Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian, Qi He

“…Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. …”
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A Cloud User Anomaly Detection Method Based on Mouse Behavior by XU Hong-jun, ZHANG Hong, HE Wei

“…The experimental results show that the proposed method can effectively detect abnormal behavior of users under the precondition of ensuring user privacy, meanwhile, it can avoid the analysis and processing of high dimensional feature data and reduce the difficulty of abnormal behavior detection.…”
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Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence by Silvia García-Méndez, Francisco de Arriba-Pérez

“…Mainly, our work contributes to an intelligent PPD screening system that combines Natural Language Processing, Machine Learning (ML), and Large Language Models (LLMS) toward an affordable, real-time, and noninvasive free speech analysis. Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMS with interpretable ML models (i.e. tree-based algorithms) using feature importance and natural language. …”
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A systematic review of effective data augmentation in cervical cancer detection by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

“…This review examines effective augmentation techniques and top-performing deep-learning models for segmentation and classification in cervical cancer detection. Analyzing 57 articles, we found that hybrid deep feature fusion with augmentation (rotation, flipping, shifting, brightness adjustments) achieved 99.8% accuracy in binary and 99.1% in multiclass classification. …”
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High-Level Codewords Based on Granger Causality for Video Event Detection by Shao-nian Huang, Dong-jun Huang, Mansoor Ahmed Khuhro

“…Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis. …”
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ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM by Fi Imanur Sifaunnufus Ms, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio

“…The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. …”
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Generalization challenges in video deepfake detection: methods, obstacles, and technological advances by LI Junjie, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…Regarding the challenges of cross-dataset detection, the paper analyzes how differences in feature distributions between datasets lead to the performance degradation of traditional deep learning models in cross-domain applications. …”
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Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection by Sheunesu Makura, Caden Dobson, Seani Rananga

“…Online banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection with network packet analysis to mitigate fraudulent activities, focusing particularly on Distributed Denial of Service (DDoS) attacks. …”
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Leveraging stacking machine learning models and optimization for improved cyberattack detection by Neha Pramanick, Jimson Mathew, Shitharth Selvarajan, Mayank Agarwal

“…Abstract The ever-growing number of complex cyber attacks requires the need for high-level intrusion detection systems (IDS). While the available research deals with traditional, hybrid, and ensemble methods for network data analysis, serious challenges are still being met in terms of producing robust and highly accurate detection systems. …”
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AAV Parameters Estimation Based on Improved Time-Frequency Ridge Extraction and Hough Transform by Yongji Yu, Yonghong Ruan, Junjie Zhong

“…First-Order short-time Fourier transform synchrosqueezed transform (FSST) is proposed for extracting micro-Doppler features. Specifically, a novel AAV parameter estimation method is investigated, which is based on an improved time-frequency ridge extraction and Hough transform, following a detailed analysis of the micro-Doppler time-frequency spectrum. …”
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Efficient lung cancer detection using computational intelligence and ensemble learning. by Richa Jain, Parminder Singh, Mohamed Abdelkader, Wadii Boulila

“…Our proposed method employs Logistic Regression, MLP Classifier, Gaussian NB Classifier, and Intelligent Feature Selection using K-Means and Fuzzy Logic to enhance detection procedures in lung cancer dataset. …”
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Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai, Yubin Xie

“…This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). …”
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Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet by Yu Zhang, Yangyang Jiao, Yinke Dou, Liangliang Zhao, Qiang Liu, Yang Liu

“…The CG-UNet model effectively detects ice-covered edges through experimental analysis, achieving optimal dataset scale (ODS) and optimal image scale (OIS) scores of 0.934 and 0.938, respectively. …”
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Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection by Tanjim Mahmud, Md. Alif Hossen Prince, Md. Hasan Ali, Mohammad Shahadat Hossain, Karl Andersson

“…Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. …”
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Hybrid Android Malware Detection and Classification Using Deep Neural Networks by Muhammad Umar Rashid, Shahnawaz Qureshi, Abdullah Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh

“…Unlike prior approaches, the proposed system integrates a multi-dimensional analysis of Android permissions, intents, and API calls, enabling robust feature extraction even under reverse engineering constraints. …”
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Leak detection and localization in water distribution systems via multilayer networks by Daniel Barros, Ariele Zanfei, Andrea Menapace, Gustavo Meirelles, Manuel Herrera, Bruno Brentan

“…Due to the intrinsic interconnected feature of water flow, including losses, this study proposes a methodology based on graph correlation and multilayer network analysis for leak detection and localization in WDNs with multiple components (infrastructure, control devices, hydraulic sensors). …”
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Review of Masked Face Recognition Based on Deep Learning by Bilal Saoud, Abdul Hakim H. M. Mohamed, Ibraheem Shayea, Ayman A. El-Saleh, Abdulaziz Alashbi

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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…It solves the problem of long model training time caused by insufficient computing power and provides a new method for the detection and analysis of abnormal features in power spectrum images.…”
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Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women by Bharti Panjwani, Jyoti Yadav, Vijay Mohan, Neha Agarwal, Saurabh Agarwal

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Contextual information based anomaly detection for multi-scene aerial videos by Girisha S, Ujjwal Verma, Manohara M M. Pai, Radhika M. Pai

“…The proposed system holistically utilizes contextual, temporal, and appearance features for the accurate detection of anomalies. A novel feature descriptor is designed to effectively capture contextual information necessary for analyzing multi-scene videos. …”
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