An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…They are trained in machine learning and deep neural network learning to detect attack patterns. There are important parameters for setting up a machine learning network, and choosing the right value for these parameters has a great impact on system accuracy. …”
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation by Nithika Vivek, Karthik Ramesh

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Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems by Ka-Kyung Kim, Joon-Seok Kim, Ieck-Chae Euom

“…However, with the advancement of technology and the digitalization of ICS, the attack space available to malicious actors has significantly expanded. Anomaly detection systems, initially implemented for detecting device faults or failures, have increasingly become the focus of research aimed at identifying attack patterns as cyberattack techniques become more sophisticated and intelligent. …”
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Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…The LADSVM approach excels at detecting anomalies in virtual machine logs characterized by strong sequential patterns and noise. …”
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Automatic eddy detection in the MIZ based on YOLO algorithm and SAR images by Nikita Sandalyuk, Eduard Khachatrian

“…The accurate identification of these eddy types is particularly important for collecting extensive statistical datasets, which are vital for understanding long-term oceanographic patterns and their impact on the Arctic climate. By fine-tuning of YOLOv8 model on an accurately labeled dataset, we achieved robust detection results with minimal training data. …”
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Validating a smart bed against polysomnography for sleep apnea detection by Farzad Siyahjani, Kostiantyn Kalenyk, Gary Garcia-Molina, Saeed Babaeizadeh

“…The algorithm aims to detect SDB events and estimate whether the apnea-hypopnea index (AHI) is ≥ 15, indicative of moderate to severe apnea. …”
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Anomaly detection method for power system information based on multimodal data by Liyue Chen, XuXiang Zhou, Peng Zhou, Xin Sun, SenSen Zheng

“…This study introduces a novel multimodal approach that integrates time-domain and frequency-domain data to improve anomaly detection accuracy and robustness. By leveraging this integration, our method captures both temporal patterns and spectral signatures, offering a more comprehensive analysis of system behavior—an advancement that significantly enhances detection performance compared to traditional techniques. …”
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Pre-Trained Language Model Ensemble for Arabic Fake News Detection by Lama Al-Zahrani, Maha Al-Yahya

“…Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. …”
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Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…Abstract Estimation and fraud detection in the case of insurance claims play a cardinal role in the insurance sector. …”
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Multiple Mobile Target Detection and Tracking in Small Active Sonar Array by Avi Abu, Nikola Mišković, Neven Cukrov, Roee Diamant

“…The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. …”
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An explainable feature selection framework for web phishing detection with machine learning by Sakib Shahriar Shafin

“…Specifically, we employ SHapley Additive exPlanations (SHAP) for global perspective and aggregated local interpretable model-agnostic explanations (LIME) to determine specific localized patterns. The proposed SHAP and LIME-aggregated FS (SLA-FS) framework pinpoints the most informative features, enabling more precise, swift, and adaptable phishing detection. …”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The problem statement of this research paper revolves around addressing challenges concerning feature selection and evolving spammer behavior and linguistic features, with the goal of devising an efficient model for spam detection. The primary objective of this endeavor was to identify the most efficacious subset of features and patterns for the task of spam detection. …”
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RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection by Aaron Alex Philip, Ziad Kobti

“… Anomaly Detection (AD) is crucial across various domains, as it identifies irregularities or unusual patterns that, if quickly addressed, can prevent financial and data losses, protect health, and prevent disasters. …”
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Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information by Chi Zhang, Jin-Woo Jung

“…Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. …”
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Improved YOLOv8 Model for Multi-type Lung Nodule Detection by BAO Qiangqiang, TANG Siyuan, LI Qingqian, WANG Naiyu, YANG Min, GU Yu, ZHAO Jinliang, GAO Jingbo, WANG Jiaxin, QU Yuhan

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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

“…The framework’s novel architecture enhances explainability by mapping detection outcomes to specific behavioral patterns while rigorous benchmarking across five public datasets (including Drebin, AndroZoo, and VirusShare) mitigates dataset bias and validates generalization. …”
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SMS spam detection using BERT and multi-graph convolutional networks by Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma

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A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“…Anomalies are subsequently identified through significant reconstruction errors, which serve as indicators of deviations from typical patterns. Experimental evaluations on the real-world PeMSD7 dataset demonstrate that the proposed TGNN + Autoencoder method improves detection accuracy by 17.33% compared to traditional methods, reduces false positives by 4.71%, and achieves a 6.02% higher F1-score relative to using TGNN or autoencoder individually. …”
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MicroRNAs in long COVID: roles, diagnostic biomarker potential and detection by Naomi-Eunicia Paval, Olga Adriana Căliman-Sturdza, Andrei Lobiuc, Mihai Dimian, Ioan-Ovidiu Sirbu, Mihai Covasa

“…Altered miRNA expression patterns during and after infection contribute to the pathogenesis of Long COVID. …”
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Sara Detection on Social Media Using Deep Learning Algorithm Development by M. Khairul Anam, Lucky Lhaura Van FC, Hamdani Hamdani, Rahmaddeni Rahmaddeni, Junadhi Junadhi, Muhammad Bambang Firdaus, Irwanda Syahputra, Yuda Irawan

“…The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. …”
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