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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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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Workplace Preference Analytics Among Graduates by Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

“…Feature selection was used to identify top-10 predictors that influence the selection of jobs in graduates' desired sectors. Various analytics methods such as Decision Tree Analysis, Random Forest Model selection, Naive Bayes Classification Method, Support Vector Machines and K-Nearest Neighbor Algorithms were employed for comparative evaluations within the workplace analytics scope. …”
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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method by Ankit Kumar, Kamred Udham Singh, Manish Kumar

“…To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.…”
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Studying the Impact of Changing Consumer Behavior During Crisis Periods Through Store Classification by Kiymet Tabak Kızgın, Selçuk Alp

“…The random forest algorithm with the highest accuracy was hybridized with 3 different classification algorithms. The hybrid model consisting of random forest and support vector machine gave the highest accuracy rate (90%) for the period including all data for store classification. …”
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Automated Classification of Exchange Information Requirements for Construction Projects Using Word2Vec and SVM by Ewelina Mitera-Kiełbasa, Krzysztof Zima

“…The proposed method uses Word2Vec for text vectorisation and Support Vector Machines (SVMs) with an RBF kernel for text classification, and it attempts to apply Word2Vec with cosine similarity for text generation. …”
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Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review* by Korie, Josephmartin Izuchukwu*, Chudi-Ajabor, Ogochukwu Gabriela, Ezeonyema, Chukwudalu Chukwuekezie, Oshim, Francisca Ogechukwu

“…The emergence of ML algorithms, such as Artificial Neural Networks (ANN), Support Vector Machines, and others, trigger this paradigm. …”
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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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Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments by Monica Dutta, Deepali Gupta, Vikas Khullar, Sapna Juneja, Roobaea Alroobaea, Pooja Sapra

“…It has widespread applications like smart homes, smart cities, robotics, etc. Primitive classification methods like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), provide a compromised performance with complex indoor environments due to light variations, intra-class similarities, and occlusions. …”
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A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

“…The key performance metrics, accuracy, precision, recall, and F1-score, are calculated to assess the efficacy of classification. Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. …”
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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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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

“…Fruit and vegetable identification and classification system is always necessary and advantageous for the agriculture business, the food processing sector, as well as the convenience shops and hypermarkets where these products are sold. …”
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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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RARE: right algorithm for the right errand; a multi-model machine learning-based approach for tourism routes and spots recommendation by Ling Luo

“…The framework employs long short-term memory (LSTM) for spot relevance prediction, support vector machine (SVM) for spot name classification, and depth first search (DFS) for optimal route generation. …”
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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. …”
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Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311 by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, Changchun Zou

“…This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). …”
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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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A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches by Sura Monther Alnedawe, Hadeel K. Aljobouri

“…In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. …”
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A Systematic Review on the Use of Big Data in Tourism by Jafar Ahangaran, Abbas Sadeghnia

“…In response to the third question of this research, which is the type and classification of big data that support this tool and methods of analysis, it should be said that the findings of the research show that the textual analysis of the data collected with the purpose of predictive analysis has been used the most in the selected articles. …”
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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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