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Mukunthan, M.A.
- Analyzing The Software Quality In Image Processing Software In Industry Using Machine Learning
Abstract Views :149 |
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Authors
Affiliations
1 Jaya Sakthi Engineering, IN
2 Department of Computer Science and Engineering, Jaya Engineering College, IN
3 Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering, IN
1 Jaya Sakthi Engineering, IN
2 Department of Computer Science and Engineering, Jaya Engineering College, IN
3 Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2674-2678Abstract
The ability of manufacturing organizations to generate defect-free, high-quality products is critical to their long-term success in the marketplace. Despite increased product diversity and complexity, as well as the necessity for cost-effective manufacturing, it is frequently important to conduct a thorough and reliable quality examination. There are bottlenecks in the manufacturing process because there are so many checks done. In this paper, we aim to automate the process of quality control in industries using a machine learning classifier that monitors the manufactured product namely the central processing unit via imaging technique. Development of a model with high quality control improves the productivity and efficacy of production that rejects the malignant and defect pieces from the supply chain. The use of imaging systems or high-speed camera enables the improvement of software quality, where the analysis is built using high clarity input images. The data processed by these imaging systems are transferred to the cyber-physical system for secured access within an organization. The results of classification of input images and process via machine learning improves the efficacy of the model over various machine learning models.Keywords
Software quality, Image Processing, Machine Learning, Cyber
References
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- Novel Deep Intelligence Method for the Detection of Environmental Pollutants Using SAR Images on Oceans
Abstract Views :121 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2953-2958Abstract
The decline of marine ecosystems poses a substantial threat to the viability of local economies that are reliant on marine life for their continued survival. Artificial intelligence (AI) and machine learning (ML) are two of the several developing technologies that have the ability to address environmental challenges. In particular, ML may be used to better analyse the oceans, keep track of shipping, maintain track of debris in the ocean, unregulated and unreported (IUU) fishing, ocean mining, reduce coral bleaching, and stop the spread of marine diseases. In this paper, we examine the rising prospects and concerns related with the application of AI in the maritime environment, as well as their potential scalability for larger results, using some use-cases to illustrate our points. The results that were obtained when the model prediction was applied to random images are evidence that the model that was suggested provides better outcomes with fewer data points.Keywords
SAR, Ocean, Pollution, Deep Intelligence, Detection.References
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