Improving Electrical Fault Detection Using Multiple Classifier Systems by José Oliveira, Dioeliton Passos, Davi Carvalho, José F. V. Melo, Eraylson G. Silva, Paulo S. G. de Mattos Neto

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A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier by Bowen Shi, Hongjian You, Huixian Wang

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Dynamic Classifier Auditing by Unsupervised Anomaly Detection Methods: An Application in Packaging Industry Predictive Maintenance by Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober, Juan Gómez-Sanchis, Antonio José Serrano-López

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INVESTIGATION OF THE CAPABILITIES OF ARTIFICIAL NEURAL NETWORKS WHEN CLASSIFYING OBJECTS DYNAMIC FEATURES by Nikita V. Laptev, Olga M. Gerget, Vladislav V. Laptev, Dmitriy Yu. Kolpashchikov

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Performance Analysis of Deep Learning Architectures in Classifying Fake and Real Images by Arya Faisal Akbar, Putu Desiana Wulaning Ayu, Dandy Pramana Hostiadi

“…This study investigates the impact of Dynamic Dropout in optimizing deep learning models, including ResNet-101, DenseNet-201, VGG-19, and AlexNet, for classifying real and synthetic images using the CIFAKE and Real and Fake Face datasets. …”
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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment by G Logeswari, K Thangaramya, M Selvi, J. Deepika Roselind

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Architecture of an automated program complex based on a multiple kernel svm classifier for analyzing malicious executable files by Алан Нафієв, Андрій Родіонов

“…The aim of the work is to create an automated system that enhances the accuracy and efficiency of malware detection by combining static and dynamic analysis into a single framework capable of processing large volumes of data with optimal time expenditure. …”
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Architecture of an automated program complex based on a multiple kernel svm classifier for analyzing malicious executable files by Alan Nafiiev, Andrii Rodionov

“…The aim of the work is to create an automated system that enhances the accuracy and efficiency of malware detection by combining static and dynamic analysis into a single framework capable of processing large volumes of data with optimal time expenditure. …”
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CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES by abdullah alsraratee, Ahmed Al-Azawei

“…This study utilizes machine learning techniques namely, K-Nearest Neighbor, Random Forest and Decision Tree to classify Android malware based on dynamic analysis. …”
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Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers by Khawla Ali Maodah, Sharaf Alhomdy, Fursan Thabit

“…Machine learning provides dynamic options for detecting known and unknown assaults, whereas typical intrusion detection systems that depend on signature or rule-based techniques find it difficult to adjust to complex cyber threats.MethodsThis study compares the efficacy of an ensemble approach (Voting Hard and Stacking) for intrusion detection in cloud environments with individual machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting, XGBoost, Naive Bayes, Support Vector Machine, and Logistic Regression. …”
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“You are doomed!" Crisis-specific and Dynamic Use of Fear Speech in Protest and Extremist Radical Social Movements by Simon Greipl, Julian Hohner, Heidi Schulze, Patrick Schwabl, Diana Rieger

“…This underscores FS's potential as an indicator for radicalization dynamics and crisis escalation. …”
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Human crime activity recognition and shooting weapon detection in video frames using the contour approximation algorithm, and FastDTW classifier by Y. V. K. Durga Bhavani, V. B. Pagi

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An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection by Wen-Chen Huang, Chun-Liang Liu

“…The purpose of this research is to propose a new method to be able to automatically detect tumor region and compare three classifiers' tumor detection performance for DMRI. …”
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NFFNet: A Deep Learning Framework for Fault Diagnosis Using Classifier-Guided Graph Attention Networks and Blind Deconvolution by Hongfei Yao, Qianzhe Xu, Qingtao Wu

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GSB: GNGS and SAG-BiGRU network for malware dynamic detection. by Zhanhui Hu, Guangzhong Liu, Xinyu Xiang, Yanping Li, Siqing Zhuang

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RiceLeafClassifier‐v1.0: A Quantized Deep Learning Model for Automated Rice Leaf Disease Detection and Edge Deployment by Oluwaseun O. Martins, Christiaan C. Oosthuizen, Dawood A. Desai

“…ABSTRACT Rice diseases critically threaten global food security, necessitating rapid, accurate detection methods. This study presents RiceLeafClassifier‐v1.0, a lightweight quantized convolutional neural network (CNN) that classifies five rice leaf conditions: blast, bacterial blight, brown spot, healthy, and red stripe, with high accuracy and real‐time performance. …”
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Utility of dynamic contrast enhancement for clinically significant prostate cancer detection by Eric V. Li, Sai K. Kumar, Jonathan A. Aguiar, Mohammad R. Siddiqui, Clayton Neill, Zequn Sun, Edward M. Schaeffer, Anugayathri Jawahar, Ashley E. Ross, Hiten D. Patel

“…Abstract Objective This study aimed to evaluate the association of dynamic contrast enhancement (DCE) with clinically significant prostate cancer (csPCa, Gleason Grade Group ≥2) and compare biparametric magnetic resonance imaging (bpMRI) and multiparametric MRI (mpMRI) nomograms. …”
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Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach by Mohammad Safaei, Jane Southworth, Cerian Gibbes, Hannah V. Herrero, Mashoukur Rahaman, Bewuket B. Tefera, Jason K. Blackburn

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A novel dynamic weighted prediction framework with stability-enhanced dynamic thresholding feature selection for neurodegenerative disease detection using gait features by Diksha Giri, Ranjit Panigrahi, Samrat Singh Bhandari, Moumita Pramanik, Akash Kumar Bhoi, Victor Hugo C. de Albuquerque

“…Methods A novel ensemble classifier, the Dynamic Weighted Prediction Framework (DWPF), and an innovative feature selection methodology, Stability-Enhanced Dynamic Thresholding (SEDT), have been proposed for neurodegenerative disease detection. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…A smaller yet relevant set of features could aid these classifiers in coping with these requirements. This work proposed using feature selection to evaluate the impact of several statistical features on the performance of ML classifiers for chatter detection during micro-milling operations, compare them to the performance of the Convolutional Neural Network algorithm, and discuss the employability of the techniques on the STM32F446RE microcontroller. …”
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