Journal Vol – 15 No -8, August 2020

Abstract:

The present paper deals with the comparison of output performance of fuzzy Proportional plus Derivative controller with conventional controllers. Fuzzy PD controller having 2 fuzzy sets for everyi/p variable and 3 fuzzy sets for o/p variable in the universe of discourse. Mathematical input-output relationships of simple fuzzy Proportional plus Derivative controller is developed via arbitrary membership functions for fuzzification, Zadeh AND operation for the evaluation of antecedent part of the rules and centroid method for defuzzification. Computer simulations show the effectiveness of Fuzzy Proportional plus Derivative controllers over the conventional controllers for time delay and nonlinear systems by choosing Triangular and Trapezoidal membership functions as input and output fuzzy sets. As a case study P.M.D.C. Servo system with saturation nonlinearity is considered with load disturbance and without load disturbance. MATLAB environment developed results are added to show the importance of the fuzzy controllers for P.M.D.C. Servo system with saturation nonlinearity with and without load disturbance using Triangular and Trapezoidal membership functions as input and output fuzzy sets.   

Keywords:

Analytical structures,fuzzification,defuzzification,fuzzy Proportional plus Derivative controllers,membership functions,MATLAB/simulink,

Refference:

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Abstract:

Today, in the technologized generation the utilization of smart computing devices has been increasing with a rapid pace in every walk of life, such as the adoption of smart watches, fitness bands, diabetic actuators, automatic machines and digital medical equipment for personal and organizational activities.In all these areas from personal to organizational and medical to satellites, the networking of devices and data transfer plays a key role. The autonomous networked computing system, that connects the physical and software components together to access, analyze and process the data for computing, communicating through networking is known as Cyber Physical System (CPS). When these systems used in different areas, they access and process a voluminous data called big data. As the big data is increasing in a large volume day by day, it has become challenging to handle such gigantic data in Cyber Physical System. So, there evolved a need to develop different tools and techniques to handle the big data in various Cyber Physical Systems. Focus of this review is to present the various tools and techniques used to manage big data in heterogeneous Cyber Physical Systems,in addition to this, it also briefs the growth and applications of Cyber Physical System.

Keywords:

Big data,Cyber Physical System,Data Analytics,

Refference:

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Abstract:

Cognitive Radio Networks (CRNs) are new type of communication networks which solves the problems of spectrum utilization and channel assignments in an important manner. Cognitive users are two types i.e Primary and Secondary users. Secondary users use the unused spectrum which is not used by the primary user i.e unlicensed users uses the licensed bandwidth with their permission. Hence, Trust and Reputation management of secondary users has gained more attention. Mainly Reputation management models are required for CRNs to clearly identify whether the Secondary user is Malicious or trusted. If the secondary user is malicious he will attack the network at different layers and degrades the performance. In this paper, a method called Multiple Nash Reputation (MNR) method is proposed to secure the CRN at two different layers namely physical and network. First, trust is separately calculated for each CR user at two different layers, physical layer and network layer using trust parameters. After that the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. The performance of MNR method is evaluated based on Energy consumption and detection accuracy.

Keywords:

Trust,Reputation,Cross Layer attack,Cognitive Radio Networks,Multiple Nash Equilibrium,

Refference:

I. Brochure, “Coexistence of wireless systems in automation technology,” in Proc. ZVEI – Central Association for Electrical and Electronic Industry, Germany, April 2018.(1)

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III. Ernesto Cadena Muñoz, Enrique Rodriguez-Colina, Luis Fernando Pedraza, Ingrid Patricia Paez, “Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP”, EURASIP Journal on Wireless Communications and Networking, Springer Open, Feb 2020

IV. GaneshDavanam,T.Pavan Kumar,M.Sunil Kumar,”Mean Bid Trust Cross Layer Trust Evaluation Model for Cognitive Radio Networks”,International Journal of Advanced Science and Technology, Vol. 29, No. 5, (2020), pp. 11450-11461

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VI. Jaydip Sen, “A Survey on Security and Privacy Protocols for Cognitive Wireless Sensor Networks”, Journal of Network and Information Security, Jun 2013

VII. Jihen Bennaceur Hanen Idoudi Leila Azouz Saidane, “Trust management in cognitive radio networks: A survey”, International Journal of Network Management, Wiley, Aug 2017(10)

VIII. Jithin Jagannath, Sean Furman, Tommaso Melodia, Andrew Drozd, “Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm”, IEEE Transactions on Mobile Computing, Vol. 18, No. 8, Aug 2019 (2)

IX. Linyuan Zhang, Guoru Ding, Qihui Wu, Yulong Zou, Zhu Han, Jinlong Wang, “Byzantine Attack and Defense in Cognitive Radio Networks: A Survey”, Elsevier, Jun 2015

X. Mee Hong Ling, Kok-Lim Alvin Yau, Geong Sen Poh, “Trust and reputation management in cognitive radio networks: a survey”, Security and Communication Networks, Wiley, Nov 2013

XI. Mitola, “Cognitive Radio Architecture Evolution,” in Proc. of the IEEE, vol. 97, no. 4, April 2009.(5)

XII. MouniaBouabdellah, Naima Kaabouch, Faissal El Bouanani, Hussain Ben-Azza, “Network layer attacks and countermeasures in cognitive radio networks: A survey”, Journal of Information Security and Applications, Elsevier, Jul 2018

XIII. Nadine Abbas, Youssef Nasser, Karim El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”, EURASIP Journal onWireless Communications and Networking, Springer, Jul 2015

XIV. Quanyan Zhu, Stefan Rass, “Game Theory Meets Network Security”, ACM, Oct 2019

XV. Saim Bin Abdul Khaliq, Muhammad Faisal Amjad, Haider Abbas, Narmeen Shafqat, Hammad Afzal, “Defence against PUE attacks in ad hoc cognitive radio networks: a mean field game approach”, Telecommunication Systems, Springer, May 2018 (3)

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Abstract:

Artificial Intelligence (AI) is poised to disrupt our world. Along with smart equipment making it possible for high-level cognitive processes like thinking, regarding, knowing, problem-solving as well as decision making, paired along with breakthroughs in data collection as well as aggregation, analytics as well as computer system processing energy, Artificial Intelligence shows opportunities to enhance as well as individual supplement intellect as well as enrich the method folks stay and function. To market sustainability, wise creation calls for a global point of view of smart manufacturing function technology. In this regard, due to demanding study efforts in the field of artificial intelligence (AI), a variety of AI-based approaches, such as machine learning, have been set up in the industry to obtain lasting manufacturing. This paper provides efficiency of data technologies that are driving the current surge in artificial intelligence.

Keywords:

Artificial Intelligence,industrial AI,sustainability,

Refference:

I. Carvalho,T.P.;Soares,F.A.A.M.N.;Vita,R.;daFrancisco,P.R.;Basto,J.P.;Alcalá,S.G.S.Asystematicliterature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 1,1–12.
II. D. Deepika, a Krishna Kumar, Monelli Ayyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
III. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
IV. Kotsiantis,S.B.;Zaharakis,I.;Pintelas, P. Supervisedmachinelearning:Areviewofclassificationtechniques.Emerg. Artif. Intell. Appl. Comput. Eng. 2007, 160, 3–24.
V. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VI. Kiran Kumar S V N Madupu, “Opportunities and Challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
VII. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi: https://doi.org/10.32628/IJSRST207257
VIII. Markham,I.S.;Mathieu,R.G.;Wray,B.A.Kanbansettingthroughartificialintelligence:Acomparativestudy of artificial neural networks and decision trees. Integr. Manuf. Syst. 2000, 11, 239–246.
IX. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131. doi: 10.1109/I-SMAC.2018.8653669
X. Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XI. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN (Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XIII. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN (Online): 2320-9801, vol 5, issue 6, june 2017
XIV. Shoban Babu Sriramoju, Naveen Kumar Rangaraju, Dr .A. Govardhan, “An improvement to the Role of the Wireless Sensors in Internet of Things” in “International Journal of Pure and Applied Mathematics”, Volume 118,No. 24,2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/
XV. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9

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Abstract:

Machine learning is concerned with algorithmically discovering styles and also relationships in data, as well as utilizing these to execute jobs such as category and also prediction in a variety of domain names. Our company now launch some pertinent jargon as well as deliver a summary of a handful of sorts of machine learning techniques. The mixed influence of new computing resources and also methods along with a boosting barrage of large datasets, is improving many investigation regions as well as may bring about technological innovations that can be used through billions of people. This paper provides the framework that uses feature models and corresponding labels for machine learning algorithms.

Keywords:

Machine Learning,classification,

Refference:

I. B. Srinivas, Shoban Babu Sriramoju, “A Secured Image Transmission Technique Using Transformation Reversal” in “International Journal of Scientific Research in Science and Technology”, Volume-4, Issue-2, February-2018, 1388-1396 [Print ISSN: 2395-6011| Online ISSN: 2395-602X]
II. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
III. G. C. de S ´ a, G. L. Pappa, as well as A. A. Freitas. Towards a method for instantly picking and also setting up multi-label classification algorithms. In Proc. GECCO Friend, web pages 1125– 1132, 2017.
IV. G. C. de S ´ a, W. J. Pinto, L. O. Oliveira, and G. L. Pappa. RECIPE: A grammar- based framework for instantly developing category pipes. In Proc. of the EuroGP Conference, webpages 246– 261, 2017.
V. J. Demsar. Statistical evaluations of classifiers over various information collections. J. Mach. Learn. Res., 7:1– 30, 2006.
VI. Kian Kumar S V N Madupu, “Challenges and CloudComputing Environments Towards Big Data”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 203-208, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207277
VII. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VIII. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
IX. Kiran Kumar S V N Madupu, “opportunities and challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
X. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
XI. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XIII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIV. M. Feurer, A. Klein, K. Eggensperger, et cetera. Dependable as well as sturdy automated ma- chine learning. In Proc. of the International Meeting on Neural Data Processing Systems, webpages 2755– 2763, 2015.
XV. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131. doi: 10.1109/I-SMAC.2018.8653669
XVI. Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVIII. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017

XIX. PushpavathiMannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT206274
XX. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXII. Shoban Babu Sriramoju, Naveen Kumar Rangaraju, Dr .A. Govardhan, “An improvement to the Role of the Wireless Sensors in Internet of Things” in “International Journal of Pure and Applied Mathematics”, Volume 118, No. 24, 2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/
XXIII. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXIV. Srinivas, MonelliAyyavaraiah, Shoban Babu Sriramoju, “A Review on Security Threats and Real Time Applications towards Data Mining” in “International Journal of Pure and Applied Mathematics”, Volume 118, No. 24, 2018, ISSN: 1314-3395 (on-line version), url: http://www.acadpubl.eu/hub/

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Abstract:

In the cloud service situation, the planning, as well as also details, is shifting into the cloud, leading to the lack of trust between clients as well as additionally cloud business. Possessing claimed that the present research on Cloud computing is mainly concentrated on the service side. At the same time, the data securities, as well as trust, have undoubtedly not been adequately looked into yet. This paper checks out into the information security issues from the info life cycle, which includes five steps when a firm makes use of Cloud computing. An info management framework is given out, featuring certainly not merely the data classification having said that also the hazard administration framework.

Keywords:

Cloud computing,big data, privacy,security,

Refference:

I. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
II. Kiran Kumar S V N Madupu, “Challenges and CloudComputing Environments Towards Big Data”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 203-208, 2014. Available at doi :https://doi.org/10.32628/IJSRSET207277
III. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
IV. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
V. Kiran Kumar S V N Madupu, “Opportunities and Challenges towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255

VI. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VII. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
VIII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
IX. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
X. Lowensohn, J. & McCarthy, C. (2009). Lessons from Twitter’s Security Breach. Available online at: http://news.cnet.com/8301-17939_109-10287558-2.html (Accessed on: November 29, 2012).
XI. Microsoft Security Bulletin MS07-049. Vulnerability in Virtual PC and Virtual Server Could Allow Elevation of Privilege (937986). URL: http://www.microsoft.com/technet/security/bulletin/ms07- 049.mspx. (November 13, 2007) (Accessed on November 20, 2012).
XII. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131. doi: 10.1109/I-SMAC.2018.8653669
XIII. Molnar, D. & Schechter, S. (2010). Self Hosting vs. Cloud Hosting: Accounting for the Security Impact of Hosting in the Cloud. In Proceedings of the Workshop on the Economics of Information Security, 2010, Harvard University, USA, June 2010.
XIV. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278

XV. PushpavathiMannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi :https://doi.org/10.32628/IJSRST207254
XVI. PushpavathiMannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XVII. PushpavathiMannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XVIII. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XIX. PushpavathiMannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT206274
XX. Security Tracker. VMWare Shared Folder Bug Lets Local Users on the Guest OS Gain Elevated Privileges on the Host OS. Security Tracker ID: 1019493. URL: http://securitytracker.com/id/1019493 (Accessed on: November 20,2012)
XXI. Xen Vulnerability. URL: http://secunia.com/advisories/26986/. (Accessed on: November 20, 2012).
XXII. Zetter, K. (2010). Google hackers Targeted Source Code of More Than 30 Companies. Wired Threat Level. January 13 2010. Available online at: http://www.wired.com/threatlevel/2010/01/google-hack- attack/ (Accessed on: November 29, 2012).

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Abstract:

Usually, Cloud Computing companies are supplied through a 3rd party service provider who has the infrastructure. Cloud Computing holds the possibility to get rid of the demands for developing of high-cost computing facilities for IT-based alternatives as well as services that the industry utilizes. It assures the delivery of a flexible IT architecture, only available via web coming from light in weight portable devices. This would certainly enable a multi-fold boost in the ability as well as capabilities of the existing and brand new software. This all-new financial concept for computing has uncovered productive ground as well as additionally is luring substantial worldwide assets. A lot of business, like economic, health care and also learning are transferring towards the cloud due to the efficiency of services provided by the pay-per-use style based upon the resources such as refining electricity utilized, bargains conducted, bandwidth absorbed, details moved, or even keeping place taking up, etc. In a cloud computing setup, the entire reports dwell over a collection of online information, allowing the stories to become accessed with digital devices. This file mostly takes note of house components in addition to cultivating computing concepts towards cloud computing.

Keywords:

Cloud computing,architecture,components,

Refference:

I. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
II. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
III. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi: https://doi.org/10.32628/CSEIT206271
IV. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255

V. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
VI. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
VII. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
VIII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
IX. M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th InternationalConferenceon InteractiveCollaborativeLearning(ICL), pp. 589 – 594, DOI=21-23 Sept.2011.
X. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XI. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XII. P. Pramod Kumar, S. Naresh Kumar, Ch. Sandeep, “FOR 4G HETEROGENEOUS NETWORKS A COMPARATIVE STUDY ON VERTICAL HANDOVER DECISION ALGORITHMS”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-15, No.-6, June(2019) pp 201-212

XIII. P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606

XIV. P. Pramod Kumar, K Sagar, “FLEXIBLE VERTICAL HANDOVER DECISION ALGORITHM FOR HETEROGENOUS WIRELESS NETWORKS IN 4G”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.-14, No.-6, November -December (2019) pp 54-66

XV. P Pramod Kumar and K Sagar 2019, “A Relative Survey on Handover Techniques in Mobility Management”, IOP Conf. Ser.: Mater. Sci. Eng.594 012027
XVI. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XVIII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XIX. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XX. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXII. P. Kalagiakos “Cloud Computing Learning,” 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku pp. 1 – 4, DOI=12-14 Oct.2011.
XXIII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXIV. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXV. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXVI. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXVII. Shailaja, P., Guru Rao, C.V. &Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXVIII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. &Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXIX. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXX. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXXI. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
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XXXIV. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039.
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XXXVI. W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp.711-716.

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Abstract:

Currently, our business performs not know just how to configure pc systems if you want to find out a lot more dependable personally. Although the techniques that have been learned operate very successfully for certain features, certainly not suited for all purposes. As an example, machine learning algorithms are, in fact, generally utilized in information mining. Likewise, in sites where documents are involved, these algorithms work and also lead far better than some other methods. As an example, in concerns featuring pep talk awareness, algorithms based on machine learning resulted better than the various different strategies. Delivered the unpredicted routine of data as well as calculating details, there prevails restored interest in administering data-driven machine learning strategies to problems for which the advancement of traditional style responses is, in fact-checked by means of modeling or even algorithmic deficiencies. This paper briefly goes over regarding the category of ML algorithms as well as additionally intersection of stats and computer science in machine learning.

Keywords:

Machine learning,algorithms,intersection,

Refference:

I. A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
II. D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technology Letters, vol. 28, no. 19, pp. 2102–2105, Apr. 2016.
III. D. Wang, M. Zhang, Z. Li, Y. Cui, J. Liu, Y. Yang, and H. Wang, “Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise,” in European Conference on Optical Communication (ECOC) 2015, Oct. 2015, pp. 1–3.
IV. D. Deepika, a Krishna Kumar, Monelli Ayyavaraiah, Shoban Babu Sriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
V. F. Lu, P.-C. Peng, S. Liu, M. Xu, S. Shen, and G.-K. Chang, “Inte- gration of Multivariate Gaussian Mixture Model for Enhanced PAM-4 Decoding Employing BasisExpansion,”in OpticalFiberCommunications Conference (OFC) 2018, Mar.2018.
VI. Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VII. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271

VIII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
IX. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
X. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XI. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIII. M. E. McCarthy, N. J. Doran, and A. D. Ellis, “Reduction of Non- linear Intersubcarrier Intermixing in Coherent Optical OFDM by a Fast Newton-Based Support Vector Machine Nonlinear Equalizer,” IEEE/OSAJournalofLightwaveTechnology,vol.35,no.12,pp.2391– 2397, Mar.2017.
XIV. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XV. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XVI. Pushpavathi Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XVII. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XVIII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN: 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XIX. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XX. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXI. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXIII. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXIV. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXV. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXVI. Shailaja, P., Guru Rao, C.V. & Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXVII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. & Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXVIII. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXIX. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXX. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
XXXI. Srinivas, Chintakindi & Rao, Chakunta & Radhakrishna, Vangipuram. (2018). Feature Vector Based Component Clustering for Software Reuse. 1-6. 10.1145/3234698.3234737.
XXXII. Subba Rao, A. & Ganguly, P. 2018, “Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems”, International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 1192.
XXXIII. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039.
XXXIV. X. Lu, M. Zhao, L. Qiao, and N. Chi, “Non-linear Compensation of Multi-CAP VLC System Employing Pre-Distortion Base on Clustering of Machine Learning,” inOptical Fiber CommunicationsConference (OFC) 2018, Mar.2018.
XXXV. Z. Ghassemlooy, and N. J. Doran, “Artificial neural network nonlinear equalizer for coherent optical OFDM,” IEEE Photonics Technology Letters, vol. 27, no. 4, pp. 387–390, Feb. 2015.

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Abstract:

A ubiquitous process to evoke the most needed data information from huge amount of unprocessed data to analyze the patterns is called as data mining which is also named as data through knowledge discovery. It helps the enterprises to extract the data information to gain knowledge for better. [I] Data mining usually deals with text for mining. Since we are using internet for the accessibility of data. In other words, we are making use of web to extract the data, modify and process the text using the WebPages. Evoking the information data which is present on internet is done using data mining is called as Web Mining.[II] It is an integral part of data mining for searching and analyzing the pattern. There are various data resources to obtain the data from web which is categorized into metadata, text documents, web links   and web content. A web mining also consist of images, videos and audio information data which are considered as multimedia data. As, many users are more keen towards extracting information in form of images and videos from the web pages , so  there’s a need of  bringing out the required multimedia  data information from unused scattered multimedia data present in the web. Here, we need to coalesce mining concepts through web into the multimedia stored data. Such concept is considered as Multimedia Web Mining, [V]It reaps the hidden information of a multimedia file as metadata, represents relationship between multimedia data files^]5. For better and efficient working performance of mining techniques, multimedia mining also index and classify the various modes of multidata such as animation, moving, still , playback  and video modes. Multimedia information is divided into two halves as organized and semi organized. Similarly web mining is categorized into utilization mining, organized mining and substance mining. In this paper, we explore the integration of multimedia with web mining for better enhancement in achieving the classification of data.

Keywords:

Web mining,levels of data mining,

Refference:

I. http://airccse.org/journal/ijcga/papers/5115ijcga05.pdf
II. https://www.researchgate.net/publication/319404075_A_Survey_on_Web_Mining_Techniques_and_Applications
III. https://www.researchgate.net/publication/230639907_A_Survey_on_Multimedia_Data_Mining_and_Its_Relevance_Today
IV. http://www.ijcstjournal.org/volume-5/issue-3/IJCST-V5I3P21.pdf
V. http://www.academia.edu/Documents/in/Web_Data_Mining
VI. https://ieeexplore.ieee.org/abstract/document/5992597
VII. https://arxiv.org/pdf/1109.1145
VIII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
IX. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
X. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XI. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIII. M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th InternationalConferenceonInteractiveCollaborativeLearning(ICL), pp. 589 – 594, DOI=21-23 Sept.2011.
XIV. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XV. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XVI. Pramod Kumar P,Thirupathi V, Monica D, “Enhancements in Mobility Management for Future Wireless Networks”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2,Issue 2, February 2013

XVII. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors Affecting Handovers and EffectiveHighlights of Handover Techniques for Next GenerationWireless Networks”, Indian Journal of Public Health Research &Development, November 2018, Vol.9, No. 11

XVIII. P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On Several Specifications in Heterogeneous Wireless Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-9, July 2019

XIX. P. Pramod Kumar ,Dr. K. Sagar, “A proficient and smart electricity billing management system ” ,International Conference on Emerging Trends in Engineering and published in Springer Nature as a Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3), July 2019.
XX. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XXI. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XXII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XXIII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XXIV. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXV. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXVI. P. Kalagiakos “Cloud Computing Learning,” 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku pp. 1 – 4, DOI=12-14 Oct.2011.
XXVII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXVIII. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXIX. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXX. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXXI. Shailaja, P., Guru Rao, C.V. &Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXXII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. &Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXXIII. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXXIV. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXXV. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
XXXVI. Srinivas, Chintakindi& Rao, Chakunta& Radhakrishna, Vangipuram. (2018). Feature Vector Based Component Clustering for Software Reuse. 1-6. 10.1145/3234698.3234737.
XXXVII. Subba Rao, A. &Ganguly, P. 2018, “Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems”, International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 1192.
XXXVIII. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039
XXXIX. W. Dawoud, I. Takouna, and C. Meinel, “Infrastructure as a Service Security: Challenges and Solutions,” 2010 7thInternational Conference on Informatics and System, pp. 1-8, March2010.
XL. W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp.711-716.

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Abstract:

In today’s generation, the internet has become an essential part of our lives for communication, banking and studying. Especially the youth has turned them into the virtual world. Have you ever speculated how many people stalk you on social media? With the rapid usage of the internet by society, it is also important to protect the information. A computer should have security in it if not it will be accessed by hackers. A hacker can illegally access the data present in a computer. Hacking the important data affects our lives adversely. Cyber-attacks are generally planned wisely. The cyber security specialists and cybercriminals started the competition which will be compared with the growth of offensive weapons and defensive ones to resist the attacks. Cyber security is that the field of science that's developing perpetually and speedily. A Cybercrime square measures currently a worldwide downside that affects innumerable spheres of human life. Every new appliance and software package become the target for cyber criminals sooner or later, therefore their makers do everything doable to be one step ahead. Nearly everything we have a tendency to see in our everyday life may require a number of the cyber security. The main ambition of the hackers is to steal confidential information or to change the data. The hackers opt for a unique way to infect the computer to gain access to it. They usually use malicious software to infect the computer. A virus is been carried by the attachments of the e-mails. When we download these attachments, the computer gets infected. Cyber security plays a major role in organizations such as governments, businesses, hospitals as these have a wide range of confidential information with them. Social networking sites became the medium for sharing information and connecting with people. One side we have an advantage as it connects people, on the other hand, it creates opportunities for cybercrimes. As an individual, we should be alert enough to secure our accounts and data.

Keywords:

cyber security ,cybercrimes,cyberattacks,ransom ware,malicious software,hackers,

Refference:

I. Azzah Kabbas, Atheer Alharthi, and Asmaa Munshi, Artificial Intelligence Applications in Cybersecurity, IJCSNS International Journal of Computer Science and Network Security, 20(2),pp.120-124, Fabruary 2020.
II. Clifton L. Smith, David J. Brooks, Security Risk Management in Security Science, 2013.
III. G.Sunil, Srinivas Aluvala,K. Ravi Chythanya, Goje. Roopa, Rajesh Mothe, Trends having huge impact on cyber security and techniques of cyber security, International Journal of Advanced Science and Technology, 29(2), pp.2701-2708, Jan.2020.
IV. G. Sunil, Srinivas Aluvala, S. Tharun Reddy, Dadi Ramesh, Dr. Revuri Varun, Various forms of cybercrime and role of social media in cyber security, International Journal of Advanced Science and Technology, 29(2), pp.2709-2715, Jan.2020.
V. G.Sunil, Srinivas Aluvala, Nagendhar Yamsani, Kanegonda Ravi Chythanya, Srikanth Yalabaka, Security Enhancement of Genome Sequence Data in Health Care Cloud, International Journal of Advanced Trends in Computer Science and Engineering, 8(2), pp.328-332, March-April 2019.
VI. Kenichi Yoshida, Kazuhiko Tsuda,Setsuya Kurahashi,Hiroki Azuma, Online Shopping Frauds Detecting System and Its Evaluation, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 4-8 July 2017.
VII. Lisa Lee Bryan, Effective Information Security Strategies for Small Business, International Journal of Cyber Criminology, 14(1), pp.341-360, January-June (2020).
VIII. Mohamed Abomhara, Geir M. Koien, Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks, Journal of Cyber Security, 4, pp. 65–88, 22 May 2015.
IX. Roopa Goje, Ramesh Dadi, Designing a collaborative detection system for detecting the threats to the cyber security in big data, Indian Journal of Public Health research & Development, 9(11), pp.730-733, November 2018.
X. Surbhi Guptha, Abhishek Singhal, Akanksha Kapoor, A literature survey on social engineering attacks: Phishing attack, 2016 International Conference on Computing, Communication and Automation (ICCCA), 29-30 April 2016.
XI. Yashpal Singh Bist, Charu Agarwal, Uttara Bansal, Online Business Frauds: A Case Study of an Online Fraud Survey Company, International Journal of Modern Engineering Research (IJMER), 2(6), Nov-Dec. 2012 pp-4396-4404.

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Abstract:

Now days, Security plays an important role in day-to-day life.  The use of the internet in human life has become the day to day activity and with the internet the use of automation devices has increased. All transaction needs to secure authentication to complete. Hence, we have introduced a Face Recognition method. It can apply in many fields such as to authenticate users, security issues etc., It mainly plays a significant role in real time surveillance systems. We implemented the Convolution neuron network to automatically create dataset and recognition with the graphical user interface. Before creating a dataset the system takes permission from the user then it creates the dataset and trains the model for farther authentication.

Keywords:

Security,deep learning,neural network,authentication,

Refference:

I E. I. Abbas, M. E. Safi And K. S. Rijab, “Face Recognition Rate Using Different Classifier Methods Based On Pca,” 2017 International Conference On Current Research In Computer Science And Information Technology (Iccit), Slemani, 2017, Pp. 37-40, Doi: 10.1109/Crcsit.2017.7965559.

II H.-W. Ng, S. Winkler. A Data-Driven Approach to Cleaning Large Face Datasets. Proc. Ieee International Conference on Image Processing (Icip), Paris, France, Oct. 27-30, 2014.

III M. R. Reshma and B. Kannan, “Approaches On Partial Face Recognition: A Literature Review,” 2019 3rd International Conference on Trends In Electronics And Informatics (Icoei), Tirunelveli, India, 2019, Pp. 538-544, Doi: 10.1109/Icoei.2019.8862783.

IV O. M. Parkhi, A.Vedaldi, A. Zisserman Deep Face Recognition British Machine Vision Conference, 2015.

V Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences, ISSN (Online): 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027 .

VI P.Kumara Swamy, Dr.C.V.Guru Rao, Dr.V.Janaki, “Functioning Of Secure Key Authentication Scheme In” In International Journal Of Pure And Applied Mathemat, Volume 118, Issue 14, Page No(S) 27 – 32, MAR. 2018, [ISSN(Print):1314-3395].

VII R. Prema and P. Shanmugapriya, “A Review: Face Recognition Techniques For Differentiate Similar Faces and Twin Faces,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (Icecds), Chennai, 2017, Pp. 2899-2902, Doi: 10.1109/Icecds.2017.8389985.

VIII Sharmila, Raman Sharma, Dhanajay Kumar, Vaishali Puranik, Kritika Gautham,”Performance Analysis Of Human Face Recognition Techniques” In 2019 Ieee

IX Sharma, Sudha and Soni, Alpesh and Malviya, Vijay, Face Recognition Based On Convolution Neural Network (Cnn) Applications in Image Processing: A Survey (April 15, 2019). Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (Raitea) 2019.

X Surface[3D] Measurement Through Easy-Snap Phase Shift Fringe Projection.” Springerprofessional.De,Https://Www.Springerprofessional.De/En/3d-Surface-Measurement-Through-Easy-Snap-Phase-Shift-Fringe-Proj/15447362. Accessed 26 Mar. 2020.

XI Sallauddin Md Et. “A Comprehensive Study on Traditional Ai and Ann Architecture.” International Journal of Advanced Science and Technology, Vol. 28, No. 17, Dec. 2019, Pp. 479–87.

XII Yerrolla Chanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced On Bidirectional LI-FI Attocell Access Point Slicing and Virtualization Using Das2 Conspire” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.

XIII Yerrolla Chanti, Dr. K. Seena Naik2, Rajesh Mothe3, Nagendar Yamsani4, Swathi Balija5” A Modified Elliptic Curve Cryptography Technique For Securing Wireless Sensor Networks” International Journal Of Engineering &Technology 2018.

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Abstract:

IoT devices, or any of the various problems inside the net update, are nonstandardregisteringdevicesthatbepartofremotelyuptodate a network and highlight theopportunityupdatetransmitstatistics[III].IoTconsistsof growing internet community past gadgets,whichcontainpcsupdated,workstations,cellphonesandmedicines, uptodateanyassortmentofactuallymoronicornonnetempowered bodily gadgets andpopularupdate.Implantedwithage, those devices can talk andfunction connection over the net, and they might be remotely located and overseen [X].To updatedis coupononline communication, being Growingage,hasupdate an appealing area of research in audiosystemhoweverPromising packages like quick assortmentcontactless discussion for mobilephone and different superior devices the same. Rigt now, valid facts and direction of NFC is up-to-date be beautifully save updated up for the headway of capacity and up to date reduce the scaffold hollow between its critical Online and alertness exercise. Proper now, proposed up-to-date NFC might be applied for sharing little evaluations along with contacts, and bootstrapping rapid institutions with percentage larger media and various records and boat Wi-Fi wireless, software content material fabric, contactless installments, examine NFC labels amongst advanced gadgets [II][I]. We more over have investing the NFC corporation business enterprise natural system and present day destiny market propensities. In diverse terms this compressive in NFC wireless duration manages advancement of statistics.

Keywords:

NFC,IOT,RIFD,BLUETOOTH.,

Refference:

I APC, Inside NFC: how near field communication works. August 17, 2011. http://apcmag.com/insidenfc-how-near-field-communication-works.htm.

II Bura Vijay Kumar1, Yerrolla Chanti2, D. Kothandaraman3, A. Harshavardhan4, Sangameshwar Kanugula5 S” INTERNET OF THINGS MIDDLEWARE ARCHITECTURE FOR COMMUNICATION” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

III D. Kothandaraman1, Y. Chanti2, B. Vijaykumar3, A. Harshavardhan4, K. Seena Naik5” Indoor Users Motion Direction Detection Using Orientation Sensor with BLE in Internet of Things” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

IV D.M. Monteiro, J.J.P.C. Rodrigues, J. Lloret, “A Secure NFC Application for Credit Transfer among Mobile Phones”, International Conference on Computer, Information and Telecommunication Systems (CITS), 2012, pp. 1- 5.

V E. Desai, M.G. Shajan, “A Review on the Operating Modes of Near Field Communication”, International Journal of Engineering and Advanced Technology (IJEAT), Volume-2, Issue-2, 2012. Ber Security (CIACS), 2014, pp. 35- 38.

VI E. Macias, J. Wyatt, “NFC Active and Passive Peer-toPeer Communication Using the TRF7970A”, April 2014, http://www.ti.com/lit/an/sloa192/sloa192.pdf.

VII Internet source ofWikipedia .com

VIII K. Seena Naik and E. Sudarshan ”Smart Healthcare Monitoring System using Raspberry Pi on IoT Platform” ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. VOL. 14, NO. 4, FEBRUARY 2019. ISSN 1819-66.

IX [N.A. Chattha. “NFC – Vulnerabilities and Defense” Conference on Information Assurance and Cyber Security (CIACS), 2014, pp. 35- 38.

X P. V. Nikitin. “An Overview of Near Field UHF RFID,” in Proc. IEEE Int. Conf. RFID, Mar. 2007, pp. 167-174.

XI Shirsha Ghosh, Joyeeta Goswami, Abhishek Kumar and Alak Majumder” Department of Electronics & Communication Engineering, National Institute of Technology, Arunachal Pradesh, Yupia, India” Issues in NFC as a Form of Contactless Communication: A Comprehensive Survey” 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India. 6 – 8 May 2015. pp.245-252.

XII V. Coskun, K. Ok, B. Ozdenizci “Near Field Communication, from theory to practice”, Wiley Publication.

XIII Yerrolla Chanti1, Seena Naik Korra2, Bura Vijay Kumar3, A. Harshavardhan4, D. Kothandaraman5 “New Technique using an IoT Robot to Oversight the Smart Domestic Surroundings” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

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Abstract:

The objective of this paper is to solve pure integer programming problems without using Gomorian constraints. In this, CMI method is used for solving linear programming problems instead of simplex method. In CMI method, there is no need to calculate net evaluations, which is essential and mandatory in pre-existing methods. By discarding the calculation of net evaluations, the iterations in the procedure gets reduced or remains atmost equal in number. After getting a non-integer value in final CMI table, here we use a reduction technique instead of adding Gomorian constraint to get the integer solution directly.The main advantage of using this reduction technique is to avoid using, any additional constraints and the Dual simplex method for getting an integer solution. With the elimination of the above processes, the integer solutions are arrived very easily. Hence this new approachof pure integer programming problemensures time conservation at various levels in deriving the optimal solutions.  This proposed method is illustrated withexamples.

Keywords:

CMI Method,LPP,IPP,Optimal Solution,Reduction technique,

Refference:

I G.B. Dantzig, Maximization of linear function of variables subject to linear inequalities Koop man cowls commission Monograph, 1951).

II Handy A.Taha: ‘Operations Research An Introduction’ 8th edition by Pearson Publication.

III Kalpana Lokhande; Pranay.Khobragade and .W. Khobragade: Alternative approach to simplex method, International journal of engineering and innovative Technology, volume 4, Issue 6, pg: 123-127.

IV P.Pandian and M.Jayalakshmi: A new approach for solving a class of pure integer linear programming problems, International journal of advanced engineering technology.

V S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI –M Technique for the solution of linear Programming problem,’ International Journal of Research and Analytical Reviews, October 2018, Volume-5, Issue-4.Pg-76-82ded.

VI S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI Method for the solution of linear formulating problem’, Journal of Emerging Technologies and Innovative Research, September 2018, Volume 5, Issue 9, Pg. 248-253.

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Abstract:

There are high necessities to create counterfeit arms for some brutal circumstances where human communications are displaying difficulties or unrealistic (for example outlandish circumstances). This paper presents data, strategies and methods which are fundamental for building a mechanical arm constrained by the developments of ordinary human arm (Gesture Robotic Arm) whose information is gaining by utilizing the Accelerometer. The improvement of this arm depends on the ARM stage in which all are interfaced with one another by utilizing lpc2148 smaller scale controller. The model of automated arm of this paper has been actualized practically.Thedeveloped mechanical arm of this paper is followed the development of human arm with a decent exactness. Usage of this arm could be normal for beating the issues, for example, picking or setting object that are away from the users.

Keywords:

Gesture Robotic Arm,Motion Perception, Accelerometer,lpc2148 smaller scale controller,

Refference:

I. Aggarwal, L., Gaur, V., & Verma, P., (2013) “Design and Implementation of a Wireless Gesture Controlled Robotic Arm with Vision”, International Journal of ComputerApplications (0975 – 8887), 79 (13), pp. 39–43.
II. Brahmani, K., Roy, K. S., & Ali, M., (2013) “Arm 7 Based Robotic Arm Control by Electronic Gesture Recognition Unit Using MEMS”, International Journal ofEngineering Trends and Technology, 4 (4), pp. 1245–1248.
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Abstract:

This paper suggests a scheme for trajectory tracking on a two wheeled mobile robot using integral sliding mode control method in the presence of external disturbances and inertia uncertainties. In this study the modified adaptive sliding mode controller for nonholonomic wheeled mobile robot is developed. Nonlinear control used to combine the kinematic and dynamic controller to follow the desired path. Firstly, the desired path is created. Secondly, the kinematic tracking controller used linear and angular velocities form reference model and feeds posture and velocities errors as input term in the sliding controller. Finally, the dynamic control was used to follow the path. Proposed control system is verified and validated using MATLAB\SIMULINK to track the required WMR trajectory and it is shown that the suggested system has better transient efficiency on different trajectories with acceptable steady stateerror.

Keywords:

Wheeled mobile robot,dynamic uncertainty,Kinematic and dynamic controller,Dynamic control,Transient efficiency,

Refference:

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