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Medicinal Uses of Cashew(Anacardium occidentale):Review

Zarqa Iqbal, Muhammad Akram, Muhammad Muddasar Saeed

The cashew nut is essential for physical and emotional well-being. It is an energetic diet and the best food medication for many illnesses. Nuts protects from malignant growth, coronary illness, circulatory strain, and various degenerative infirmities connected to aging. It can be used for several medicinal purposes. Every part of cashew has some important medicinal properties. Cashew kernel contains proteins, carbohydrates, vitamins, and fats which help gain energy. Cashew nuts are used for many purposes like blood sugar, weight loss, cancer, cold and flu, aging, urinary disorders, digestive disorders, and bone relaxation. In comparing cashew with other nuts, Cashew kernels have good properties, including proteins, carbohydrates, fibers, and minerals, then almond, hazelnut, and walnuts. Cashew kernels contain the highest number of proteins (21%) when compared with Almonds (20.8%), Hazelnuts (15.6%), and Macadamia Nuts (60.9%). Cashew contain large amount of carbohydrate (22%) compared to other nuts like almonds, walnuts, etc. There are some strong benefits of Cashew for health. They boost the immune system, act as an antioxidant, remove gallstones, and beneficial for anemia. They are good sources of healthy fats that are necessary for our body to absorb fat-soluble vitamins.

Antihypertensive, coronary illness, protein, carbohydrate, fiber

Reference:

1. Allen, K. G., L. M. Klevay, et al. (1977). “The zinc and copper content of seeds and nuts.”
2. Bes‐Rastrollo, M., J. Sabaté, et al. (2007). “Nut consumption and weight gain in a Mediterranean cohort: the SUN study.” Obesity 15(1): 107-107.
3. Cardoso, B. R., G. B. S. Duarte, et al. (2017). “Brazil nuts: Nutritional composition, health benefits and safety aspects.” Food Research International 100: 9-18.
4. Cordeiro, T. A. “A REVIEW ON THE NUTRITIONAL ASPECTS AND HEALTH BENEFITS OF CASHEW (ANACARDIUMOCCIDENTALE).” Principal’s Message: 85.
5. Davis, C. D. (2003). “Low dietary copper increases fecal free radical production, fecal water alkaline phosphatase activity and cytotoxicity in healthy men.” The Journal of nutrition 133(2): 522-527.
6. de Oliveira, E. F., H. C. Paula, et al. (2014). “Alginate/cashew gum nanoparticles for essential oil encapsulation.” Colloids and Surfaces B: Biointerfaces 113: 146-151.
7. Dendena, B. and S. Corsi (2014). “Cashew, from seed to market: a review.” Agronomy for sustainable development 34(4): 753-772.
8. Allen, K. G., L. M. Klevay, et al. (1977). “The zinc and copper content of seeds and nuts.”
9. Bes‐Rastrollo, M., J. Sabaté, et al. (2007). “Nut consumption and weight gain in a Mediterranean cohort: the SUN study.” Obesity 15(1): 107-107.
10. Cardoso, B. R., G. B. S. Duarte, et al. (2017). “Brazil nuts: Nutritional composition, health benefits and safety aspects.” Food Research International 100: 9-18.
11. Cordeiro, T. A. “A REVIEW ON THE NUTRITIONAL ASPECTS AND HEALTH BENEFITS OF CASHEW (ANACARDIUMOCCIDENTALE).” Principal’s Message: 85.
12. Davis, C. D. (2003). “Low dietary copper increases fecal free radical production, fecal water alkaline phosphatase activity and cytotoxicity in healthy men.” The Journal of nutrition 133(2): 522-527.
13. Fetuga, B., G. Babatunde, et al. (1975). feedingstuff potential of cashewnut scrap kernel meal. Proceedings of the Conference on Animal Feeds of Tropical and Subtropical Origin.
14. Fraser, G. E. (1994). “Diet and coronary heart disease: beyond dietary fats and low-density-lipoprotein cholesterol.” The American journal of clinical nutrition 59(5): 1117S-1123S.
15. Hu, F. B., J. E. Manson, et al. (2001). “Types of dietary fat and risk of coronary heart disease: a critical review.” Journal of the American college of Nutrition 20(1): 5-19.
16. Layokun, S., A. Obawole, et al. (1986). “Investigation of cashew apple juice as a substrate for single cell protein production.” Journal of Food science 51(1): 237-238.
17. Maia, J. G. S., E. H. A. Andrade, et al. (2000). “Volatile constituents of the leaves, fruits and flowers of cashew (Anacardium occidentale L.).” Journal of food composition and analysis 13(3): 227-232.
18. Nagaraja, K. (1987). “Lipids of high-yielding varieties of cashew (Anacardium occidentale L.).” Plant Foods for Human Nutrition 37(4): 307-311.
19. Nayudamma, Y. and C. Koteswara Rao (1967). “Cashew testa: its use in leather industry.” Industrial Cashew Journal 4: 12-13.
20. Odunsi, A. (2002). “Effect of feeding reject cashew kernel meal on pre and early-laying performance of pullet.” Archivos de Zootecnia 51(196): 423-429.
21. Oliveira, N., R. Leal, et al. (2015). “The importance of the cashew nut (Anacardium occidentale L.) coat: a review.” American International Journal of Contemporary Scientific Research 2(8): 09-41.
22. Patil, P. (2017). “Indian cashew food.” Integr Food Nutr Metab 4(2): 1-5.
23. Rani, T. J., K. Prasad, et al. (2017). “Evaluation of antiobesity activity of ethanolic extract of cashew apple against high fat diet induced obesity in rodents.” The FASEB Journal 31: lb565-lb565.
24. Rico, R., M. Bulló, et al. (2016). “Nutritional composition of raw fresh cashew (Anacardium occidentale L.) kernels from different origin.” Food science & nutrition 4(2): 329-338.
25. Santos, B., G. Juliano, et al. (2013). “Modelling the acidification curve of tropical fruits: cashew apple, coconut, kiwi, mango, papaya, red guava, watermelon and yellow melon.” International Food Research Journal 20(2): 763.
26. Schirato, G. V., F. M. F. Monteiro, et al. (2006). “O polissacarídeo do Anacardium occidentale L. na fase inflamatória do processo cicatricial de lesões cutâneas.” Ciência Rural 36(1): 149-154.
27. Karthick, R., et al. “Overcome the challenges in bio-medical instruments using IOT–A review.” Materials Today: Proceedings (2020). https://doi.org/10.1016/j.matpr.2020.08.420
28. Karthick, R., et al. “A Geographical Review: Novel Coronavirus (COVID-19) Pandemic.” A Geographical Review: Novel Coronavirus (COVID-19) Pandemic (October 16, 2020). Asian Journal of Applied Science and Technology (AJAST)(Quarterly International Journal) Volume 4 (2020): 44-50.
29. Sathiyanathan, N. “Medical Image Compression Using View Compensated Wavelet Transform.” Journal of Global Research in Computer Science 9.9 (2018): 01-04.
30. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for Ht-MPSOC.” International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41. https://doi.org/10.1504/IJAIP.2021.114581
31. Sabarish, P., et al. “An Energy Efficient Microwave Based Wireless Solar Power Transmission System.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012013
32. Vijayalakshmi, S., et al. “Implementation of a new Bi-Directional Switch multilevel Inverter for the reduction of harmonics.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012026
33. Karthick, R., and M. Sundararajan. “Hardware Evaluation of Second Round SHA-3 Candidates Using FPGA (April 2, 2014).” International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014) 2.2.
34. Karthick, R., et al. “High resolution image scaling using fuzzy based FPGA implementation.” Asian Journal of Applied Science and Technology (AJAST) 3.1 (2019): 215-221.
35. P. Sabarish, R. Karthick, A. Sindhu, N. Sathiyanathan, Investigation on performance of solar photovoltaic fed hybrid semi impedance source converters, Materials Today: Proceedings, 2020, https://doi.org/10.1016/j.matpr.2020.08.390
36. Karthick, R., A. Manoj Prabaharan, and P. Selvaprasanth. “Internet of things based high security border surveillance strategy.” Asian Journal of Applied Science and Technology (AJAST) Volume 3 (2019): 94-100.
37. Karthick, R., and M. Sundararajan. “A novel 3-D-IC test architecture-a review.” International Journal of Engineering and Technology (UAE) 7.1.1 (2018): 582-586.
38. Karthick, R., and M. Sundararajan. “Design and implementation of low power testing using advanced razor based processor.” International Journal of Applied Engineering Research 12.17 (2017): 6384-6390.
39. Karthick, R., and M. Sundararajan. “A Reconfigurable Method for TimeCorrelatedMimo Channels with a Decision Feedback Receiver.” International Journal of Applied Engineering Research 12.15 (2017): 5234-5241.
40. Karthick, R., and M. Sundararajan. “PSO based out-of-order (ooo) execution scheme for HT-MPSOC.” Journal of Advanced Research in Dynamical and Control Systems 9 (2017): 1969.
41. Karthick, R. “Deep Learning For Age Group Classification System.” International Journal Of Advances In Signal And Image Sciences 4.2 (2018): 16-22. 42. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020). http://doi.org/10.4038/jnsfsr.v48i4.10340
43. Suresh, Helina Rajini, et al. “Suppression of four wave mixing effect in DWDM system.” Materials Today: Proceedings (2021). https://doi.org/10.1016/j.matpr.2020.11.545 44. M. Sheik Dawood, S. Sakena Benazer, N. Nanthini, R. Devika, R. Karthick, Design of rectenna for wireless sensor networks, Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2020.11.905
45. M. Sheik Dawood, S. Sakena Benazer, R. Karthick, R. Senthil Ganesh, S. Sugirtha Mary, Performance analysis of efficient video transmission using EvalSVC, EvalVid-NT, EvalVid, Materials Today: Proceedings,2021. https://doi.org/10.1016/j.matpr.2021.02.287

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Prognostic System for Heart Disease using Machine Learning: A Review

Mahesh Kumar, K.Premkumar, R.Senthilkumar

In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are fed into Classification methods of Machine Learning are been used to predict risk of heart disease.

HDPS, Machine Learning, Features Classification, Random forest algorithm, Dataset, Data-flow.

Reference:

1. Heart Disease Dataset – https://www.kaggle.com/c/heart-disease dated: Sept 2018
2. Effective heart disease prediction using hybrid machine learning techniques Senthilkumar Mohan, ChandrasegarThirumalai, Gautam Srivastava IEEE Access 7, 81542-81554, 2019
3. Heart Disease Prediction Using Machine Learning Algorithms A Singh, R Kumar – 2020 International Conference, 2020-ieeexplore.ieee.org
4. Heart disease prediction using machine learning techniques: a survey by VV Ramalingam, Ayantan Dandapath, M Karthik Raja International Journal of Engineering and Technology 7(2.8), 684-687, 2018
5. Prediction of heart disease using machine learning Aditi Gavhane, GouthamiKokkula, Isha Pandya, Kailas Devadkar 2018
6. Karthick, R., et al. “Overcome the challenges in bio-medical instruments using IOT–A review.” Materials Today: Proceedings (2020). https://doi.org/10.1016/j.matpr.2020.08.420
7. Karthick, R., et al. “A Geographical Review: Novel Coronavirus (COVID-19) Pandemic.” A Geographical Review: Novel Coronavirus (COVID-19) Pandemic (October 16, 2020). Asian Journal of Applied Science and Technology (AJAST)(Quarterly International Journal) Volume 4 (2020): 44-50.
8. Sathiyanathan, N. “Medical Image Compression Using View Compensated Wavelet Transform.” Journal of Global Research in Computer Science 9.9 (2018): 01-04.
9. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for Ht-MPSOC.” International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41. https://doi.org/10.1504/IJAIP.2021.114581
10. Sabarish, P., et al. “An Energy Efficient Microwave Based Wireless Solar Power Transmission System.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012013
11. Vijayalakshmi, S., et al. “Implementation of a new Bi-Directional Switch multilevel Inverter for the reduction of harmonics.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012026
12. Karthick, R., and M. Sundararajan. “Hardware Evaluation of Second Round SHA-3 Candidates Using FPGA (April 2, 2014).” International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014) 2.2.
13. Karthick, R., et al. “High resolution image scaling using fuzzy based FPGA implementation.” Asian Journal of Applied Science and Technology (AJAST) 3.1 (2019): 215-221.
14. P. Sabarish, R. Karthick, A. Sindhu, N. Sathiyanathan, Investigation on performance of solar photovoltaic fed hybrid semi impedance source converters, Materials Today: Proceedings, 2020, https://doi.org/10.1016/j.matpr.2020.08.39015. Karthick, R., A. Manoj Prabaharan, and P. Selvaprasanth. “Internet of things based high security border surveillance strategy.” Asian Journal of Applied Science and Technology (AJAST) Volume 3 (2019): 94-100.
16. Karthick, R., and M. Sundararajan. “A novel 3-D-IC test architecture-a review.” International Journal of Engineering and Technology (UAE) 7.1.1 (2018): 582-586.
17. Karthick, R., and M. Sundararajan. “Design and implementation of low power testing using advanced razor based processor.” International Journal of Applied Engineering Research 12.17 (2017): 6384-6390.
18. Karthick, R., and M. Sundararajan. “A Reconfigurable Method for TimeCorrelatedMimo Channels with a Decision Feedback Receiver.” International Journal of Applied Engineering Research 12.15 (2017): 5234-5241.
19. Karthick, R., and M. Sundararajan. “PSO based out-of-order (ooo) execution scheme for HT-MPSOC.” Journal of Advanced Research in Dynamical and Control Systems 9 (2017): 1969.
20. Karthick, R. “Deep Learning For Age Group Classification System.” International Journal Of Advances In Signal And Image Sciences 4.2 (2018): 16-22.
21. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020). http://doi.org/10.4038/jnsfsr.v48i4.10340
22. Suresh, Helina Rajini, et al. “Suppression of four wave mixing effect in DWDM system.” Materials Today: Proceedings (2021). https://doi.org/10.1016/j.matpr.2020.11.545
23. M. Sheik Dawood, S. Sakena Benazer, N. Nanthini, R. Devika, R. Karthick, Design of rectenna for wireless sensor networks, Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2020.11.905
24. M. Sheik Dawood, S. Sakena Benazer, R. Karthick, R. Senthil Ganesh, S. Sugirtha Mary, Performance analysis of efficient video transmission using EvalSVC, EvalVid-NT, EvalVid, Materials Today: Proceedings,2021. https://doi.org/10.1016/j.matpr.2021.02.287

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33-38

STC database for SQL Range Queries digital apps with Privacy Preserving

Sudhin Chandran, M.Abhijith, Priya

Businesses and people outsource database to realize helpful and low-cost applications and administrations. In arrange to supply adequate usefulness for SQL inquiries, numerous secure database plans have been proposed. In any case, such plans are helpless to protection leakage to cloud server. The most reason is that database is facilitated and handled in cloud server, which is past the control of information proprietors. For the numerical extend inquiry (“>”, “<”, etc.), those plans cannot give adequate protection security against viable challenges, e.g., security spillage of measurable properties, get to design. Besides, expanded number of questions will definitely spill more data to the cloud server. In this paper, we propose a two-cloud engineering for secure database, with a arrangement of crossing point conventions that give security conservation to different numeric-related extend questions. Security analysis shows that privacy of numerical information is strongly protected against cloud providers in our proposed scheme.

security spillage, cloud server, numerical information

Reference:

1. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
2. C. Wang, Q. Wang, K. Ren, N. Cao, and W. Lou, “Toward secure and dependable storage services in cloud computing,” IEEE Transactions on Services Computing, vol. 5, no. 2, pp. 220–232, 2012.
3. K. Xue and P. Hong, “A dynamic secure group sharing framework in public cloud computing,” IEEE Transactions on Cloud Computing, vol. 2, no. 4, pp. 459–470, 2014.
4. J. W. Rittinghouse and J. F. Ransome, Cloud computing: implementation, management, and security. CRC press, 2016.
5. D. Zissis and D. Lekkas, “Addressing cloud computing security issues,” Future Generation Computer Systems, vol. 28, no. 3, pp. 583–592, 2012.
6. H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches,” Wireless Communications and Mobile Computing, vol. 13, no. 18, pp. 1587–1611, 2013.
7. R. A. Popa, C. Redfield, N. Zeldovich, and H. Balakrishnan, “CryptDB: protecting confidentiality with encrypted query processing,” in Proceedings of the 23rd ACM Symposium on Operating Systems Principles. ACM, 2011, pp. 85–100.
8. C. Curino, E. P. Jones, R. A. Popa, N. Malviya et al., “Relational cloud: A database-as-a-service for the cloud,” 2011, http://hdl.handle.net/1721. 1/62241.
9. D. Boneh, D. Gupta, I. Mironov, and A. Sahai, “Hosting services on an untrusted cloud,” in Advances in Cryptology-EUROCRYPT 2015. Springer, 2015, pp. 404–436.
10. X. Chen, J. Li, J. Weng, J. Ma, and W. Lou, “Verifiable computation over large database with incremental updates,” IEEE Transactions on Computers, vol. 65, no. 10, pp. 3184–3195, 2016.
11. X. Chen, J. Li, X. Huang, J. Ma, and W. Lou, “New publicly verifiable databases with efficient updates,” IEEE Transactions on Dependable and Secure Computing, vol. 12, no. 5, pp. 546–556, 2015.
12. S. Benabbas, R. Gennaro, and Y. Vahlis, “Verifiable delegation of computation over large datasets,” in Annual Cryptology Conference. Springer, 2011, pp. 111–131.
13. W. Li, K. Xue, Y. Xue, and J. Hong, “TMACS: A robust and verifiable threshold multi-authority access control system in public cloud storage,” IEEE Transactions on Parallel & Distributed Systems, vol. 27, no. 5, pp. 1484–1496, 2016.
14. K. Xue, Y. Xue, J. Hong, W. Li, H. Yue, D. S. Wei, and P. Hong, “RAAC: Robust and auditable access control with multiple attribute authorities for public cloud storage,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 4, pp. 953–967, 2017.

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49-59

Identification of plant Syndrome using IPT

Madan Mohan M, Malathi V, VinothiniI N

Agricultural productivity is something on which Indian economy highly depends. This is the one of the reasons that disease detection in plants plays a vital role in agriculture field, as having disease in plants are unavoidable. If proper care is not taken in this area, then it causes serious effects on plants and due to which the overall agriculture yield will be affected. For instance, a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself if detected properly by identifying the symptoms of diseases can result in increased productivity. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers diseases classification techniques that can be used for plant leaf disease detection. Image segmentation is one of the method which will segment the raw images in to two or more clusters and the programmed algorithm will work fine in analyzing these clusters for disease classification and prediction of type of disease that a plant leaf gets affected

Image processing, Detection, Identification of plant leaf diseases, Convolutional neural network

Reference:

1. Konstantinos P. Ferentinos: Deep Learning models for plant disease detection and Diagnosis. Computers and electronics in ariculture(2018)
2. Vishal Mani Tiwari&Tarun Gupta “Plant Leaf Disease Analysis using Image Processing Technique with Modified SVM-CS Classifier”, 2017.
3. Pallavi.S. Marathe “Plant Disease Detection using Digital Image Processing and GSM” International Journal of Engineering Science and Computing, April 2017.
4. Xihai Zhang, (Member, Ieee), Yue Qiao, Fanfeng Meng, Chengguo Fan, And Mingming Zhang “Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks” in proceedings of IEEE June 26, 2018.
5. M.N. Abu Bakar, A.H. Abdullah, N. Abdul Rahim, H. Yazid, S.N. Misman and M.J. Masnan” Rice Leaf Blast Disease Detection Using Multi-Level Color Image Thresholding” in proceedings of Research Gate, Article· August 2018.
6. Rajneet Kaur and Manjeet Kaur, “A Brief Review on Plant DiseaseDetection using in Image Processing”, IJCSMC, Vol. 6, Issue. 2, February 2017.7. Sandesh Raut and Amit Fulsunge, “Plant Disease Detection in Image Processing Using MATLAB”, IJIRSET Vol. 6, Issue 6, June 2017.
8. K. Elangovan and S. Nalini “Plant Disease Classification Using Image Segmentation and SVM Techniques”, IJCIRV, Volume 13, Number 7, 2017.
9. Sonal P Patel and Arun Kumar Dewangan “A Comparative Study on Various Plant Leaf Diseases Detection and Classification” (IJSRET), Volume 6, Issue 3, March 2017.
10. R.Rajmohan and M.Pajany, “Smart paddy crop disease identification and management using deep convolution neural network & SVM classifier”, International journal of pure and applied mathematics, vol 118, no 5, pp. 255-264, 2017.
11. Wenjiang Huang, Qingsong Guan, JuhuaLuo, Jingcheng Zhang, Jinling Zhao, Dong Liang, Linsheng Huang, and Dongyan Zhang, “New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases”, IEEE journal of selected topics in applied earth observation and remote sensing,Vol. 7, No. 6, June 2014
12. Karthick, R., et al. “Overcome the challenges in bio-medical instruments using IOT–A review.” Materials Today: Proceedings (2020). https://doi.org/10.1016/j.matpr.2020.08.420
13. Karthick, R., et al. “A Geographical Review: Novel Coronavirus (COVID-19) Pandemic.” A Geographical Review: Novel Coronavirus (COVID-19) Pandemic (October 16, 2020). Asian Journal of Applied Science and Technology (AJAST)(Quarterly International Journal) Volume 4 (2020): 44-50.
14. Sathiyanathan, N. “Medical Image Compression Using View Compensated Wavelet Transform.” Journal of Global Research in Computer Science 9.9 (2018): 01-04.
15. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for Ht-MPSOC.” International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41. https://doi.org/10.1504/IJAIP.2021.114581
16. Sabarish, P., et al. “An Energy Efficient Microwave Based Wireless Solar Power Transmission System.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012013
17. Vijayalakshmi, S., et al. “Implementation of a new Bi-Directional Switch multilevel Inverter for the reduction of harmonics.” IOP Conference Series: Materials Science and Engineering. Vol. 937. No. 1. IOP Publishing, 2020. doi:10.1088/1757-899X/937/1/012026
18. Karthick, R., and M. Sundararajan. “Hardware Evaluation of Second Round SHA-3 Candidates Using FPGA (April 2, 2014).” International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014) 2.2.
19. Karthick, R., et al. “High resolution image scaling using fuzzy based FPGA implementation.” Asian Journal of Applied Science and Technology (AJAST) 3.1 (2019): 215-221.20. P. Sabarish, R. Karthick, A. Sindhu, N. Sathiyanathan, Investigation on performance of solar photovoltaic fed hybrid semi impedance source converters, Materials Today: Proceedings, 2020, https://doi.org/10.1016/j.matpr.2020.08.390
21. Karthick, R., A. Manoj Prabaharan, and P. Selvaprasanth. “Internet of things based high security border surveillance strategy.” Asian Journal of Applied Science and Technology (AJAST) Volume 3 (2019): 94-100.
22. Karthick, R., and M. Sundararajan. “A novel 3-D-IC test architecture-a review.” International Journal of Engineering and Technology (UAE) 7.1.1 (2018): 582-586.
23. Karthick, R., and M. Sundararajan. “Design and implementation of low power testing using advanced razor based processor.” International Journal of Applied Engineering Research 12.17 (2017): 6384-6390.
24. Karthick, R., and M. Sundararajan. “A Reconfigurable Method for TimeCorrelatedMimo Channels with a Decision Feedback Receiver.” International Journal of Applied Engineering Research 12.15 (2017): 5234-5241.
25. Karthick, R., and M. Sundararajan. “PSO based out-of-order (ooo) execution scheme for HT-MPSOC.” Journal of Advanced Research in Dynamical and Control Systems 9 (2017): 1969.
26. Karthick, R. “Deep Learning For Age Group Classification System.” International Journal Of Advances In Signal And Image Sciences 4.2 (2018): 16-22.
27. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020). http://doi.org/10.4038/jnsfsr.v48i4.10340
28. Suresh, Helina Rajini, et al. “Suppression of four wave mixing effect in DWDM system.” Materials Today: Proceedings (2021). https://doi.org/10.1016/j.matpr.2020.11.545
29. M. Sheik Dawood, S. Sakena Benazer, N. Nanthini, R. Devika, R. Karthick, Design of rectenna for wireless sensor networks, Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2020.11.905
30. M. Sheik Dawood, S. Sakena Benazer, R. Karthick, R. Senthil Ganesh, S. Sugirtha Mary, Performance analysis of efficient video transmission using EvalSVC, EvalVid-NT, EvalVid, Materials Today: Proceedings,2021. https://doi.org/10.1016/j.matpr.2021.02.287 .

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60-69

Year 2021

Medicinal Uses of Cashew(Anacardium occidentale):Review

Prognostic System for Heart Disease using Machine Learning: A Review

STC database for SQL Range Queries digital apps with Privacy Preserving

Identification of plant Syndrome using IPT

Automatic Face Mask Detection Using Python

Automatic Control for Home Applications using IoT

Modern Automated System for Traffic Signals

Android Based Diet Consultant using Rule Pattern-based algorithm

Cloud Based Bookmark Manager

Chat Robot for Medical Applications

AN INTRUSION DETECTION SYSTEM MODEL FOR DETECTING KNOWN AND INNOVATIVE CYBER ATTACKS USING SVM ALGORITHM

CONSUMER PERCEPTION TOWARDS APPLE PRODUCTS WITH REFERENCE TO TIRUCHIRAPPALLI DISTRICT

Machining Behaviour of Aluminium 6063 Strengthen with Aluminium Oxide and Chicken Bone Ash Produced by Modern Technique

Speech Based Controlled Techniques using NLP

AR Watch Try – On Application for Android Devices

A Multiple Sensor Data-Fusion for EFD Using IoT

IoT Based Intruder Prevention using Fogger