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Murugan, J. Senthil
- Analyzing The Software Quality In Image Processing Software In Industry Using Machine Learning
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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, CyberReferences
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- A Deep Learning Based Analysis of Oil Spilled Images To Minimize Pollution in Marine Environment
Abstract Views :99 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Tagore Engineering College, India., IN
3 Department of Marine Engineering, AMET Deemed to be University, India., IN
1 Department of Computer Science and Engineering, Veltech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Tagore Engineering College, India., IN
3 Department of Marine Engineering, AMET Deemed to be University, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2914-2920Abstract
The rising demand for oil and increased shipping capacity are significant contributors to the pollution of the world seas and oceans that is caused by human activity. Oil spills on the world waterways are another major cause of this pollution. Because of the growing demand for oil and the capability of the maritime transport industry, oil spills on seas and oceans have become a significant source of pollution in recent years. It is of the utmost importance that oil spills are constantly monitored and that measures are taken to clean them up as quickly as is humanly possible. This is since oil spills can have devastating effects not only on the local ecosystem but also on the economies of states that are located along the shore. Because of the ongoing threats that are posed to marine life, biodiversity, and habitats, it is of the utmost importance to be able to keep a watch on oil spills from a distance, recognise them, and take action to clean them up. This is essential. In the past ten years, developments in remote sensing data collection, computing capability, cloud computing infrastructure, and cuttingedge SqueezeNet algorithms have led to significant advancements in oil spill detection. These developments have been responsible for most of the progress. These technological advancements have made it possible to identify oil spills more accurately.Keywords
Oil Spill, Shipping, Pollution, SqueezeNet.References
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