Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…However, the combination of EfficientNetB0 pre-training with ML Support Vector Machines (SVM) produced optimal results with accuracy, sensitivity, specificity, precision, F1-Score, and AUC, of 99.4%, 98.7%, 99.1%, 99%, 98.8%, and 100%, respectively. …”
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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. …”
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Modeling forest canopy structure and developing a stand health index using satellite remote sensing by Pulakesh Das, Parinaz Rahimzadeh-Bajgiran, William Livingston, Cameron D. McIntire, Aaron Bergdahl

“…The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. …”
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AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data by Saleh I. Alzahrani, Wael M. S. Yafooz, Ibrahim A. Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh

“…In terms of AUC-ROC, the kernel Support Vector Machine (SVM) outperformed others, achieving 71% accuracy, with precision, recall, F-measure, and area under the curve values of 0.7, 0.75, 0.59, and 0.72, respectively. …”
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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…On the HAM10000 and ISIC 2018 datasets, we trained and assessed three distinct ML models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), and Support Vector Machine (SVM). Overall performance was increased by the combined predictions made with the Max Voting technique. …”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). …”
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Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges by Tomer Raitsis, Yossi Elgazari, Guy E. Toibin, Yotam Lurie, Shlomo Mark, Oded Margalit

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An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication by Prabhath Samarathunga, Hossein Rezaei, Maheshi Lokumarambage, Thushan Sivalingam, Nandana Rajatheva, Anil Fernando

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Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…To achieve ultimate efficiency, the current study experimented with a range of machine-learning algorithms. The algorithms include Support Vector Machines (SVM), Random Forests (RF), Decision Trees, K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN). …”
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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…The results show that the proposed hybrid model outperforms traditional models such as RFR, support vector machine, K-nearest neighbor, LSTM, and QLSTM in terms of MAE, RMSE, and bias. …”
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High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies by Sheng Liu, Zhenzhen Ren, Weihua Wang, Kai Zhong, Jinhong Yang, Hongwei Ning

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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

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Studying the Impact of Changing Consumer Behavior During Crisis Periods Through Store Classification by Kiymet Tabak Kızgın, Selçuk Alp

“…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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Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management by Xiaoyi Jiang

“…From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” …”
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A Performance Analysis of Business Intelligence Techniques on Crime Prediction by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Ruth Wario

“…Four different classification algorithms that is; decision tree (J48), Naïve Bayes, Multilayer Perceptron and Support Vector Machine were compared to find the most effective algorithm for crime prediction. …”
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Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin by Ilham Kurniawan, Duwi Cahya Putri Buani, Abdussomad Abdussomad, Widya Apriliah, Rizal Amegia Saputra

“…Classification is a data mining method that determines categories in data groups to support more accurate predictions and analysis. Therefore, three machine learning classification algorithms such as, support vector machine, naive bayes and random forest, were used in this experiment. to determine recipients of Raskin assistance. …”
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基于SVM与GA参数优化的齿轮箱断齿故障诊断方法研究 by 张星辉, 康建设, 曹端超, 孙磊, 滕红智

“…A new method of gearbox fault diagnosis based on SVM（Support vector machine） and GA（Genetic algorithm）which is used to optimize parameters is presented.Firstly,the raw vibration signal is preprocessed by Time Synchronous Average algorithm.Then,the signal wavelet packet decomposition is carried out,standard deviation of wavelet packet coefficients of the signals is considered as the fault feature vector,and the normalization process of the fault feature vector is carried out.In the end,the fault feature vector is used as the input of SVM.In this process,the Daubechies order,wavelet packet decomposition level,c and g of SVM are optimized by GA.After that,the optimized parameter is used in training model which will be used for fault diagnosis.The experimental result shows that SVM and GA can be used to effectively diagnose faults of gearbox.…”
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Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology by Li Shengjia, Ma Yali, Zhao Yongsheng, Pu Dajun, Yan Shidang

“…Then, the prediction model of meshing contact characteristics of the system is established using support vector machine and random forest algorithm, which realizes the efficient prediction of meshing contact characteristics of the system. …”
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Fault diagnosis of the mine ventilation system based on OCKIELM by Zhiyuan Shen, Qizheng Wang

“…Compared to support vector data description, principal component analysis, and one-class support vector machine methods, this method exhibits superior performance across F1, area under the curve, and G-mean metrics.…”
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