iosr-JCE   Next Generation Computing Technologies

Abstract: Information is indispensable for human development as air is essential for the survival of living being. The rate of change brought about by information technologies has a key effect on the way we live, work worldwide. The Library services are also actively changing according to the changing digital environment. Today Libraries use current trends in all activities including, selection, sorting and dissemination of information. Today everyone is looking out for ease of access of information and services from libraries. Internet of things which is now the talk of the world is playing a vital role in all aspects of life. Today we can see that there are more than 15 million interconnected and electronic devices in operation globally. The most common example of internet of things tools that is used is the RFID technology This paper is written in order to provide an overview of the RFID technology, its history, RFID technology components and how it works and also pros and cons of the RFID technology is also discussed. This study will also give an idea for the Libraries that are planning to implement automated Library Management System using RFID Technology in future.

Keywords -Libraries, RFID Tags, RFID Technology, RFID Reader, Radio Waves

[1] Radio-frequency identification. (2017, September 29). In Wikipedia, the Free Encyclopedia. Retrieved 06:00, October 5, 2017,from https://en.wikipedia.org/w/index.php?title=Radio-frequency_identification&oldid=802966994

[2] Syed Md. Shahid, "Use of RFID Technology in Libraries: a New Approach to Circulation, Tracking, Inventorying, and Security of Library Material , Library Philosophy and Practice Vol. 8, No. 1 (Fall 2005) ISSN 1522-0222

[3] Narayanan A, Sanjay Singh and Somashekaran M , Implementing RFID in Library: Methodologies, Advantages and Disadvantages. Scientific Information Resource Division, IGCAR.

[4] Jay Singh, Navjit Brar, Carmen Fong, The State of RFID Applications in Libraries, .Information Technology and Libraries" Chicago25.1 (Mar 2006): 24-32.

[5] A. Larsan Aro Brian, L. Arockiam and P. D. Sheba Kezia Malarchelvi , An IOT based secured smart library system with NFC based book tracking International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 11 Issue 5 –NOVEMBER 2014.p. 18-21.

Abstract: Protein structure is the three dimensional arrangements of atoms (amino acids) in a protein molecule .The three dimensional protein structure prediction is used to easily understand the function and molecular level of a protein. In this paper summarized about some evolutionary algorithms, methods and tools that are used for solving the protein tertiary structure prediction.

Keywords: protein structure, three dimensional protein structure prediction, evolutionary algorithms

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[3]. Mount DM (2004). Bioinformatics: Sequence and Genome Analysis. 2. Cold Spring Harbor Laboratory Press. ISBN 0-87969-712-1.
[4]. Zhang Y (2008). "Progress and challenges in protein structure prediction". Curr Opin Struct Biol. 18 (3): 342–8. PMC 2680823 . PMID 18436442. doi: 10.1016/j.sbi.2008.02.004
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Abstract: Protein structure prediction is one of the most important goals pursued by bioinformatics, it is highly important in medicine (drug design) and structural bio informatics. Three dimensional protein structures are determined by its coordinates X, Y and Z. Algorithms, methods, applications and databases with various techniques addressed to predict the three dimensional protein structures. The prediction methods of 3D protein structures are categorized into comparative, folding and a b initio prediction. By using the algorithms it determines and predicts protein structure from its amino acid sequences.

Keywords- Protein structure prediction, Methods, Dataset, Application.

[1]. Schnell, J.R., Chou, J.J., 2008. "Structure and mechanism of the M2 proton channel of influenza A virus Nature" 451, 591–595.
[2]. Erfan Khajia, Masoumeh Karamib, Zahra Garkani-Nejadc, n aGraduate Student, Department of Complex Adaptive Systems, University of Gothenburg, Gothenburg, Sweden Department of Bio-Chemistry,
[3]. Iran Military University of Medical Sciences, West Fatemy St, North Karegar Ave, Tehran, Iran,"3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction" E. Khaji et al. / Journal of Theoretical Biology 391 (2016) 81–87.
[4]. BorkoBoskovic , Janez Brest , Faculty of Electrical Engineering and computer science , University of Maribor Slovenia , " Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic – polar model and cubic lattice" . Applied Soft computing 45(2016) 61-70.
[5]. Leonardo Correa, Bruno Borguesan, CamiloFartan, Mario Inostroza-Ponta, and Marcio Dorn, L. Corrˆ ea, B. Borguesan, and M. Dorn , the Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil. C. Farfán and M. Inostroza-Ponta are with the Dep. de IngenierıaInformática, Center for Biotechnology and Bioengineering, U de Santiago,Santiago, Chile."AMemetic Algorithm for 3-D Protein Structure Prediction Problem", Ieee/Acm Transactions On Computational Biology &Bioinformatics, Vol. 15, No. 1, April 2016.

Abstract: Clustering is a technique of data mining. It aims at decision natural separation of data. Objects are grouped depending on the similar nature they share. Similarity is measured depending on different parameters like number of parameters which are in common or lowest allowed difference between any parameters of an object. Data mining techniques include classification, clustering, mining frequent pattern, and correlation. Out of all these, in few decades clustering has gain wide attention of researchers. Clustering involves grouping of data objects which are similar in nature. This helps in the abstraction process of huge amount of data. Once the abstraction process is complete group of data can be represented in more compact manner. This is nothing but data............

[1]. Y. Yang. An evaluation of statistical approaches to text categorization. Journal of information Retrieval, 1(1/2):67—88, 1999.
[2]. K. Tzeras and S. Hartman. Automatic indexing based on bayesian inference networks.
In Proc 16th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'93).
[3]. C. Apte, F. Damerau, and S. Weiss. Text mining with decision rules and decision trees. In Proceedings of the Conference on Automated Learning and Discovery, Workshop 6: Learning from Text and the Web, 1998.
[4]. D. D. Lewis and M. Ringuette. Comparison of two learning algorithms for text categorization. In Proceedings of the Third Annual Symposium on Document Analysis and Information Retrieval (SDAIR'94), 1994.
[5]. Thorsten Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In European Conference on Machine Learning (ECML).

Abstract: Advancement in database and networking technologies had made information storage and data sharing easier. The data mining techniques are used to access the shared data either in centralized or in the distributed environment for knowledge discovery. The knowledge extracted can be used by the shared organization to have mutual benefits. If the data used in mining contains the person specific information, it is important to protect the customer personal data. At the same time the data is to be utilized to guarantee valid analysis results. Privacy preserving data mining focus on providing protection to the personal information stored in the database and also to provide...........

Keywords: Data Security, Privacy, k-anonymity, l-diversity, t-closeness

[1] Aggarwal, C. C., Yu, P. S, A general survey of privacy-preserving data mining models and algorithms. Privacy- preserving data mining, 2008, 11-52.
[2] Sweeney, L, k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10 (05), 557-570.
[3] Wang, K., Fung, B., Dong, G,. Integrating private databases for data analysis. Intelligence and Security Informatics, 2005, 23-41.
[4] Wong, R. C. W., Li, J., Fu, A. W. C., Wang, K., (α, k) -anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, 754-759.
[5] Chiu, C. C., Tsai, C. Y, A k-anonymity clustering method for effective data privacy preservation. Advanced Data Mining and Applications, 2007, 89-99..

Abstract: The huge network of devices connected to the Internet, including smart phones and tablets and almost anything with a sensor on it- cars, machines in production plants, jet engines, wearable devices, and more. These "things" collect and exchange data. IoT cuts diagonally different application domain verticals ranging from civilian to security sectors. These domains include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their bequest infrastructure to support IoT. The Internet of Things refers to the ever-growing network of physical objects that mark an IP address for internet connectivity, and the communication that occurs between these objects and other Internet-enabled devices and systems. Thermostats, cars, lights, refrigerators, and more appliances can all be linked to the IoT. Therefore, in this paper we learn the essentials of this promising technology.

Keywords: Thermostats, smart phones, tablets, healthcare, mining.

[1] Kosmatos, E.A., Tselikas, N.D. and Boucouvalas, A.C. (2011) Integrating RFIDs and Smart Objects into a Unified Internet of Things Architecture. Advances in Internet of Things: Scientific Research, 1, 5-12.
[2] Aggarwal, R. and Lal Das, M. (2012) RFID Security in the Context of "Internet of Things". First International Conference on Security of Internet of Things, Kerala, 17-19 August 2012.
[3] Biddlecombe, E. (2009) UN Predicts "Internet of Things". Retrieved July 6.
[4] Reinhardt, A. (2004) A Machine-to-Machine Internet of Things.
[5] Higgins, K.T. (2015). Working with the next generation of plant pros. Food Processing [Online]. Retrieved July 31, 2015.

Abstract: The substantial growth in power demand throughout globe has resulted in more production with high quality outcome. The power energy consumption needs to be increased hastily due to industrial development and usage litheness. Thermal plant consistency and stability needed to improve the quality is a inspiring task due to advent of active environmental features which roots abnormalities in regular functioning philosophy. Mostly energy outcome of coal fired utility boiler is attained using standard active set points. Thermal plant unit efficiency of the boiler replicates the outcome of coal to steam conversion process which excludes un-ignorable energy loss. This proposed effort tries to derive optimal process criteria to minimize and control energy loss. The detail knowledge about fuel.........

Keywords— Analysis, Boiler, Knowledge, Loss, Mining

[1] Christina Athanasopoulou & Vasilis Chatziathanasiou 2009, 'Intelligent System for Identification and Replacement of Faulty Sensor Measurements in Thermal Power Plants(IPPAMAS:PART 1)' Expert System with Applications,Elsevier, no.36, pp. 8750-8757.
[2] Dewangan, DN,Manoj Kumar Jha & Banjare,YP 2014,'Reliability Investigation of Steam Turbine used in Thermal Power Plant' International Journal of Innovative Research in Science ,Engineering and Technology, vol. 3, no. 7, pp. 14915-14923.
[3] Fang, GAO, Pu Han, Yong-Jie ZHAI & Yuan LU 2012,'Computational Intelligence i Low NOx Emission Combustion for Coal Fired Power Plants' International Journal of Advancements in Computing Technology, vol. 4,no. 14.
[4] Firas B Ismail Alnaimi & Hussain H.AL-Kayiem 2011, 'Artificial Intelligent System for Steam Boiler Diagnosis Based on Superheater Monitoring' Journal of Applied Sciences, no. 11, pp. 1566-1572.
[5] Genesis Murehwa, Davison Zimwara, Wellington Tumbudzuku & Samson Mhlanga 2012, ' Energy Efficiency Improvement in Thermal Power Plants' International Journal of Innovative Technology and Exploring Engineering, vol. 2, issue. 1.

Abstract: Healthcare organisations and industries produces huge amount of data everyday. These vast information can be extracted and analysed to obtain patterns which can be used to forcast or predict the future events. Many healthcare organizations have already started collecting healthcare records to systematically use those data to identify patterns and improve health management of a particular population thereby providing improved care and reduced manual work and cost. This paper focuses on the survey of different dengue fever classification and study of a particular population where people are mostly diagnosed with dengue infection. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centres for better health policy-making, detection of disease outbreaks. We have collected data from government hospitals. We have applied these data in our fuzzy logic and generated decision tree and compared the performance of other techniques.

Keywords: Data Mining,, Decision Tree, Dengue, Fuzzy Logic, Healthcare

[1] F. Ibrahim, M. N Taib, W. A. B. Wan Abas, C.G. Chan and S. Sulaiman, A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN), Computer Methods and Programs in Biomedicine, No.79, 2005, pp. 273-281.
[2] L. Tanner, M. Schreiber, J.G. Low, A. Ong, T.Tolfvenstam, Y.L. Lai, L.C. Ng, Y.S. Leo, L.Thi Puong, S.G. Vasudevan, C.P. Simmons,M.L. Hibberd and E.E. Ooi, Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness, PLoS Neglected Tropical Disease, Vol.2, 2008.
[3] T. Faisal, F. Ibrahim and M.N. Taib, A noninvasive intelligent approach for predicting the risk in dengue patients, Expert Systems with Application,Vol.37, No.3, 2010, pp. 2175- 2181.
[4] World Health Organization, Guideline for Treatment of Dengue Fever/Dengue Haemorrhagic Fever, 1999
[5] M. Anbarasi, E. Anupriya, N.ch.s.n.Iyengar, Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm, International Journal of Engineering Science and Technology,2010

Abstract: Super-resolution (SR) is a technique to enhance the resolution of an image without changing the camera resolution, through using software algorithms. In this context, this paper proposes a fully automatic SR algorithm, using a recent nonparametric Bayesian inference method based on numerical integration, known in the statistical literature as integrated nested Laplace approximation (INLA). By applying such inference method to the SR problem, this paper shows that all the equations needed to implement this technique can be written in closed form. Moreover, the results of several simulations show that the proposed algorithm performs better than other SR algorithms recently proposed..

Keywords: Bayesian inference, Closed form, Integrated Nested Laplace Approximation (INLA), Nonparametric, Super-resolution (SR)

[1] N. P. Galatsanos, V. Z. Mesarovic, R. Molina, and A. K. Katsaggelos, "Hierarchical Bayesian image restoration from partiallyknown
blurs," IEEE Trans. Image Process., vol. 9, no. 10, pp. 1784–1797, Oct. 2000.
[2] M. Tipping and C. Bishop, "Bayesian image super-resolution," in Advances in Neural Information Processing Systems,
Cambridge, MA: MIT Press, pp. 1303–1310, 2003.
[3] T. Pham, L. Van Vliet, and K. Schutte, "Robust fusion of irregularly sampled data using adaptive normalized convolution,"
EURASIP J. Appl.Signal Process., vol. 2006, pp. 1–12, Jan. 2006.
[4] M. Vetterli, "Superresolution images reconstructed from aliased images," in Proceedings of SPIE/IS&T Visual Communications
and Image Processing Conference, T. Ebrahimi and T. Sikora, Eds., vol. 5150 of Proceedings of SPIE, Switzerland, 2003.
[5] P. Vandewalle, S. Süsstrunk, and M. Vetterli, "A frequency domain approach to registration of aliased images with application to
superresolution," EURASIP J. Appl. Signal Process, vol. 2006, pp. 1–14, Mar. 2006/

Abstract: In modern world user have started to search their requirement through their mobile User can access information through Mobile environment more easily regardless of user location. Accessing information can be of any type of searching a location. Searching can be to identify their nearest educational Institution, Petrol Bunk, Play area, Restaurant and so on. Spatial queries as utilize to access better information through the mobile at point of the world. KNN, Range query are popularly available to identify the required location.KNN can also apply for business purpose such as Banking, statistical process, Social network. In banking sector KNN is utilized to identify the nearest ATM for transaction, Loan decision, Bank credit risk analysis with k-nearest neighbor classifier. The K-NN is modified as K-NN classifier for the above process under banking sector. K-NN can be modified as K-NN Extract for Business...........

Keywords – K-NN, spatial, queries, classifiers

[1] K. VenkateswaraRao, A .Govardhan, K.V .ChalapatiRao, "Spatial temporal Data Mining:Issues ,Task & Application",IJCSES",Vol 3.No,February 2012
[2] E.Baby Anitha,,Dr.K.Duraisamy, "Prediction of vehicle Movements using spatial Mining: A Recent survey",IJART,Vol.2 Issue 4,2012,pp-1-4.
[3] Bindiya, M.Varghese ,Unnikrishnan, A,Poulose, Jacob.K, Spatial clustering Algorithms-An Overview",AJCSIT,2013,1-8,ISSN 2249-5126

[4] gourav rahangdale, mr.manish agirwar, dr.mahesh motvani" IJCSI International journal of computer science issues",volume 13,issue 5,sep 2016

[5] aida krichene abdel moula "accounting and management information systems", volume 14,no.1,pp 79106,2015

[6] American Statistical Association, " journal of statistical software" November 2012,volume 51,issue 7 .

Abstract: Cloud computing has been the buzzing Word over the last few years but surprisingly and whether we realize it or not we are using it as well. Gmail, Facebook, DropBox, Skype, Paypal, SalesForce.com are all examples of cloud computing. Cloud Computing refers to on-demand delivery of resources through the Internet. In traditional data storage systems, Server room contains a Data base server, Mail server, networking, firewalls, routers, modem, switches, QPS (Query Per Second means how much queries or load will be handled by the server), configurable system, high net speed and the maintenance engineers. To establish such IT infrastructure, we need to spend lots of money. To overcome all these problems and to reduce the IT infrastructure cost, Cloud Computing comes into existence. This paper intend to throw lights on Cloud computing, its need, how it works, pros and cons.

Keywords:- Coherence, Economy – of – Scale, QPS

[1] Alexa Huth and James Cebula "The Basics of Cloud Computing‟, United States Computer Emergency Readiness Team. (2011).
[2] Anitha Y, "Security Issues in cloud computing", "International Journal of Thesis Projects and Dissertations "(IJTPD) Vol. 1, Issue 1, PP :( 1-6), Month: October 2013.
[3] Balachandra Reddy Kandukuri, Rama Krishna Paturi and Dr. AtanuRakshit, "Cloud security issues" In Services Computing, 2009. IEEE International Conference on, page 517520, 2009..

Abstract: When sending humans to the place, where no one has gone before, there are a multitude of variables to consider, and NASA (National Aeronautics and Space Administration) is hard at work researching the health and safety risks of a future Mission to Mars. The stakes are high, but NASA realized from the get-go that it needed to focus less on developing the perfect analytic model and more on building a data science process that empowers decision-makers to use analytics to answer a multitude of continually changing questions. This paper investigates, "How data science coupled with rocket science to get humans to Mars?"

Keywords: Analytic – mission - data science – rocket science

[1] Instruments that are used to gather data: http://economictimes.indiatimes.com/articleshow/60273566. cms?utm_ source=contentofinterest&utm_medium=text&utm_campaign=cppst.
[2] What will be NASA's biggest data challenge?: https://techcrunch.com/2016/11/19/how-data-science-and-rocket-science-will-get-humans-to-mars/
[3] An Overview of Advanced Analytics: http://searchbusinessanalytics.techtarget.com/definition/advanced-analytics
[4] About Leidos and its role:
[5] https://www.cloudera.com/solutions/gallery/lockheed-leidos-collaborative-advanced-analytics-and-data-sharing-platform-caads.html

Abstract: Data mining is used to transform the data available from the web servers to track the continually changing attitude of online customers. The Business Organizations which are involved in online trading must classify them accurately. Based on the preferences and expectations of various levels of online customers the buying behaviour and pattern also vary. This research paper discusses about the Multivariate Decision Tree Algorithm to perform the Supervised Classification of Online Customers. According to Market Basket Analysis Classification process is taken place. This Classification will be certainly helpful to the organizations to address the expectations of online buyers and lead them to successful sustainability in the online trading. Multivariate Decision Tree deals with attribute correlation. It uses linear machine co-relation of attributes that influences online purchase. Entropy is used to measure.........

Keywords: DataMining, Classification, Online Buyers, Linear Machine,Univariate Decision Tree,Multivariate Decision Tree,Market Basket Analysis.

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