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Showing result 16 - 20 of 349 essays matching the above criteria.
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16. Unsupervised Anomaly Detection and Explainability for Ladok Logs
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Anomaly detection is the process of finding outliers in data. This report will explore the use of unsupervised machine learning for anomaly detection as well as the importance of explaining the decision making of the model. READ MORE
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17. Deep Learning-Based Anomaly Detection for Predictive Maintenance of Cold Isostatic Press
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Predictive maintenance is an automated technique that analyses sensor data from industrial systems to enable downtime planning. Available for this study is unlabelled data from sensors placed in proximity to hydraulic system outlets of a cold isostatic press. READ MORE
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18. Unsupervised Detection of Interictal Epileptiform Discharges in Routine Scalp EEG : Machine Learning Assisted Epilepsy Diagnosis
University essay from Uppsala universitet/Avdelningen Vi3Abstract : Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. READ MORE
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19. Industrial 3D Anomaly Detection and Localization Using Unsupervised Machine Learning
University essay from Linköpings universitet/DatorseendeAbstract : Detecting defects in industrially manufactured products is crucial to ensure their safety and quality. This process can be both expensive and error-prone if done manually, making automated solutions desirable. READ MORE
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20. Anomaly Detection with Machine Learning using CLIP in a Video Surveillance Context
University essay from Linköpings universitet/DatorseendeAbstract : This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. READ MORE