Accounting Support Using Artificial Intelligence for Bank Statement Classification by Marco Lecci, Thomas Hanne

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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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Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines by Emmanuel Ahishakiye, Fredrick Kanobe

“…These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. …”
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Assessing the Level of Employment in the Informal Sector of the Economy of Russian Regions Using Modern Machine Learning Methods by Aleksey Nikolaevich Borisov, Aleksandr Ivanovich Borodin, Roman Vladimirovich Gubarev, Evgeny Ivanovich Dzuyba, Oksana Mikhaylovna Kulikova

“…In the course of solving the classification problem using a modern machine learning method (LightGBM), the key factors affecting the level of employment in the informal sector of the economy of Russian regions were identified. …”
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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

“…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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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“… The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. …”
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DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM by Syaiful Anam, Abdi Negara Guci, Fery Widhiatmoko, Mila Kurniawaty, Komang Agus Arta Wijaya

“…Support Vector Machine (SVM) is one of the superior ML approaches. …”
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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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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

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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. …”
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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

“…This study employs a dataset comprising gas sensor measurements that encompassing gasses, such as Liquid Petroleum Gas (LPG), Compressed Natural Gas (CNG), Methane, Propane, and others. Various machine learning classifiers, including K-Nearest Neighbors, Decision Tree, Support Vector Machines, XGBoost, and others, are compared with ExAIRFC-GSDC for gas detection. …”
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Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers by Firman Aziz, Mutia Maulida, Jafar Jafar, Nurafni Shahnyb, Norma Nasir, Ampauleng Ampauleng

“…This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. …”
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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

“…Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10. …”
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Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods by Yu. M. Beketnova

“…The author carried out a comparative analysis of the results obtained by classification methods — the logistic regression method, decision trees (algorithms of two-class decision forest, Adaboost), support vector machine (algorithm of two-class support vector machine), neural network methods (algorithm of two-class neural network), Bayesian networks (algorithm of two-class Bayes network). …”
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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

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Comparative Analysis of a Quantum SVM With an Optimized Kernel Versus Classical SVMs by Matheus Cammarosano Hidalgo

“…Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. …”
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Hybrid Machine Learning-Based Multi-Stage Framework for Detection of Credit Card Anomalies and Fraud by Hatoon S. Alsagri

“…The proposed framework utilizes a multi-stage classification system that employs multiple classifiers, i.e., logistic regression, support vector machine (SVM) XGBoost, Random Forest, K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). …”
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An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal by Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial

“…The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. …”
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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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