Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…Traditional methods for detecting insider threats, such as Random Forest and Isolation Forest, suffer from high computational resource consumption, poor feature representation, and sensitivity to noise. …”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…Such entropy metrics can be included within feature subsets, thus making the feature set richer for subsequent analysis and machine learning applications.…”
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ALGORITHM OF UNMANNED AERIAL VEHICLES ACOUSTIC SIGNALS DETECTION BASED ON ANALYSIS OF FRACTAL DIMENSION by Leonid M. Artushin, Mykola V. Buhaiov

“…It is established that the values of FD for broadband noise are subordinated to the normal probability density distribution law. An algorithm for detecting of UAV acoustic signals based on the analysis of FD with a constant probability of false alarm is proposed. …”
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Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection by Edeh Michael Onyema, B. Gunapriya, Balasubramanian Prabhu Kavin, Mueen Uddin, Priyan Malarvizhi Kumar, Tehseen Mazhar, Mamoon M. Saeed

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Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection by Kun Zheng, Haoshan Liang, Hongwei Zhao, Zhe Chen, Guohao Xie, Liguo Li, Jinghua Lu, Zhangda Long

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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection. by Naif Almakayeel, E Laxmi Lydia, Oleg Razzhivin, S Rama Sree, Mohammed Altaf Ahmed, Bibhuti Bhusan Dash, S P Siddique Ibrahim

“…Various deep learning (DL) and artificial intelligence (AI) technologies have been successfully implemented for the analysis of medical images, namely grading of diabetic retinopathy (DR), breast cancer detection, skin cancer detection, and so on. …”
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AI-Assisted MRI Analysis for Multiple Sclerosis: Lesion Detection and Brain Atrophy Assessment by Ioana-Andreea CÎRLIG, Ioana-Andreea GHEONEA

“…This study aimed to assess the performance and clinical relevance of AI-based MRI analysis for lesion detection and brain atrophy quantification in patients with MS. …”
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Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models by Ncedo S. Maseko, Bonginkosi A. Thango, Nkateko Mabunda

“…Advanced diagnostic techniques are essential for timely fault detection and predictive maintenance. This study investigates the application of machine learning (ML) techniques in transformer fault detection using Frequency Response Analysis (FRA) data. …”
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An extrinsic random-based ensemble approach for android malware detection by Nektaria Potha, V. Kouliaridis, G. Kambourakis

“…Malware detection is a fundamental task and associated with significant applications in humanities, cybersecurity, and social media analytics. …”
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Detection and analysis of organic contaminants in potable water based on three-dimensional fluorescence spectroscopy by CHEN Fang, ZHANG Xiaoyan, HUANG Pingjie, HOU Dibo, ZHANG Guangxin, ZHAO Jiajia, HE Jiping

“…Support vector machine was applied to classify measured samples based on feature information of relative concentration value, which can distinguish normal water samples from the ones polluted by organics.Through the analysis of the experimental results, we could find that the method effectively detects the water samples contaminated by organics. …”
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A Distributed Detection Method for Quality-related Faults in Complex Non-stationary Industrial Processes by Jie DONG, Daye LI, Yanmei WEI, Kaixiang PENG, Hui YANG

“…Therefore, this study proposes a new distributed quality-related fault detection method for complex non-stationary industrial processes based on dynamic and static feature fusion. …”
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Explainable correlation-based anomaly detection for Industrial Control Systems by Ermiyas Birihanu, Imre Lendák

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Deep Learning-Based Medical Object Detection: A Survey by Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee

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Listening deeper: neural networks unravel acoustic features in preterm infant crying by Yuta Shinya, Taiji Ueno, Masahiko Kawai, Fusako Niwa, Seiichi Tomotaki, Masako Myowa

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Whole-genome sequencing of rough Brucella melitensis in China provides insights into its genetic features by Xiaowen Yang, Dongri Piao, Lingling Mao, Bo Pang, Hongyan Zhao, Guozhong Tian, Hai Jiang, Biao Kan

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Multi-Modal Deep Learning for Lung Cancer Detection Using Attention-Based Inception-ResNet by Mohamed Hosny, Ibrahim A. Elgendy, Mousa Ahmad Albashrawi

“…Nevertheless, existing DL models for lung cancer detection suffer from critical limitations, including reliance on single imaging modalities, insufficient datasets, suboptimal feature extraction techniques and constrained generalizability. …”
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An improved chilli pepper flower detection approach based on YOLOv8 by Zhi-Yong Wang, Cui-Ping Zhang

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Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model by Jitendra Kumar Samriya, Amit Kumar, Ashok Bhansali, Meena Malik, Varsha Arya, Wadee Alhalabi, Bassma Saleh Alsulami, Brij B. Gupta

“…This research explores the application of blockchain technology and sentiment analysis to create a privacy-focused system for detecting and analyzing fake web recommendations. …”
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Experimental Investigations to Study the Effectiveness of Cepstral Features to Detect Surface Fatigue Wear Development in a FZG Spur Geared System Subjected to Accelerated Tests by Muniyappa AMARNATH, I.R. PRAVEEN KRISHNA, Ramalingam KRISHNAMURTHY

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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Effic... by Shadi Jacob Khoury, Yazeed Zoabi, Mickey Scheinowitz, Noam Shomron

“…By applying interpretability techniques to both machine learning and deep learning models, we aimed to improve understanding of symptomatology and enhance early detection of COVID-19 cases. Notably, even though less than 1% of our cohort reported having a sore throat, this symptom emerged as a significant indicator of active COVID-19 infection, appearing 7 out of 9 times in the top four most important features across all methodologies. …”
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