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The Intrusion Detection System helps people and organization to detect the attacks, hackers, their logging information and report these information to the owner of the computer system. The Intrusion Detection System not only identifies the attack on the computer system, it also determines problems with current security policies. The popular conventional security mechanisms are – authentication and firewall security. The authentication protects the computer integrity and security from unauthorized person but it cannot prevent authorized (legitimate) users from performing harmful operations on a computer system. On the other hand firewall only security from some internal attacks to the computer peripherals and information, it cannot provide complete security from outside attacks on the internet. The intrusion detection system is a powerful technology that provides security from both the inside as well as outside attacks. In the world of communication, we exchange our data with another users using internet. Also in the age of cloud computing our data is stored on the remote computer which can be accessed using Internet. Therefore, security of data is big concern for different users. We need not only to protect the data, which exchanged through internet but also to protect the stored data from different types of attacks. An Intrusion Detection System does all the above activities for us. Successful Intrusion Detection Systems protect computer systems from various types of computer system attacks. We can construct Intrusion Detection Systems on various platforms. One such platform is data mining.

Intrusion Detection System, Classifications of IDS

[1] Suthaharan, S. (2012). An iterative ellipsoid-based anomaly detection technique for intrusion detection systems. 2012 Proceedings of IEEE Southeastcon.
[2] Ng, J., Joshi, D., & Banik, S. M. (2015). Applying Data Mining Techniques to Intrusion Detection. 2015 12th International Conference on Information Technology - New Generations.
[3] Zhou, Z., Liu, L., & Han, G. (2015). Survival Continuity on Intrusion Detection System of Wireless Sensor Networks. 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).
[4] Mehmood, T., & Rais, H. B. M. (2016). Machine learning algorithms in context of intrusion detection. 2016 3rd International Conference on Computer and Information Sciences (ICCOINS).
[5] Jaiswal, A., Manjunatha, A. S., Madhu, B. R., & Murthy, P. C. (2016). Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning. 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT).
[6] Borkar, A., Donode, A., & Kumari, A. (2017). A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS). 2017 International Conference on Inventive Computing and Informatics (ICICI).
[7] Samrin, R., & Vasumathi, D. (2017). Review on anomaly based network intrusion detection system. 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT).
[8] Zhang, Q., Qu, Y., & Deng, A. (2018). Network Intrusion Detection Using Kernel-based Fuzzy-rough Feature Selection. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[9] Gupta, D., Singhal, S., Malik, S., & Singh, A. (2016). Network intrusion detection system using various data mining techniques. 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS).
[10] Wankhade, K., Patka, S., & Thool, R. (2013). An efficient approach for Intrusion Detection using data mining methods. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[11] Sultana, A., & Jabbar, M. A. (2016). Intelligent network intrusion detection system using data mining techniques. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).
[12] Sharma, B., & Gupta, H. (2014). A Design and Implementation of Intrusion Detection System by Using Data Mining. 2014 Fourth International Conference on Communication Systems and Network Technologies.
[13] El Moussaid, N., & Toumanari, A. (2014). Overview of intrusion detection using data-mining and the features selection. 2014 International Conference on Multimedia Computing and Systems (ICMCS).
[14] Deepa, V. K., & Geetha, J. R. R. (2013). Rapid development of applications in data mining. 2013 International Conference on Green High Performance Computing (ICGHPC).
[15] Bjerkestrand, T., Tsaptsinos, D., & Pfluegel, E. (2015). An evaluation of feature selection and reduction algorithms for network IDS data. 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA).
[16] China Appala Naidu, R., & Avadhani, P. S. (2012). A comparison of data mining techniques for intrusion detection. 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
[17] Ariafar, E., & Kiani, R. (2017). Intrusion detection system using an optimized framework based on datamining techniques. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI).
[18] Elekar, K. S. (2015). Combination of data mining techniques for intrusion detection system. 2015 International Conference on Computer, Communication and Control (IC4).
[19] Das, A., & Sathya, S. S. (2012). A fuzzy approach to feature reduction in KDD intrusion detection dataset. 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT’12).
[20] Prachi Tembhare1, Neeraj Shukla (2017)[20] An Integrated and Improved Scheme for Efficient Intrusion Detection in Cloud.
[21] P. Rutravigneshwaran (2017) A Study of Intrusion Detection System using Efficient Data Mining Techniques.

Anil Lamba, "A Survey of various machine learning techniques used in Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.557-563, 2019.

In this paper we analyze the E-Government and their various aspects. E-Government is the use of information and communication technologies (ICTs). It improves the communication into public sector organizations. Electronic government means all government facilities make useful for all. We can say that easy and corruption free facilities for all the citizens. E-Government is used in most of the countries because of its various advantages. Because of Fast growth in Internet and information technology, many governments all over the world have changing their services from the habitual to electronic. Primarily E-Government facilitates to decrease expensive cost in construction of infrastructure but it implements large savings towards government’s performance. Citizens can increase of the governmental services from anywhere and anytime. E-Government is measured as a tool for simple administration of government activities. The advantages of this review paper could be useful to those who want to learn about e-Government and its outcome especially the government officials dealing with e-Government strategy and implementation.

e-Government, electronic, information, communication, technology

[1] Zahir Irani, Peter E.D. Love, Ali Montazemi, “e-government: Past, present and future", European Journal of Information Systems, pp.103-105, 2007.
[2] Premkumar, G., Alfred T. Ho, and Pallavi Chakraborty, “E-government evolution: an evaluation of local online services”, International Journal of Electronic Business 4, no. 2, pp.177-190, 2006.
[3] Hongxiu, L., Reima, S. (2009), “A Proposed Scale for measuring E-service Quality”, International Journal of u- and e-service, Science and Technology, 2, 1.
[4] Seifter, J., and J. Chung. "Using e-government to reinforce government-citizen relationships." Social Science Computer Reviews 27, pp.3-23, 2008.
[5] Jensen, Michael C., William H. Mackling, “Theory of the Firm: Managing Behavior, Agency Cost, and Ownership Structure” , Journal of Financial Economics, Volume 3, 1976.
[6] Mintzberg, Henry, “Managerial Work: Analysis from Observation, Management Science”, volume 18, October 1971.
[7] Rogers W’O Okot-Uma, “Electronic Governance: Re-inventing Good Governance”, Commonwealth Secretariat London.
[8] Fassnacht, Martin, Ibrahim Koese, "Quality of electronic services: Conceptualizing and testing a hierarchical model." Journal of service research , 19-37, 2006.
[9] Christos Halaris, Magoutas B., Papadomichelaki X., and Mentzas G. , “Classification and synthesis of quality approaches in e-government services”, Emerald Group Publishing Limited, Vol. 17 No. 4, pp. 378-401, 2007.
[10] Esteves, José, Rhoda C. Joseph, "A comprehensive framework for the assessment of eGovernment projects.", Government information quarterly, vol. 25, issue. 1, PP.118-132, 2008.
[11] Jansen, Jurjen, Sjoerd de Vries, Paul van Schaik, "The contextual benchmark method: Benchmarking e-government services." Government Information Quarterly, vol. 27, issue. 3, pp. 213-219, 2010.
[12] Rowley, Jennifer, "An analysis of the e-service literature: towards a research agenda" , Internet research, Vol.16, Issue. 3, pp.339-359, 2006.
[13] Behkamal, Behshid, Mohsen Kahani, Mohammad Kazem Akbari, "Customizing ISO 9126 quality model for evaluation of B2B applications", Information and software technology, Vol.51, Issue. 3, pp. 599-609, 2009.
[14] Chutimaskul, Wichian, Suree Funilkul, Vithida Chongsuphajaisiddhi, "The quality framework of e-government development", In Proceedings of the 2nd international conference on Theory and practice of electronic governance ACM, pp. 105-109., 2008.
[15] Behkamal, Behshid, Mohsen Kahani, and Mohammad Kazem Akbari, "Customizing ISO 9126 quality model for evaluation of B2B applications", Information and software technology Vol.51, Issue. 3, pp.599-609, 2009.
[16] Chutimaskul, Wichian, Suree Funilkul, and Vithida Chongsuphajaisiddhi, "The quality framework of e-government development", In Proceedings of the 2nd international conference on Theory and practice of electronic governance ACM, pp. 105-109, 2008.
[17] Magoutas, Babis, Kay-Uwe Schmidt, Gregoris Mentzas, Ljiljana Stojanovic, "An adaptive e-questionnaire for measuring user perceived portal quality", International Journal of Human-Computer Studies, Vol.68, Issue.10, pp.729-745, 2010.
[18] Bertot, John C., Paul T. Jaeger, and Justin M. Grimes. "Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies", Government information quarterly, Vol.27, Issue.3, pp.264-271, 2010.
[19] Pazalos, Konstantinos, Euripidis Loukis, and Vassilios Nikolopoulos, "A structured methodology for assessing and improving e-services in digital cities", Telematics and Informatics, Vol.29, Issue.1, pp.123-136, 2012.
[20] Joseph S. Nye Jr. John D. Donahue, “Governance in a Globalization World” , Visions of Governance For the 21st century Brookings Institution Press.
[21] Kettl, D. F., “The Transformation of Governance”, John Hopkins University Press, U.S.A. 2002.
[22] Heeks, Richard, and Savita Bailur, "Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice", Government information quarterly, Vol.24, Issue.2, pp.243-265, 2007.
[23] Leitner, C., “eGovernment in Europe: The State of Affairs”, European Institute of Public Administration, Maastricht, the Netherlands, 2003.
[24] Michiel Backus, “E-Governance and Developing Countries”, Introduction and examples, Research Report, Vol.3, April.2001.

Phramaha Wattana Losanta, S. N. Deshmukh, "A Review on Local E-Government And Informaiton Quality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.564-568, 2019.

The design and development of an interactive Smart Mirror is for the ambient home environment as well as for commercial uses in various industries. The project which would display data on the mirror and the data and would be managed by the Raspberry Pi. The smart mirror implemented as a personalized digital device equipped with peripherals such as Raspberry Pi, microphone, speakers, LED monitor, webcam covered with a sheet of reflective mirror provides one of the most basic common amenities such as weather of the city, latest updates of news and headlines and local time corresponding to the location and Alexa voice assistant using face recognition.

Smart Mirror, Raspberry Pi, Weather, Time, News, Face recognition and Alexa voice assistant

[1] Mohammed Ghazal, Tara al Hadithy,Yyasmina al Khalil, Muhammad Akmal and Hassan Hajjdiab, " a Mobile-programmable smart mirror for ambient IoT environments", in 5th international conference on future internet of things and cloud workshops, 2017.
[2] Muhammed Mu’izzudeen, Yusri Shahreen Kasim, Rohayanti Hassan, Zubaile Abdullah Husni Ruslai, Kamaruzzaman Jahidin, Mohammad Syafwan Arshad, " Smart Mirror for Smart Life", in IEEE Conference publication, 2017.
[3] .Ivette Cristina Araujo Garcia, Eduardo Rodrigo Linares Salmon, Rosario Villalta Riega, Alfredo Barrientos Padilla, "Implementation and Customization of a Smart Mirror through a Facial Recognition Authentication and a Personalized News Recommendation Algorithm", in 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017.
[4] Oihane Gomez-Carmona, Diego Casado-Mansilla, "SmiWork: An Interactive Smart Mirror Platform or Workplace Health Promotion", 2017.
[5] Ramya .S , Saranya. S , Yuvamalini. M, "The Smart Mirror", in International Journal of Advanced Research, Ideas and Innovations in Technology, 2018.
[6] Vaibhav Khanna, Yash Vardhan, Dhruv Nair, Preeti Pannu, " Design And Development Of A Smart Mirror Using Raspberry Pi ", in International Journal of Electrical, Electronics and Data Communication, 2018.
[7] Derrick Gold, David Sollinger, Indratmo, "SmartReflect : A modular smart mirror application platform", in 7th Annual Information Technology, Electronics and Mobile Communication Conference(IEMCON), 2016.
[8] S Athira, Frangly Francis, Radwin Raphel, N S Sachin, Snophy Porinchu, Seenia Francis, "Smart mirror : A novel framework for interactive display", in International Conference on Circuit, Power and Computing Technologies(ICCPCT), 2016.
[9] Kun Jin, Xibo Deng, Zhi Huang, Shaochang Chen, "Design of the Smart Mirror Based on Raspberry PI", in 2nd IEEE Advanced Information Managements, Communicates, Electronics and Automation Control Conference(IMCEC), 2018.
[10] A Comparison of facial recognition`s algorithms (2017)
(https://www.theseus.fi/bitstream/handle/10024/132808/Delbiaggio_Nicolas.pdf?sequence=1).

Khurd Aishwarya .S, Shweta .S. Kakade, R. M. Dalvi , "Futuristic Smart Mirror," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.569-572, 2019.

With natural calamities, plant disease also plays a major role in severe damage of agricultural product. Hence it is very much important to prevent the crop from being affected by different type of diseases. Likewise betel-vine which is also known as “the green gold of India” is affected by different kind of diseases during its short life period. But leaf rot disease affects the plant all over the year, which is a great loss to the farmer, as twenty million of people of our country make their livelihood directly or indirectly from betel vine. Here I proposed two methods to detect the affected area and quantify the area exactly in betel vine leaf so that, leaves can be protected from severe damage by applying exact amount of pesticides as needed in time and this is the novel aim behind this research work. Hence two methods are simulated namely Otsu’s global thresholding and K-means clustering to get the ROI clearly after segmentation and finally made a comparison to know, which one is giving better result. By applying Otsu’s methodology (PM-1), it is evident from table that the precision of (PM-1) is very high, but the recall value is low, as the average recall value is only 52%. But experimental results shown that K-means is better one with very high precision and high recall value where, the average recall value is 0.9366 or 93.66%.

Segmentation, ROI, Detection, Quantify, Precision, Recall

[1] M Jhuria, A kumar and R Borse, ”Image processing for Smart farming, detection of Disease and Fruit Grading,” Proceedings of the 2013 International Conference on Image Information processing, pp. 521-526, 2016
[2] A K Dey, M Sharma, M.R.Meshram, ”Image Processing Based
Leaf Rot Disease, Detection of Betel Vine”, Proceedings of the 2016 International Conference on computational Modeling and Security, CMS, pp.748-754 , 2016
[3] J. Vijaykumar, S. Arumugam, ”Early Detection of Powdery mildew Disease for Betel vine plants Using Digital Image Analysis”, International Journal of Modern Engineering Research, vol.2.Issue 4, pp. 2581-2583, 2012
[4] P.Tamilshankar, Dr.T.Gunasekar,”Computer Aided Diseases Identification for Betel Leaf”, International Research Journal of Engineering Technology ,Vol 2, Issue 9, pp. 2577-2581, 2015

[5] J.Vijayakumar, ”Powdery mildew disease Identification in Pachaikodi variety of Betel vine plants using Histogram and Neural Network based Digital Imaging Techniques, International Research Journal of Engineering Technology, Vol 3, Issue 2, pp. 145-1152, 2016
[6] Abdullah NE, Rahim AA, Hashim H, Kamal MM “Classification of Rubber tree leaf diseases using multilayer Perceptron neural network”, Proceedings of IEEE 2007 5th Student Conference on Research and Development. , pp 16.
[7] M Bhange, H.A.Hingoliwala, “Smart Framing: Pomegranate Disease Detection Using Image Processing”, Proceedings of 2015 International Symposium on Computer Vision and Internet (VisionNet’15),pp. 280-288
[8] Ehsan Kiani, Tofik Mamedov, “ Identification of Plant Disease Infection using soft-computing : Application to modern botany”,9th International Conference on Theory and application of Soft Computing ,Computing with Words and Perception, pp. 24-25 ,2017
[9] Riddhi H. Shaparia, Narendra M.Patel, Zankhana H.Shah “Flower Classification using Different Color Channel”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, issue 2,pp. 1-6, 2019
[10] V. Davis, S. Devane, "Diagnosis of Brain Hemorrhage Using Artificial Neural Network", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp.20-23, 2017

Sannihita Pattanaik, Chandra Sekhar Panda, "Diseased area Detection & Quantification of Betel-vine leaves, Affected by Leaf rot disease," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.573-577, 2019.

Design and simulation of novel operational transconductance amplifier (O.T.A.) based on SOI FinFET technology has been performed. The simulation results indicate that there is an increase in DC gain by 44.4%, C.M.R.R. by 1.5% , P.S.R.R. by 52.6%. The bandwidth of FinFET based O.T.A. is 161.301 MHz and CMOS O.T.A. is 1.00 MHz. The proposed circuit is designed using LTspice simulation at 1.5V supply Voltage. PTM 32 nm SOI FinFET has been employed for simulation. Comparative analysis of analog performance parameters of FinFET based O.T.A. and conventional CMOS based O.T.A. is also carried out.

FinFET, CMOS, O.T.A., PTM, C.M.R.R., P.S.R.R., DC Gain

[1]. Mythry, Sarin V., et. al. "Design and Analysis of High Gain CMOS Telescopic OTA in 180nm Technology for Biomedical and RF Applications." International Journal of Microelectronics Engineering, Vol. 1, No.1 , 2015.
[2]. Kushwah, Ravindra Singh, and Shyam Akashe. "FinFET Based Tunable Analog Circuit: Design and Analysis at Technology." Chinese journal of engineering , 165945, 8 P-P, Volume 2013.
[3]. Matsukawa, Takashi, et. al. "Decomposition of on-current variability of nMOS FinFETs for prediction beyond 20 nm." IEEE Transactions on Electron Devices 59.8,2012.
[4]. M Nizamuddin, Sajad A Loan, et.al., “Design, simulation and comparative analysis of CNT based Cascode Operational Transconductance Amplifiers”, Nanotechnology, IOP Publishing Ltd , U.K. Volume 26 , Number 39, 02 October 2015.
[5]. Sajad A Loan, M Nizamuddin et.al., “Design and Comparative Analysis of High Performance Carbon Nanotube Based Operational Transconductance Amplifiers”, NANO: World Scientific, Hong Kong Vol. 10, No. 3, 2015.
[6]. M Nizamuddin, , et al. "Design, Simulation and the Comparative Analysis of Carbon Nanotube Field Effect Transistors Based Multistage Operational Amplifiers." Journal of Nanoelectronics and Optoelectronics 12.10, 2017
[7]. Shirazi, Mahdi, and Alireza Hassanzadeh. "Design of a low voltage low power self-biased OTA using independent gate FinFET and PTM models." AEU-International Journal of Electronics and Communications 82 2017.
[8]. Ragheb, A. N., and Hyung Won Kim. "Ultra-low power OTA based on bias recycling and subthreshold operation with phase margin enhancement." Microelectronics Journal 60, 2017.
[9]. Thakker, Rajesh A., et al. "A novel architecture for improving slew rate in FinFET-based op- amps and OTAs." Microelectronics Journal 42.5, 2011.
[10]. Subramaniana, V., et al. "Device and circuit-level analog performance trade-offs: a comparative study of planar bulk FETs versus FinFETs." Electron Devices Meeting, 2005. IEDM Technical Digest. IEEE International. IEEE, 2005.
[11]. Vidhi Tiwari1 , Pratibha Adkar, “Implementation of IoT in Home Automation using android application”, Vol.7, Issue.2, pp.11-16, April International Journal of Scientific Research in Computer Science and Engineering, 2019.
[12]. A. Fasiku, Ayodeji Ireti, “Comparison of Intel Single-Core and Intel Dual-Core Processor Performance”, Volume-1, Issue-1 ,International Journal of Scientific Research in Computer Science and Engineering, Jan- Feb-2013.

M Nizamuddin, Divisha Sharma, "FinFET based Operational Transconductance Amplifier for Low Power Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.578-581, 2019.

Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. One important problem is mining data streams in extremely large databases (e.g. 100 TB). Satellite and computer network data can easily be of this scale. However, today’s data mining technology is still too slow to handle data of this scale. In addition, data mining should be a continuous, online process, rather than an occasional one-shot process. Organizations that can do this will have a decisive advantage over ones that do not. One particular instance is from high speed network traffic where one hopes to mine information for various purposes, including identifying anomalous events possibly indicating attacks of one kind or another. A technical problem is how to compute models over streaming data, which accommodate changing environments from which the data are drawn. This is the problem of “concept drift” or “environment drift.” This problem is particularly hard in the context of large streaming data. How may one compute models that are accurate and useful very efficiently? For example, one cannot presume to have a great deal of computing power and resources to store a lot of data, or to pass over the data multiple times. Hence, incremental mining and effective model updating to maintain accurate modeling of the current stream are both very hard problems.

Data Stream, Data Stream Mining, Concept Drift/Environment Drift

[1] Latifur Khan1,Wei Fan, Data Stream Mining and Its Applications, June, 2012.
[2] Chen, S., Wang, H., Zhou, S., Yu, P (2008). Stop chasing trends: Discovering high order models in evolving data, In: Proc. ICDE, pp. 923–932 (2008).
[3] Gaber MM, Zaslavsky A, Krishnaswamy S. Mining data streams: a review. ACM SIGMOD Rec 2005,
[4] Mohamed Medhat Gaber, Advances in data stream mining, Volume 2, Januar y / Februar y 2012.
[5] C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, "A framework for projected clustering of high dimensional data streams," in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, 2004, p.863.
[6] Gaber MM, Zaslavsky A, Krishnaswamy S. Mining data streams: a review. ACM SIGMOD Rec 2005,
[7] Nicolás García-Pedrajas • Aida de Haro-GarcíaScaling up data mining algorithms: review and taxonomy,Received: 4 June 2011 / Accepted: 26 September 2011 / Published online: 13 January 2012
[8] Madjid Khalilian, Norwati Mustapha, MD Nasir Suliman, MD Ali Mamat,” A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets”,Vol.1, IMECS 2010,march 2010.
[9] R.S. Walse , G.D. Kurundkar , P. U. Bhalchandra,”A Review: Design and Development of Novel Techniques for Clustering and Classification of Data” in IJSRCSE, Vol.06 , Special Issue.01 , pp.19-22, Jan-2018.
[10] A.Jenita Jebamalar “Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools” Int. J. Sc. Res. in Network Security and Communication ,Volume-6, Issue-6, December 2018.
[11] Himanshi , Komal Kumar Bhatia, “Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques” Int. J. Sc. Res. in Network Security and Communication , Volume-6, Issue-2, April 2018.

K. Rajasekhar, P. Venkata Maheswara, "Measuring Different Tasks for Unstructured Data and High Speed Data in Data Stream Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.582-589, 2019.

Internet users in the world increasing rapidly. At the present time, the best way of conveying information is the World Wide Web. There are so many websites for learning, shopping, selling, businesses and many more. The expansion of Internet usage will result in increasing web data speedily. To exploit the information of internet usage, it becomes necessary to extract the access behavior of the users.Web usage mining is one of such Data mining technique used for mining, web access log.These access logs are saved on the web server.Access log is the records of all the user, requests for a particular file from a website.Web usage mining will help in improving the design of the website and the personalization of the content. This paper gives the comparative study of web usage mining, it also summarizes the web usage mining approach like pre-processing, pattern discovery, pattern analysis, visualization. This survey listed various research work done by the researcher. It delivers numerous techniques and algorithms used in web usage mining.

server log, access log, web usage mining, pre-processing, user identification, session identification, clustering, classification, pattern discovery & analysis

[1] Daniel T. Larose, Discovering knowledge in data: An Introduction to Data Mining, USA: A John Wiley & Sons, INC, publication, 2005.
[2] Bing Liu, Web data mining: Exploring Hyperlinks, Contents, and usage data, German: Springer-Verlag Berlin Heidelberg, pp 527-540, 2007, ISBN 978-3-642-19459-7.
[3] R. Kosala and H. Blockeel, Web mining research: A survey, ACM SIGKDD Explore. 2 (2000) 1–15
[4] Qingyu Zhang and Richards S. Segall, International Journal of Information Technology & Decision-Making Vol. 7, No. 4 (2008) 683–720
[5] M. Eirinaki and M. Vazirgiannis, “Web mining for web personalization,” ACM Trans. Inter. Tech., Vol. 3, No. 1, pp. 1-27, 2003
[6] B.Lalithadevi, A.Merry Ida, A New Approach For Improving World Wide Web Techniques in Data Mining, International Journal of Advanced Research in Computer Science and Software Engineering, volume 3,issue1, January 2013
[7] M. Aldekhail, Application and Significance of Web Usage Mining in the 21st Century: A Literature Review, International Journal of Computer Theory and Engineering, Vol. 8, No. 1, February 2016
[8] Murat Ali Bayir, Ismail Hakki Toroslu, Ahmet Cosar and Guven Fidan “Discovering more accurate Frequent Web Usage Patterns,” arXiv0804.1409v1, 2008
[9] Michal Munk, Jozef Kapusta, Peter Švec, Constantine the Philosopher University in Nitra, Department of Informatics, Tr. A.Hlinku 1, 949 74 Nitra, Slovakia, “Data Pre-processing Evaluation for Web Log Mining: Reconstruction of Activities of a Web Visitor”, International Conference on Computational Science, ICCS 2010
[10] Mr. Shivkumar Khosla, Mrs. Varunakshi Bhojane, Department of Computer Engineering, Mumbai University, India, “Capturing Web Log and Performing Pre-processing of the User’s Accessing Distance Education System”, International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol.2, Issue.5, Sep.-Oct. 2012
[11] V. Chitraa, Dr. Antony Selvadoss Thanamani, A Novel Technique for Session Identification in Web Usage Mining Pre-processing, International Journal of Computer Application (0975 8887) Volume 34 No. 9, November 2011.
[12] Chaitra L Mugali, Pre-Processing and Analysis of Web Server Logs, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163, Issue 8, Volume 2 (August 2015)
RAJASHREE SHETTAR ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY
RAJASHREE SHETTAR ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY
[13] Rajashree shettar, sequential pattern mining from web log data, IJESAT, ISSN:2250–3676, Volume-2, Issue-2, 204 – 208
[14] P. Fournier-Vige, “Mining partially-ordered sequential rules common to multiple sequences,” IEEE Transactions on Knowledge and Data Engineering, vol. 27(8), pp. 2203–2216, 2015.
[15] P. Fournier-Viger, T. Gueniche, et al, “ERMiner: sequential rule mining using equivalence classes,” The International Symposium on Intelligent Data Analysis, pp. 108–119, 2014.
[16] S. Padmaja et al., International Journal of Engineering and Technology (IJET), Vol 8 No 1 Feb-Mar 2016
[17] Viswanathan K, Mayilvahanan K, and R. Christy Pushpaleela, “Performance Comparison of SVM and C4.5 Algorithms for Heart Disease in Diabetic”, International Journal of Control Theory and Applications, ISSN: 0974-5572, Volume 10, Number 25, 2017.
[18] Ketan D. Patel, “Pre-processing on web server log data for web usage pattern discovery”, International Journal of Computer Applications (0975 – 8887) Volume 165 – No.10, May 2017
[19] Reeny Zackarias, “Predicting Users with Similar Behaviour Through Session”, International Journal of Advanced Engineering and Research Development (IJAERD) Volume 4, Issue 3, March -2017, e-ISSN: 2348 – 4470 [20] Jiaoling Du, Xiangqi Zhang, Hongmei Zhang and Lei Chen, "Research and Improvement of Apriori Algorithm", IEEESixth International Conference on Science and Technology, pp.117-121,2016. [21] V. Chitraa and Antony Selvadoss Thanamani, “Clustering of Navigation Patterns using Bolzwano_WeierstrassTheorem”, Indian Journal of Science and Technology,Vol8(12),69283, June 2015.PP1-9 [22] P. Sukumar, “Review on Modern Data Pre-processing Techniques in Web Usage Mining (WUM),” International Conference on Computational Systems and Information Systems for Sustainable Solutions,978-1-50901022-6/16/IEEE(2016). [23] S S Patil and HP Khandagale, “Enhancing Web Navigation Usability Using Web Usage Mining Techniques”, International Research Journal of Engineering and Technology IRJET, vol 4 6, June 2016. [24]S Sharma and S S Lodhi, “Development of Decision Tree Algorithm for Mining Web Data Stream”, International Journal of Computer Applications, March 2016. [25] Shlin He, Qingwei Lin, et al, “Identifying Impactful Service System Problems Via Log Analysis”, ESE/FSE’18, November 4–9,2018, lake-Buena-Vista, Florida, USA. [26] Sonia Sharma et al, “Customer Behaviour Analysis using Web Usage Mining”, International Journal of Scientific Research in Computer Science and Engineering, vol 5, issue 6, pp4750, December (2017).
[27] Madihah Mohd Saudi, et al,” An Efficient Data Transformation Technique for Web Log”, WCE 2017, July 5–7,2017, London, UK.
[28] TAWFIQ A. AL-ASDI, et al, “An Efficient Web Usage Mining Algorithm Based on Log File Data”, Journal of Theoretical and Applied Information Technology,31 October 2016 vol 92 No 2, ISSN:1992 – 8645.
[29] Arjun Ram Meghwal and Dr. Arvind K Sharma,” Identifying System Error through Web Server Log File in Web Log Mining”, International Journal of Computer Science And Technology,Vol.7, ISSN 1, Jan–March 2016 [30] Jayanti Mehra and Dr. R S Thakur, “An Efficient method for Web Log Pre-processing and Page Access Frequency using Web Usage Mining”, International Journal of Applied Engineering Research ISSN 0973–4562 Vol–13,November–2(2018),pp1227–1232. [31] B. Rajeshwari, “Web Page Prediction Using Web Mining”, IRJET, Vol:5, Issue 5, May 2018, e–ISSN:2395–0056. [32] Aanum Shaikh, “Web Usage Mining Using Apriori and FP Growth Algorithm”, International Journal of Computer Science and Information Technology, Vol– 6, pp 354–357,2015

Sonam Singh Gurjar, Khushboo Agrawal, "Comprehensive Overview On Web Usage Mining Its Task & Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.590-599, 2019.

Medical diagnosis via image processing and machine learning is considered one of the most important issues of artificial intelligence systems. In this paper, we present a machine learning approach to detect whether an MRI image of a brain contains a tumour or not. The results show that such an approach is very promising. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues, which is necessary for planning treatment. Deep Learning is a new machine-learning arena that increased a lot of attention over the earlier few ages. It was extensively useful to numerous bids and established to be an influential machine-learning tool for many of the complex difficulties. In this paper, we used Deep Neural Network classifier, which is one of the DL architectures for classifying a dataset of 66 brain MRIs into four classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumours. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the assessment of the presentation was quite good over all the presentation measures.

Machine learning, Deep learning, Deep neural network, Discrete wavelet transform, Principle component analysis, Fuzzy c-means, Magnetic resonance images

[1] A.R. Kavitha, L. Chitra, R. kanaga “Brain tumor segmentation using genetic algorithm with SVM classifier”, Int J Adv Res Electr Electron Instrum Eng, 5 (3) (2016).
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W. JaiSingh, Preethi Nanjundan, "An Investigation on Brain Tumour Segmentation using Various Machine Learning Approaches," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.600-603, 2019.

The most important practice in India from the very beginning of Indus valley civilization era is agriculture on which both farmer and general people are dependent. But the economic contribution of agriculture is gradually declining. One of the major factors affecting agriculture in India is water shortage. It is caused due to use of inefficient traditional methods for irrigation. So an automated irrigation system is essential to avoid this water wastage. In this system the soil moisture is monitored using different sensors and as required watering is done. Here soil moisture and temperature are the main parameters to be focused on. The data collected is then passed on to fuzzy logic to increase efficiency, optimization and precision. The main purpose of this project is to monitor the soil moisture using Smartphone by sending SMS.

IOT, FIS, Technology, Agriculture, Irrigation

[1] S.N. Deepa, S.N. Sivanandam (Author), “Fuzzy Inference System” Principles of Soft Computing” Wiley publication.
[2] https://www.arduino.cc/en/ArduinoCertified/IntelEdison
[3] https://www.dfrobot.com/category-36.html
[4] https://www.metergroup.com/environment/articles/how-calibrate-soil-moisture-sensors
[5] https://software.intel.com/en-us/intel-system-studio-iot-edition-guide-for-c-troubleshooting-and-faq

Ravindra Kerkar, Kishor Bhosale, Gousiya Khanche, Madhura Pillai, "IoT Based Intelligent Irrigation System Using Intel Edison and Fuzzy Inference System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.604-607, 2019.

Coreference resolution is an important processing step for semantic analysis of a text in NLP. It facilitates in better understanding of the text. So coreference resolution tool becomes a necessity for every NLP process meant for text understanding or generation. The task of selecting a tool from a range of available open source coreference resolution tools can be challenging. This paper presents a study of these available open source coreference resolution tools with the aim to select a better performing tool that can be integrated into an NLP pipeline with ease. After the initial theoretical study of 13 open source coreference tools, a black box testing approach has been followed for testing the performance of 5 selected tools for their performance, usage and ease of integration for building an NLP application like summarization system, dialogue system etc. The performance evaluation is done using standard CoNLL 2012 coreference dataset for English language. The coreference marked output is evaluated against the manually tagged gold standard dataset. The performance is analyzed to select the best performing coreference tool for practical applications.

Coreference resolution, coreference tools, entity resolution, NLP

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[23] H.M. Alfawareh, S. Jusoh, “Resolving ambiguous entity through context knowledge and fuzzy approach. “International journal on computer science and engineering (IJCSE), 3(1), pp.410-422, 2011.

Sandhya Singh, Krishnanjan Bhattacharjee, Hemant Darbari, Seema Verma, "Analyzing Coreference Tools for NLP Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.608-615, 2019.