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Sharma, Deepak
- Compression Using Deflater Algorithm with Encryption using AES-256 of HEVC Videos for more Effective Transmission
Abstract Views :163 |
PDF Views:0
Authors
Affiliations
1 Department of CSE, SBSSTC, Ferozepur, Punjab, IN
1 Department of CSE, SBSSTC, Ferozepur, Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 22 (2016), Pagination: 126-133Abstract
A very high demand of the computer technology and internet technology, multimedia service has become a new interested area. But there are lots of problem like a huge amount of data, high speed play, bandwidth limitation. For enhancing the security and speed, HEVC-high efficiency video coding provides better compression with greater quality and low bandwidth usage. Several algorithms have been proposed for efficient and secure streaming of HEVC/HD video files. These algorithms are only useful for encrypting high resolution videos. In the proposed algorithm, a combination of AES-256 encryption algorithm and Deflator compression algorithm is used for efficient encrypted data delivery with compressed size. Experimental results proves that proposed algorithms gives better encryption and smarter compression of all type of multimedia files as compared to previous algorithms with minimum processing time.Keywords
Transmission, Deflater, Compression, Base 64 Encoder, Encryption, AES-256.Full Text
- Investigation of Slurry Erosion Behavior using Slurry Erosion Impact Test Rig
Abstract Views :89 |
PDF Views:0
Authors
Affiliations
1 Scholars of Mechanical Engineering Department, IET BHADDAL, Ropar (Punjab), IN
1 Scholars of Mechanical Engineering Department, IET BHADDAL, Ropar (Punjab), IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 25 (2017), Pagination: 230-236Abstract
In the industries, there are different types of materials used for the different kind of purposes & works. Materials are selected for various purposes according to their different kind of properties. But after long interval of time material get eroded due to erosion. Erosion is mainly caused by sliding & colliding action of solid, liquid & gaseous particles over the surface of component in service. Present work investigated the slurry erosion behavior of AISI304 Steel under different condition such, velocity, impact angle and concentration. Slurry erosion testing was carried out for the duration of 120 mins. Testing was carried out on laboratory developed apparatus. For proper interaction of various parameters and to analyze their influence statistical technique such as Taguchi l9 orthogonal array was used for Design of Experiment. Result obtained by the experimentation was further analyzed using Taguchi approach. Regression equations are modeled for modeling of the slurry erosion process of AISI 304 steel. It was found that AISI 304 Steel shown best resistance at impact angle of 90°. It was found that velocity has significant effect on the erosion rate of material.Keywords
Slurry Erosion, Material, Rig, Statistical Technique, Erosion Rating.Full Text
References
- Thakur Lalit, Arora Navneet, Introduction, A comparative study on slurry and erosion behavior of HVOF sprayed Wc-Co coatings
- https://en.wikipedia.org/wiki/Erosion_corrosion
- http://www.ansys-blog.com/erosion-fluid-dynamics-modeling/
- Sharma Mithlesh, Goyal D.K, Kumar naresh& Sharma Raman , Introduction, Effect of Velocity on Erosion Performance of 13Cr4Ni and H.V.O.F. Coatings 5. https://en.wikipedia.org/wiki/Slurry
- Disease Detection Using Soft Computing
Abstract Views :126 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, DAV University, Jalandhar, IN
2 Associate Professor, DAV University, Jalandhar, IN
1 Research Scholar, DAV University, Jalandhar, IN
2 Associate Professor, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 253-260Abstract
Disease detection using soft computing is an emerging field that utilizes various techniques from the domain of artificial intelligence and machine learning to accurately diagnose diseases. Soft computing techniques, such as neural networks, fuzzy logic, and genetic algorithms, are used to build intelligent systems that can analyze complex data and patterns to identify the presence of diseases. In this research paper author has put his efforts to explore the application of soft computing in the diagnosis of disease. Author choose fuzzy logic as the soft computing technique and explore the work done by various researchers for disease diagnosis using fuzzy logic. Author concluded that disease detection using soft computing is a promising area of research that has the potential to transform the field of healthcare. By harnessing the power of artificial intelligence and machine learning, we can improve the accuracy and efficiency of disease diagnosis, leading to better patient outcomes and a healthier society.Keywords
Soft Computing, Fuzzy Logic, Disease Diagnosis.Full Text
References
- .Ramya, R., & Palanisamy, V. (2018). A fuzzy logic-based approach for tuberculosis diagnosis. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 51-56. https://doi.org/10.1109/ctems.2018.8471944
- .Mustapha, A., Bakar, S. A., & Abu-Bakar, S. A. R. (2016). A fuzzy logic-based decision support system for breast cancer diagnosis. Journal of Medical Systems, 40(8), 183. https://doi.org/10.1007/s10916-016-0549-3
- .Marimuthu, R., & Venkatesan, P. (2015). A fuzzy logic-based approach for heart disease diagnosis. International Journal of Advanced Research in Computer Science and Software Engineering, 5(10), 280-283. http://ijarcsse.com/Before_August_2017/docs/papers/Volume_5/10_October2015/V5I10-0338.pdf
- .Naik, N. H., & Raja, K. B. (2015). A hybrid fuzzy logic and artificial neural network approach for diabetes diagnosis. International Journal of Applied Engineering Research, 10(10), 25231-25244. http://www.ripublication.com/ijaer15/ijaerv10n10_31.pdf
- .Zaidi, S. A., Khan, M. A., & Rizvi, S. M. M. (2014). A fuzzy expert system for the diagnosis of hepatitis B. Journal of Medical Systems, 38(12), 138. https://doi.org/10.1007/s10916-014-0138-1
- .Salleh, A. M. A., & Wahab, N. A. (2012). A fuzzy logic approach for the diagnosis of dengue fever. Procedia Engineering, 41, 1649-1655. https://doi.org/10.1016/j.proeng.2012.07.377
- .Sheikh, M. B. E., & Fadaei, K. I. (2012). A fuzzy expert system for the diagnosis of glaucoma. Journal of Medical Systems, 36(4), 2321-2327. https://doi.org/10.1007/s10916-011-9724-1
- .Sheikh, M. B. E., & Zarei, M. H. (2013). A fuzzy expert system for the diagnosis of prostate cancer. Journal of Medical Systems, 37(6), 9956. https://doi.org/10.1007/s10916-013-9956-y
- .Akinola, A. T., Adeyemo, A. O., & Soriyan, O. S. (2019). A fuzzy logic-based approach for the diagnosis of Alzheimer's disease. Journal of Medical Systems, 43(9), 290. https://doi.org/10.1007/s10916-019-1381-3
- . Sheikh, M. B. E., & Al-Jasser, H. F. (2012). A fuzzy expert system for the diagnosis of thyroid diseases. Journal of Medical Systems, 36(6), 3599-3605. https://doi.org/10.1007/s10916-012-9854-8
- . Ramya, R., & Palanisamy, V. (2016). A fuzzy logic-based approach for tuberculosis diagnosis. International Journal of Computer Applications, 146(7), 39-43.
- . Mustapha, A., Bakar, S. A., & Abu-Bakar, S. A. R. (2014). A fuzzy logic-based decision support system for breast cancer diagnosis. Expert Systems with Applications, 41(4), 1476-1482.
- . Marimuthu, R., & Venkatesan, P. (2013). A fuzzy logic-based approach for heart disease diagnosis. International Journal of Computer Applications, 75(16), 8-11.
- . Naik, N. H., & Raja, K. B. (2013). A hybrid fuzzy logic and artificial neural network approach for diabetes diagnosis. International Journal of Computer Applications, 77(10), 21-26.
- . Zaidi, S. A., Khan, M. A., & Rizvi, S. M. M. (2012). A fuzzy expert system for the diagnosis of hepatitis B. Journal of Medical Systems, 36(6), 3699-3710.
- . Salleh, A. M. A., & Wahab, N. A. (2015). A fuzzy logic approach for the diagnosis of dengue fever. Procedia Computer Science, 72, 245-252.
- . Sheikh, M. B. E., & Fadaei, K. I. (2013). A fuzzy expert system for the diagnosis of glaucoma. Journal of Medical Systems, 37(3), 9918.
- . Sheikh, M. B. E., & Zarei, M. H. (2013). A fuzzy expert system for the diagnosis of prostate cancer. Journal of Medical Systems, 37(5), 9957.
- . Akinola, A. T., Adeyemo, A. O., & Soriyan, O. S. (2014). A fuzzy logic-based approach for the diagnosis of Alzheimer's disease. Expert Systems with Applications, 41(6), 3065-3070.
- . Sheikh, M. B. E., & Al-Jasser, H. F. (2013). A fuzzy expert system for the diagnosis of thyroid diseases. Journal of Medical Systems, 37(4), 9934.