Volume-07 , Special Issue-14 , May - 2019 Go Back

WSN (Wireless Sensor Network) is one of the most standardized administration network utilized and applicable in some business and modern applications, On of its account, its specialized main improvement in a processor, corresponding, and its lowest control use in registering or applicable gadgets. The network is worked with hubs that are mainly to watch the environmental measure like temperature, mugginess, weight, position or movement of objects or particles, sense and vibration, noise and furthermore. By increasing its lifetime, the power of safeguarding measures is essential and necessary to upgrade and to increase the lifetime of network in WSN. Algorithms that use clusters are especially developed to improve and increase the network lifetime. One of the main technique that is used to increase the network lifetime is Sensor node Clustering. In this technique, data can be mainly aggregated at the Cluster head. Particle swarm optimization (PSO) is one of the most basic, successful and advancement algorithm used to increase the lifetime of network. It helps in framing or grouping the clusters and the data is aggregated at the Cluster head and then it is passed to Base station (BS)

WSN, PSO, Cluster head, LEACH protocol

[1]Kennedy, Eberhart and Shi [1] and become first researcher to intend and identify for stylish behavior of fishes.
[2] Prof. N.V.S.N Sarma, Mahesh Gopi “Implementation of Energy Efficient Clustering by usage of Firefly Algorithm in WSN” International Congress held on CSE, ECE, Electrical, and CE in 2014.
[3] Rajeev Kumar, Dalip Kumar proposed “Hybrid Swarm Intelligence Energy Efficient based Clustered Routing Algorithm for WSN” published on Hindawi Publishing Corporation, in the Journal of Sensors Volume in 2016.
[4] Varsha Gupta, Shashi Kumar Sharma proposed an “Cluster Head Selection Using some Modified ACO” held on Fourth International Conference on the Soft Computing for Problem Solving the Springer in 2015.
[5] Sunil R. Gupta, Dr. N.G. Bawane, proposed an “A Clustering Solution for WSN based on Energy Distribution &GA”, held on International Conference on Emerging Trends in Engineering and Technology.
[6] Raghavendra V. Kulkarni, Senior Member, IEEE, and Ganesh Kumar Venayagamoorthy, Senior Member, IEEE “Particle Swarm Optimization in WSN: A Brief Survey’’
[7] S. K. Singh, M. Singh, and D. Singh, paper on “A survey of energy efficient hierarchical cluster-based routing in WSN,``
[8] Kuila, P., & Jana, P. K. proposed Energy efficient clustering and routing algorithms mainly for WSN: PSO Engineering Applications related of AI, 33, 127-140.
[9]Singh, A. (in the year 2016). Proposed a LEACH based energy efficient routing protocol, WSN. International Conference held on ELE, ECE, and OT (ICEEOT), 4654-4658
[10] Fateh boutekkouk, Fatima Taibi, Khawla Meziani proposed “A hybrid approach to increase the lifespan of heterogeneous WSN” held The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks.
[11] Rejina Parvin, Vasanthanayaki C, “Particle Swarm Optimization based Clustering by preventing residual nodes in WSN”, IEEE Sensors Journal, 1530-437X IEEE, 2015.
[12] P.Leela, K.Yogitha “Hybrid Approach for Energy Optimization in WSN” held International Conference on Innovations in Engineering and Technology ICIET.
[13] Wei Qu, Mengmeng Yang, “An Energy efficient Routing Control Strategy Based on Genetic Optimization”, IEEE, in June 29 to July 4 in 2014.
[14] X. Liu, “Atypical hierarchical routing protocols for WSN A review,`` IEEE Sensors J., vol. 15, no. 10, pp. 5372_5383, in the year Oct. 2015.

Priyanka M D, Mallikarjuna M, "Network Life Time Analysis of WSNs Using Particle Swarm Optimization", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.1-6, 2019.

In today’s world, number of communication protocols for both long and short distance communication purpose, long distance protocols is USB (Universal Serial Bus), ETHERNET, PCI-EXPRESS. SPI (Serial Peripheral interface) and I2C are used for short distance communication protocols. SPI is one of the commonly used bus protocol for connecting peripheral devices to microprocessor .SPI is full duplex, high speed an synchronous bus protocol used for on-board or intra-chip communication In this project the configurable architecture of SPI Protocol with Wishbone Interface has been designed .The main advantage of this design is it overcomes the weaknesses of traditional SPI Bus protocol. As the complexity of the circuit is numerous so there is need of verification methodology to quench the product failure. This project emphasizes on verification of SPI master core verification using Universal Verification Methodology.

SPI, Wishbone, UVM, SystemVerilog

[1]. Zhili Zhou, Zheng Xie, Xin’an Wang and Teng Wang “Development of verification environment for SPI master interface using SystemVerilog” ICSP,2012 IEEE 11th international Conference.
[2]. T.Liu and Y.Wang, “IP design of universal multiple devices SPI interface” in AntiConterfeiting, Security and identification (ASID), 2011 IEEE International Conference on IEEE,2011,pp. 169-172.
[3]. W.Ni and J. Zhang, “Research of reusability based on UVM verification,” in 2015 IEEE 11th International Conference on ASIC, Nov 2015,pp. 1-4.
[4]. A.K. Swain and K. Mahapatra, “Design and Verification of WISHBONE bus interface for System-on-Chip integration,” in india Conference (IINDICON),2010 Annual IEEE .IEEE,2010,pp. 1-4.
[5].K. Fathy and K. Salah, “An Efficient Scenario Based Testing Methodology Using UVM,” in 2016 17th International Workshop on Microprocessor and SOC Test and Verification (MTV), Dec 2016, pp. 57–60.
[6]. R.Prasad and C.S.Rani, “UART IP CORE VERIFICATION BY USING UVM,” IRF International Conferences, 15 2016
[7].K.Aditya, M.Sivakumar,F.Noorbasha, and P.B.Thummalakunta, “Design and Functional Verification of a SPI Master Slave Core Using System Verilog ,” International Journal of Computional Engineering Research,05 2018.
[8]. S.Anantha , M.K.Kumar, and J.K. Bhandari, “Design and verification of SPI,” International Journal of Engineering Development and Research(IJEDR), vol 1, pp. 130-136,Dec.2014.
[9]. Srot and Simon, SPI Master core Specification, 2004. [online]. Available: https://opencores.org /project/spi
[10]. R.Herveille, SPI Core Specification, 2003.[online]. Available: https://opencores.org/project/simple_spi

Punith Kumar M B, Sreekantesha H N, "Design and Verification of Serial Peripheral Interface Master Core Using Universal Verification Methodology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.7-11, 2019.

This project is based on providing solution to retail giants to address their current Assortment strategy and increase their profit. Assortment AI is an AI project under the trade name reMark which is a social analytical platform of Kigyan Techno Solutions that provides retail marketing solutions. The concept of the project: Today majority of the giant retail companies are facing a lot of issues in their current assortment planning of their products. These include the total dump of products being 45%. This wrong assortment planning leads to products being out of stock which causes loss to businesses and major customer dissatisfaction, also this assortment planning requires a lot of manual strategies which are very costly and hence these assortment strategies then turn out to be costly, time taking, biased and working on mostly non relevant data. Due to the above factors the retail companies have understood that the current assortment planning strategies are not working and are only causing business loss and customer attrition. Hence there is a need of new assortment plan. The project that we are currently working on is building Assortment AI Module in a retail market and so the project is an Artificial Intelligence block which will process the current situation and will build a proper assortment plan. This would then address various problems that the retail companies were facing and present them with proper assortment plan which would include unbiased assortment strategy, the dump would be reduced to less than 20%, increase the customer satisfaction and reduce the customer attrition

Kigyan Techno Solutions,data science,attrition,retail,assortment dumpstrategy

[1] K. M. Murali, L. Michael, E.K. Barbara, "Why is AssortmentPlanning so Difficult for Retailers? A Framework and Research Agenda", Journal of Retailing, vol. 85, no. 1, pp. 71-83, 2009.Show Context CrossRef Google Scholar.
[2]M. K. Mantrala, K. Manfred, S. Leopold, Krafft Manfred, MuraliK. Mantrala, "Entrepreneurship in Retailing: Leopold Stiefel`s ‘Big Idea’ and the Growth of Media Markt-Saturn" in Retailing in the 21st Century: Current and Emerging Trends, Berlin/Heidelberg/New York:Springer., 2008.
[3]K. Wong, Major Retailers Rev Up Green Campaigns, February 2008.
[4]P.D. Larson, R.A. DeMarais, "Psychic stock: An independent variable category of inventory", International Journal of Physical Distribution and Logistics Management, vol. 20, no. 7, pp. 28-34, 1990.
[5]Albuquerque P, Bronnenberg BJ (2009) Estimating demand heterogeneity using aggregated data: An application to the frozen pizza category. Marketing Sci. 28(2):356–372. [6] David A. Freedman (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 26. A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression equation has two or more explanatory variables on the right hand side, each with its own slope coefficient
.[7] Hilary L. Seal (1967). "The historical development of the Gauss linear model". Biometrika. 54 (1/2): 1–24. doi:10.1093/biomet/54.1-2.1. JSTOR 2333849.
[8] Yan, Xin (2009), Linear Regression Analysis: Theory and Computing, World Scientific, pp. 1–2, ISBN 9789812834119, Regression analysis ... is probably one of the oldest topics in mathematical statistics dating back to about two hundred years ago. The earliest form of the linear regression was the least squares method, which was published by Legendre in 1805, and by Gauss in 1809 ... Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the sun.

S.K. Begur, K. Gururaj, K.K. Bhowmik, K. Kumar, K. Malik, S.A. Rabbani, N. Tengli, "Retail Assortment Planning Using Data Science", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.12-17, 2019.

Social media(twitter) is easily conveyed organization for the enrolled people that may fuse content, photos, chronicles and hyperlinks. Individuals post whereabouts, opinions and information to help or against social media. The most terrified subject is terrorist strikes happening far and wide. Terrorist exploits the web-based life to consistently impart utilizing code signs or to build their backhanded proximity. The words with the hash sign related with them are broke down, get the evaluation of the twitter posts. This paper displays a methodology for sentiment analysis on terrorist related posts and to deal with the slants with their geolocations. Machine learning procedures like KNN (K-Nearest Neighbor), Random Forest are connected and the information is prepared utilizing Exploratory Data Analysis. The results are looked at and exhibited.

Sentiment Analysis; Exploratory Data Analysis; KNN; Random Forest; Geolocations

[1] Ali, Govand A., "Identifying Terrorist Affiliations through Social Network Analysis Using Data Mining Techniques,". M.S Theses, Dept. Information Technology, Valparaiso Univ., Indiana, USA, 2016.
[2] Enghin Omer, “Using Machine Learning to Identify Jihadist Messages on Twitter,”. M.S Theses, Dept. Information Technology, Uppsala Univ., Sweden, 2015.
[3] M. Rowe and H. Saif, “Mining pro-ISIS radicalisation signals from social media users,” in Proceedings of the 10th International Conference on Web and Social Media, 2016.
[4] Abhishek Barve, “Terror Attack Identifier: Classify using KNN, SVM, Random Forest algorithm and alert through messages,” International Research Journal of Engineering and Technology (IRJET), Vidyalankar Institute of Technology, India, 2018.
[5] Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R, “Sentiment Analysis of Twitter Data,” in Proc of ACL HLT Conf, 2011.
[6] Go, A., Bhayani, R., and Huang, L, “Twitter Sentiment Classification using Distant Supervision,” Technical Report, Stanford Digital Library Technologies Project, 2009.
[7] Juan DU, Zhi an Yi. “A New KNN Categorization Algorithm for Harmful Information Filtering”, 2012 IEEE.
[8] Mateusz Budnik, Iwona Pozniak-Koszalka, Leszek Koszalka, “The Usage of the k-Nearest Neighbour Classifier with Classifier Ensemble”, 12th International Conference on Computational Science and Its Applications, 2012 IEEE.
[9] Mohammad Abdul Wajeed, T. Adilakshmi,” Semi- Supervised Text Classification Using Enhanced KNN”.2011 IEEE.
[10] Lijun Wang, Xiqing Zhao,” Improved Knn Classification Algorithms Research in Text Categorization”. 2012 IEEE.
[11] Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon,” What is Twitter, a Social. or a News Media?”. International World Wide Web Conference Committee (IW3C2), April 26–30, 2010.
[12] G. Kesavaraj, Dr.S. Sukumaran, “A Study on Classification Techniques in Data Mining”, IEEE 4th ICCCNT - 2013 July 4 - 6, 2013.

Dharanija. G, B. Chandana Priya, B. Manasa Sai, G.V. Vishnu Vardhan Reddy, Sujatha. K, "A Diagnosis System Framework for the Time-series analysis of the Terrorism attacks Worldwide", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.18-22, 2019.

Campus Career Management Portal is online application to lessen the distance between member of the work force and provider of the work. The Principle goal is to make the enrolment process of colleges easy. This Campus Career Management Portal is structured based on Provider of work and Member of the work force. Campus Career Management Portal allows member of work force to enrol subtleties that belongs to them such as abilities and involvement in any other activities, and after that on the other hand even it grants providers of work to post their necessities with the Structure. Campus Career Management framework is useful for the activity suppliers for example organizations which need representatives, work searchers who need work, (for both Experienced and fresher`s). These entries primary point is to give the opportunities accessible to the activity searchers without taking any charge from them in IT innovations. Online test can also be taken by students. Campus Career Management Portal will consequently send sends to all activity searchers whose abilities are coordinated with the necessity. The system is an application that is designed is viably utilized with the login credentials

Member of the Workforce,provider of the work, Represntatives

[1] Mandeep Pannu, Qussay Salih, Carson yuen, Zhen Hong Li ,Edwin Tinu, “Web based Project Management System for small to midsize business”, IEEE Transaction, in 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEEE IEMCON), Canada, 2018.
[2] Khampheth Bounnady, Khampaseuth Phanthavong, Somsanouk Pathoumvanh, Keokanlaya Sihalath “Comparision the processing speed between PHP and ASP.NET”, IEEE , in 13th International Conference on Electrical Engineering,/Electronics, Computer Telecommunications and Information Technology (ECTI-CON), Thailand, 2016.
[3] PunnamKumari and Rainu Nandal “A Research Paper onWebsite Development OptimizationUsing XAMPP/PHP”, In international Journal of advanced Research in Computer Science India, 2017.
[4] Yu Ping, Hu Hong-Wei, Zhou Nan,“Design and Implementation of a MySQL database backup and recovery system.” IEEE in proceeding of the 11th World Congress in Intelligent Control and Automation”, China. 2015.

A Jesee, A Mounika, Anupama, Anusha Roy , Basavaraj S H, "Campus Career Management System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.23-27, 2019.

Stock price prediction has always been a curious, interesting and complex topic in business studies. Stock market is very unreliable for forecasting since there are no major rules or algorithms to estimate or predict share price in the stock market. Several methods like Random Forest analysis, neural networks, time series analysis algorithms like ARIMA, statistical analysis, SVM and many more have been used to predict the stock price of shares in the stock market but not all of these implementations have been correctly identified as a consistent acceptable prediction tool. This paper presents a comparative study of time series analysis using Autoregressive Moving Average i.e. (ARIMA MODEL) and Tableau (a powerful business intelligence tool) to predict the closing index of Google Inc. This paper also presents a process to build stock price prediction model with the help of time series analysis i.e. (ARIMA).The model has been built with the help of R Programming and Tableau. With the upcoming of machine learning and neural networks many researchers are trying to predict the stock price of companies and the trend that it will follow in the near future because it affects the investors as well as the competitors that are present for that company in the market. The prediction of stock price can also be done using neural networks, SVM etc. But here time series analysis has been used because it is easy to implement and it gives better results for short term predictions. The results obtained by ARIMA model shows that it is one of the best methods for the analysis of time series data

Stock market, forecasting, ARIMA Model, Business intelligence

[1] N. Chatterjee, S. Mohan, “Extraction-Based Single-Document Summarization Using Random Indexing”, Proc. 19th IEEE International Conference on Tools with Artificial Intelligence, Greece, pp. 448-455, 2007.
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Gaurav Priyadarshi, Avneet Ranjan, Sharath Kumar, Bipul Mohanta, "Stock Price Prediction Using Time Series Analysis and Business Intelligence", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.28-31, 2019.

Theatrical Events of fear like Terrorism based operations have be sighted everywhere throughout the world. This surveillance system analyses and speculates the possible suspects depending upon their conducts, records them into Watch-List, classifies them and stores their images in the Government cloud. Whenever the suspects in the Watch-List land at any Local camera resembling movement cams, Metro stations and Airports which are served by the government cloud, it recognizes them using facial recognition software. Instantly the framework alarms the experts and significant information is sent to the Military Intelligence. Based on the rundowns and photos gathered by the Military surveillance systems, they coordinate with the machine which utilizes facial acknowledgment programming to perceive the suspects in nearby reconnaissance cameras which are served by the administration cloud. The most problems faced is issues of privacy, there is a strict law for invading of public privacy just on suspicion without any proof. Hence its not possible for putting a surveillance on any individual. This machine resolves as it provides physical proofs of why that individual is being surveillance, moreover even common crimes within the city could be stopped by the local authority as facial recognition spots this individual in any camera. The application of this machine is limitless as terabytes of data is available in the local cloud just the accumulating this data and processing through proper channel is necessary

Terrorism, machine learning, Local Cloud, Government Cloud

[1] Andrey I. Kapitanov, Ilona I. Kapitanova,Vladimir M. Troyanovskiy, Vladimir F. Shangin, Nikolay O. Krylikov National Research University of Electronic Technology Zelenograd, Moscow, Russia Approach to Automatic Identification of Terrorist and Radical
Content in Social Networks Messages, 2018
[2] Norshuhani Zamin: A Comprehensive Survey on Security in Cloud
Computing, 2009 Computation World:
[3] Vassilis Plachouras Thomson Reuters, Corporate Research and Development 1 Mark Square, London, EC2A 4EG, United Kingdom Email: vassilis.plachouras@thomsonreuters.com Information Extraction of Regulatory Enforcement Actions: From Anti-Money Laundering Compliance to Countering Terrorism Finance, 2015
[4] Jochen L. Leidner Thomson Reuters, Corporate Research and Development 1 Mark Square, London, EC2A 4EG, United Kingdom Email: jochen.leidner@thomsonreuters.com Information Extraction of Regulatory Enforcement Actions: From Anti-Money Laundering Compliance to Countering Terrorism Finance, 2015
[5]https://www.cfr.org/backgrounder/tracking-down-terrorist-financing,
2019
[6] B.Thuraisingham.: and Counter-Terrorism. CRS Press / Chapman Hall, Web Data Mining and Applications in Business, 2015
[7]https://www.cfr.org/backgrounder/tracking-down-terrorist-financing,
2019

Prajwal Kulkarni, Sneha Pattar, Gopal Krishna Shyam, "Analysis of Security Threats using Machine Learning and Cloud Computing Technology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.32-35, 2019.

Tourism is one of the major revenue earners for any country. Many countries growth depends mostly on their tourism industry-generated income. While exploring, many tourists sometimes face some situations where they are not able to reach out other locals of the place for help because there is a problem in the communication or sometimes are majorly not aware of the place. The travellers need to plan their trips well in advance keeping their time and destination in mind. Sometimes, in a hurry, they tend to forget some places for their trip and regret later. This happens when there is no proper planning for the trip. Keeping an eye on all these issues, we introduce goTripper Chatbot. This is an application which is build using OpenNLP (natural language processing) which makes it possible for the bot to understand natural language human text/voice input and initiate further work. It includes technologies like machine learning and artificial intelligence which helps the bot learn of its own and become smart enough to respond back to the user with time. This not only resolves the communication problem people face but also have different features which are required by a tourist when he/she is on their trip. It also provides the user with a scheduled plan of the trip that he/she may follow. Being a ChatBot, it will be easy to use with minimum work efforts required, time efficient and is very economical.

ChatBot, tourism, tourists, natural language processing, weather, maps, live chat, OpenNLP, intents, entities

[1] Martin C. Brown, “Python: The Complete Reference”, McGraw-Hill Publication, India, pp. 720, 2018.
[2] Sebastian Raschka, “Python Machine Learning”, Packt Publishing Limited; 2nd Revised edition, India, pp. 622, 2017.
[3] Prateek Joshi, “Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers”, Packt Publishing; 1 edition, India, pp. 448, 2017
[4] Christopher M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Springer; 1st ed. 2006. Corr. 2nd printing 2011 edition, India, pp. 738, 2011
[5] Bhavika R. Ranoliya,” Chatbot for university related FAQs”, In the Proceedings of the 2017 IEEE International Conference, Udupi, India, 2017
[6] Nudtaporn Rosruen,” Chatbot Utilization for Medical Consultant System”, In the Proceedings of the 2018 IEEE International Conference, Bangkok, Thailand, 2019
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Monalisha Bandyopadhyay, Mitali Sahoo, Mayur L Rangani, Jyoti K Mirji, "goTripper ChatBot for Tourism", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.36-40, 2019.

MANET is a continuously self-configuring, in infrastructure less network of mobile devices where they are connected wirelessly. TCP protocol which is a reliable protocol, which is widely developed for wired networks. TCP protocols have different TCP variants to detect and control congestion in the network. However, all these variants do not succeed in showing similar performances of controlling congestion in MANE. In this paper,we analyzed the performance of three main congestion controlling TCP variants such as NEW RENO, SACK and VEGAS in AODV (Ad-hoc on demand distance vector) and DSR (Dynamic source routing) reactive routing protocols. File Transfer Protocol(FTP)application is used to provide network traffic between nodes. Different scenarios are created and the average values of each performance metrics such as Jitter, Throughput, and Packet drop and end-to-end delay are used to evaluate the performance

Congestion Control, NEW RENO, SACK, VEGAS, DSR, AODV

[1] Tomar, Poonam, &Prashant Panse,"A Comprehensive Analysis and Comparison of TCP Tahoe,TCPRenoandTCPLite.", International Journal of Computer Scienceand Information Technologies, Vol.2, No5,pp.2467-2471, 2011.
[2] Chitkara, M. and M.W. Ahmad,“Review on manet: characteristics, challenges, imperatives and routing protocols”, International Journal of Computer Science and Mobile Computing. 3(2): p. 432-7, 2014.
[3] YuvarajuB.N&NiranjanNChiplunkar,“ScenarioBasedPerformanceAnalysisofVariants ofTCPusingNS2-Simulator”, International JournalofAdvancements inTechnology, Vol.1,No2, pp.223-233, 2010.
[4] Neha Arora,, “Comparative Analysis of Routing Protocols And TCP in MANETS”, InternationalJournal ofEmerging TrendsinEngineering &Technology , Vol. 02,No.1,pp.19-28, 2013.
[5] SuneelKumarDuvvuri&Dr.S.RamaKrishna,“PerformanceEvaluationofTCPalternatives inMANET using Reactive Routing Protocol”, International Journal ofModern Computer Science,Vol.4,No.4,pp.35-39, 2016.
[6] M.Jehan&Dr.G.Radhamani, “Scalable TCP:Better Throughputin TCP Congestion Control Algorithms onMANETs”, International Journal ofAdvanced Computer Science and Applications ,pp.14-18, 2011.
[7] Iffat Syad, Sehrish Abrejo &Asma Ansari,“analysis of proactive and reactive MANET routingprotocols under selected TC Pvariants”, InternationalJournalofAdhoc,Sensor&Ubiquitous Computing, Vol.4,No.4,pp.17-26, 2013.
[8] Hrituparna Paul,AnishKumar Saha,Partha Pratim Deb &Partha Sarathi,“Comparative Analysis of Different TCP Variantsin MobileAd-HocNetwork”, International Journal of Computer ApplicationsVol.52,No.13,pp.19-22, 2012.

Shibani Shetty, Deeksha Kotian, Divya, Khatheeja Rukshana, Shifana Begum, "Study on Various TCP Variants of Reactive Routing Protocols with Their Performance Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.41-44, 2019.

The proliferation of technologyis infiltrating all the aspects of life dramatically, as a result of which world is becoming dynamic and complex. Brain-Computer Interface (BCI) or Brain-Machine Interface (BMI) is an emerging field of technology whose goal is to make a real-time path between brain and electronic devices such as computers, robots, artificial limbs, self-driving cars and everything which can be connected with the internet. In BMI, brain or cerebral control these by transmitting and receiving electrical signals. This paper presents an idea of how with the help of technology we can control things.or this an example is explained where the system is based on steady-state evoked potential (SSVEP) is used, where it is made to give mobile number as an input. The buttons are typed on a virtual keypad similar to normal keypad. Each key or a button is assigned with a particular key and SSVEP is used to judge their frequencies

BCI, BMI, SSVEP

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[5] Sergey D. Stavisky, “Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements”, Scientific Reports pp 1-5,Issue 05 November 2018.
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