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Facial Expression Recognition based Restaurant Scoring System Using Deep Learning
Mr. Anumula Srinivas.
Pages: 1-7 | First Published: 05 Aug 2022
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Abstract
Food plays an important role in every human being’s life. Taking this as major objective several people are coming up with numerous new restaurants. Many of these restaurants are with less or no staff. Because of this reason, the rating of most of the restaurants is inaccurate. To overcome this problem and give a practical and exact rating to restaurants, this paper presents a Facial Expression Recognition-based Restaurant Scoring System with a pre-trained CNN model. By this model, customers can rate the food as well as the environment of the restaurants in three different expressions (Satisfied, Neutral, Disappointed). For implementing and using this system, we consider an application and server.
Keywords: Automated restaurants, convolutional neural network, facial expression, restaurant scoring, unmanned restaurants

REFERENCES
1. Hassansain Saeed, Ali Shouman, Mais Elfar, Mostafa Shabka, Shikharesh Majumdar, and Chung Horng-Lung, “Near- field communication sensors and cloud- based smart restaurant management system,” in Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 686-691, 2016.
2. Florian Schroff, Dmitry Kalenichenko, and James Philbin, “FaceNet: a unified embedding for face recognition and clustering,” n Proceedingsof the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815-823, 2015.
3. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretti, and Hartwig Adam, “MobileNets: efficient convolutional neural networks for mobile vision applications, ” arXiv preprint arXiv:1704.04861, 2017
4. Janne Tommola, Pedram Ghazi, Bishwo Adhikara, and Heikki Huttunen, “Real-time system for facial analysis,” in Proceedings of the 7th European Workshop on Visual Information Processing (EUVIP’18), 2018.
5. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
6. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
7. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.

8. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
9. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
10. Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
11. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
12. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
13. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
14. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
15. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
16. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
17. Ardra, S., & Viswanathan, A. (2012). A Survey on Detection and Mitigation of Misbehavior in Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).

Forewarn Traffic Signal with Ambulance Detection By GPS and Light Standard Signal Using Machine Learning
Dr. N. Vinaya Kumari
Pages: 8-14 | First Published: 05 Aug 2022
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Abstract
Imagine being stuck in a traffic jam, it’s pretty aggravating and all of a sudden you hear a siren of an ambulance and everyone around us start making space for the ambulance to pass and relived to reach its destination, but it is unfeasible in every circumstance. Ambulance services are one of the mainly affected services in traffic jams. Let’s talk about few statistics, according to a report published by Times of India about 146,133 people were killed in road accidents in India in the year 2016. Unfortunately, about 30% of deaths are caused due to delayed ambulance. Another Indian government data shows more than 50% of heart attack cases reach hospital late. Providing prompt and effective emergency health services by the ambulances is a challenge in country like India. So, the main core is to make sure that ambulances should be prime concerned during traffic jams and help them reach their destined hospitals instantaneously. The exercise behind this is whenever the ambulance finds a nearby traffic signal within the radius of 500 metres, it will immediately send the request to the control room for checking the current status of the signal in which the ambulance has to cross. What it takes is that mainly we need to train an SVM classifier which is a machine learning module on extracted features to differentiate between Vehicle and Non-Vehicle objects. For feature extraction to have a robust feature set and to increase accuracy level we will be using Histogram of Oriented Gradients (HOG). Using this we should be able to get further details of vehicles like their colour. Scikit-image python library provides us with the necessary API for calculating HOG feature. Once we have the prediction model, it’s time to use it on our test images. Prediction model will be applied in a special technique called Sliding Windows. By Eliminating false positives, we should be able to identify the Ambulance. Now we improve traffic clearance by also using street lights for detecting the approach of ambulance by placing lightweight detector CSL-YOLO on street lights and train Machine Learning module to identify the ambulance and share its location with control room to intimate its approach. Now when it is within 500 meters, we display ambulance symbol onto traffic lights and instruct the citizens to move towards the right allowing the free space on the left for the ambulance to reach the destination. By this we can conclude that this development will help in management of services and reduces risk of life.

References 
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10. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
11. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic 

approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
12. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
13. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
14. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
15. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
16. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).

Image-Based Plant Disease Detection by Comparing Deep Learning and Machine Learning Algorithms
Dr A. Vishwanath
Pages: 15-21 | First Published: 05 Aug 2022
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Abstract
Plant diseases area unit the most issue two-faced in agriculture. As population can increase, the assembly of plants in addition can increase and due to plant diseases it's going to have a control on the assembly of food.The traditional methodology used for illness detection is knowledgeable visual observation. but it's very sophisticated to go look out the illness manually as a result of the time interval and knowledge of the plant's diseases. So, it had been necessary to develop a system that detected the illness in less time and value effective manner.We discuss the employment of machine learning and deep learning to sight diseases in plants automatically.Using a public dataset of fifty four,306 photos of pathological and healthy plant leaves collected below controlled conditions, we have a tendency to tend to coach a deep convolutional neural network to identify fourteen crop species and twenty six diseases (or absence thereof). The trained model achieves academic degree accuracy of 9ty nine.35% on a held-out take a glance at set, demonstrating the practicability of this approach. Overall, the approach of coaching job deep learning models on additional and additional large and publicly out there image datasets presents a clear path toward smartphone-assisted illness identification on a huge world scale.
Keywords: machine learning, plant diseases, deep learning

References
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3. Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. "Using deep learning for image-based plant disease detection." Frontiers in plant science 7 (2016): 1419.
4. Fujita, E., et al. "A practical plant diagnosis system for field leaf images and feature visualization." International Journal of Engineering & Technology 7.4.11 (2018): 49-54. classification." IEEE Transactrions on systems, man, and cybernetics 6 (1973):610-612.

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8. Haykin, simon. Neural networks: a Comprehensive foundation. Prentice Hall PTR, 1994.
9. Szegedy, Christian et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
10. Duan, Kai-Bo, and S. Sathiya Keerthi. "Which is the best multiclass SVM method? An empirical study." International workshop on multiple classifier systems. Springer, Berlin, Heidelberg, 2005.
11. Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009.
12. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
13. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
14. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
15. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
16. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
17. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016) 18. S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar “Semantic Risk Analysis Model Cancer Data Prediction”, Journal on Science Engineering and Technology, Vol 5 Issue No 2 2018
19. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
20. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.

Implementation of Efficient Biometric-Based Access for Secured Cloud Services
Mr. G. Vikram
Pages: 22-31 | First Published: 05 Aug 2022
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Abstract
In our data-driven culture, the need for remote data storage and computing services has grown exponentially, requiring secure access to such data and services. This study proposes a new biometric-based authentication system for secure access to remote (cloud) servers. The proposed approach treats the user's biometrics as confidential credentials. It then uses the user’s biometric data to generate a unique ID and uses it to generate the user's private key. In addition, we present an efficient way to generate session keys for secure messaging between two interlocutors using two biometric templates. That is, you don't have to store your private and session keys somewhere. The proposed approach follows formal security analysis using detailed RealorRandom (ROR) model-based formal security analysis, informal (unmathematical) security analysis, and widely accepted automated Internet security verification., Can withstand multiple known attacks against (passive / active) attackers. Protocol and application (AVISPA) tools. Finally, numerous and comparative studies have shown the effectiveness and usefulness of the proposed approach.
Keywrods: Authentication, biometric-based security, cloud service access, session key.

References 
1. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari and Dr. S. Sivaprakash” Improve Design and Analysis of Friend-to-Friend Content Dissemination System”Vol 3 Issue No 3 2021 DOI: http:// dx.doi.org/10.35338/EJASR.2021.3301
2. C. Neuman, S. Hartman, K. Raeburn, “The kerberos network authentication service (v5),” RFC 4120, 2005.
3. “OAuth Protocol.” [Online]. Available: http://www.oauth.net/
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5. G. Wettstein, J. Grosen, and E. Rodriguez, “IDFusion: An open architecture for Kerberos based authorization,” Proc. AFS and Kerberos Best Practices Workshop, June 2006.
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7. B. Neuman and S. Stubblebine, “A note on the use of timestamps as nonces,” Oper. Syst. Rev., vol. 27, no. 2, pp. 10–14, 1993.
8. J. Astorga, E. Jacob, M. Huarte, and M. Higuero, “Ladon :endto-end authorisation support for resource-deprived environments,” IET Infomration Security, vol. 6, no. 2, pp. 93–101, 2012.
9. S. Zhu, S. Setia, and S. Jajodia, “LEAP: efficient security mechanisms for large-scale distributed sensor networks,” Washington D.C., USA, October 2003, pp. 62–72.
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11. P. Kaijser, T. Parker, and D. Pinkas, “SESAME: The solution to security for open distributed systems,” Computer Communications, vol. 17, no. 7, pp. 501–518, 1994.
12. G. Wettstein, J. Grosen, and E. Rodriguez, “IDFusion: An open architecture for Kerberos based 

authorization,” Proc. AFS and Kerberos Best Practices Workshop, June 2006.
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15. O. Althobaiti, M. Al-Rodhaan, and A. Al-Dhelaan, “An efficient biometric authentication protocol for wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 2013, pp. 1–13, 2013, Article ID 407971, http://dx.doi.org/ 10.1155/2013/407971.
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18. M. Park, H. Kim, and S. Lee, “Privacy Preserving Biometric-Based User Authentication Protocol Using Smart Cards,” in 17th International Conference on Computational Science and Engineering, Chengdu, China, 2014, pp. 1541–1544.
19. P. K. Dhillon and S. Kalra, “A lightweight biometrics based remote user authentication scheme for IoT services,” Journal of Information Security and Applications, vol. 34, pp. 255 – 270, 2017.
20. S. D. Kaul and A. K. Awasthi, “Security Enhancement of an Improved Remote User Authentication Scheme with Key Agreement,” Wireless Personal Communications, vol. 89, no. 2, pp. 621–637, 2016.
21. D. Kang, J. Jung, H. Kim, Y. Lee, and D. Won, “Efficient and Secure Biometric-Based User Authenticated Key Agreement Scheme with Anonymity,” Security and Communication Networks, vol. 2018, pp. 1–14, 2018, Article ID 9046064, https://doi.org/10.1155/2018/9046064.
22. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
23. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
24. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
25. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
26. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
27. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
28. Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
29. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
30. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
31. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
32. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.

A Study on the Role of Ecopreneurship in Sustainable Development
Dr. R. Sethu Ravi
Pages: 29-33 | First Published: 05 Aug 2022
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ABSTRACT
Our environment faces lot of environmental problems, it is clear that past strategies used to address these challenges have failed to prevent environmental degradation. It is therefore time to pay attention to the role that entrepreneurs can play in solving our environmental problems. Ecopreneurs can help preserve our ecosystems, counteract climate change, improve fresh water supply, maintain biodiversity, and reduce environmental degradation and deforestation. This paper focuses on the role of entrepreneurship in sustainable development.

Keywords: Ecopreneurship, sustainable development. 

Received: 11th July 2019         Accepted: 15th July 2019    Published: 25th August 2019                                                 
  

REFERENCES

  1.  A Framework for ecopreneurship – Shin, Rudy (Professor at IIM Ahmadabad) 

  2. Larson, A.L. (2000) „Sustainable Innovation through an Entrepreneurship Lens‟, Business Strategy and the Environment.  

  3. Lober, D.J. (1998) „Pollution Prevention and Corporate Entrepreneurship‟, Journal of Organizational Change Management.  

  4. Pastakia, A. (1998) „Grassroots Ecopreneurs: Change Agents for a Sustainable Society‟, Journal of Organizational Change Management 

  5. Tibor, T., and I. Feldman (1996) ISO 14000: A Guide to the New Environmental Management

  6. Standards (Chicago: Irwin Professional).

  7. Timmons, J. (1986) „Growing Up Big‟, The Art and Science of Entrepreneurship.

  8. Wiklund, J. (1999) „The Sustainability of the Entrepreneurial Orientation–Performance

  9. Relationship‟, Entrepreneurship: Theory and Practice.

  10. Cobb, John B. (1998). Can Corporations assume responsibility for the environment?

  11. Volery, Thierry. (2006). Ecopreneurship: rationale, current issues and futures challenges.

  12.  www.researchgate.net

  13. www.businessweek.com 

  14. www.businessethics.com