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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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Hazard assessment can be supported by Artificial Intelligence (AI) techniques, such as machine learning, which are used for data exploration to identify features of interest in the data. …”
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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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Detection of Valvular Heart Diseases From PCG Signals Using Machine and Deep Learning Models: A Review by Ayappasamy Kannan, Manob Jyoti Saikia, Sushant Kumar, Sumit Datta

“…Artificial intelligence (AI) predictions are widely used to address challenges in the heart health sector, such as providing clinical decision support. …”
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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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Can Different Cultivars of <i>Panicum maximum</i> Be Identified Using a VIS/NIR Sensor and Machine Learning? by Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. …”
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High-resolution mapping of orchard distribution across Italy by Francesco Lodato, Ryan Jayne, Marco Santonico, Maurizio Pollino, Flavio Bellino, Laura De Gara, Bruno Basso

“…By increasing the granularity of these subclasses in a LULC coarse dataset, this approach provides improved support for agricultural management, landscape planning, and related sectors, benefiting agricultural authorities, research institutions, and farmers. …”
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Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA by Manar Alassaf, Ali Mustafa Qamar

“…Therefore, various experiments were conducted to investigate the effects of one-way ANOVA and to select important features concerning the performance of different supervised machine learning classifiers. Support Vector Machine and Naïve Bayes achieved the best results with one-way ANOVA as compared to the baseline experimental results in the collected dataset. …”
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Improving SMART learning: Course completion via AI-driven hybrid system integration in big data by Abdellah Bakhouyi, Amine Dehbi, Lahcen Amhaimar, Said Broumi, Mohamed Talea, Abderrahim Khalidi

“…The hybrid system implements state-of-the-art machine learning modeling techniques, including Decision Trees, Support Vector Machines, and Naïve Bayesian Classifiers for analyzing the performance of the students' data as well as for predicting successful completion of the course. …”
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

“…To overcome this, an explainable hybrid framework has been proposed that integrates DenseNet201 for deep feature extraction with a Support Vector Machine (SVM) classifier for robust binary classification. …”
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Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids by Ali Jaber Almalki

“…By fusing supervised learning models (Random Forest) with unsupervised learning algorithms (Isolation Forest, One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF), and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)), this study presents a unique hybrid technique for identifying power theft. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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A comprehensive review of AI-based brain-computer interface with prefrontal cortex and sensory-motor rhythms systemization for rehabilitation by Anna Latha M, Ramesh R

“…Key findings: The results show that the random forest classifier is more suitable for eye state classification, achieving an accuracy up to 99.80 %, and support vector machine classification provides a higher accuracy of 100 % for MI conditions. …”
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Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network by Yingisani Chabalala, Elhadi Adam, Mahlatse Kganyago

“…However, the heterogeneity and complexity of the study area—composed of smallholder mixed cropping systems with overlapping spectra—constituted an obstacle to the application of optical pixel-based classification using machine learning (ML) classifiers. Given the socio-economic importance of fruit tree crops, the research sought to map the phenological dynamics of these crops using deep neural network (DNN) and optical Sentinel-2 data. …”
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Enhancing tool condition monitoring in friction stir welding with probabilistic neural network algorithm by Balachandar Krishnamurthy, Jegadeeshwaran Rakkiyannan

“…A feature importance study is conducted using a decision tree algorithm, which selects only the most significant features to reduce computational complexity.ResultFeature classification is then performed using various machine learning and deep learning algorithms, including Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Cascade Correlation, GMDH Polynomial Neural Networks, and Linear Discriminant Analysis Among these classifiers, Probabilistic Neural Networks (PNN) consistently deliver the best results as 91.25% under 1,400 rpm.DiscussionBased on these findings, the Probabilistic Neural Network algorithm is identified as a robust and reliable prediction model for monitoring FSW tool conditions.…”
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Is a voluntary healthy food policy effective? evaluating effects on foods and drinks for sale in hospitals and resulting policy changes by Cliona Ni Mhurchu, Magda Rosin, Stephanie Shen, Bruce Kidd, Elaine Umali, Yannan Jiang, Sarah Gerritsen, Sally Mackay, Lisa Te Morenga

“…Forty-three sites were audited, encompassing 229 retail settings (serviced food outlets and vending machines). In total, 8485 foods/drinks were classified according to Policy criteria. …”
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