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Probabilistic memory auto-encoding network for abnormal behavior detection in surveillance video.
Neural Netw
March 2025
Department of Computer Science University of California, Santa Barbara, 93106, California, US. Electronic address:
Abnormal behavior detection in surveillance video, as one of the essential functions in the intelligent surveillance system, plays a vital role in anti-terrorism, maintaining stability, and ensuring social security. Aiming at the problem of extremely imbalance between normal behavior data and abnormal behavior data, the probabilistic memory model-based network is designed to learn from the distribution of normal behaviors and guide the detection of abnormal behavior. An auto-encoding model is employed as the backbone network, and the gap between the predicted future frame and the real frame is used to measure the degree of abnormality.
View Article and Find Full Text PDFResearch Progress on the Application of MALDI-MSI in Hair Analysis.
Fa Yi Xue Za Zhi
December 2024
Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Mass spectrometry imaging (MSI) is an emerging high-tech "photography" method combining mass spectrometry and image visualization technologies, which can simultaneously monitor the spatial distribution of multiple molecules in biological samples. In recent years, the characteristics of this technology have been used to visually study the distribution of drugs in hair, among which matrix-assisted laser desorption ionization-mass spectrometry imaging(MALDI-MSI) technology has been most widely applied in hair analysis. Based on the principle of MALDI-MSI technology, this paper discusses the key technical elements of MALDI-MSI technology applied to hair analysis and reviews the research achievements of MSI in hair analysis.
View Article and Find Full Text PDFThe INNOVATE framework to foster ethics of artificial intelligence.
Recenti Prog Med
March 2025
Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, USA.
ChatGPT, the latest advancement in Artificial Intelligence (AI), represents one of the most advanced and rapidly evolving chatbot technologies to date. Its capability to provide swift and intelligent responses has garnered admiration from scientists and educators globally. Particularly, the healthcare sector stands to gain significantly from the integration of systems like ChatGPT, with benefits including enhanced productivity, reduced expenses, and improved patient outcomes.
View Article and Find Full Text PDFAn interpretable dimensional reduction technique with an explainable model for detecting attacks in Internet of Medical Things devices.
Sci Rep
March 2025
Faculty of Organization and Informatics, University of Zagreb, Varazdin, Croatia.
The security of Internet of Medical Things (IoMT) devices is crucial for ensuring the integrity and reliability of patients' medical data. These devices, operating over TCP and ICMP protocols, are highly susceptible to cyberattacks. While machine learning models can detect these attacks with acceptable accuracy, their operational mechanisms remain unclear, leaving the decision-making process of the models undefined.
View Article and Find Full Text PDFRice leaf disease classification using a fusion vision approach.
Sci Rep
March 2025
Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu, India.
Rice serves as a fundamental staple for a significant portion of the global population, playing an essential role in ensuring food security worldwide. However, the continuous threat of various diseases risks both yield and quality. Detecting these diseases at an early stage is very important for effective management of these risks.
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