Water quality anomaly detection research based on GRU-PINN model by Zhao Xinyu

“…Feature selection is then performed based on feature importance ranking and mutual information analysis. …”
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The Possibilities of Modern Ultrasound Scanning in Uveal Melanoma Extrascleral Growth Detection by A. G. Amiryan, S. V. Saakyan, G. P. Zakharova, A. Yu. Tsygankov

“…In patients with EG, domed and irregular tumors were more often detected. The results of densitometric analysis of the tumor in patients with and without EG showed significantly higher acoustic density in patients with EG in the area of the base (A1) and central part (A2) of the tumor. …”
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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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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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Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection by Abu Sarwar Zamani, L. Anand, Kantilal Pitambar Rane, P. Prabhu, Ahmed Mateen Buttar, Harikumar Pallathadka, Abhishek Raghuvanshi, Betty Nokobi Dugbakie

“…Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. …”
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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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MSO‐DETR: Metric space optimization for few‐shot object detection by Haifeng Sima, Manyang Wang, Lanlan Liu, Yudong Zhang, Junding Sun

“…Finally, the decoder processes the high‐level feature information as the decoding of the query object and detects objects by predicting their locations and corresponding task encodings. …”
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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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Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images by Wenbo Pang, Yi Ma, Huiyan Jiang, Qiming Yu

“…To address this issue, we perform a labels correction module with feature similarity by constructing feature centers for typical cells in each category. …”
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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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Anomaly detection in cropland monitoring using multiple view vision transformer by Xuesong Liu, Yansong Liu, He Sui, Chuan Qin, Yuanxi Che, Zhaobo Guo

“…Comparative analysis with state-of-the-art algorithms reveals the superiority of the proposed model. …”
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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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Detection of SSL/TLS protocol attacks based on flow spectrum theory by Shize GUO, Fan ZHANG, Zhuoxue SONG, Ziming ZHAO, Xinjie ZHAO, Xiaojuan WANG, Xiangyang LUO

“…Network attack detection plays a vital role in network security.Existing detection approaches focus on typical attack behaviors, such as Botnets and SQL injection.The widespread use of the SSL/TLS encryption protocol arises some emerging attack strategies against the SSL/TLS protocol.With the network traffic collection environment that built upon the implements of popular SSL/TLS attacks, a network traffic dataset including four SSL/TLS attacks, as well as benign flows was controlled.Considering the problems that limited observability of existing detection and limited separation of the original-flow spatiotemporal domains, a flow spectrum theory was proposed to map the threat behavior in the cyberspace from the original spatiotemporal domain to the transformed domain through the process of “potential change” and obtain the “potential variation spectrum”.The flow spectrum theory is based on a set of separable and observable feature representations to achieve efficient analysis of network flows.The key to the application of flow spectrum theory in actual cyberspace threat behavior detection is to find the potential basis matrix for a specific threat network flow under the condition of a given transformation operator.Since the SSL/TLS protocol has a strong timing relationship and state transition process in the handshake phase, and there are similarities between some SSL/TLS attacks, the detection of SSL/TLS attacks not only needs to consider timing context information, but also needs to consider the high-separation representation of TLS network flows.Based on the flow spectrum theory, the threat template idea was used to extract the potential basis matrix, and the potential basis mapping based on the long-short-term memory unit was used to map the SSL/TLS attack network flow to the flow spectrum domain space.On the self-built SSL/TLS attack network flow data set, the validity of the flow spectrum theory is verified by means of classification performance comparison, potential variation spectrum dimensionality reduction visualization, threat behavior feature weight evaluation, threat behavior spectrum division assessment, and potential variation base matrix heatmap visualization.…”
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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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iSeizdiag: toward the framework development of epileptic seizure detection for healthcare by Ashish Sharma, Ashish Sharma, Akshat Saxena, Mradul Agrawal, Kunal Kishor, Deepti Kaushik, Prateek Jain, Arvind R. Yadav, Manob Jyoti Saikia, Manob Jyoti Saikia

“…Different neurological disorders are represented as different waves on EEG records.MethodThis paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. …”
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A comprehensive survey on techniques, challenges, evaluation metrics and applications of deep learning models for anomaly detection by Mudita Kohli, Indu Chhabra

“…Analyzing network packets for identifying deviations from the standard behavior is called anomaly detection. Deep learning tools have emerged as a promising alternative over classical machine learning approaches due to their proficiency in feature modeling, appraise detection rate, and mirror cognitive development. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. …”
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Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules by Khalfalla Awedat, Masoud Alajmi, Mustafa Elfituri

“…This study presents a novel deep-learning-based approach to enhance fault detection in PV systems by customizing the Atrous Spatial Pyramid Pooling (ASPP) module within a U-Net architecture. …”
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