Realtime Monitoring of Animal Behavior Using Deep Learning Models by Romesa Rao, Salman Qadri, Rao Kashif

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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). …”
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OSFS‐Vague: Online streaming feature selection algorithm based on vague set by Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu

“…Its main idea is to combine uncertainty and three‐way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous method. …”
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A multi-voter multi-commission nearest neighbor classifier by Suyanto Suyanto, Prasti Eko Yunanto, Tenia Wahyuningrum, Siti Khomsah

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Performance Analysis of Diabetes Detection Using Machine Learning Classifiers by Hung Huynh, Liu Hui, Ngoc Han Nguyen, Ruixuan Qiao

“…Three types of machine learning classifiers are used: Tree-based, Function-based, and Rule-based. …”
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Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…The variance inflation factor (VIF) of the wrapper feature selection approach with decision tree classification algorithm yields 99.63% accuracy and 4.3% error rate compared to other classification algorithms and wrapper feature selection techniques.…”
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Impact of computing platforms on classifier performance in heart disease prediction by Beenish Ayesha Akram, Muhammad Irfan, Amna Zafar, Sidra Khan, Rubina Shaheen

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Predict Diabetes Using Voting Classifier and Hyper Tuning Technique by Chra Ali Kamal, Manal Ali Atiyah

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P-TAME: Explain Any Image Classifier With Trained Perturbations by Mariano V. Ntrougkas, Vasileios Mezaris, Ioannis Patras

“…We apply P-TAME to explain the decisions of VGG-16, ResNet-50, and ViT-B-16, three distinct and widely used image classifiers. …”
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Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans by Umair Khan, Armughan Ali, Salabat Khan, Farhan Aadil, Mehr Yahya Durrani, Khan Muhammad, Ran Baik, Jong Weon Lee

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Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity by Nasser Shoeibi, Majid Abrishami, Seyedeh Maryam Hosseini, Mohammad-Reza Ansari-Astaneh, Razieh Farrahi, Bahareh Gharib, Fatemeh Neghabi, Mojtaba Abrishami, Mehdi Sakhaee, Mehrdad Motamed Shariati

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The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status by Niloofar Minbashi, Markus Bohlin, Carl-William Palmqvist, Behzad Kordnejad

“…Applying SMOTE improved the sensitivity, precision, and F-measure of delayed departures by 20% for decision trees and by 30% for random forests. Overall, random forests show a relative better performance in detecting all three departure classes before and after applying SMOTE. …”
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Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters by Poonam Shrivastava, Neeta Tripathi, Bikesh Kumar Singh, Bhupesh Kumar Dewangan

“…Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC> 82%, and accuracy > 99%). …”
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Network intrusion detection based on improved KNN algorithm by Hongsheng Bao, Jie Gao

“…This poses a serious threat to cyber security and even national security. Therefore, a new three-branch decision soft increment K-nearest neighbor algorithm is proposed, representing the class cluster as an interval set. …”
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A novel three-way distance-based fuzzy large margin distribution machine for imbalance classification by Li Liu, Jinrui Guo, Ziqi Yin, Rui Chen, Guojun Huang

“…To solve these problems, we propose a novel three-way distance-based fuzzy large margin distribution machine (3W-DBFLDM). …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…The three experiments were conducted with and without hyperparameter tuning. …”
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Optimizing Cardiovascular Risk Assessment with a Soft Voting Classifier Ensemble by Ammar Oad, Zulfikar Ahmed Maher, Imtiaz Hussain Koondhar, Karishima Kumari, Hammad Bacha

“…Rest of classifiers gave average scores. It means the proposed method provided best results while compared with Decision tree, Logistic regression, Support Vector Classifier (SVC), SVM Kernel, K Nearest Neighbor and Naïve Bayes. …”
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COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS by Desy Lusiyanti, Selvy Musdalifah, Agusman Sahari, Iman Al Fajri

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Deep learning for prediction and classifying the dynamical behaviour of piecewise-smooth maps by Vismaya V S, Bharath V Nair, Sishu Shankar Muni

“…The decision tree classifier best predicts the border collision bifurcation for the 1D normal form map, the random forest, and the 1D tent map. …”
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