LAVID: A Lightweight and Autonomous Smart Camera System for Urban Violence Detection and Geolocation by Mohammed Azzakhnini, Houda Saidi, Ahmed Azough, Hamid Tairi, Hassan Qjidaa

“…Due to the advancement of artificial intelligence methods, many automatic video analysis tasks like violence detection have been studied from a research perspective, and are even beginning to be commercialized in industrial solutions. …”
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Application of the Genetic Algorithm in the Network Intrusion Detection System Using NSL-KDD Data by Naglaa Ibrahim, Hana Usman

“…For rules generation NSL-KDD Data Set is used which include, KDDTrain and KDDTest, 125973 and 22544 records respectively, each record  consists of 41 features and one class attribute for specifying   normal and abnormal connection (complete train and test data are used), In order to get rid of redundancy and inappropriate features Principal  Component Analysis (PCA) is used for selecting (5)  features. …”
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Non-Contact Laser Ultrasound Detection of Internal Gas Defects in Lithium-Ion Batteries by Dongxia Tang, Chenguang Xu, Guidong Xu, Sen Cui, Sai Zhang

“…Through both time-domain and frequency-domain analysis of the ultrasonic features, the results demonstrate that the signal amplitude attenuation characteristics of ultrasound in media with acoustic impedance mismatches can be used for precise detection and quantitative characterization of gas defect regions within the battery. …”
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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…K-means++ algorithm is employed to perform clustering analysis on YOLOv4 model prior frames in this paper, and smaller size prior frames are set to capture small face information to solve the missing detection problem of small face targets in scenes. …”
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Case report: Detection and treatment of a rare submucosal gland duct adenoma of the esophagus by Yu Zhou, Daoxing He, Zhigang Huang, Zhaoyi Chen, Zheng Zhou, Hui Duan, Jie Xu

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Incipient Fault Detection of Rolling Element Bearings Based on Deep EMD-PCA Algorithm by Huaitao Shi, Jin Guo, Zhe Yuan, Zhenpeng Liu, Maxiao Hou, Jie Sun

“…Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. …”
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Replica attack detection method for vehicular ad hoc networks with sequential trajectory segment by Yan Xin, Xia Feng

“…In terms of semi-supervised support vector machine, we establish a detection model using spatio-temporal trajectories of different identities as input samples, which include features of both conspiracy and non-conspiracy attack scenarios. …”
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Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning by Berk Özel, Muhammad Shahab Alam, Muhammad Umer Khan

“…In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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Abnormal Power Fluctuation Detection of Wind Turbines Based on Subband Processing and Correlation Coefficient by CHEN Gang, HU Kaikai, CHEN Yanan, ZHANG Jiayou, XIONG Wenhao

“…Wind turbine power signals are nonlinear and non-stationary signals, and their abnormal fluctuation Frequencies are uncertain, which makes it difficult to extract and detect abnormal power fluctuation features. This paper proposes a method for detecting abnormal power fluctuations of wind turbine based on subband processing and correlation coefficient. …”
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Evaluating YOLO Models for Efficient Crack Detection in Concrete Structures Using Transfer Learning by Muhammad Sohaib, Muzamal Arif, Jong-Myon Kim

“…This work introduces a comprehensive analysis of various YOLO models for detecting cracks in concrete structures, aiming to assist in the selection of an optimal model for future detection and segmentation tasks. …”
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Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal by Pragati Tripathi, M. A. Ansari, Tapan Kumar Gandhi, Rajat Mehrotra, Md Belal Bin Heyat, Faijan Akhtar, Chiagoziem C. Ukwuoma, Abdullah Y. Muaad, Yasser M. Kadah, Mugahed A. Al-Antari, Jian Ping Li

“…Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. …”
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Harnessing multimodal approaches for depression detection using large language models and facial expressions by Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier

“…This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.…”
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A Spatio-Temporal Attention Network With Multiframe Information for Infrared Small Target Detection by Donghui Liu, Wenlong Zhang, Zicheng Feng, Xiaoliang Sun, Rui Zhang, Yang Shang

“…Most algorithms focus solely on extracting features from the spatial domain. However, this approach often leads to suboptimal detection performance. …”
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Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection by Ilhan Firat Kilincer

“…In the last part of the study, the effect of the features in the CL2-IDS dataset on the classification is interpreted with SHapley Additive exPlanations (SHAP), an Explainable Artificial Intelligence (XAI) method. …”
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Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection by Ranpreet Kaur, Hamid GholamHosseini, Maria Lindén

“…An image segmentation network based on deep learning is then used to extract lesion regions for detailed analysis and calculate the optimized classification features. …”
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Dielectrophoresis-Enhanced Microfluidic Device with Membrane Filter for Efficient Microparticle Concentration and Optical Detection by Young-Ho Nam, Seung-Ki Lee, Jae-Hyoung Park

“…These results highlight the device’s potential for precise and efficient microparticle concentration and detection.…”
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Expanding and Interpreting Financial Statement Fraud Detection Using Supply Chain Knowledge Graphs by Shanshan Zhu, Tengyun Ma, Haotian Wu, Jifan Ren, Daojing He, Yubin Li, Rui Ge

“…These relationships also aid in the assessment and detection of financial fraud. Recent studies employing graph neural networks (GNNs) have demonstrated enhanced detection capabilities by integrating corporate financial features with supply chain relationships, surpassing traditional methods that rely solely on financial features. …”
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Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors by Javier de las Morenas, Lidia M. Belmonte, Rafael Morales

“…However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific focus on implementing an efficient and non-intrusive edge-based solution. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…This approach is commonly employed for spatial extraction of features, permitted the model to detect patterns that may indicate possible attacks. …”
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