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Pramodhini, R.
- Image and Video Anomaly Detection Using AI Based Deepanomaly Detectors
Abstract Views :52 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, IN
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
3 Department of Mechanical Engineering, Pravara Rural Engineering College, IN
4 Department of Information Technology, M. Kumarasamy College of Engineering, IN
5 Department of Computer Science and Information Technology, Jazan University,, SA
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, IN
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
3 Department of Mechanical Engineering, Pravara Rural Engineering College, IN
4 Department of Information Technology, M. Kumarasamy College of Engineering, IN
5 Department of Computer Science and Information Technology, Jazan University,, SA
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3161-3167Abstract
In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.Keywords
Anomaly Detection, Deep Learning, Image Analysis, Computer Vision, Video ProcessingReferences
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- Y. Hao, X. Wang and X. Gao, “Spatiotemporal Consistency-Enhanced Network for Video Anomaly Detection”, Pattern Recognition, Vol. 121, pp. 1-12, 2022.
- A. Berroukham and I. Boulfrifi, “Deep Learning-Based Methods for Anomaly Detection in Video Surveillance: A Review”, Bulletin of Electrical Engineering and Informatics, Vol. 12, No. 1, pp. 314-327, 2023.
- Y. Liu, J. Liu, J. Lin, M. Zhao and L. Song, “Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection”, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, No. 5, pp. 2498-2502, 2022.
- Y. Liu, J. Liu, J. Lin, M. Zhao and L. Song, “Amp-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System”, IEEE Transactions on Industrial Informatics, Vol. 87, No. 2, pp. 1-13, 2023.
- T. Ganokratanaa and N. Sebe, “Video Anomaly Detection using Deep Residual-Spatiotemporal Translation Network”, Pattern Recognition Letters, Vol. 155, pp. 143-150, 2022.
- G. Wang, Y. Wang, J. Qin, D. Zhang and D. Huang, “Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles”, Proceedings of European Conference on Computer Vision, pp. 494-511, 2022.
- W. Liu, S. Shan and X. Chen, “Diversity-Measurable Anomaly Detection”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12147-12156, 2023.
- R. Raja and D.K. Saini, “Analysis of Anomaly Detection in Surveillance Video: Recent Trends and Future Vision”, Multimedia Tools and Applications, Vol. 82, No. 8, pp. 12635-12651, 2023.
- J. Fioresi and M. Shah, “Ted-Spad: Temporal Distinctiveness for Self-Supervised Privacy-Preservation for Video Anomaly Detection”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13598-13609, 2023.
- Q. Zhang, G. Feng and H. Wu, “Surveillance Video Anomaly Detection via Non-Local U-Net Frame Prediction”, Multimedia Tools and Applications, Vol. 81, No. 19, pp. 27073-27088, 2022.
- D.R. Patrikar and M.R. Parate, “Anomaly Detection using Edge Computing in Video Surveillance System”, International Journal of Multimedia Information Retrieval, Vol. 11, No. 2, pp. 85-110, 2022.
- A. Barbalau, J. Dueholm, B. Ramachandra and M. Shah, “SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection”, Computer Vision and Image Understanding, Vol. 229, pp. 1-16, 2023.
- W. Wang, F. Chang and C. Liu, “Mutuality-Oriented Reconstruction and Prediction Hybrid Network for Video Anomaly Detection”, Signal, Image and Video Processing, Vol. 16, No. 7, pp. 1747-1754, 2022.
- Swarm Intelligence Embedded Data Mining for Precision Agriculture Advancements
Abstract Views :42 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, IN
2 Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, IN
3 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
4 Department of Computer Applications, IFTM University, IN
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, IN
2 Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, IN
3 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
4 Department of Computer Applications, IFTM University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3218-3223Abstract
The present study investigates the potential of Swarm Intelligence (SI) in driving breakthroughs in Precision Agriculture (PA). It focuses on the research of mining techniques to uncover novel insights and developments in the field of PA. Social informatics (SI) is an academic discipline that focuses on the examination of collective behaviour within both herbal and synthetic structures. In order to gather, analyse, and synthesise information, SI utilises self-sufficient mobile devices known as Autonomous Mobile Agents (AMAs). These entities refer to robotic and computational frameworks that engage in mutual interaction, facilitating the examination of collective intelligence. This essay examines the potential impact of utilising the System of International Units (SI) on enhancing the accuracy and precision of commodity production and control in the field of production agriculture (PA). It also highlights the existing advancements that have been achieved in this regard. This analysis examines possible uses of Swarm Intelligence in the Public Administration (PA) industry, as well as the challenges that need to be solved in order to enhance the efficiency and accuracy of PA operations.Keywords
Swarm Intelligence, Embedded Data Mining, Precision Agriculture, Machine Learning, Artificial Intelligence, Crop Yield.References
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- G.P. Obi Reddy and G. Ravindra Chary, “Applications of Geospatial and Big Data Technologies in Smart Farming”, Proceedings of International Conference on Smart Agriculture for Developing Nations: Status, Perspectives and Challenges, pp. 15-31, 2023.
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- N.A. Farooqui and R. Mehra, “IOT based Automated Greenhouse using Machine Learning Approach”, International Journal of Intelligent Systems and Applications in Engineering, Vol. 10, No. 2, pp. 226-231, 2022.
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- S. Selvi and V. Saravanan, “Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network”, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.
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