An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM by Zhen Chen, ZhenWan Li, Jia Huang, ShengZheng Liu, HaiXia Long

“…The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. …”
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AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction by Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Ali, Orhan Unal, Shahriar Faghani

“…In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. …”
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Sensitivity of the particle size determined inline by Photon Density Wave Spectroscopy with respect to sample properties and measurement settings during emulsion copolymerization r... by Usue Olatz Aspiazu, Sebastian Zimmermann, Marvin Münzberg, Jose Ramon Leiza, Maria Paulis

“…In this work, a sensitivity analysis of the PDWS results with respect to relevant sample properties and measurement settings is carried out. It is investigated how the distances between the emission and detection fibers together with the laser modulation frequencies, and uncertainties of the input parameters (refractive index, density, and solids content of the dispersed phase) needed for data analysis affect the reduced scattering coefficients and particle diameters obtained from inline PDWS measurements. …”
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Time Series Foundation Model for Improved Transformer Load Forecasting and Overload Detection by Yikai Hou, Chao Ma, Xiang Li, Yinggang Sun, Haining Yu, Zhou Fang

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Enabling real-time road anomaly detection via mobile edge computing by Zengwei Zheng, Mingxuan Zhou, Yuanyi Chen, Meimei Huo, Dan Chen

“…We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

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FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection by Xiuxian Zhang, Hongwei Zhao, Weishan Zhang, Shaohua Cao, Haoyun Sun, Baoyu Zhang

“…In response to this trend, federated time series anomaly detection enables collaborative analysis across distributed sensing nodes without exposing raw data. …”
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Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities by Denis Leite, Emmanuel Andrade, Diego Rativa, Alexandre M. A. Maciel

“…Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). …”
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The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review by Luis B Elvas, Ana Almeida, Joao C Ferreira

“…The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. …”
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Applying EQTransformer to laboratory earthquakes: detecting and picking acoustic emissions with machine learning by Jack Sheehan, Qiushi Zhai, Lindsay Yuling Chuang, Timothy Officer, Yanbin Wang, Lupei Zhu, Zhigang Peng

“…In both cases, EQT performed within the standards advertised for EQT on earthquake data and with similar precision to MultiNet. Our results indicate that the EQT model pre-trained using global seismic data can be directly applied to accurately pick AE events in laboratory settings, with robust performance across multiple recording channels. …”
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PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography by Erdem Yanar, Fırat Hardalaç, Kubilay Ayturan

“…Additionally, comparative experiments were conducted using tabular clinical data from over 10,000 patients, enabling a direct evaluation of image-based and structured-data-based classification pipelines. …”
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Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition by Fengqin Tang, Xiaozong Wang, Xuejing Zhao, Chunning Wang

“…Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. …”
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YOLOv12 for Human Object Detection in Real-time Video Surveillance Systems by Yohanes Bowo Widodo, Sondang Sibuea, Rano Agustino

“…With this approach, the system can generate relevant information for rapid decision-making, such as monitoring human behavior in public settings and detecting suspicious activity. The analysis of this study highlights the significant potential of YOLO in improving real-time video surveillance systems and demonstrates that more accurate object detection capabilities can improve overall public safety. …”
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BlaOXA-48 Genotypic Detection of Carbapenem Resistance in Isolates of Pseudomonas Aeruginosa by Shaista Bakhat, Yasmeen Taj, Faisal Hanif, Saman Nadeem

“…Data was statistically analyzed by SPSS version 23.0. …”
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Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT by Mateusz Koziński, Doruk Oner, Jakub Gwizdała, Catherine Beigelman-Aubry, Pascal Fua, Angela Koutsokera, Alessio Casutt, Argyro Vraka, Michele De Palma, John-David Aubert, Horst Bischof, Christophe von Garnier, Sahand Jamal Rahi, Martin Urschler, Nahal Mansouri

“…Deep neural networks (DNNs) have not previously been used for BOS detection. This study aims to train a DNN to detect BOS in CT scans using an approach tailored for low-data scenarios. …”
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Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends by Rajashree Nambiar, Ranjith Bhat, Balachandra Achar H V

“…However, challenges remain in data scarcity, class imbalance, and generalizability to clinical settings. …”
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Cross-lingual hate speech detection using domain-specific word embeddings. by Ayme Arango Monnar, Jorge Perez Rojas, Barbara Polete Labra

“…THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Hate speech detection in online social networks is a multidimensional problem, dependent on language and cultural factors. …”
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A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection by Sainag Nethala, Pronoy Chopra, Khaja Kamaluddin, Shahid Alam, Soltan Alharbi, Mohammad Alsaffar

“…This research not only underscores the potential of our proposed approach in malware detection but also sets a foundation for future advancements. …”
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Swin-PSO-SVM: A Novel Hybrid Model for Monkeypox Early Detection by Hager Saleh, Sherif Mostafa, Alshimaa H. Ismail, Sumit Srivastava, Saeed Hamood Alsamhi

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