Volume-5 , Issue-6 , Dec 2017, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics

  Oluigbo Ikenna V., Nwokonkwo Obi C., Ezeh Gloria N., Ndukwe Ngoziobasi G.

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.1-12, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.112

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The impact of Internet of Things (IoT) and big data cannot be overemphasized. The aim of this research is to propose a research model for predicting the role of data analytics and Internet of Things in revolutionizing the healthcare industry in Nigeria, and thereafter assess the impact of proposed IoT and big data analytics model to the healthcare sector of Nigerian economy. This study used a quantitative research approach and an analytical field survey, considering five federal medical centers across the country, as well as a number of IT experts and academician. In proposing a model for healthcare industry revolution through big data analytics and Internet of Things, six drivers was identified – Remote Sensing Technology, Cloud Computing, Big Data Center, Data Visualization, Standard User Interface and Display, and Wireless Network Gateways. The predicted model shows that all six drivers individually and collectively significantly influences healthcare industry revolution in Nigeria. In assessing the impact of the proposed model on the healthcare industry, the researcher identified five key operational and management areas. Based on the MANOVA result, the researcher concludes that the proposed model of IoT and big data analytics will have significant impact on healthcare industry in Nigeria if adopted. The researcher thus recommends that for a positive revolution of healthcare industry in Nigeria, the need for Internet of Things devices and technologies, as well as big data analytics is very paramount; Top management and relevant stakeholders should provide maximum support. The healthcare sector should also harness the various benefits of these recent technologies.

  Key-Words / Index Term
  Big Data Analytics, Collaborative Disease Management, Healthcare Industry Revolution, Internet of Things, Remote Health Monitoring

  References
  [1] R. Lopez “An Introduction to the Internet of Things (IoT): Part 1 of the IoT Series”, Lopez Research LLC 2269 Chestnut Street #202 San Francisco, CA 94123, pp. 1-6, November 2013.
  [2] S. Vijayakannan “Transforming Healthcare through Internet of Things”, Project Management Practitioner’s Conference, Architecting Project Management: For Transforming Lives, Nimhans Convention Centre, Bangadore, pp. 1-26, November 2014.
  [3] IERC “European Research Cluster on the Internet of Things”, Internet of Things - Pan European Research and Innovation Vision, pp. 1-10, October, 2011.
  [4] T.H. Davenport “Analytics 3.0”, Harvard Business Review, pp. 68, December 2013
  [5] Y.M. Thivaanraj, R. Vinothkumar “Internet of Things: Benefits and Risk of Smart Healthcare Application”, Journal of Information Systems Research and Innovation, Vol. 10, Issue 3, pp. 37-42, 2016
  [6] R.H. Kim “Cure Performance and Effectiveness of Portable Smart Healthcare Wear System Using Electro-conductive Textiles”, Procedia Manufacturing, 3 (Ahfe), pp. 542–549, 2015
  [7] R. Karen, E. Scott, C. Lyman “The Internet of Things: An Overview, Understanding the Issues and Challenges of a More Connected World”, Internet Society, Galerie Jean-Malbuisson, 15 CH-1204 Geneva, Switzerland, pp. 1-50, 2015
  [8] E.A. Kosmatos, N.D. Tselikas, A.C. Boucouvalas “Integrating RFIDs and Smart Objects into a Unified Internet of Things Architecture. Advances in Internet of Things”, Scientific Research, Vol. 1, pp. 5-12, 2011
  [9] H. Jun-Wei, Y. Shouyi, L. Leibo, Z. Zhen, W. Shaojun “A Crop Monitoring System Based on Wireless Sensor Network”, Procedia Environmental Sciences, Vol. 11, pp. 558– 565, 2011
  [10] J. Healey, N. Pollard, B. Woods “The Healthcare Internet of Things: Rewards and Risks”, Atlantic council, Brent scowcroft center on International Security, Washington DC, pp. 1-18, 2015
  [11] B. Hamilton “Big Data is the Future of Healthcare”, Cognizant 20-20 Insights, pp. 1-7, September 2012
  [12] W. Lidong, A.A. Cheryl “Big Data in Medical Applications and Healthcare”, American Medical Journal, Vol. 6, Issue 1, pp. 1-8, 2015
  [13] V. Sharma, N.K. Joshi “The Evolution of Big Data Security through Hadoop Incremental Security Model”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue 5, pp. 3489-3493, 2015
  [14] R. Hermon, P.A.H Williams “Big Data in Healthcare: What is it used for?” Originally published in the Proceedings of the 3rd Australian eHealth Informatics and Security Conference. Held on the 1-3 December, 2014 at Edith Cowan University, Joondalup Campus, Perth, Western Australia, pp. 40-49, 2014
  [15] D. Niewolny “How the Internet of Things Is Revolutionizing Healthcare”, White paper on freescale Semiconductors, pp. 1-8, 2013
  [16] P. Zhibo “Technologies and Architectures of the Internet of Things (IoT) for Health and Well-being”, Doctoral Thesis, KTH – Royal Institute of Technology Stockholm, Sweden, pp. 1-75, 2013
  [17] K. Alok, S. Sampada “Healthcare applications of the Internet of Things: A Review”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5, Issue 5, pp. 6229-6232, 2014
  [18] K. Pahlavan, P. Krishnamurthy, A. Hatami, M. Ylianttila, J.P. Makela, R. Pichna, J. Vallstron “Handoff in Hybrid Mobile Data Networks”, Mobile and Wireless Communication Summit, Vol. 7, pp. 43-47, 2007
  [19] EY “Big Data: Changing the Way Businesses Compete and Operates”, Insight on Governance, Risks and Compliance, EYGMLimited, 2014
  [20] K. Priyanka, N. Kulennavar “A survey on big data analytics in health care”, International Journal of Computer Science and Information Technologies, Vol. 5, pp. 5865-5868, 2014
  [21] N.P. Terry “Protecting patient privacy in the age of big data”, UMKC Law Review, Vol. 81, pp. 385-415, 2013
  [22] I.V. Oluigbo, B.C. Asiegbu, G.N. Ezeh, O.C. Nwokonkwo “Group Membership Prediction of an Outsourced Software Project: A Discriminant Function Analysis Approach”, International Journal of Scientific Research in Multidisciplinary Studies, Vol. 3, Issue 4, pp. 12-18, 2017
  

  Citation
  Oluigbo Ikenna V., Nwokonkwo Obi C., Ezeh Gloria N., Ndukwe Ngoziobasi G., "Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.1-12, 2017

• Open Access   Article

  Fuzzy Association Rule Mining- A Survey

  Pramod Pardeshi, Ujwala Patil

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.13-18, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.1318

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The World Wide Web has become a huge repository of the hypertexts and documents. The rapid growth of web logs and texts has gained a lot of attention from the researchers for extracting the interesting rule for designing of the web pages, drawing the customer preference, analysing the customer behaviour and decision making for serving the organizations with better services. Such decisions are made by analysing different web parameters such as the server log, registration information, access time, session period, page hits and other relative information left by user. This paper presents a survey on various techniques such as fuzzy logic and rule mining for finding the customer behaviour that helps in better decision making and enhancing the performance of the system.

  Key-Words / Index Term
  Video watermarking, feature selection, rough set theory, motion vectors, particle swarm optimization

  References
  [1] Ujwala Patil and Sachin Pardeshi, “A Survey on User Future Request Prediction: Web Usage Mining”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, Issue 3, pp. 121-124, 2012.
  [2] Rajni Pamnani and Pramila Chawan, “Web Usage Mining: A Research Area in Web Mining”, pp. 2-3, 2012.
  [3] Kavita Sharma, Gulshan Shrivastava and Vikas Kumar, “Web Mining: Today and Tomorrow”, 3rd International Conference on Electronics Computer Technology, Vol. 1, pp. 399-403, 2011.
  [4] Jiawei Han, Jian Pei, Yiwen Yin, “Mining Frequent Patterns without Candidate Generation”, In Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp. 1–12, 2000.
  [5] Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood and Samad Ahmadi, “Web usage mining with evolutionary extraction of temporal fuzzy association rules”, Knowledge-Based Systems, Vol.54, pp. 66-72, 2013.
  [6] Binu Thomas and G. Raju, “A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules”, Hindawi Publishing Corporation, Vol.2013, pp. 1-10, 2013.
  [7] Victoria Nebot and Rafael Berlanga, “Finding association rules in semantic web data”, Knowledge-Based Systems, Vol.25, pp. 51-62, 2012.
  [8] S. Veeramalai, N. Jaisankar and A. Kannan, “Efficient Web Log Mining Using Enhanced Apriori Algorithm with Hash Tree and Fuzzy”, International Journal of Computer Science & Information Technology, Vol.2, No. 4, pp. 60-74, 2010.
  [9] Liu Jian and Wang Yan-Qing, “Web Log Data Mining Based on Association Rule”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Vol.11, pp. 1855-1859, 2011.
  [10] Tarek F. Gharib, Hamed Nassar, Mohamed Taha and Ajith Abraham, “An efficient algorithm for incremental mining of temporal association rules”, Data and Knowledge Engineering, Vol.69, pp. 800-815, 2010.
  [11] Ujwala Manoj Patil and Prof. Dr. J. B. Patil, “MINING FUZZY ASSOCIATION RULES FROM WEB USAGE QUANTITATIVE DATA” CSITY, SIGPRO, AIFZ, NWCOM, DTMN, GRAPHHOC, pp. 89–98, 2016.
  [12] R GEETHARAMANI, P REVATHY and SHOMONA G JACOB, “Prediction of users webpage access behaviour using association rule mining” Sadhana, Vol. 40, part 8, pp. 2353–2365, 2015.
  [13] Richa Soni and Gurpreet Kaur, “ Web Usage Mining: Personalization of Web Usage Data”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 2, 2014
  [14] Chhavi Rana, “A Study of Web Usage Mining Research Tools”, Int. J. Advanced Networking and Applications, Vol. 03 Issue:06, pp. 1422-1429, 2012.
  [15] Stephen G. Matthews, GONGORA, Mario A., HOPGOOD, Adrian A. and AHMADI, Samad, “Temporal Fuzzy Association Rule Mining with 2-tuple Linguistic Representation”, IEEE International Conference on Fuzzy Systems, pp 1-8, 2012.
  

  Citation
  Pramod Pardeshi, Ujwala Patil, "Fuzzy Association Rule Mining- A Survey," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.13-18, 2017

• Open Access   Article

  Car License Plate Detection Using Veda

  M.Bhargavi, Sajja.Radharani

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.19-26, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.1926

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The VEDA is one of the emerging techniques in the field of Image Processing.Car-license-plate detection (CLPD) comprises of two main steps. The first step comprises of converting a color image into grayscale values, which raises the speed of the CLPD method. Then input to Adaptive threshold (AT) to segment the image and binarised it, then apply unwanted-line elimination algorithm (ULEA) to remove unwanted lines thus enhance the image, then VEDA is applied. The second part comprises of CLPD method which processes low resolution images taken by a web camera. Now the VEDA detects the vertical edges,the desired plate details based on color information are highlighted. Now,performing statistical and logical operations the candidate region will be extracted. Finally,the license plate (LP) is detected.

  Key-Words / Index Term
  Car-License Plate Detection(CLPD),Vertical Edge Detection Algorithm(VEDA), Unwanted-Line Elimination Algorithm(ULEA),AdaptiveThresholding(AT),LicensePlate(LP)

  References
  [1] S. N. Huda, K. Marzuki, Y. Rubiyah, and O. Khairuddin, “Comparison of feature extractors in license plate recognition,” in Proc. 1st IEEE AMS, Phuket, Thailand, 2007, pp. 502–506.
  [2] S. Thanongsak and C. Kosin, “The recognition of car license plate for automatic parking system,” in Proc. 5th Int. Symp. Signal Process. Appl., Brisbane, QLD, Australia, 1999, pp. 455–457
  [3] H. Bai and C. Liu, “A hybrid license plate extraction method based on edge statistics and morphology,” in Proc. 17th Int. Conf. Pattern Recognit., Cambridge, U.K., 2004, pp. 831–834.
  [4] M. Fukumi, Y. Takeuchi, H. Fukumoto, Y. Mitsura, and M. Khalid, “Neural network based threshold determination for Malaysia license plate character recognition,” in Proc. 9th Int. Conf. Mechatron. Technol., 2005, pp. 1–5.
  [5] H.-H. P. Wu, H.-H. Chen, R.-J. Wu, and D.-F. Shen, “License plate extraction in low resolution video,” in Proc. IEEE 18th Int. Conf. Pattern Recognit., Hong Kong, 2006, pp. 824–827
  [6] J.-W. Hsieh, S.-H. Yu, and Y. S. Chen, “Morphology-based license plate detection from complex scenes,” in Proc. 16th Int. Conf. Pattern Recognit., Quebec City, QC, Canada, 2002, pp. 176–179.
  [7].Mei Yu and Yong Deak Kim Ajou ,”An Approach to Korean License Plate Recognition Based on Vertical Edge Matching”,in IEEE Xpl.,2000,pp.2975-2980.
  [8]Abbas M. Al-Ghaili, Syamsiah Mashohor, Abdul Rahman Ramli, and Alyani Ismail, “Vertical-Edge-Based Car-License-Plate Detection,” in IEEE Tras. On.Veh.Tec.,Vol 62, 2013, pp. 26–38.
  

  Citation
  M.Bhargavi, Sajja.Radharani, "Car License Plate Detection Using Veda," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.19-26, 2017

• Open Access   Article

  Security Issues in Big Data with Cloud Computing

  Anitya Kumar Gupta, Srishti Gupta

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.27-32, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.2732

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  In this paper, we examine security issues for cloud computing, Big Data, Map Reduce and Hadoop environment. The fundamental focus is on security issues in distributed computing that are connected with huge information. Big data information applications are an awesome advantage to associations, business, organizations and numerous substantial scale and little scale ventures. We additionally talk about different conceivable answers for the issues in distributed computing security and Hadoop. Distributed computing security is creating at a quick pace which incorporates PC security, system security, data security, and information protection. Distributed computing assumes an extremely indispensable part in securing information, applications and the related foundation with the assistance of arrangements, innovations, controls, and enormous information instruments. In addition, distributed computing, enormous information and its applications, focal points are prone to speak to the most promising new frontiers in science.

  Key-Words / Index Term
  Big Data, Cloud Computing, Hadoop, HDFS (Hadoop Distributed File System), Map Reduce

  References
  [1] Ren, Yulong, and Wen Tang. "A Service Integrity Assurance Framework For Cloud Computing Based On Mapreduce."Proceedings of IEEE CCIS2012. Hangzhou: 2012, pp 240 –244, Oct. 30 2012-Nov. 1 2012
  [2]N, Gonzalez, Miers C, Redigolo F, Carvalho T, Simplicio M, de Sousa G.T, and Pourzandi M. "A Quantitative Analysis of Current Security Concerns and Solutions for Cloud Computing.". Athens: 2011. pp 231 – 238, Nov. 29 2011- Dec. 1 2011
  [3] A. Haripriya, A.P. Siva Kumar, "An Effective Patient Treatment Plan Recommendation with Predicted Treatment Time Using Hadoop", International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.155-158, 2017.
  [4] Ujjwal Agarwal, "Cloud Computing: BDaaS and HDaaS (Big Data as a Service and Hadoop as a Service)", International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.131-134, 2017.
  [5] "Security-Enhanced Linux.” Security-Enhanced Linux. N.p. Web. 13 Dec 2013.
  [6] Big data adoption- Infrastructure consideration –TCS White paper
  

  Citation
  Anitya Kumar Gupta, Srishti Gupta, "Security Issues in Big Data with Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.27-32, 2017

• Open Access   Article

  Online Signature Verification using Dynamic Properties

  EdigaLingappa, Geetavani.B, JambulaHareesha

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.33-38, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.3338

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Authentication of persons has been known as a paramount part in society. It is the only means to have a record of the person performing a transaction on his own will. As technology evolved security started to be a main concern as authentication problems increase. There are many authentication methods such as Knowledge based, Biometrics and Ownership based. All these authentication schemes are best at their respective use cases. Knowledge based schemes forces to remember passwords, ownership based requires to carry a thing which performs authentication and finally biometrics methods which are unique to a person requires for the person to safeguard his biological data.The best way of authentication since old times is by using a signature. Verification of the signature is one of the biometric methods used in recognition systems. APIs for verification, storing data and processing coordinates. The user‘s signature input is given using a touch interface of an Android mobile device. The user has to first register his signature and the properties of this signature are used to verify the signature for later transactions. Dynamic properties such as pressure, button status and the co-ordinates are used to provide a secure authentication. This process is called as online signature verification.This signature based verification is used at banking where there is transaction of high value. For land registration, instead of signing the documents, we can use online signature which cannot be forged easily

  Key-Words / Index Term
  Dynamic Time Warping (DTW) algorithm; Online Signature; Dynamic properties; Biometric methods; OnlineSignatureVerification

  References
  [1] Madasu Hanmandlu, Farrukh Sayeed, Shantaram Vasikarla, ‖Online Signature Verification using the Entropy Function‖, 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
  [2] Xiaoyu Song, Xinghua Xia and Fangjun Luan, ―Online Signature Verification Based on Stable Features Extracted Dynamically‖, 2016 IEEE Transactions on Systems, Man, and Cybernetics: Systems
  [3] Michał Lech, Andrzej Czyżewski ―A handwritten signature verification method employing a tablet‖, 2016 Signal Processing: Algorithms, Architectures Arrangements, and Applications (SPA)
  

  Citation
  EdigaLingappa, Geetavani.B, JambulaHareesha , "Online Signature Verification using Dynamic Properties," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.33-38, 2017

• Open Access   Article

  Sentiment Detection from Punjabi Text using Support Vector Machine

  Gagandeep Kaur, Kamaldeep Kaur

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.39-46, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.3946

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This paper focuses on sentiment analysis on Punjabi News Articles using Support Vector Machine. Sentiment analysis is a field of Natural Language Processing and it is the most trending field of research. Sentiment analysis on Punjabi language is to be performed because of increasing amount of Punjabi content over the web, provides an important aspect for the researchers, organizations, and governments to analyze the user-generated content and get the useful information from it. With the increase in the amount of information being communicated via regional languages like Punjabi, comes a promising opportunity of mining this information. Support Vector Machine approach is used by proposed system to classify the content. Support Vector Machine is a supervised machine learning approach that is be used for classification and regression problems. However, it is mostly used for classification problems. So there is a need to analyze the Punjabi language content and get better understanding of Punjabi text. The work focuses on detecting positive or negative sentiment from Punjabi content. The results of the proposed system depict remarkable accuracy. The accuracy of sentiment analysis on Punjabi news articles using Support vector machine is found to be 90%.

  Key-Words / Index Term
  Sentiment Analysis, Natural Language Processing, Punjabi News Articles, Support Vector Machine

  References
  [1] P. Panday, “A survey of Sentiment classification techniques used for Indian regional languages ,” International Journal on Computational Science and Applications, vol. 5, pp. 13-26, April 2015.
  [2] A. Balahur and G. Jacquet, “Sentiment analysis meets social media – Challenges and solutions of the field in view of the current information sharing context,” International Journal Of Engineering And Computer Science, vol. 3, pp. 428–432, July 2015.
  3] J. Kaur and R. Saini, “A study and analysis of opinion mining research in indo-aryan, dravidian and tibeto-burman language families,” International Journal of Data Mining and Emerging Technologies, vol. 4, no. 2, pp. 53-60, July 2014.
  [4] A. Kaur and V. Gupta, “Proposed algorithm of sentiment analysis for Punjabi text,” International Journal of Science and Technology, vol. 6, no. 4, pp. 180-183, May 2014.
  [5] A. Sharma, “Sentiment analyzer using Punjabi language,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 9, pp. 5904-5905, September 2014.
  [6] R. Prabowo and M. Thelwall, “Sentiment analysis: a combined approach,” International Journal of Computer Applications & Information Technology, vol. 4, no. 8, pp. 143-157, July 2009.
  [7] A. Das and S. Bandyopadhyay, “Opinion-Polarity Identification in Bengali,” International Arab Journal of Information Technology, vol. 2, no. 9, pp. 169-182, May 2010.
  [8] K. Mouthami and A. Baloglu, “Sentiment analysis and classification based on textual reviews,” International Conference on Internet and Web Applications and Services, chicago, 2010, pp. 10-17.
  [9] N.C. Troussas, and A. Agarwal, “Sentiment analysis in twitter using machine learning techniques,” International Journal of Computer Science and Engineering, vol. 4, no. 2, p. 3038, June 2011.
  [10] N. Garg “Movie review mining in Punjabi,” International Journal of Application or Innovation in Engineering & Management, vol. 2, no. 12, pp. 372-375, December 2013.
  [11] J. Kaur, J. Kumar, and R. SAINI, "A study of text classification natural language processing algorithms for Indian languages,” International Journal of Computer Applications & Information Technology, vol. 4, no. 9, pp. 162-167, July 2015.
  [12] A. Das, “Opinion-polarity identification in Bengali,” International Journal of Computer Science, vol. 10, no. 5, pp. 169-182, April 2010.
  [13] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 1, no. 5, pp. 1093-1113, April 2014.
  [14] J. Bhaskar, K. Sruthi, and P. Nedungadi, "Hybrid approach for emotion classification of audio conversation based on text and speech mining,” International Conference on Information and Communication Technologies, Amritapuri, pp. 635-643, April 2015.
  [15] R. Kumar and R. Vadlamani , “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 37, no. 11, pp. 14-46, June 2015.
  [16] V. Kumar, “Opinion mining and sentiment analysis,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 39, no. 10, pp. 141-166, April 2015.
  [17]U. Jain and A. Sandu, “Emotion Detection from Punjabi Text using Hybrid Support Vector Machine and Maximum Entropy Algorithm,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 11, p. 5, November 2015.
  [18]V.K. Singh and R. Rani, “Sentiment analysis of movie reviews for aspect-level sentiment classification,” International Journal of Advanced Computer Research, vol. 9, no. 5, pp. 10-39, April 2015.
  

  Citation
  Gagandeep Kaur, Kamaldeep Kaur, "Sentiment Detection from Punjabi Text using Support Vector Machine," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.39-46, 2017

• Open Access   Article

  Customer Behaviour Analysis using Web Usage Mining

  Sonia Sharma, Munishwar Rai

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.47-50, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.4750

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This paper attempts to determine the veiled information and identifying user behavior on the web by using the web data sources. With the help of this information, Prediction of user behavior can be done very easily. The user pattern is analyzed by applying the free tool similar web and page score on the website of the particular organization. This tool tries to conduct web mining in a domain independent manner. The experimental results provide an easier way to navigate the website and improve the website design architecture. This work deliberates the detailed results of a website in a specific education domain application. The main objective of conducting the study on websites of the different universities is to discern the pattern of behavioural expressions and user characteristics so that the organization can improve the structure of website and can upload the more information in a proper way.

  Key-Words / Index Term
  Web usage mining, Consumer Behaviour, Pattern

  References
  [1] Li Yong-hong; Liu Xiao-liang, "Research of data mining based on e-commerce," Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on , vol.4, no., pp.719,722, 9-11 July 2010
  [2] Chaoyang Xiang; Shenghui He; Lei Chen, "A Studying System Based on Web Mining," Intelligent Ubiquitous Computing and Education, 2009, International Symposium pp.433,435, 15-16 May 2009
  [3] Li Mei; Feng Cheng, "Overview of Web mining technology and its application in e-commerce," Computer Engineering and Technology (ICCET), 2010, 2nd International Conference on , vol.7, no., pp.V7-277,V7-280,16-18 April 2010
  [4] Yadav, M.P.; Feeroz, M.; Yadav, V.K., "Mining the customer behavior using web usage mining in e-commerce," Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on , vol., no., pp.1,5, 26-28 July 2012 .
  [5] Zhiwu Liu; Li Wang, "Study of Data Mining Technology Used for E-commerce," Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on , vol., no., pp.509,512, 1-3 Nov. 2010
  [6] S.Yadav, K.Ahmad, J.Shekhar, ”Analysis of Web Mining Applications and Beneficial Areas”, International Journal of IUM, vol.12, Issue 2, 2011.
  [7] Shen Zihao; Wang Hui, "Research on E-Commerce Application Based on Web Mining," Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on , vol., no., pp.337,340, 22-23 June 2010
  [8] Tang, Hewen; Yan, Honglin; Zengfang Yang; Yu Ma; Chunping Li, "Application of Data Mining in Electronic Commerce," Information Engineering, 2009. ICIE `09. WASE International Conference on , vol.1, no., pp.631,634, 10-11 July 2009
  

  Citation
  Sonia Sharma, Munishwar Rai, "Customer Behaviour Analysis using Web Usage Mining," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.47-50, 2017

• Open Access   Article

  Intelligent Reverse Sensor for Electric Wheelchair

  Sooraj Surendran, Gnanaguru G

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.51-53, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.5153

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This research article focuses on the design and implementation of a microcontroller based Automatic Reverse Sensor for Electric Wheelchair. The purposed model presents a reliable and cost effective obstacle detection technique and thereby identifying the obstacles only in reverse motion. The system functions in accordance to the controlled inputs from the sensor to the microcontroller.

  Key-Words / Index Term
  Electric wheelchairs, E-vehicles, Ultrasonic range finder, Obstacle detection

  References
  [1] Disable persons in India “A statistical profile 2016”
  [2] Parking sensor, Wikipedia
  [3] Sunrise medical’s reversing camera
  [4] Microchip’s PIC 16F877A datasheet
  [5] Ultasonic range sensor
  

  Citation
  Sooraj Surendran, Gnanaguru G, "Intelligent Reverse Sensor for Electric Wheelchair," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.51-53, 2017

• Open Access   Article

  Peer 2 Peer System Using Sort

  V. V.Sharma, R. B. Wagh

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.54-57, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.5457

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Peer 2 peer system act as both clients as well as server. This system exposes them to malicious activity. Without central server this system can share their files between them easily. To decrease the attacks of malicious peer the system builds trust between each other. A good peer always gives fair recommendations after uploading authentic files. Both service as well as recommendation attacked performed by malicious peer. Both service based attack as well as recommendation based attack are detect by Self Organizing Trust Model (SORT). To learn trust information of other peer, Peer send query to other peer that interacted in past. Any peer wants to download or upload file from another peer. So peer get recommendation from other node, Peer decides that the node is good or malicious based on recommendation. Peer does not interact with node if it is found malicious but also separate the malicious node from the network. If node found good peer then both can interact with each other. A separate history of interactions for each acquaintance is stored by peer.

  Key-Words / Index Term
  peer to peer system, trust, management, reputation, security

  References
  [1] Ahmet Burak Can and Bharat,‟A Self-Organizing Trust Model for Peer-to-Peer Systems‟IEEE Trans.Dependable and Secure Computing,vol 10, No.1, 2013.
  [2] J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility,” IEEE Trans. Electron Devices, vol. ED-11, pp. 34-39, Jan. 1959
  [3] Aberer.K and Despotovic.Z “Managing Trust in a Peer-2-Peer Information System” Proc. 10th Intl Conf. Information and Knowledge Management (CIKM), (2001),.
  [4] Kamvar.S, Schlosser.M, and Garcia-Molina.H„The (Eigentrust) Algorithm for Reputation Management in P2P Networks‟ Proc. 12th World Wide Web Conf. (WWW), ,(2003).
  [5] R.S.Sinju and C.Felsy, “ Managing Trust Relationship in Peer to Peer System” vol.2,Issue-2,pp-29-36,April-June 2014.
  [6] A. Abdul-Rahman and S. Hailes, “Supporting Trust in Virtual Communities,” Proc. 33rd Hawaii Int’l Conf. System Sciences (HICSS), 2000.
  [7] L. Mui, M. Mohtashemi, and A. Halberstadt, “A Computational Model of Trust and Reputation for E-Businesses,” Proc. 35th Hawaii Int’l Conf. System Sciences (HICSS), 2002.
  [8] A. Jøsang, E. Gray, and M. Kinateder, “Analysing Topologies of Transitive Trust,” Proc. First Int’l Workshop Formal Aspects in Security and Trust (FAST), 2003.
  [9] E. Terzi, Y. Zhong, B. Bhargava, Pankaj, and S. Madria, “An Algorithm for Building User-Role Profiles in a Trust Environment,” Proc. Fourth Int’l Conf. Data Warehousing and Knowledge Discovery (DaWaK), vol. 2454, 2002
  [10] Z. Despotovic and K. Aberer, “Trust-Aware Delivery of Composite Goods,” Proc. First Int’l Conf. Agents and Peer-to-Peer Computing, 2002.
  [11] H. Yu, M. Kaminsky, P.B. Gibbons, and A. Flaxman, “Sybilguard: Defending against Sybil Attacks via Social Networks,” ACM SIGCOMM Computer Comm. Rev., vol. 36, no. 4, pp. 267-278, 2006.
  [12] N. Tran, B. Min, J. Li, and L. Subramanian, “Sybil-Resilient Online Content Voting,” Proc. Sixth USENIX Symp. Networked Systems Design and Implementation (NSDI), 2009.
  [13] K. Hoffman, D. Zage, and C. Nita-Rotaru, “A Survey of Attack and Defense Techniques for Reputation Systems,” ACM Computing Surveys, vol. 42, no. 1, pp. 1:1-1:31, 2009.
  [14] K. Aberer, A. Datta, and M. Hauswirth, “P-Grid: Dynamics of Self- Organization Processes in Structured P2P Systems,” Peer-to-Peer Systems and Applications, vol. 3845, 2005.
  

  Citation
  V. V.Sharma, R. B. Wagh, "Peer 2 Peer System Using Sort," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.54-57, 2017

• Open Access   Article

  A Comparative Study and New Model for Smart Mirror

  D.K. Mittal, V. Verma, R. Rastogi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.6 , pp.58-61, Dec-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.5861

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This paper shows a comparison and design of a smart device – Smart Mirror. The smart mirror discussed here is mainly for home environment. This paper highlights some of the smart mirrors from different companies. These smart mirrors are not widely used due to cost or high requirements of hardware. The proposed smart mirror will be operated by Raspberry Pi and will be connected by real world through internet. The smart mirror will consist Raspberry Pi, LED monitor, speakers, camera, microphone with two-way mirror and acrylic glass. With the help of voice recognition api the mirror will communicate with the user through voice commands and responds them accordingly. The mirror could also support human gestures. The mirror will highlight some basic amenities like time, local news, weather. The mirror will also perform some advance functions such as booking a ride on Uber, face recognition, etc. This mirror with artificial intelligence will provide an extraordinary experience to the user.

  Key-Words / Index Term
  Artificial Intelligence, Raspberry Pi, Smart Mirror, Machine Learning, Home Automation

  References
  [1] Preeti Pannu Vaibhav Khanna, Yash Vardhan, Dhruv Nair, Design and Development of a Smart Mirror Using Raspberry PI, IJEEDC, Volume-5, Issue 1, January 2017.
  [2] “A Health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box”, IEEE Transactions on Industrial Informatics 2014, pp. 2180-2191.
  [3] S. Fang, L. Xu, H. Pei, and Y. Liu, “An Integrated Approach to Snowmelt Flood Forecasting in Water Resource Management,” IEEE Transactions on Industrial Informatics, vol.10, no.1, pp.548-558, 2014.
  [4] L. Li, “echnology designed to combat fakes in the global spply chain,” Business Horizons, vol.56, no.2, pp.167-177, 2013.
  [5] L. Xu, “Introduction: Systems Science in Industrial Sectors,” Systems Research and Behavioral Science, vol.30, no.3, pp.211-213, 2013.
  [6] S. Hodges, S. Taylor, N. Villar, J. Scott, D. Bial, and P.T. Fischer, “Prototyping Connected Devices for the Internet of Things,” Computer, vol.46, no.2, pp.26-34, Feb. 2013.
  [7] Z. Pang, Q. Chen, J. Tian, E. Dubrova, and L. Zheng, “Ecosystem analysis in the design of open platform-based in-home healthcare terminals towards the internet-of-things,” 15th International Conference on Advanced Communication Technology, pp.529-534, 2013.
  [8] Z. Pang, “Technologies and Architectures of the Internet-of-Things (IoT) for Health and Well being,” PhD Thesis, Royal Institute of Technology (KTH), Stockholm, Sweden, 2013.
  [9] X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin, “Smart community: an internet of things application,” IEEE Communications Magazine, vol.49, no.11, pp.68-75, Nov. 2011.
  [10] B. Schuz, et al., “Medication beliefs predict medication adherence in older adults with multiple illnesses,” Journal of Psychosomatic Research, vol. 70, no. 2, pp. 179-187. 2011.
  [11] J. Gao, Z. Pang, Q. Chen, and L.-R. Zheng, “Interactive packaging solutions based on RFID technology and Controlled Delamination Material,” IEEE International Conference on RFID, pp.158 165, Apr. 2010.
  [12] K. Ashton “That `Internet of Things` Thing,” RFID Journal, Jun. 2009.
  [13] P. Mayberry, “Increasing Pharmaceutical Compliance through Better Packaging,” Annual National Symposium on Patient Compliance, 2009.
  [14] C. E. Koop, et al., “Future delivery of health care: Cybercare,” IEEE Engineering in Medicine and Biology Magazine, vol.27, no.6, pp.29-38, Nov. 2008.
  [15] L. Li, and W. Benton, “Hospital technology and nurse staffing management decisions,” Journal of Operations Management, vol.24, no.5, pp.676-691, 2006.
  [16] B. Bhakar, T. Jagadish Kumar, M.V. Kamla , “A Security Determination-Reaction Architecture for Heterogeneous Distributed Network,” International Journal of Scientific Research in Computer Science and Engineering, vol.3, no.4, pp.312-338, 2006.
  [17] L. Li, and C. Markowski, “An analysis of hospital capacity management patterns using Miles and Snow Topology,” International Journal of Management and Enterprise Development, vol.3, no.4, pp.312-338, 2006.
  

  Citation
  D.K. Mittal, V. Verma, R. Rastogi, "A Comparative Study and New Model for Smart Mirror," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.58-61, 2017