goals, in which the model was constructed using the one-versus-one support vector machines method with a radial basis function kernel. …”
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Workplace Preference Analytics Among Graduates by Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

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A hybrid approach to financial big data analysis using extended ensemble learning and optimized spark streaming by Muhammad Babar

“…The core ensemble combines K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and K-Neighbors Classifier (KNC) to improve classification robustness and generalization. …”
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Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method by Febiana Angela tanesab, Rangga Pahlevi Putra, Aviv Yuniar Rahman

“…According to IRRI, rice farmers experience crop losses of up to 37% each year due to pests and diseases. This study aims to classify rice plant diseases using the Multi-Class Support Vector Machine (M-SVM) method based on leaf images. …”
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Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Bu... by Galih Arisona, Alief Pascal Taruna, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…This research aims to improve the detection of electricity theft through a machine learning-based model utilizing the Support Vector Machine (SVM) classification technique. …”
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Early Prediction Detection of Retail and Corporate Credit Risks Using Machine Learning Algorithms by Mohamed A. Hamada, Karim Farag, Adejor E. Abiche

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Enhancing revenue generation in Bangladesh’s FinTech sector: a comprehensive analysis of real-time predictive customer behavior modeling in AWS using a hybrid OptiBoost-EnsembleX m... by Avijit Chowdhury

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A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson’s Disease Detection from Electroencephalogram Signals by Sankhadip Bera, Zong Woo Geem, Young-Im Cho, Pawan Kumar Singh

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Comparison of Rating-based and Inset Lexicon-based Labeling in Sentiment Analysis using SVM (Case Study: GoBiz Application Reviews on Google Play Store) by Hiliah Firda, Pacu Putra, Nabila Rizky Oktadini, Putri Eka Sevtiyuni, Allsela Meiriza

“…It compares two labeling methods—Rating-Based and Inset Lexicon—and evaluates them using the Support Vector Machine (SVM) algorithm. The analysis process includes data selection, text preprocessing, data transformation using TF-IDF, SVM implementation with 10-fold cross-validation, and result visualization through word clouds. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…Feature scaling and normalisation were performed across the dataset to standardise the features. Six machine learning models that comprised support vector machine, Adaboost, Logistic regression, a K-nearest neighbour, neural network and random forest classifier were trained to predict the occurrence of reworks in sustainable buildings. …”
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Application of machine learning techniques for churn prediction in the telecom business by Raji Krishna, D. Jayanthi, D.S. Shylu Sam, K. Kavitha, Naveen Kumar Maurya, T. Benil

“…These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. …”
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Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

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Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM by Pawanjit Singh Ghatora, Seyed Ebrahim Hosseini, Shahbaz Pervez, Muhammad Javed Iqbal, Nabil Shaukat

“…In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). …”
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