Volume-07 , Special Issue-12 , May - 2019 Go Back

Driven by the frequent technological changes and innovation, obsolescence has become a major challenge that cannot be ignored in which the life cycle of the components is often shorter than that of their systems. Basically, obsolescence problems are often sudden and not planned which causes delays and extra costs. On the other side forecasting appears to be one of the most efficient solutions to solve this problem. This paper aims to provide new light and help industries to generate different solutions to the problems of obsolescence. Specifically it presents a framework for forecasting the obsolescence based on random forest (RF) algorithm which has proven as the best predictor for forecasting obsolescence risk based on a previous comparative study with a high degree of accuracy.

components, Data minig, Obsolescence

[1] Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning- Connor Jennings, Student Member, IEEE, Dazhong Wu, and Janis Terpenny(SEPTEMBER 2016)
[2]Forecasting technology and part obsolescence-Peter Sandborn(July 2015).
[3]Forecasting Obsolescence Risk using Machine Learning- Connor Jennings, Dazhong Wu, Janis Terpenny-Center for e-Design Industrial and Manufacturing Systems Engineering Pennsylvania State University State College, Pennsylvania, 16801, USA
[4]Electronic Part Obsolescence Forecasting Based on Time Series Modeling-Jungmok Ma1 and Namhun Kim2,#
[5] Electronic Part Life Cycle Concepts and Obsolescence Forecasting- Rajeev Solomon, Peter A. Sandborn, Member, IEEE, and Michael G. Pecht, Fellow, IEEE
[6]Obsolescence and Life Cycle Management for Avionics(November 2015)
[7]A Random Forest Method for Obsolescence Forecasting-Grichi1Y. Beauregard1, T. M. Dao1
[8]Estimating obsolescence risk from demand data - a case studyWillem van Jaarsveld* and Rommert Dekker-( January 18, 2010).
[9]Using Learning-based Filters to Detect Rule-based Filtering Obsolescence by Francis Wolinski 1,2,Frantz Vichot 1,3 & Mathieu Stricker 1,4
[10] Generic Tools and Methods for Obsolescence Control G6rard Gaillat Thomson-CSF Technologies et MNthodes B.P. 56 91401 Orsay, France
[11] Obsolescence and Life Cycle Management for Avionics By Federal Aviation Administration William J. Hughes Technical Center Aviation Research Division Atlantic City International Airport New Jersey 08405
[12] Obsolescence by D. KAYE GAPEN SIGRID P. MILNER

Sarangi Choudhari, Nisha Balani, Parul Jha, "Improving Accuracy of Obsolescence Detection Using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.111-113, 2019.

Software testing is an integral part of the software development life cycle. In order to achieve the testing on individual module or complete integration testing of the system a quality analyst makes test cases for it, covering the different scenarios against which validity of the working of the system is checked. A test case is nothing but a set of conditions and data with it under with it a quality analyst will test a system and check if working according to specification or not. More focus is made on automating the testing of the software, resulting in the creation of the several automation testing framework such as Selenium, Apache Jmeter etc. Similarly attention on creating the test cases is also made. As user stories are written in the natural language, natural language processing can be a way of extracting information from it. Conformiq provides the same functionality. In agile development methodology such as extreme programming such automation is really advantageous which provided quite flexibility and adaptive to change provided.

Software Testing, Automation,Test Cases, Selenium, Conformiq, Agile Methodology, Natural Language Processing, Test Driven Development

[1] Ahlam Ansari ; Mirza Baig Shagufta ; Ansari Sadaf Fatima ; Shaikh Tehreem , “Constructing Test cases using Natural Language Processing” , Published in: 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)
[2] Andrew Rendell, “Effective and Pragmatic Test Driven Development”, Published in: Agile 2008 Conference
[3] “ISO/IEC/IEEE International Standard - Systems and software engineering -- Life cycle processes --Requirements engineering”, Published in: 1 Dec. 2011
[4] J.Zalewski, “How to write the SRS documentation, following IEEE Std. 830.”, ISM 4331, J.Zalewski, September 2003.
[5] Roger Pressman , “Software Engineering A Practitioner`s Approach 7th Edition” , McGraw-Hill, a business unit of The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, NewYork, NY 10020. Copyright © 2010 by The McGraw-Hill Companies, Inc.
[6] Hécio A. Soares and Raimundo S. Moura, “A methodology to guide writing Software Requirements Specification document”, Departamento de Informática Instituto Federal do Piauí and Departamento de Computação Universidade Federal do Piauí, 2015 IEEE.
[7] Jai Gaur ; Akshita Goyal ; Tanupriya Choudhury ; Sai Sabitha, “A walk through of software testing techniques”, 2017.
[8] Itir Karac ; Burak Turhan, “What Do We (Really) Know about Test-Driven Development?” , IEEE Software ( Volume: 35 , Issue: 4 , July/August 2018 )
[9] J.Zalewski, “How to write the SRS documentation, following IEEE Std. 830.”, ISM 4331, J.Zalewski, September 2003.
[10] M. Costantino ; R.G. Morgan ; R.J. Collingham ; R. Carigliano , “Natural language processing and information extraction: qualitative analysis of financial news articles” , Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)
[11] Ron Patton , “Software engineering”. 800 E. 96th St., Indianapolis,
Indiana, 46240 USA.
[12] Shi Zhong ; Chen Liping ; Chen Tian-en, “Agile planning and development methods”,
Published in: 2011 3rd International Conference on Computer Research and Development. Shanghai, China.
[13] Suresh Thummalapenta ; Saurabh Sinha ; Nimit Singhania ; Satish Chandra, “Automating test automation”, Published in: 2012 34th International Conference on Software Engineering (ICSE), Zurich, Switzerland.
[14] W.T. Tsai ; D. Volovik ; T.F. Keefe, “Automated test case generation for programs specified by relational algebra queries”
[15] Roger Pressman , “Software Engineering A Practitioner`s Approach 7thEdition” , McGraw-Hill, a business unit of The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, NewYork, NY 10020. Copyright © 2010 by The McGraw-Hill Companies, Inc.

Saurabh Mahajann, Mona Mulchandani, Samir Ajani, "Test Case Automation Using Natural Language Processing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.114-117, 2019.

Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. Sentiment analysis is a technology with great practical value; it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Internet has opened the new doors for information exchange and the growth of social media has created unprecedented opportunities for citizens to publicly raise their opinions, but it has serious bottlenecks when it comes to do analysis of these opinions. Even urgency to gain a real time understanding of citizens concerns has grown very rapidly. Since, the viral nature of social media which is fast and distributed one, some issues get rapidly distributed and unpredictably become important through this word of mouth opinions expressed online which in turn has known as sentiments of the users. The decision makers and people do not yet realized to make sense of this mass communication and interact sensibly with thousands of others with the help of sentiment analysis.

Sentiment analysis, text mining, literature review, Natural Language Processing, Question Answering

[1]. The evolution of sentiment analysis - Mika V. Mäntylä, Daniel Graziotin , MiikkaKuutila (2017)
[2]. Sentiment Analysis of Twitter Data - ApoorvAgarwal, BoyiXie Ilia Vovsha Owen Rambow Rebecca Passonneau, Department of Computer Science, Columbia University, New York
[3]. Research On Sentiment Analysis: The First Decade, Oskar Ahlgren(2015)
[4]. Sentiment analysis: Measuring opinions, ChetashriBhadanea,HardiDalalb, HeenalDoshic, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015)
[5]. A survey on sentiment analysis challenges,DoaaMohey El-Din Mohamed Hussein,Faculty of Computers and Information, Cairo University, Cairo, Egypt(2016)
[6]. Recent Trends in Deep Learning Based Natural Language Processing ¬- Tom Young, DevamanyuHazarika, SoujanyaPoria, Erik Cambria(2018)
[7]. Comparison of Text Sentiment Analysis based on Machine Learning - Xueying Zhang, XianghanZheng, (2016 IEEE)
[8]. Fundamentals of Sentiment Analysis:Concepts and Methodology,A.B. Pawar, M.A. Jawale and D.N. Kyatanavar (2016)
[9]. A Survey on Hate Speech Detection using Natural Language Processing, Anna Schmidt , Michael Wiegand (2017)
[10]. A Survey on Hate Speech Detection using Natural Language Processing, Anna Schmidt,MichaelWiegand
[11]. Research paper On Sentiment Analyzer By Using A Supervised Joint Topic Modeling Approach, Samruddhi S. Raut, Dr. Hemant R. Deshmukh, Asst Prof. AnkitR.Mune.cessing and the Web, DragomirRadev,MirellaLapata.
[12]. Natural Language Processing and the Web, DragomirRadev,MirellaLapata.
[13]. Analyzing Scientific Papers Based on Sentiment Analysis, DoaaMohey El-Din Mohamed Hussein
[14]. Sentiment Analysis and Opinion Mining: A Survey, G.Vinodhini,RM.Chandrasekaran

Sayali Meshram, Nisha Balani, Parul Jha, "Sentiment Analysis of Statement using Natural Language Processing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.118-120, 2019.

The Internet of things is a current major developing technology, which is a network of everyday physical objects that enhances the quality of lifestyle. Application of the internet of things encounters dealing with huge amount of data. One of the directions of big data is this huge amount of data with respect to the internet of things. As the name implies, big data refers to the data that cannot be analyzed by traditional data processing software. The key challenge of this phenomenon is to use a proper way to analyse, which can provide useful features from the data absorbed by the perception layer of the internet of things in order to provide feedback to end users, which helps them in better decision making and improves the performance of the corresponding internet of things network. Analysis of big data in theinternetof things is obviously a hard task. Data storages are distributed and there should be parallel data processing. Transmission of the data across the network can slow down because of the massive amount of data. In this regard, this paper focuses on how to analyze the massive and heterogeneous data of the internet of things in a proper way. At first, the internet of things and the big data are discussed separately with architectures, applications, challenges etc. Since these two technologies are interrelated, data analysis in the internet of things is discussed with various methodologies and challenges. Finally, the study discusses a proper framework that can analyze the big data in the internet of things (IOT) in an efficient way.

Internet of Things, machine learning, cloud data, forecasting, load

[1] T.T. Mulani and S.V.Pingle (March 2016). “Internet of things.” International research journal of multidisciplinary studies &sppp`s [online], Vol. 2, Special Issue 1, ISSN: 2454-8499.
[2] S. Chandrakanth, K.Venkatesh, J.U. Mahesh, K.V.Naganjaneyulu, “Internet of Things,” International Journal of Innovations & Advancement in Computer Science, Vol. 3, Issue 8, ISSN 2347 – 8616, October 2014.
[3] D. Evans, “Internet of things: How the next evolution of the internet is changing everything,” Cisco internet business solutions group, 2011.
[4] M. Villari, A. Celesti, M. Fazio, A. Puliafito, “AllJoyn Lambda: an Architecture for the Management of Smart Environments in IoT,” International conference on IEEE, pp 9-14, November 2014.
[5] C. Ifrim, A.M. Pintilie, E. Apostol, C. Dobre, F. Pop (2017), “The art of advanced healthcare applications in big data and IoT systems,” In advances in mobile cloud computing and big data in the 5G era[online], C.X. Mavromoustakis et al. (eds.), Springer International Publishing Switzerland, pp 133-149, 2017. Available: https://cs.pub.ro
[6] D. Mourtzis, E. Vlachou, N. Milas (2016). “Industrial big data as a result of IoT adoption in manufacturing,” 5th CIRP Global web conference research and innovation for future production[online], Vol. 55, pp 290-295, Available: http://www.sciencedirect.com
[7] Y. Sun, H. Song, A.J. Jara, R. Bie, “Internet of things and big data analytics for smart and connected communities,” IEEE access, vol. 14, August 2015.
[8] P. Goel, D. Grag, “The internet of things: A main source of big data analytics,” Computer engineering and intelligent systems, vol. 8, ISSN 2222-1719, pp 12-16, 2017
[9] H. Cai, B. Xu, L. Jiang, A. V. Vasilakos, “IoT- Based big data storage systems in cloud computing: perspectives and challenges,” IEEE internet of things journal, vol. 4, pp 75-87, February 2017.
[10] A. Bera, A. Kundu, N.R.D. Sarkar, D. Mou, “Experimental analysis on big data in IoT-based architecture,” Proceedings of the international conference on data engineering and communication technology, Springer Singapore, pp 1-9, 2017.
[11] Y. Simmhan, S. Perera, “Big data analytics platforms for real-time applications in IoT,” Big data analytics, Springer India, pp 115-135,
2016.
[12] X. Liu, N. Iftikhar, X. Xie, “Survey of real-time processing systems for big data,” Proceedings of the 18th International database engineering & applications symposium, ACM, pp 356-361, July 2014.

Sneha Kharole, Nisha Balani, Parul Jha, "A Cloud Platform for Big IoT Data Analytics by Combining Batch and Stream Processing Technologies", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.121-124, 2019.

In India sustainable agriculture development is essential to meet food demands, economic growth and poverty reduction. Climate change having adverse effect on agriculture and traditional practices followed are planting, fertilizing and harvesting against the predetermined schedule. Precision agriculture can be used to mitigate the climate change. The work objective is optimal usage of water in irrigation, proper nutrient management to plant and avoid crop losses due to diseases and pests with proper scheduling of sprays. In this context, we have proposed an agro advisory system for the pomegranate field. Wireless sensor network is deployed on field and will continuously monitoring real time environmental, soil, hydrological and crop specific parameters. Those are important for growth, productivity and quality in agriculture. An agro advisory will be disseminated to the farmers according to real time field conditions . The experimental result analysis of proposed system shows improvement over traditional followed methods

Precision agriculture, optimal usage, agro advisory, quality in agriculture

[1]. CROPSAP (Horticulture) team of E pest surveillance: 2013: Pests of Fruits (Banana, Mango and Pomegranate) E Pest Surveillance and Pest Management Advisory (ed. D.B. Ahuja), Jointly published by National Centre for Integrated Pest Management, New Delhi and State Department of Horticulture, Commissionerate of Agriculture, Pune, MS.
[2] A. Tripathy, J. Adinarayana, K. Vijayalakshmi, S. M. U. Desai, S. Ninomiya, M. Hirafuji, and T. Kiura, “Knowledge discovery and leaf spot dynamics of groundnut crop through wireless sensor network and data mining techniques,” Computers and Electronics in Agriculture Elsevier, vol. 107, pp. 104–114, June 2014.
[3] D. P, S. Sonkiya, P. Das, M. V. V., and M. V. Ramesh, “ Cawis: Context aware wireless irrigation system,” in Computer, Communications, and Control Technology (I4CT), 2014 International Conference on, 2014, pp. 310–315.
[4] B. B. Bhanu, K. R. Rao, J. V. N. Ramesh, and M. A. Hussain, “Agriculture field monitoring and analysis using wireless sensor networks for improving crop production,” in 2014 Eleventh International Conference on Wireless and Optical Communications Networks (WOCN), 2014, pp. 7.
[5] T. D. Le and D. H. Tan, “Design and deploy a wireless sensor network for precision agriculture,” in Information and Computer Science (NICS), 2015 2nd National Foundation for Science and Technology Development Conference on, 2015, pp. 294–299.
[6] M. Bhange and H.A.Hingoliwala, “Smart farming: Pomegranate disease detection using image processing,” Procedia Computer Science, Elsevier, vol. 58, pp. 280–288, Augest 2015.
[7] C. Akshay, N. Karnwal, K. A. Abhfeeth, R. Khandelwal, T. Govindraju, D. Ezhilarasi, and Y. Sujan, “Wireless sensing and control for precision green house management,” in Sensing Technology (ICST), 2012 Sixth International Conference on, 2012, pp. 52–56.
[8] A. Mittal, K. P. Chetan, S. Jayaraman, B. G. Jagyasi, A. Pande, and P. Balamuralidhar, “mkrishi wireless sensor network platform for precision agriculture,” in Sensing Technology (ICST), 2012 Sixth International Conference on, 2012, pp. 623–629.
[9] D. Anurag, S. Roy, and S. Bandyopadhyay, “Agro-sense: Precision agriculture using sensor-based wireless mesh networks,” in Innovations in NGN: Future Network and Services, 2008. K-INGN 2008. First ITU-T Kaleidoscope Academic Conference, 2008, pp. 383–388.
[10] H. Chang, N. Zhou, X. Zhao, Q. Cao, M. Tan, and Y. Zhang, “A new agriculture monitoring system based on wsns,” in 2014 12th International Conference on Signal Processing (ICSP), Oct 2014, pp. 1755–1760.

Trupti Deshkar, Samir Ajani, "Iot Based predication Analysis System for Precision Agriculture", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.125-128, 2019.

Discovering Knowledge from various health data domains requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can unidentified identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. This concept motivates us to create a trust level security authentication. It means, this healthcare database will be fully secured using cryptography algorithm of encryption and decryption using AES algorithm and authentication will be controlled on “Trust Level Security”. It means that if any researcher or organization need to access this data, then he/she must have at least above average trust level. We score 1 as minimum trust level 10 as maximum trust level and 5 as an average trust level. Trust level will be calculated on the basis of how much other organizations and researchers trust on A researcher or organization.

Data Security; Health Data Warehouse; Privacy Preserved Record Linkage; Data Mining

[1]T.R. Sahama, and P.R Croll, “A Data Warehouse Architecture for Clinical Data Warehousing,” Australasian Workshop on Health Knowledge Management and Discovery,2007.
[2] J. H. Weber-Jahnke & C. Obry “Protecting privacy during peer-to-peer exchange of medical documents”, Inf Syst Front (2012), Springer, 14:87–104
[3] H.C. Kum, A. Krishnamurthy, A. Machanavajjhala et. at., “Privacy preserving interactive record linkage (PPIRL), ” J Am Med Inform Assoc vol. 21, 2014, pp. 212–220.
[4] J. Liang, L. Chen, and S. Mehrotra, “Efficient Record Linkage in Large Data Sets,” In Proc. of the Eighth International Conference on Database Systems for Advanced Applications, 2003.
[5] Your medical record is worth more to hackers than your credit card.
[6] P. Christen, “Automatic record linkage using seeded nearest neighbour and support vector machine classification,” In Proc. of the 14th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
[7] S. I. Khan and A.S.M.L. Hoque, “Privacy and security problems of national health data warehouse: a convenient solution for developing countries,” In Proc. of the International Conference on Networking Systems and Security (NSysS). IEEE,2016.
[8]C. F. Andrea, C. Danielle, T.M. Matthew et. al., “Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records,” BMC Medical Informatics and Decision Making, Vol. 13, 2013, pp.13:71.
[9] J.A. Lyman, K. Scully, and J.H. Harrison, “The development of health care data warehouses to support data mining,” Clin Lab Med. 28,1 2008, pp. 55-71
[10] P. Christen, “Automatic record linkage using seeded nearest neighbor and support vector machine classification,” In Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
[11] E.A. Sauleau, J. Paumier, and A. Buemi, “Medical record linkage in health information systems by approximate string matching and clustering,” BMC Med Inform Decision Making, Vol. 5, 2005, pp.32– 44.
[12] A.B. McCoy, A. Wright, Kahn M. et al., “Matching identifiers in electronic health records: implications for duplicate records and patient safety,” BMJ Qual Saf Vol. 22, 2013, pp.219–24.
[13] C. F. Andrea, C. Danielle, T.M. Matthew et. al., “Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records,” BMC Medical Informatics and Decision Making, Vol. 13, 2013, pp.13:71.
[14] N. K. Abel, P. C. John, L. J. Kathryn et al., “Design and implementation of a privacy preserving electronic health record linkage tool in Chicago,” Journal of the American Medical Informatics Association, 2015, pp.1-9.

Varsha Katiwal, Nisha Balani, Priyanka Dudhe, "Health Data Integration with Secured Record Linkage and Trust-Level Security Based Authentication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.129-132, 2019.

The proposed work is a system for automatic control of fish farming using sensors. Aqua-culture, also called as aqua farming, is the farming of aquatic organisms like fish, crustaceans and crabs and many other acquatic organisms by using the various sensors to detect the condition of the aquatic environment and reduce the the risks by detection. The proposed system assists remote monitoring of the fish farming system based on Internet of Things (IOT) for real-time monitor and control of a fish farming environment. The main objective of this manuscript is to provide an automatic monitoring system that helps us in saving time, money & power of the farmers. IOT technologies have revolutionized farm production in the country recently. In this fish farming process we use different sensors like pH value, temperature and water level sensors. By using these sensors we automate the work and it will also be easy to monitor the fish farming remotely from other location.

Aqua-culture, Fish Farming, Internet of Things (IOT), Level Sensor, pH Sensor

[1] Arshak, K., Gill, E., Arshak, A. And Korostynska, O. Investigation of tin oxides as sensing layers in conductimetricinterdigitated pH sensors. Sensors and Actuators B: Chemical 127 (1) (2007) 42-53.
[2] Adhikari, B. and Majumdar, S. Polymers in sensor applications. Progress in Polymer Science (Oxford), 2012.
[3] Dyck, A.J. and Sumaila, U.R. Economic impact of ocean fish populations in the global fishery. Journal of Bioeconomics12 (3) (2010) 227-243.
[4] Graham, M. And Haarstad, H. 4 Transparency and Development: Ethical Consumption through Web 2.0 and the Internet of Things. Open Development: Networked Innovations in International Development 79 (2014).
[5] Gigli, M. and Koo, S. Internet of Things, Services and Applications Categorization. Advances in Internet of Things (2011).
[6] Zhu, H.J. Global Fisheries Development Status and Future Trend Analysis. Taiwan Economic Research Monthly 33 (3) (2010).
[7] Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems 29 (7) (2013) 1645-1660.
[8] Kawarazuka, N. and Béné, C. Linking small-scale fisheries and aquaculture to household nutritional security: an overview. Food Security 2 (4) (2010) 343-357.
[9] Chang, K.L. The Study of the Effect of Product Attributes on Consumers’ Purchase Intention and Brand Loyalty for Smartphone. Southern Taiwan University of Science and Technology, Department of Business Management, 2010.
[10] Raja, K.S. and Kiruthika, U. An energy efficient method for secure and reliable data transmission in wireless body area networks using relaodv. Wireless Personal Communications 83 (4) (2015) 2975-2997.
[11] Dey, M.M., Paraguas, F.J., Kambewa, P. And Pemsl, D.E. The impact of integrated aquaculture–agriculture on small‐scale farms in Southern Malawi. Agricultural Economics 41 (1) (2010) 67-79.
[12] Pacheco, O. Outcome of Water temperature on fish culture, 2013.
[13] Lee, P.G. A review of automated control systems for aquaculture and design criteria for their implementation. Aquacultural Engineering 14 (3) (1995) 205-227.
[14] Bartolome, P. Fallout of PH value in fish farming, 2014.
[15] Poonam, Y.M. and Mulge, Y. Remote temperature monitoring using LM35 sensor and intimate android user via C2DM service. International Journal of Computer Science and Mobile Computing 2 (6) (2013) 32-36.
[16] Sung, W.T. Effects of Dissolved oxygen on aquaculture, 2012.

M. Mulchandani, D. Marshettiwar, R. Varma, "Autofish Monitoring System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.133-138, 2019.

This website is designed to attend to all your needs- from buying property, selling property or renting /leasing property. Here you found better opportunity to invest your value of entire life. PROLAND application is an internet portal dedicated to meet every aspect of the user needs in the real Estate industry. It is application where property owners, brokers and investors can exchange information, selling and buying property, quickly effectively and inexpensively. It features commercial and residential properties for sells and rents. The PROLANDS application show the names of all property owners, how long a particular holder owned it, and the price of the land when it was sold. Rarely will a PROLAND application mention capital improvements to the property. PROLAND is a web application. It can be accessed by any where in the world.it overcome the mediators, it direct communication between owner and purchaser.

Methodology, Real estate property, Field practice, Valuer, Valuation. Information Visualization, Real Estate Information

[1] Emil JanulewiczMcGill Liu (Dave) LiuMcGill Universityliu.liu2@mail.mcgill.ca "Chinese real estate company website to information analysis" Feb 2009
[2] Jia Sheng, Ying Zhou, Shogun Li "Analysis of Real Estate Proposed sysemt Market Localization"2nd International Conference on Education, Management and Social Science (ICEMSS 2014)
[3] Lv jianliang1, iangying , "The Research on E-commerce Applied in Real Estate Enterprises"2012 International Conference on Innovation and Information Management vol 36 (ICIIM 2012)
[4] M. Kartika, Smita Dange, Swati Kinhekar, 4Girish B Trupti G, Sushant R. REAL ESTATE APPLICATION USING SPATIAL DATABASE Nov 2012
[5] Mingyuan Yu , Donghui Yu, Lei Ye, Xiwei Liu," Visualization Method Based on Cloud Computing for Real Estate Information" The Fourth International Conferences on Advanced Service Computing SERVICE COMPUTATION 2012 .
[6] Timothy H. Greer, Mirza B. Murtaza Technologies To Improve The Decision-Making Process Of Real Estate Appraisers: XML, Intelligent Agents, Avms, And Web Services
[7] Xu Wei, Lu Guiying, Jade Ling Qing Xiong Yanfu, the new ebusiness models in the modern enterprise applications – based on the example of Guangxi Sugar Network Social Survey. Coastal Enterprises and Science & Technology, 2008 (09)
[8] Y. Manolopoulos, A. Nanopoulos, A.N. Papadopoulos, Y. Theodoridi “Rtrees: Theory and Applications” Springer 2005
[10] Zhangxi Lin Gary D.Anderson, and T.J Anderson "web based services for rea estate:model and implementation" IT PRO Feb 2006
[9] Zhangxi Lin Gary D.Anderson, and T.J Anderson "web based services for real estate: model and implementation" IT PRO Feb 2004

A. Bhoyar, S. Ramteke, A. Kumbhare, "Proland (Real Estate Management Portal)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.139-141, 2019.

Advances in data mining techniques are now making it possible to analyze a large amount of stock data for predicting future price trends. The candlestick charting is one of the most popular techniques used to predict short-term stock price trends, i.e., bullish, bearish, continuation. While the charting technique is popular among traders and has long history, there is still no consistent conclusion for the predictability of the technique. The trend of stock prices often continues after intervals of several days because stock prices tend to fluctuate according to announcements of important economic indicators, economic and political news, etc. To cope with this kind of stock price characteristics, this paper focuses on a dynamic programming algorithm for retrieving similar numerical sequences. The proposed algorithm also handles a relative position among a stock price, 5-day moving average, and 25-day moving average to take into account where the price occurs in price zones. Experimental results on the daily data of the Nikkei stock average show that the proposed algorithm is effective to forecast short-term trends of stock prices. Candlesticks are especially popular because they give investors a very clear visual image of a stock’s progress. They provide deeper insight into the direction of the market as compared to other types of charts. Most investors feel that candlestick charts are more visually informative and appealing; therefore it is easier to draw inferences from them. A candlestick provides an encapsulated picture of the stock movement so investors can easily compare the opening and closing prices, as well as the high and low. The candlestick charting technique probably began sometime after 1850. Despite of its long history and popularity, mixed results are obtained in the studies on candlestick charting. Negative conclusions to the predictability of candlesticks are reported, while positive evidences are provided for several candlestick chart patterns in experiments using the U.S. and the Asian stock markets. It is also pointed out that candlestick chart pattern recognition is subjective. The candlestick chart patterns are often qualitatively described using words and illustrations.

Market Efficiency, Candlestick Technical, Analysis Charting

[1]. C++ GUI programming with Qt4 by Jasmin Blanchette, Maek Summerfield” for getting the link between C++ and Qt.
[2]. www.qt.io for understanding candlestick related information.
[3]. www.researchgate.net for candlestick technical trading strategies
[4]. www.quora.com for the basic advantages and disadvantages related to technical issues of Qt framework.
[5]. www.cppdepend.com for information related to C++.
[6]. www.codeproject.com for the features of C++ and Qt.
[7]. www.sourceforge.net for Qt GUI module

R. Mathew, C. Chimurkar, N. Balani, "To Display Candlesticks by Using QT Framework", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.142-144, 2019.

Electronic image and data inserting framework have number of critical interactive media applications. Now a days, consideration is paid to reversible data hiding (RDH) in encoded images is more, since it maintains the highest quality property that the original cover can be losslessly recovered after inserted data is extracted while securing the image content’s confidentiality. RDH is a technology used to hide data inside image for high security and can fully recover the original image and private data . All earlier methods fixed data by reversibly vacating room from the encrypted images, which may result to some errors on data extraction and/or image restoration. In this paper, we put forward another method in which we simply encrypt an image without its header by using our new technique. Hence it is easy for the data hider to reversibly fixed data in the encrypted image. The projected technique can achieve real reversibility, that is, data extraction and image recovery are free of any error.

Encryption, Decryption and Reversible Data hiding

[1] T. Kalker and F.M.Willems, “Capacity bounds and code constructions for reversible data-hiding,” in Proc. 14th Int. Conf. Digital Signal Processing (DSP2002), 2002, pp. 71–76.
[2] W. Zhang, B. Chen, and N. Yu, “Capacity-approaching codes for reversible data hiding,” in Proc 13th Information Hiding (IH’2011),LNCS 6958, 2011, pp. 255–269, Springer-Verlag.
[3] W. Zhang, B. Chen, and N. Yu, “Improving various reversible data hiding schemes via optimal codes for binary covers,” IEEE Trans.
[4] J. Fridrich and M. Goljan, “Lossless data embedding for all image formats,” in Proc. SPIE Proc. Photonics West, Electronic Imaging, Security and Watermarking of Multimedia Contents, San Jose, CA, USA, Jan. 2002, vol. 4675, pp. 572–583.
[5] Z. Ni, Y. Shi, N. Ansari, and S. Wei, “Reversible data hiding,” IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354–362, Mar. 2006.
[6] X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011.
[7] W. Hong, T. Chen, and H.Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199–202, Apr. 2012.
[8] X. Zhang, “Separable reversible data hiding in encrypted image,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 826–832, Apr. 2012.
[9] M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, “On compressing encrypted data,” IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2992–3006, Oct. 2004. 562 IEEE Transactions On Information Forensics And Security, Vol. 8, No. 3, March 2013
[10] W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Image Process., vol. 19, no. 4, pp. 1097–1102, Apr. 2010
[11] Miscelaneous Gray Level Images [Online]. Availabe:http://decsai.ugr.es/cvg/dbimagenes/g512.php

Sonali Jambhulkar, Priyanka Dudhe, "Protection Reversible Data Hiding in Encrypted Images by Distributing Memory before Encryption", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.145-147, 2019.