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Electricity, gas and water are the basic needs of our life. We cannot imagine our life without these resources. To sustain our lives and make our household living easier, we depend on these different resources. The household living can only get better if we’re able to utilize the resources efficiently and in a sustained manner. Analytical Data Representation helps to study the data related to different events to help and analyze various related activities to aid in several purpose. The idea of Analytical Data Representation in this project is to track and keep the record of consumption of various resources like water, gas, electricity and others of a building of a certain area. It is highly recommended and required for modern lifestyle as it reduces the time to retrieve the information and details of resource consumption easily. Calculating the resource consumption details of a building manually requires more time and effort and if the consumption target is to be set or consumption details of previous year or month has to be viewed, it’s another hard work. This tiresome work can be automated using the concept of Analytical Data Representation.

Resources, Sustain, Efficiency, Consumption, Recommended

[1] Henrique Pombeiro, André Pina, Carlos Silva,“Analyzing residential electricity consumption patterns based on Consumer`s segmentation”, CEUR Workshop Proceedings. 923. pp.29-38,2012.
[2] D.r. K.R Subramanian ,“The crisis of consumption of natural resources”,International Journal of Recent Innovations in Academic Research,Volume-2, Issue-4, pp. 8-19,2018.
[3] Satish Palaniappan, Raghul Asokan, Srinivas Bharathwaj, N. Sujaudeen, “Automated Meter Reading System - A Study. International Journal of Computer Applications”, Volume-116, pp.39-46, 2015.
[4] C. Wei and Y. Li, "Design of energy consumption monitoring and energy-saving management system of intelligent building based on the Internet of things," 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, pp. 3650-3652, 2011 .

Sandip Mishra , "Analytical Data Representation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.68-72, 2019.

In industries, machinery and devices, the speed of rotating part or motors needs to be measured to monitor or to control the system especially in servomechanism. Tachometer is an instrument which is used to measure the rotation speed of a shaft or disk, as in a motor or other machine. It measures the rotational speed in the unit revolution per minute (RPM). This paper provides a detailed study on the design and development of contactless tachometer based on different techniques. First design of a simple, easy to implement magnetic type Reed relay based tachometer, Second design of Hall sensor based tachometer and finally IR based tachometer by using low cost linear digital integrated circuits (ICs). We have developed a model which counts the rotation using different sensor techniques and analyzed for the sensitivity for handling speeds with vast range of rpm. With the help of a microcontroller speed can be calculated and displayed.

Servomechanism, tachometer, RPM, Reed relay, Hall sensor

[1] Prateek Mishra1, Shikhar Pradhan, Siddhartha Sethiya, Vikas Chaudhary “Contactless Tachometer With Auto Cutt Off ”International Research Journal of Engineering and Technology, Volume: 04 Issue: 04 Apr 2017.
[2] Varnika Dwivedi, Ravindra Parab, Satyendra Sharma “Design of a Portable Contact-less Tachometer using Infrared Sensor for Laboratory Application” International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, p-ISSN: 2395-0072, Volume No.6 Issue No.6, June 2019, pp: 1324-1328.
[3] Kathirvelan J, Babu Varghese, Ubaid A Ponnary, Fajas Kamar, Renju Thomas Jacob “Hall Effect Sensor Based Portable Tachometer for RPM Measurement” International Journal of Computer Science and Engineering Communications- IJCSEC. Vol.2,Issue.1,February,2014. ISSN:2347–8586, pp: 100-105.
[4] Hall Effect in semi conductors safa kasap, Department of electrical engineering , university of Saskatchewan
[5] A. S. M. Bakibillah, Muhammad Athar Uddin, Shah Ahsanul Haque “Design, Implementation and Performance Analysis of a Low-cost Optical Tachometer” IIUC STUDIES, ISSN 1813-7733 Vol.- 7, December 2010 (Published in December 2011) (p 107-116).
[6] https://www.electrical4u.com/optoisolator-construction-and-operating-principle-of-optoisolator/
[7] Cariappa P K, Pooja D, Shwetha A, Sudharani B T, Geetha M N “Contact-Less Tachometer” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181, NCESC - 2018 Conference Proceedings, Vol-6, Issue-13, pp-1-3.
[8] M. Ehikhamenle, B.O. Omijeh “Design And Development of A Smart Digital Tachometer Using At89c52 Microcontroller” American Journal of Electrical and Electronic Engineering, vol 5, No-1,2017.
[9] Nitin Singh, Raghuvir S. Toma “Design of a Low-Cost Contact-Less Digital Tachometer with Added Wireless Feature” International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-7, December 2013, pp: 21-23.

Smita Prajapati, Ravindra Parab, "Design of Digital Tachometers Based on Different sensing Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.73-78, 2019.

Some further theoretic properties of the scheme called non-deranged permutation Group, especially in relation to ascent block were identified and studied in this paper. This was done first through some computations on this scheme using prime numbers . A recursion formula for generating maximum number of block and minimum number of block were developed and it’s also observed that is equidistributed with for any arbitrary permutation group and it in decreasing order for non-deranged permutations it also established that the number of ascent block in is .

Ascent Number, Ascent set ,Ascent block and Γ1-non deranged permutations

[1] K.O. Aremu, A.H Ibrahim., S.Buoro and F.A.Akinola, Pattern Popularity in -non deranged permutations: An Algebraic and Algorithmic Approach. Annals. Computer Science Series15(2) (2017)115-122.
[2] K. O. Aremu, O. Ejima, and M. S. Abdullahi, On the fuzzy non deranged permutation Group Asian Journal ofMathematics and Computer Research, 18(4) (2017),152-157

[3] B. Clarke, A note on some Mahonian statistics, sem. Lothar.combin.53 (2005),Aricle B53a
[4] L. Euler, Institutiones Calculi differntialis in “opera omnia series prime “ Volx, (1913),Teubner,Leipzig.
[5] A.I. Garba and A.A. Ibrahim, A New Method of Constructing a Variety of Finite Group Based on Some Succession Scheme. International Journal of Physical Sciences 2(3) (2010),23-26.
[6] A.I. Garba, O. Ejima, K.O. Aremu and U. Hamisu, Non standard Young tableaux of -non deranged permutation group . Global Journal of Mathematical Analysis5(1) (2017), 21-23.
[7] G.N Han, Une transformation fondamentale sur les rearrangements de mots,Adv. Math. 105 (1994),26-41
[8] I. Haglund and L. Steven , An extension of the Foata map to standard Young tableaux, Sem. Lothar.Combin. 56 (2006),Article B56c
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[11]M. Ibrahim and A.I. Garba Exedance on -non deranged permutations proceedings of Annual National Conference of Mathematical Association of Nigeria (MAN) , (2018), 197-201.
[12]M. Ibrahim and A.I. Garba, Descent on -non deranged permutation group Journal of Mathematical Association of Nigeria ABACUS , 46(1) (2019),12-18.
[13] P.A. MacMahon ,.Combinatory Analysis Vol. 1 and 2 (1915), Cambridge University Press(reprinted by Chesea,New York,1955)
[14] B. Sagan, A maj statistics for set partitions European J. Combin. 2 (1991), 69-79
[15]R. Simion and D. Stanton, Specialization of generalized laguerre polynomials SIAM J. Math. Anal. 25(2) (1994), 712-719
[16] R. Simion and D. Stanton, Octabasic Laguerre polynomials and permutation statistics, J.Comput. Appl. Math.68(1-2) (1996),297- 329
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M. Ibrahim, M. Muhammad, "Standard Representation of Set Partitions of Γ1 non-deranged permutations," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.79-84, 2019.

Financial Services is a heavily regulated industry and organizational complexity that is driven by business segments, product lines, customer segments, a multitude of channels and transaction volumes. The role of data onto the financial services institutes has grown exponentially in recent years and is advancing rapidly. Traditional data solutions were built based on the demands of earlier days using technologies available at that point in time. However, the ever-growing amount of data and the insights that can now be extracted from it have rendered these solutions obsolete. A modern technology and advanced analytical solutions can only handle current demands and achieve business goals. Todays, Machine Learning (ML) gains traction in digital businesses and embraces it as a tool for creating operational efficiencies. The ML algorithm can analyze thousands of data sources simultaneously, something that human traders cannot possibly achieve. They help human traders squeeze a slim advantage over the market average. In addition, it has given the vast volumes of trading operations that small advantage often translate into significant profits. Robust architecture designs is one of the common traits of a successful enterprise financial ecosystem. This article discusses the use cases, benefits and pitfalls and the requirements of ML architecture to financial services institutes. This proposed ML architecture provides a fully functional technical picture for developing a cohesive business solution.

Advanced Analytics, Machine Learning, Machine Learning Model, Machine Learning Architecture, Financial Service Institutes, Digital Business

[1] Addepto, Data Science in Finance – Why it is Beneficial to Use it, 2019.
[2] Akli Adjaoute. The AI Disconnect in the Financial Services Industry, Few industries are leveraging AI to the full extent of the technology’s power, 2019.
[3] Anastasia D, Seven Exciting Uses of ML in FinTech, 2018.
[4] Why you need a Digital Data Architecture to build a Sustainable Digital Business, 2017.
[5] C. Belém, L. Santos, and A.Leitão, “On the Impact of Machine Learning. Architecture without Architects?” in CAAD Futures, Seoul, South Korea., 2019.
[6] Carlton E. Sapp, Preparing and Architecting for ML, 2017.
[7] Daniel Faggella, Machine Learning in Finance – Present and Future Applications, 2019.
[8] Daniela Ventura, Diego Casado-Mansilla, Juan López-de-Armentia, Pablo Garaizar, Diego López-de-Ipiña, Vincenzo Catania, ARIIMA: A Real IoT Implementation of a ML Architecture for Reducing Energy Consumption, Springer International Publishing, 2014.
[9] DataStax Enterprise Reference Architecture, www.datastax.com.
[10] Elena Moldavskaya. Top Five Machine Learning Use Cases for the Financial Industry, Intetics Inc., 2018
[11] Elma, Machine learning in European financial institutions Study, 2018.
[12] Håkon Hapnes Strand. A Lightweight Machine Learning Architecture for IoT Streams, 2019.
[13] Introduction to Machine Learning Architecture, 2019.
[14] Joseph E. Beck, Beverly Park Woolf, Carole R. BealADVISOR: A machine learning architecture for intelligent tutor construction, American Association for Artificial Intelligence, 2000.
[15] Justin Boyan, Dayne Freitag, Thorsten Joachims, A Machine Learning Architecture for Optimizing Web Search Engines, In AAAI Workshop on Internet-based Information Systems, 1996.
[16] Karsten Egetoft, Data-Driven Analytics: Practical Use Cases for Financial Services, 2019.
[17] LB Shyamasundar, P Jhansi Rani. A Multiple-Layer Machine Learning Architecture for Improved Accuracy in Sentiment Analysis, Computational Intelligence, Machine Learning and Data Analytics, the Computer Journal, 2019.
[18] Machine Learning: How to Build Scalable Machine Learning Models, 2019.
[19] Mark Labbe, AI in Financial Services Helps Speed Consumer Interaction, 2019.
[20] Nikhil Gokhale, Ankur Gajjaria, Rob Kaye, Dave Kuder, AI Leaders in Financial Services Common Traits of Frontrunners in the Artificial Intelligence Race, 2018.
[21] Sidney D`Mello, Stan Franklin, Uma Ramamurthy, Bernard Baars. A Cognitive Science-Based Machine Learning Architecture, American Association for Artificial Intelligence, 2006.
[22] Tanmoy Ray, Scopes of ML and AI in Banking & Financial Services, ML & AI, The Future of Fintechs, 2017.
[23] Techwave, Machine Learning Use Cases in Finance, 2018.
[24] Thatchanamoorthy Lakshmanan, Anti-Money Laundering Powered by RegTech, 2017.
[25] William Markito. An Open Source Reference Architecture for Real-Time Stock Prediction. 2015.
[26] Palanivel K, Chithralekha T. “Big Data Reference Architecture for e-Learning Analytical Systems”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 6, Issue. 1, pp.55-67, 2018.
[27] Palanivel, K. “Modern Network Analytics Architecture Stack to Enterprise Networks”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.7, Issue.4, pp.263-280, Apr 2019.
[28] Kurt Stockinger, Nils Bundi, Jonas Heitz and Wolfgang Breymann. “Scalable architecture for Big Data financialanalytics: user‑defined functions vs. SQL”, Journal of Big Data, Vol.6, Issue.46, 2019.
[29] Andersen, T.G., Bollerslev, T., Frederiksen, P.H., and Nielsen, M.Ø., Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns. Working paper, Northwestern University, 2006
[30] Steinfeld, K. Dreams May Come. In Acadia 2017, 590–599, 2017

K. Palanivel, "Machine Learning Architecture to Financial Service Organizations," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.85-104, 2019.

With the rapid development in the technology, and every device connected to the internet and increase in wireless sensing devices, the spectrum is becoming more and more congested. To solve the spectrum scarcity problem, Cognitive Radio technology is used. The details about the function of cognitive radio such as spectrum sensing, spectrum management, spectrum decision and spectrum handoff are illustrated in this paper. Cognitive radio senses the spectrum for the presence of idle spectrum and allocates the unused frequency band to the cognitive user. When the secondary user is transmitting the data, the cognitive radio senses for the unused spectrum. If the primary user wants to access the channel, then the cognitive radio allocates the secondary user in the nearby unused frequency band. In this paper we are mainly focusing on narrow band spectrum sensing. Under narrow band spectrum sensing various detection techniques such as Energy detection, Matched filter, Covariance detector, Waveform detector and Cyclo-stationary detection are discussed in detail below. The efficiency of the spectrum sensing can be increased with the cooperative spectrum sensing in which multiple secondary users cooperate in sensing the spectrum.

Cognitive Radio, Spectrum Sensing, Narrowband Spectrum Sensing, Wideband Spectrum Sensing, Cooperative Sensing

[1] Wael Guibene, Monia Turki, Bassem Zayen and Aawatif Hayar, “Spectrum sensing for cognitive exploiting spectrum discontinuities detection”, Guibene et al. EURASIP Journal on Wireless Communication and Networking 2012, 2012:4, pp. 1-9, (SPRINGER).
[2] Lu Lu, Xiangwei Zhou, Uzoma Onunkwo and Geoffrey Ye Li, “Ten years of research in spectrum sensing and sharing in cognitive radio”
[3] Lu Lu, Xiangwei Zhou, Uzoma Onunkwo and Geoffrey Ye Li, “Ten years of research in spectrum sensing and sharing in cognitive radio”,
Lu et al. EURASIP Journal on Wireless Communication and Networking 2012, 2012:28, pp. 1-16, (SPRINGER).
[4] Peter Steenkiste, Douglas Sicker, Gary Minden, Dipankar Raychaudhuri, “Future Directions in Cognitive Radio Network Research” , NSF Workshop Report, March 9-10, 2009, pp. 1-39.
[5] Milind M. Buddhikot “Understanding Dynamic Spectrum Access: Models, Taxonomy and Challenges” , IEEE DySPAN 2007, Dublin, April 17-21, 2007.
[6] Qing Zhao and Brian M. Sadler, “A Survey of Dynamic Spectrum Access”, IEEE Signal Processing Magazine, May 2007, pp. 79- 89
[7] Badr Benmammar1 Asma Amraoui1 , Francine Krief2, “A Survey on Dynamic spectrum Access Techniques in Cognitive Radio Networks”, International Journal of Communication Networks and Information Security (IJCNIS), Vol. 5, No. 2, August 2013, pp. 69- 79.
[8] Federal and C. Commission, "Spectrum Policy Task Force," Rep. ET Docket no,02-135, Nov. 2002.
[9] S. Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications," IEEE Journal On Selected Areas In Communications, vol. 23, NO. 2, February 2005.
[10] J. Ma, et al., "Signal Processing in Cognitive Radio," Proceedings of the IEEE, vol. 97, No. 5, pp. 805-822, May 2009
[11] G. Ganesan and Y. Li, "Agility improvement through cooperative diversity in cognitive radio," in Proc IEEE Global Telecomm. Conf. (Globecom), St. Louis, Missouri, USA, vol. 5, pp. 2505–2509, Nov./Dec. 2005.
[12] F. Digham, et al., "On the energy detection of unknown signals over fading channels," in Proc. IEEE Int. Conf.Commun., vol. 5, Seattle, Washington, USA, pp. 3575– 3579, May 2003
[13] A. Ghasemi and E. Sousa, "Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks," in Proc.IEEE Consumer Commun. And Networking Conf., Las Vegas, Nevada, USA, pp. 1022–1026, Jan. 2007.
[14] W Ejaz, NU Hasan, MA Azam, HS Kim, “Improved local spectrum sensing for cognitive radio networks”. EURASIP J. Adv. Signal Process (2012). http:// asp.eurasipjournals.com/content/2012/1/242.
[15] KG Smitha, AP Vinod, PR Nair, “Low power DFT filter bank based two-stage spectrum sensing”, in IEEE Proceedings of International Conference on Innovations in Information Technology (IIT). (UAE, March 2012), pp. 173–177
[16] W Ejaz, NU Hasan, HS Kim, “SNR-based adaptive spectrum sensing for cognitive radio networks”. Int. J. Innov. Comput. Inf. Control. 8(9), 6095–6106 (2012)
[17] S Geethu, GL Narayanan, “A novel high speed two stage detector for spectrum sensing”. Elsevier Procedia Technol. 6, 682–689 (2012)
[18] S Maleki, A Pandharipande, G Leus, “Two-stage spectrumsensing for cognitive radios”, in IEEE Proceedings of International Conference on Acoustic Speech and Signal Processing (USA, March 2010), pp. 2946–2949
[19] PR Nair, AP Vinod, KG Smitha, AK Krishna, “Fast two-stage spectrum detector for cognitive radios in uncertain noise channels”. IET Commun. 6(11), 1341–1348 (2012)
[20] W Yue, B Zheng, Q Meng, W Yue, “Combined energy detection one-order cyclo-stationary feature detection techniques in cognitive radio systems”. J China Univ. Posts Telecomm. 17(4), 18–25 (2010)
[21] L Luo, NM Neihart, S Roy, DJ Allstot, “A two-stage sensing technique for dynamic spectrum access”. IEEE Trans. Wirel. Commun. 8(6), 3028–3037 (2009)
[22] J. Mitola and J. Maguire, G.Q., “Cognitive radio: making software radios more personal," Personal Communications”, IEEE, vol. 6, no. 4, pp. 1318, 1999.
[23] RK. Sahoo, DP. Mohapatra, MR. Patra, “A Firefly Algorithm Based Approach for Automated Generation and Optimization of Test Cases”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.54-58, 2016.
[24] K.Ravi Kumar,V Manikandan, “Detection of Node Capture Attack in Wireless Sensor Networks” International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.4, pp.56-61, August (2018)

Praneeth P. Jain, Pradeep R. Pawar, Prajwal Patil, Devasis Pradhan, "Narrowband Spectrum Sensing in Cognitive Radio: Detection Methodologies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.105-113, 2019.

With rapid development of online web shops and E-commerce data, it is evident that users get convenience in different fields such as lexical similarity, Vocabulary mismatch, information retrieval etc. Faceted search is becoming a popular method to allow the user to interactively search in online web shops and product comparison sites. Trying to figure out retrieval of information using facet search to reduce the number of search results quickly to improve the search results. There are many attributes, for example, filter, facet value, facet and facet count, which can also be used for information retrieval towards the user search query. Over the years, all kinds of improve search results techniques have tried to simplify this task such as WebPT, NextGen and Kareo. This paper gives a detailed survey of some recent algorithms of faceted search, the attributes handled by them and the methods used by them.

Faceted Search, Semantic link, Data Mining, Probabilistic Model, Spatial Database, Navigation System, Information Retrieval

[1] Ying Liu, Soon Chong Johnson Lim and Wing Bun Lee, “Multi-Facet product information search and retrieval using semantically annotated product family ontology”, doi:10.1016/j.ipm.2009.09.001, pp 479-493, 2010.
[2] Flavius Frasincar, Damir Vandic and Jan-Willem van Dam, “Facet product search powered by Semantic web”, doi:10.1016/j.dss.2012.02.010, pp 425-437, 2012.
[3] Yannis Tzitzikas, Nikos Manolis and Panagiotis Papadakos, “Faceted exploration of RDF/S datasets”, doi.10.1007/s10844-016-0413-8, J Intell Inf Sys (2017), pp 157-171, 2017.
[4] Anusree Radhakrishnan, “Query facet Engine for easier search Results”, International conference on circuits power and computing technologies (ICCPCT)
[5] Anthony C. Robinson and Sterling D. Quinn, “A brute force method for spatially-enhanced multivariate facet analysis”,doi.org/10/1016/j.compenurbsys, 2017.
[6] Andreas Rauber and Serwah Sabetghadam, “A faceted approach to reachability analysis of graph modelled collections”, International journal of Multimedia Information Retrieval (2018)
[7] Xiangyu Fan and Xi Niu, ACM Transactions on
Information Systems, “Understanding Faceted search from data science and human factor Perspectives”, Vol. 37 No.2, Article 14, January 19
[8] Siji Mol K Sijimol, International journal for scientific Research and Development, “A survey on Faceted Product Search Engines”, Vol. 6 2321-0613.
[9] Lan Huang, “A distributed Multi-Facet search engine of microblogs based on SolrCloud”, American journal of software engineering, vol.5 no.1 20-26, 2017.
[10] Daniel Sonntag, “Integrated Decision support by combining textual information, faceted search and information visualization”, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems.
[11] Hak-Jin Kim, Yongjun Zhu, Wooju Kim and Taimao Sun, “Dynamic faceted navigation in decision making using Semantic Web technology”, http://dx.doi.org/10.1016/j/dss/2014/01.010.
[12] Ales Bosnjak and Vili Podgorelec, “Upgrade of a current research information system with ontologically supported semantic search engine”, http://dx.doi.org/10/1016j/eswa.2016.09.01.
[13] Rajvardhan Patil, Zheng Xin Chen and Yong Shi, “A perspective from Optimization”, International conferences on Web Intelligence and Intelligent Agent Technology, DOI 10.1109/WI-IAT.2012.188.
[14] Leo Breiman, “Using and Understanding Random Forests”, Statistics Department, Vol. 3 No.1, 2002.
[15] Paul Hugh Cleverley and Simon Burnett, “A data driven information needs model for faceted search”, Information Science 41, pp 97-113, 2015.
[16] Hak-Jin Kim, Youngjun Zhu, Wooju Kim and Taimao Sun, “Dynamic faceted navigation in decision making using semantic web technology”, Decision Support System. 61 pp 59-68, 2014.
[17] Xi Niu, Tao Zhang and Hsin-liang Chen, “Study of user search activities with two discovery tools at an academic library”, Internation Journal Humanities and Computer Interaction, pp 422-433, 2014.

Yogesh, Shalu, Komal Kumar Bhatia, Neelam Duhan, "An Anatomy of Faceted Search on World Wide Web," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.114-120, 2019.

Ground water resources are a lifeline for living begins on this earth. These resources have been used in the agriculture field, drinking water and industry sector. The researcher believes that India is gradually moving towards the excess use and contamination of groundwater. In India 89% of ground water used in the irrigation sector, making it the highest category user in the country. Last decade, there has been a continued increase in ground water utilization for irrigation and it would decrease surface water level. In the conventional systems to farmer personally visited Wells or tanks checking water availability and check irrigation condition in the field by focusing various aspects such as observe water Level in wells or tanks, pump condition for on or off, for testing water quality required the manual collection of water samples and detect any intrusion in agriculture field. This older system had drawbacks such as time-consuming, wastage of ground water in irrigation. So to mitigate all drawbacks, there must be a requirement of effective utilization of ground water resources in the agriculture field using the Internet of things (IoT). Propose work which makes use of various technologies like, Internet of things, sensors, actuators, and WiFi module. It will provide many services to the farmer in agriculture field include automatic pump on/off the system, which make help to save the water without human interaction, finding the amount of water using water level sensors, weather observation, Intrusion detection at water resources area and test water quality of ground water, etc.

WiFi, Raspberry Pi 3b+, Ultrasonic sensor, PIR sensor, DHT sensor, Water Quality, Turbidity Sensor, PI camera

[1] N. Ahmed, D. De, I. Hussain, “Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas,” IEEE INTERNET OF THINGS JOURNAL, VOL. 5, NO. 6, DECEMBER 2018.
[2] A. Khanna, S. Kaur, ”Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture,” Computers and Electronics in Agriculture 157 (2019) 218–231.
[3] O. Elijah, T.A. Rahman, I. Orikumhi, C. Y. Leow, N.Hindia, "An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE INTERNET OF THINGS JOURNAL, VOL. 5, NO. 5, OCTOBER 2018.
[4] P. Srinivasulu, R.Venkat, M.S.Babu, K Rajesh, "Cloud Service Oriented Architecture (CSOA) for Agriculture through the Internet of Things (IoT) and Big Data," 2017 International Conference on Electrical, Instrumentation, and Communication Engineering (ICEICE2017).
[5] M. Shirode, M. Adaling, J. Biradar, T. Mate, " IOT Based Water Quality Monitoring System," International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN: 2456-3307.
[6] Oborkhale, Lawrence I (Ph.D.), Abioye A. E. , Egonwa B. O., Olalekan T. A. "Design and Implementation of Automatic Irrigation Control System,” OSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. II (July – Aug. 2015), PP 99-111.
[7] Dr. Veena S, Mahesh K, Rajesh M, Salmon S, "The Survey on Smart Agriculture Using IoT," International Journal of Innovative Research in Engineering & Management (IJIREM) ISSN: 2350-0557, Volume-5, Issue-2, March-2018.
[8] N A Aswathi, Dr. R Suresh, "Automatic System for Agriculture and Domestic Plant watering using Drip Irrigation," International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056, Volume: 03 Issue: 07 | July-2016 p-ISSN: 2395-0072.
[9] N. Vaitheeka, S. Manoj Kumar, "COGNITIVE INTELLIGENCE OF INTERNET OF THINGS (IoT) IN AGRICULTURE INDUSTRY -A PRIMER STUDY ON SMART FARMING," International Journal of Pure and Applied Mathematics Volume 118 No. 20 2018, 1835-1841 SSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version).
[10] M. B. KALPANA, ”Online Monitoring Of Water Quality Using Raspberry Pi3 Model B,” (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH Volume No.4, Issue No.6, October – November 2016, 4790-4795.
[11] Y.nanda Kishore, J.Krishna Chaitanya, ”Automatic Monitoring and Controlling of Irrigation System Using Wireless Sensor Networks and GSM,” International Journal of Science, Engineering and Technology Research, Volume 3, Issue 9, September 2014
[12] R. Suhag, ”Overview of Ground Water in India,” LPR Legislative Research, February 2016

D. B. Kale, A. Taneja, D. Rewardikar, "Smart Utilization of Ground Water Resources in Agriculture Field using the Internet of things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.121-124, 2019.

The growing popularity of social media, E-commerce, blogs and any social field created a new platform where anyone can discuss and exchange his/her views, ideas , suggestions and experiences about any product or services in market. This state of affairs open a new area of research called Opinion Mining and Sentiment Analysis. Opinion Mining and Sentiment Analysis is an extension of Data Mining that extracts and analyzes the unstructured data automatically. In this review paper our aim is to present the details study over Opinion Mining and Sentiment Analysis, its different techniques , methods etc.

Introduction, Sentiment analysis techniques , Literature review, Comparative analysis , Conclusion

[1] Mejova, Y. (2009). Sentiment Analysis: An Overview. Comprehensive exam paper, available http:// www. cs. uiowa. edu/~ ymejova/ publications/ Com ps Yelena Mejova. pdf [2010-02-03].
[2] X Fang, J Zhan” Sentiment analysis using product review data” , Journal of Big Data , Issue 1/2015,2015.
[3] M. Levene , A. Mudinas, D. Zhang, “Combining lexicon and learning based approaches for concept leve lsentiment analysis”, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, New York ,NY, USA, Article 5, pp. 1-8,2 012.
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Sakshi Koli, Ram Narayan , "Review Paper On Sentiment Analysis Technique By Different Machine Learning Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.125-129, 2019.

In this paper we have explained about the mobile computing features, limitations which are necessarily should know to us for the future enhancements over the services which are provided by the Cloud Servers (CS). A mobile computing is basically a combination of the cloud computing and the mobile devices whose combination provides us the services over cloud which includes: data storage, data security. Also in this paper our aim is to provide a robust anonymous mutual authentication schemes to provide the effective and the secure data access services to its users. The scheme which used will be helpful to replay attackers and will support creation, modification, to read the information which is put away in the cloud servers. This paper likewise addresses client renouncement. Besides, our verification and access control plot is decentralized and hearty, not at all like different access control plans intended for mists which are incorporated. The correspondence, calculation, and capacity overheads are practically identical to incorporated approaches.

Mobile Cloud Computing, Cloud Servers, Security, Smart Cards, Networking

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Karuna Rana, Himanshu Yadav, Chetan Agrawal, "A Review on Mutual Authentication and Location Privacy in Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.130-134, 2019.

The pricing schemes that are in practice today and analyze why they do not solve the ISPs problem from the growing data traffic. The Internet can incentivize users to spread out their bandwidth consumption more evenly across different times of the day, and thus help ISPs to overcome the problem of peak congestion. Congestion pricing is not a new idea in itself, but for IP network data plan we learn from our wired and wireless ISP collaborators that the time for its implementation has finally arrived

User Provided Networks, Internet Service Provider, Broadband pricing, Data plans, Network Economics

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Venugopal A., Niranchana D., "The Time Dependent Pricing (TDP) by Mobile Network Operators using Broad Band Pricing Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.135-140, 2019.