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Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation.
Sensors (Basel)
December 2024
Division of Computer Science & Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea.
Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high computational and memory costs. In particular, it is often composed of real-time collected data that tends to be noisy, making preprocessing an essential step.
View Article and Find Full Text PDFNAILS: Normalized Artificial Intelligence Labeling Sensor for Self-Care Health.
Sensors (Basel)
December 2024
The Abdus Salam International Centre for Theoretical Physics (ICTP), 34151 Trieste, Italy.
Visual examination of nails can reflect human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking at the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling.
View Article and Find Full Text PDFEnhanced Intrusion Detection for ICS Using MS1DCNN and Transformer to Tackle Data Imbalance.
Sensors (Basel)
December 2024
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300132, China.
With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed.
View Article and Find Full Text PDFEnhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence.
Toxics
November 2024
The Dean's Office, Defense and Security University "Dr. Franjo Tuđman", 10000 Zagreb, Croatia.
Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades.
View Article and Find Full Text PDFBeyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management-A State-of-the-Art Narrative Review.
Int J Mol Sci
December 2024
Department of Cardiology & Preventive Cardiology Outpatient Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece.
The global rise in obesity underscores the need for effective weight management strategies that address individual metabolic and hormonal variability, moving beyond the simplistic "calories in, calories out" model. Body types-ectomorph, mesomorph, and endomorph-provide a framework for understanding the differences in fat storage, muscle development, and energy expenditure, as each type responds uniquely to caloric intake and exercise. Variability in weight outcomes is influenced by factors such as genetic polymorphisms and epigenetic changes in hormonal signaling pathways and metabolic processes, as well as lifestyle factors, including nutrition, exercise, sleep, and stress.
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