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Janaki, R.
- Design & Development of Automated Digital Indenter for Microhardness Measurement
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Authors
Affiliations
1 Dept. of Mechanical Engg., R.V. College of Engineering, IN
1 Dept. of Mechanical Engg., R.V. College of Engineering, IN
Source
Manufacturing Technology Today, Vol 9, No 2 (2010), Pagination: 13-16Abstract
Micro hardness testing of materials is widely used in industry, since, in many cases, hardness values have been found to correlate with other physical properties such as wear, fatigue and tensile strength. Currently, the dedicated hardness testers are too expensive with limited features. Thus this paper discusses the designing of a PC-based, real-time hardness measurement system and integrating the same with the existing Vision Inspection System for rapid dimensions measurement and online metal characterization, at the manufacturers' site.Keywords
Digital Indenter, Microhardness Measurement, Surface Roughness.
- Classification of Social Media Content and Improved Community Detection (C&CD) Using VGGNet Learning and Analytics
Abstract Views :78 |
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Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3181-3186Abstract
Social media platforms generate vast amounts of data, necessitating efficient content classification and community detection methods. This study addresses this challenge through the utilization of VGGNet analytics, a powerful deep learning architecture. We employed a two-step approach, beginning with VGGNet-based content classification to categorize social media posts. Subsequently, a community detection algorithm was applied to identify distinct user groups based on their interactions and content preferences. This research contributes an novel framework that seamlessly integrates VGGNet for content analysis and community detection, enhancing the understanding of user behavior in social media platforms. The proposed method aims to provide more accurate and insightful results compared to traditional approaches. Our experiments on diverse social media datasets demonstrate the effectiveness of the VGGNet-based approach. The content classification accurately assigns posts to relevant categories, while the community detection algorithm identifies cohesive user groups. The results highlight the potential for improved content recommendation systems and targeted marketing strategies.Keywords
Community Detection, Content Classification, Social Media, VGGNet Analytics, Deep Learning.References
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