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In the applications of data mining, finding a association amongst a number of datasets is an essential concern to be focused. Correlation is generally employed in a statistical tool that supports in computing the association amongst datasets. The correlation coefficient supports in determining the strength in addition to the direction amongst two datasets and generally utilized in the real-valued datasets. In huge databases, there are various fields with mixed data types, like real, nominal and ordinal possesses values of missing information. In this paper, an effort has been made for computing the correlation coefficient between real-valued and nominal-valued dataset with missing values.

Data Mining, Real-valued data, Nominal-Valued data and Missing values

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[20] Shahid Ali Khan, Praveen Dhyani, ”Data Mining: Using C++ to Measure Correlation between Real-Valued and Nominal Valued datasets," International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN: 2347-5552, Volume-3, Issue-2, March 2015.

Shahid Ali Khan, Praveen Dhyani, "Missing Data Imputation to Measure Statistic for Data Mining Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1215-1220, 2019.

In today’s technology world, a majority of users across the world have access to Internet for communication via fingerprint ,images, audio and video. It is a need to understand and recognize the behavior of such larger text information on people by analysing their finger. This Paper focuses on collect a database of fingerprint images, we design a neural network algorithm for fingerprint recognition.In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the data base corressponding to 20 individuals. At the end, a comparative study of the performance of different classifiers is discussed.

Sentiment analysis , deep learning , fingerprint detection

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Kuntesh, Raj Kumar, "Reconstructing Fingerprint Images Using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1221-1224, 2019.

People are getting smart day by day with the upcoming technologies and they want everything fast and up to date. Everyone want their work complete with just one click. So we thought of works where people actually waste their precious time where they should not. One of them was standing in queue at the billing counter in malls. So we designed the system in which there is a trolley with barcode scanner and raspberry pi. All the scanned barcodes will be transferred to pi and through pi to android application. On the android app all items with quantity, price will be displayed and the customer can pay total bill through the app. So, basically the project is based on online billing of bought products. This eliminates the process of standing in queue and saves the time of customer. Customer can also delete or add item during shopping according to his budget and comfort.

Android App, Barcoad Scanner, Trolly Unit, RaspberryPi

[1] Akshay Kumar, Abhinav Gupta, S Balamurugan , S Balaji and Marimuthu R, "Smart Shopping Cart", Issue IEEE Conference 18 Dec 2017.
[2] Xuan Liu, Haitao Zhang, Jingxian Fang, Guan Guan, Yundi Huang, "IN-TELLIGENT SHOPPING CART WITH QUICK PAYMENT BASEDON DYNAMIC TARGET TRACKING", Issue IEEE Conference 19 Dec 2016.
[3] Y. Berdaliyev and A. P. James, "RFID-Cloud Smart Cart System", Issue 2016 International Conference on Advances in Computing, Communica-tions and Informatics (ICACCI), Jaipur, India, 2016, pp. 2346-2352.
[4] J C.Narayana Swamy, Dr. D Seshachalam, Saleem Vila Shari, "Smart RFID based Interactive Kiosk Cart using wireless sensor node", Issue 6-8 Oct 2016 International Conference on Computation System and Informa-tion Technology for Sustainable Solutions (CSITSS), Bangalore, 2016, pp. 459-464.
[5] Ezhilazhang C, Adithya R, Burhanuddin Y. L, Charles F, "Automatic Product Detection and Smart Billing for Shopping using Li-Fi" ,Issue IEEE Conference 9 Jan 2017.
[6] Prasiddhi K.Khairnar, Dhanashri H. Gawali, "Innovative Shopping Cart for Smart Cities", Issue IEEE Conference Vol 2, MAY 19-20 Issue IEEE, 2017 PP.1067-1071.
[7] Agarwal Isha Sanjay, Chawandke Manasi Prashant, "RFID Based Super market Shopping System",2017 International Conference on Big Data, IoT and Data Science (BID), Vishwakarma Institute of Technology, Pune , Dec 20-22, 2017.

P.L. Sitaprao, P.M. Pawale, M.P. Gajare, "Android App Based Wireless Billing for Shopping Malls," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1225-1227, 2019.

In this exploration work, big data gathered from smart devices have been utilized to recover the human activity patterns to enhance smart home occupant`s health status, as there is a great deal of financial investment in the advanced transformation as a push to give healthier biological communities to individuals. which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications.In this transformation a more of smart-devices are prepared around and gives an arranged data that can be utilized to investigate the health data. In this examination, the work mostly centers on breaking down the big data separated from human activities for frequent pattern mining, cluster analysis, prediction to quantify and investigate the energy consumption changes likewise by inhabitants. This paper speaks to the need of breaking down energy-consumption pattern dependent on the machine level, which is totally identified with person behavior.

Smart Devices, Human Activity Patterns, Smart home, Cluster Analysis, Bayesian network

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[14] S. Shrimohammadi, A. Yassine and S. Singh, “Incremental mining of frequent power consumption patterns from smart maters big data”, in Proc. IEEE Electrical Power Energy Conf. (EPEC), Oct. 2016..
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[16] Mrs. Bhawana Mathur, Dr. Manju Kaushik: “Comparative study of k-means and Hierarchical Clustering Techniques”, International journal of Software & Hardware Research in Engg, 2014.
[17] Dr. M Nagalakshmi, Dr. I Surya Prabha, K Anil, Big Data Map Reducing Technique Based Apriori in Distributed Mining. International Journal of Advanced Research in Engineering and Technology, 8(5), 2017, pp 19 – 28.
[18] Parag C. Shukla and Dr. Kishor Atkotiya, Big Data Analytics: What It Is and What It Isn‟t, Characteristics, Classification, Challenges and Importance. International Journal of Computer Engineering & Technology, 8(6), 2017, pp. 60–66.
[19] K. Prema and Dr. A.V. Sriharsha, Differential Privacy in Big Data Analytics for Haptic Applications. International Journal of Computer Engineering & Technology, 8(3), 2017, pp. 11–19.
[20] Naga Raju Hari Manikyam and Dr. S. Mohan Kumar, Methods and Techniques To Deal with Big Data Analytics and Challenges In Cloud Computing Environment. International Journal of Civil Engineering and Technology, 8(4), 2017, pp. 669-678.

P. Geethanjali, "A survey on Large Scale Data Analysis on Human Activity Patterns for health prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1228-1231, 2019.

In this paper, we have presented simulated and measured results of tunable monopole circular microstrip antenna for wide frequency impedance bandwidth which covers low frequency wireless applications. The proposed antenna consists of two identical slots placed on either side of feed axis having fixed dimensions of L1=0.446 cm, L2= 0.6 cm and width W1= 0.4cm. A stub of fixed width is loaded on left side of patch having with Ws=0.9 cm .The upper and lower length of stub is varied from LUS=0.62 to 1.22 cm and LLS=0.457 to 1.057 cm to tune an antenna for wide frequency impedance bandwidth from 1.7425 GHz to 1.4725 GHz. This variation also increases the impedance bandwidth from 163 to 200%when simulated where as it varies from 98.6 to 164 % when measured and gives a peak gain of 1.9079 dB. The proposed antenna can cover all low frequency microwave applications like GPS, GSM, DCS, PCS, UMTE, LTE, Wi-Fi, WLAN, Wi-MAX, FCC ID etc., The simulated results are in good agreement with experimental results .The VSWR is less than 2. The radiation patterns are nearly Omni directional nature both in E and H plane.

Identical slots, stub, TMCMSAWB and FIBW

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Biradar Rajendra, S. N. Mulgi, "Tunable Monopole Circular Microstrip Antenna for Wide FIBW covering Low frequency wireless applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1232-1236, 2019.

An ad hoc network is a network that is independent of pre-established infrastructure and has the capability of handling any damage or changes in the topology. Ad hoc networks can be either static ad hoc network (SANET) or mobile ad hoc network (MANET). In SANET, the nodes have no moving property and they are fixed at one place within the network whereas MANET is a group of wireless nodes that can move and self-organize themselves to form a network for a temporary purpose. Nodes in the MANET have the liberty to join/leave the network due to their mobility property. This paper makes strive to explore the impact of LANMAR routing protocol in SANET and MANET environments.

Ad hoc Network, MANET, SANET, LANMAR, Fisheye, EXata

[1] M. Ilyas, “The Handbook of Ad Hoc Wireless Networks,” CRC Press, 2003.
[2] Diaa Eldein Mustafa Ahmed, Othman O. Khalifa, “A Comprehensive Classification of MANETs Routing Protocols”, International Journal of Computer Applications Technology and Research, Vol. 6, Issue. 3, pp.141-158, 2017.
[3] Neeraj Verma, Sarita Soni, “A Review of Different Routing Protocols in MANET”, International Journal of Advanced Research in Computer Science, Vol. 8, No.3, 2017.
[4] Mehran Abolhasan, Tadeusz Wysocki, Eryk Dutkiewicz, “A review of routing protocols for mobile ad hoc networks”, Faculty of Engineering and Information Sciences , 2004.
[5] F. Bai, N. Sadagopan and A. Helmy, “IMPORTANT: A framework to systematically analyze the Impact of Mobility on Performance of Routing protocols for Adhoc Networks”, IEEE INFOCOM, pp. 825-835, 2003.
[6] Camp, T., Boleng, J., Davies, V, “A survey of mobility models for ad hoc network research”, Wireless Communications and Mobile Computing, Vol.2, Issue.5, pp.483–502, 2002.
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[8] Chingrace Guite, Kamaljeet Kaur Mangat, "A Study on Energy Efficient VM Allocation in Green Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.37-40, 2018.
[9] Mario Gerla, “Landmark Routing Protocol (LANMAR) for Large Scale Ad Hoc Networks”, Internet Draft,draft-ietf-manet-lanmar-05.txt, work in progress, 2003.
[10] Yeng-Zhong Lee, Jason Chen, Xiaoyun Hong, Kaixin Xu, Teresa Breyer, and Mario Gerla, “Experimental Evaluation of LANMAR, a Scalable Ad-Hoc Routing Protocol”, “MINUTEMAN” project, 2003-08.
[11] P. F. Tsuchiya, “The Landmark Hierarchy: a new hierarchy for routing in very large networks,” In Computer Communication Review, vol.18, no.4, pp. 35-42, 1988.
[12] G. Pei, M. Gerla and X. Hong, “LANMAR: Landmark Routing for Large Scale Wireless Ad Hoc Networks with Group Mobility”, Proceedings of IEEE/ACM MobiHOC 2000, Boston, 2000.
[13] G. Pei, M. Gerla, and T.-W. Chen, “Fisheye State Routing: A Routing Scheme for Ad Hoc Wireless Networks”, Proceedings of ICC 2000, New Orleans, LA, 2000.
[14] M. Gerla, “Fisheye state routing protocol (FSR) for ad hoc networks”, Internet Draft, draft-ietf-manet-fsr-03.txt, work in progress, 2002.
[15] Anurag Singh, Rajnesh Singh, Sunil Gupta, "Evaluating the Performance of TCP over Routing Protocols in MANETs Using NS2", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.4, pp.1- 4, 2018.

Anveshini Dumala, S. Pallam Setty, "Performance Analysis of LANMAR Routing Protocol in SANET and MANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1237-1242, 2019.

Requirement gathering is one of the most crucial phases in the software development life cycle and the change life cycle. Any discrepancy in requirements leads to Technical Debt , has comparatively more impact than other phases of the software development life cycle and has an adverse impact on the software life expectancy. Most of the times, requirements discussed are very superficial that leads to architects and developers assuming so many details. These assumptions mostly end up being discrepancies. Therefore, having precise complete and unambiguous requirements in the initial phase makes the design and development less error prone due to reduced or minimized surprises. Thus, adequate and unambiguous requirements are the key elements of software success. Along with un-ambiguity and completeness, correct prioritization is vital. Incorrect or misleading prioritization results into inaccurate estimation and unmanageable scope. Therefore having a common vocabulary for prioritization along with precise and detailed requirements can help keeping Technical Debt minimized and longer life expectancy of the software. There are many techniques of the requirement gathering. In this paper, this author proposes a method for requirement structuring and prioritization.

Requirement Gathering, Requirements Prioritization, Software Engineering, Software Architecture, Software Quality, Software Life Expectancy

[1] Microsoft Corporation, Canada, “Microsoft Attention Spans Research Report”, www.scribd.com. [Online] Spring 2015
[2] S. S. Dhupkar, “Measuring Software Life Expectancy”, International Journal of modern Trends in Engineering and Research, Vol. 3, Issue 10, pp. 178-184, 2016
[3] S. S. Dhupkar, “Technical Debts, Impact and Settlement”, International Journal for Research in Applied Science and Engineering Technology, Vol. 5, Issue 11, 2017

Saurabh Dhupkar, "Tree Structure of Requirements and HKEH Prioritization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1243-1247, 2019.

The nodes lifetime maximization is one of the serious problems in the wireless sensor networks (WSNs). Clustering technique demonstrate to the most popular solution for enhancing sum of expended energy of WSNs. Every sensor can oversee an occasion and send data to its cluster head (CH) which totals and transmits data to the BS (BS) through different CHs in the network in clustered WSNs. This situation induces the hot spot` issue where closer CHs to the BS will die prior due to the overwhelming transfer data. Unequal clustering techniques have attempted to tackle this issue and control the extent of each cluster in the network. In this paper, we proposed another unequal clustering calculation called energy degree distance unequal clustering algorithm (EDDUCA) planning with maintenance to adjust energy utilization and expand the lifetime of the network. EDDUCA utilizes the `Sierpinski triangle` technique so as to divide network into unequal clusters. The derived outcomes show that EDDUCA-M can adequately adjust the energy utilization and thusly can extend the lifetime of network.

Unequal clustering, WSN, Energy efficiency, Lifetime, Cluster maintenance

[1] Arjunan, S. and Pothula, S., “A survey on unequal clustering protocols in Wireless Sensor Networks”, Journal of King Saud University-Computer and Information Science, 2017.
[2] Kumar, D., Aseri, T. C., & Patel, R. B,”EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks”, Computer Communications, Vol.32, pp. 662–667, 2009.
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[6] Ye, M., Li, C., Chen, GH., & Wu, J.,” EECS: An energy efficient clustering scheme in wireless sensor networks”, In 24th IEEE international performance, computing, and communication conference, pp. 535–540, 2005.
[7] Jun, Y., Weiming, Z., Weidong, X., Daquan, T., &Jiuyang, T. ,”Energy efficient and balanced cluster-based data aggregation algorithm for wireless sensor networks”, Procedia Engineering, Vol. 29, pp.2009–2015, 2012.
[8] Li, C., Ye, M., Chen, G., & Wu, J. “An energy-efficient unequal clustering mechanism for Wireless sensor network”, In IEEE international conference on mobile adhoc and sensor systems conference, pp. 596–640, 2005.
[9] Song, M., Chenglin, Z., Zheng, Z., &Yabin, Y, ”An improved fuzzy unequal clustering algorithm for wireless sensor network” ,Mobile Network Application Journal, LLC, pp.206–214, 2012.
[10] Arjunan, S. and Sujatha, P., “Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol”.,Applied Intelligence, pp.1-18, 2018.
[11] Uthayakumar J, Vengattaraman T, Dhavachelvan P. ,”A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks”, Ad Hoc Networks,. pp.149-57,2009.

N. Hema Rajini, "Optimal Unequal Clustering Maintenance Algorithm Using Sierpinski Triangle for Image Transmission in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1248-1252, 2019.

Extracting information from a particular dataset in various sectors and transforms it into different useful form for a particular process is called data mining. The data mining will manipulate a data to establish patterns for making decisions in needy situations. This type of process in data mining will lead the researchers to evaluate N number of process. The growth of the country lies on the background of education system. Now educational data mining deals lot of issues that may lead different form of solutions. The main objective of this paper is to compare the different classification techniques using weka tool. Using a weka tool were Navies Bayes, J48, AdaBoostM1, LMT and SMO algorithms are utilized for performing classification techniques.

Data mining, Classification, Naïve bayes, J48, AdaboostM1, LMT and SMO

[1] S. Vijayarani and M. Muthulakshmi, "Comparative analysis of bayes and lazy classification algorithms," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 8, pp. 3118-3124, 2013.
[2] Marie Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[3] Namrata Ghuse, Pranali Pawar, Amol Potgantwar, "An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.27-33, 2017.
[4] Himanshi, Komal Kumar Bhatia, "Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.50-53, 2018.
[5] M. F. Uddin and J. Lee, "Predicting good fit students by correlating relevant personality traits with academic/career data," in Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2016: IEEE Press, pp. 968-975.
[6] D. Rajeshinigo and J. P. A. Jebamalar, "Educational Mining: A Comparative Study of Classification Algorithms Using Weka," Innovative Res. Comput. Commun. Eng, 2017.
[7] A. B. E. D. Ahmed and I. S. Elaraby, "Data mining: A prediction for student`s performance using classification method," World Journal of Computer Application and Technology, vol. 2, no. 2, pp. 43-47, 2014.
[8] P. Kaur, M. Singh, and G. S. Josan, "Classification and prediction based data mining algorithms to predict slow learners in education sector," Procedia Computer Science, vol. 57, pp. 500-508, 2015.
[9] V. Ramesh, P. Parkavi, and P. Yasodha, "Performance analysis of data mining techniques for placement chance prediction," International Journal of Scientific & Engineering Research, vol. 2, no. 8, p. 1, 2011.
[10] D. K. Tiwary, "A Comparative study of classification algorithms for credit card approval using weka," GALAXY International Interdisciplinary Research Journal, GIIRJ, vol. 2, no. 3, pp. 165-174, 2014.
[11] Deepika Mallampati, "An Efficient Spam Filtering using Supervised Machine Learning Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.33-37, 2018.
[12] M. N. Amin and M. A. Habib, "Comparison of different classification techniques using WEKA for hematological data," American Journal of Engineering Research, vol. 4, no. 3, pp. 55-61, 2015.
[13] R. Kaur and V. Chopra, "Implementing AdaBoost and enhanced AdaBoost algorithm in web mining," International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 7, pp. 306-311, 2015.
[14] G. Taneja and A. Sethi, "Comparison of classifiers in data mining," International Journal of Computer Science and Mobile Computing, vol. 3, no. 11, pp. 102-115, 2014.
[15] F. Alam and S. Pachauri, "Detection using weka," Advances in Computational Sciences and Technology, vol. 10, no. 6, pp. 1731-1743, 2017.
[16] I. Charalampopoulos and I. Anagnostopoulos, "A comparable study employing weka clustering/classification algorithms for web page classification," in 2011 15th Panhellenic Conference on Informatics, 2011: IEEE, pp. 235-239.

T. Thilagaraj, N. Sengottaiyan, "Implementation of Classification Algorithms in Educational Data using Weka Tool," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1253-1257, 2019.

Various statistical data for different weather condition obtained using metrological reports can be used to analyze and predict the feasibility to set up a standalone photovoltaic system at different geographical locations. Various steps involved in modeling the performance of photovoltaic systems include the physical parameters of the surrounding environment that helps in determining the output power generated. For any standalone system, a design and installation at best location is required to predict the optimal renewable energy capabilities. In this paper we intend to analyze the solar irradiation for a specific period through which we can calculate DC power generated. Solar parameters, power parameters and the Power Loss parameters are determined using system advisor model (SAM) which in turn helps us to predict the actual power generated. This virtual simulation facilitates the initial design consideration to set up a standalone power plant. The gross energy yield is obtained considering all losses and this supports to calculate the output power for a given solar module.

Photovoltaic, Modeling, Solar Irradiance, Standalone, Geographical Information system (GIS)

[1] Joshua S. Stein, Christopher P. Cameron, Sandia National Laboratories, Ben Bourne SunPower Corporation, Adrianne Kimber First Solar, Jean Posbic BP Solar, and Terry Jester, Hudson Clean Energy, “A Standardized approach to PV System performance model Validation” Presented at 35th IEEE PVSC, Honolulu, HI June 25, 2010
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[14] M. Chegaar et al. “Effect of illumination intensity on solar cells parameters Energy Procedia” 36 ( 2013 ) 722 – 729. Published by Elsevier
[15] G. Barker Mountain Energy Partnership Longmont, Colorado P. Norton National Renewable Energy Laboratory presented at the Solar 2003 Conference: America’s Secure Energy Austin, Texas June 21–26, 2003
[16] Caisheng Wang, Senior Member, IEEE, and M. Hashem Nehrir, Senior Member, IEEE “Power Management of a Stand-Alone Wind/Photovoltaic/Fuel Cell Energy System” IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 23, NO. 3, SEPTEMBER 2008 pg957-67
[17] S. Joshi and D.K. Rai, “Design and Simulations of Load Management Impact on Power System” International Journal of Scientific Research in Computer Science and EngineeringVol.5, Issue.6, pp.79-82. E-ISSN: 2320-7639
[18] S. Joshi and D.K. Rai, “Design and Simulations of Load Management Impact on Power System” International Journal of Scientific Research in Computer Science and Engineering Vol.5, Issue.6, pp.75-78 E-ISSN: 2320-7639

Surendra H.H., Seshachalam D., Sudhindra K.R., "Statistical Analysis of Solar Energy Resources of polycrystalline silicon module for a Standalone system in Indian context," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1258-1262, 2019.