Volume-6 , Issue-4 , Apr - 2018 Go Back

Every Indian Family issued ration card based on which given subsidized food grains are distributed. For distribution and storing of food grains, an effective smart approach is proposed in this paper. Due to manual distribution sometimes the user does not get actually measured food grains as it gets replaced with a poor quality of food grains. The main purpose of designing the system is to provide security for grains which are received from the government by the interfacing officer locking system. The proposed design provides ease of use to the customers. The presented system is offline as a database of user and officer is stored in memory according to the particular areas. The authentication is provided for the officer who loads the grains in a container and for the customer to receive the grains. Interchanging of food grains is prohibited after proper verification of identification number.

Ration card, security,offline, verification

[1] N. Adiba, A.K. Singh, "Automated ration distribution system using RFID and GSM", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 5, Issue 7. Pp.1-7, 2014
[2] C. Mahatme, S. Mahakalkar, "Auto Rationing system", International Journal of Pure and Applied Research in Engineering and Technology (IJPRET), volume 4. Pp.1-7, 2014
[3] J. Patel, D. Mantala, "Ration Vending machine using fingerprint recognition", International Journal of Advance Research and Innovative Ideas in Education (IJARIIE), Volume 3, Issue 2. pp.1-6, 2017
[4] P.Naveenkumar, N.Suthanthira Vanitha, "Automatic Rationing system using embedded system Technology", International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), volume 1, Issue 8 . pp.1-4, 2013
[5] M. Zagare, G. Bodade, "Automatic Rationing system by using controller", International Journal of Research in Advent Technology (IJRAT). Pp.1-4, 2016
[6] P. Pedwal, "Real Time Automatic Ration Material Distribution System", IOSR Journal of Computer Engineering (IOSR-JCE), pp.1-5, 2016
[7] Yuvaraju .M , Pranesh K.A., "Fair Price Shop Automated Vending Machine Design Using RFID and GSM Communication Technology", International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 4, Issue 6, pp.1-8, 2016
[8] Kamalanathan .P, "Automatic Paper Vending Machine", International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 4, pp.1-6, 2015
[9] S. Raikar M, G. D. Kamlapura, "Automated Ration Wending Machine for a Ration Shop Using RFID Card", International Journal of Science and Research (IJSR), pp.1-5, 2015
[10] N. Adiba, S. Priyam, "Automated Ration Distribution System Using RFID/UID and IoT", International Journal of Advance Electrical and Electronics Engineering (IJAEEE), pp.1-5, 2017
[11] Y.K. Sharma, K.B. Shiva Kumar, "Multi-Modality Biometric Assisted Smart Card Based Ration Distribution System", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 4, Issue 6, pp.1-11, 2014
[12] P. Pedwal, S. Borkar, "Real Time Automatic Ration Material Distribution System", International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 5, Issue. 3, pp.1-6, 2016
[13] K. Bogawar, N. Kayarkar, A. Biswas, "Intelligent Rationing System using RFID &GSM", International Journal of Engineering Science and Computing(IJECS), Volume 7 Issue No.3, pp.1-3, 2017
[14] R.A. Jaid, C.K. Kadam, "An Overview of Automatic Rationing System", International Journal of Informative & Futuristic Research (IJIFR), Vol. 2, Issue. 6, pp.1-6, 2015

V.Barai, N.Balla, C.Khobragade, S.Mahajan,Avinash Maurya , "Smart Grain Dispenser with Officer Locking System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.550-555, 2018.

Speech of human beings is the reflection of the state of mind. Proper evaluation of these speech signals into stress types is necessary in order to ensure that the person is in a healthy state of mind. More than a decade has passed since research on stress types in speech identification has become a new field of research in line with its ‘big brothers’ speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In this work we propose a Recurrent Neural Network classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The algorithm assists the system to learn the speech patterns in real time and self-train itself in order to improve the classification accuracy of the overall system. The proposed system is suitable for real time speech and is language and word independent.

RNN, MFCC, Stress Classification, Feature Selection

[1] Schuller, Bjorn, et al., “Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first Challenge”, Speech Communication 53.9, pp. 1062-1087, 2011.
[2] Anagnostopoulos, Christos-Nikolaos, Theodoros Iliou, and Ioannis Giannoukos, “Features and Classifiers for Emotion Recognition from Speech: A survey from 2000 to 2011”, Artificial Intelligence Review 43.2, pp.155-177, 2015.
[3] Dipti D. Joshi, M. B. Zalte, “Speech Emotion Recognition: A Review”, Journal of Electronics and Communication Engineering (IOSR-JECE) 4.4, pp.34-37, 2013.
[4] Ververidis, Dimitrios, and Constantine Kotropoulos, “Emotional Speech Recognition: Resources, Features, and Methods”, Speech Communication 48.9, pp.1162-1181, 2006.
[5] El Ayadi, Moataz, Mohamed S. Kamel, and Fakhri Karray, “Survey on Speech Emotion Recognition”, Features, classification schemes, and databases, Pattern Recognition 44.3 pp. 572-587,2011.
[6] Scherer, Klaus R., “Vocal Communication of Emotion: A review of research paradigms”, Speech communication 40.1, pp.227-256, 2003.
[7] Vogt, Thurid, Elisabeth Andre, and Johannes Wagner, “Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realization, Affect and emotion in human-computer interaction”, Springer Berlin Heidelberg, pp. 75-91, 2008.
[8] Burkhardt, Felix, et al., “A Database of German Emotional Speech”, INTER-SPEECH, Lisbon, Portugal, vol. 5, pp.1-4, 2005.
[9] Kwon, Oh-Wook, et al, “Emotion Recognition by Speech Signals, INTER-SPEECH, pp.1-4, 2003.
[10] Campbell, N. “Recording and Storing of Speech Data”. In: Proceedings LREC, pp. 12-25, 2002.
[11] Cowie, R., Douglas-Cowie, E., Savvidou, S., McMahon, E., Sawey, M., Schroder, M. Feeltrace, “An Instrument for Recording Perceived Emotion in Real Time”, In: Proceedings of the ISCA Workshop on Speech and Emotion, pp.19-24, 2000.
[12] Devillers, L., Cowie, R., Martin, J.-C., Douglas-Cowie, E., Abrilian, S., McRorie, M.: “Real life emotions in French and English TV video clips: an integrated annotation protocol combining continuous and discrete approaches”, 5th International Conference on Language Resources and Evaluation LREC, Genoa, Italy.2006.
[13] Douglas-Cowie, E., Campbell, N., Cowie, R.P. “ Emotional speech: Towards a new generation of databases”. Speech Communication 40(1–2), pp.33-60, 2003.
[14] Douglas-Cowie, E., et al.: “The description of naturally occurring emotional speech”. In: Proceedings of 15th International Congress of Phonetic Sciences, Barcelona, 2003.
[15] http://audacity.sourceforge.net/download.
[16] A. J. Robinson, "An Application of Recurrent Nets to Phone Probability Estimation," IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 298-305, 1994.
[17] Oriol Vinyals, Suman Ravuri, and Daniel Povey, "Revisiting Recurrent Neural Networks for Robust ASR," in ICASSP, 2012.
[18] A. Maas, Q. Le, T. O Neil, O. Vinyals, P. Nguyen, and A. Ng, "Recurrent neural networks for noise reduction in robust asr," in INTERSPEECH, 2012.
[19] BOGERT, B. P.; HEALY, M. J. R.; TURKEY, J. W.: “The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo Autocovariance, Cross-Cepstrum and Saphe Cracking”, Proceedings of the Symposium on Time Series Analysis, (M. Rosenblatt, Ed) Chapter 15, New York: Wiley, pp.209-243, 1963.

N.P. Dhole, S.N. Kale, "Study of Recurrent Neural Network Classification of Stress Types in Speech Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.256-360, 2018.

Internet of Things (IoT) can be defined as a network of physical objects, devices that contain embedded technology (like intelligent sensors, controllers etc.) which can communicate, sense, or interact with internal and external systems. In other words, when objects can sense and communicate, it changes how and where decisions are made, and who makes them and accordingly operations can be carried out. A latest and very fast emerging shift in networking and communications is the Internet of Things. This technology provides an easier way of communication of devices with the minimal interaction of human. IoT takes a leap because of advancement in network interconnections and computing ability to propose fresh techniques. With computation, connectivity, and data storage becoming more advanced there has been an expansion of IoT based application solutions in diversified domains right from health care to public safety, agriculture, from assembly line scheduling to manufacturing and various other technological domains. Different IoT based applications have been explored and possible approach for enhancing the use of this technology have been depicted in this paper. Future directions and suggestions for effectively and efficiently improving the IoT based application areas have been shown. This paper will provide a better insight to carry out research in the field of IoT.

Embedded technology, Interconnectedness, Networking, and Computation

[1] Tsai, Chun-Wei, et al. "Data mining for internet of things: A Survey. “Communications Surveys & Tutorials, IEEE 16.1 (2014): 77-97.
[2] Stankovic, John. "Research directions for the internet of things." Internet of Things Journal, IEEE 1.1 (2014): 3-9.
[3] M. Zorzi, A. Gluhak, S. Lange, A. Bassi, From Today`s Intranet of Things to a Future Internet of Things: A Wirelessand Mobility-Related View, IEEE Wireless Communication 17 (2010) 43–51.
[4] N. Honle, U.P. Kappeler, D. Nicklas, T. Schwarz, M. Grossmann, Benefits of Integrating Meta Data into a Context Model, in: 2005: pp. 25–29.
[5] Karimi, Kaivan, and Gary Atkinson. "What the Internet of Things (IoT) needs to become a reality." White Paper, FreeScale and ARM (2013).
[6] Stankovic, John. "Research directions for the internet of things." Internet of Things Journal, IEEE 1.1 (2014): 3-9.
[7] Gubbi, Jayavardhana, et al. "Internet of Things (IoT): A vision, architectural elements, and future directions." Future Generation Computer Systems 29.7 (2013): 1645-1660.
[8] "Understanding the Internet of Things (IoT) “, July 2014. R.Weber, Internet of Things-New security and privacy challenges, Computer Law & Security Review, 26:23– 30, 2010.

T.S.Praveena, M.Naga Jyothi, M.Surya Prakash, "Contemporary Progression, Agile Applications and Impending Scope of Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.381-385, 2018.

In Natural Language Processing research, Named entity recognition acts as an important tool. To improve the quality of search results, while searching through the internet , the automatic Named entity recognition(NER) and classification in the text plays very important role. Many natural language processing applications like question answering, document clustering, document summarization uses the output of Named Entity Recognition. Even today, the highly accurate Named Entity Recognition (NER) is a challenge, In this paper, a novel approach using unsupervised learning is proposed to automatically create gazette for Named entity recognition and Named entity extraction. The main purpose of approach is to automate the named entity recognition task, as manually recognizing named entities is cumbersome. Manually labeling so huge number of entities is effort intensive and can lead to wrong classification of entities.

Named Entity Recognition, Named Entity Extraction, Natural Language Processing, Machine Learning, Information Retrieval

[1] Rudra Murthy V , Pushpak Bhattacharyya," A deep Learning solution for NER" ,17th International Conference on Intelligent Text Processing and Computational Linguistics,Turkey, April 2016
[2] Rudra Murthy V , Mitesh Khapra,Dr.Pushpak Bhattacharya,"Sharing Network Parameters for Cross-lingual Named Entity Recognition ", July 2016
[3] Guillaume Lample,Miguel Ballesteros,Sandeep Subramanian, Kazuya Kawakami ,Chris Dyer, "Neural Architectures for Named Entity Recognition", Proceedings of NAACL-2016
[4] Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He,Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig," Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding",Proc. IEEE/ACM transaction on audio, speech and language ,VOL. 23(2015) pp530-539
[5] Sangameshwar Patil , Sachin Pawar , Girish Palshikar, “Named entity extraction using Information distance", 1st Indian Workshop on Machine Learning, IIT Kanpur,India,2013
[6] Pawar Sachin,Rajiv Srivastava,Palshikar G.K.2012, "Automatic Gazette Creation for Named Entity Recognition and Application to Resume Processing"", In Proceedings of ACM COMPUTE 2012 Conference, Pune, India.
[7] Palshikar, G.K., 2011, "Techniques for named entity recognition: a survey", TRDDC Technical Report, pp.191-217
[8] Nadeau, D., & Sekine, S. (2007),"A survey of named entity recognition and classification. Lingvisticae Investigations", 30, 3–26. doi:10.1075/li.30.1.03nad
[9] Ekbal, A., & Bandyopadhyay, S. (2010),"Improving the performance of a NER system by post-processing and voting. In Structural, Syntactic and Statistical Pattern Recognition", LNCS 5342 (pp. 831−841), Springer
[10] Krishnarao, A., Gahlot, H., Srinet, A., & Kushwaha,D. (2009),"A comparison of performance of sequential learning algorithms on the task of named entity recognition for Indian languages", In proceedings of the International Conference on Computational Science(ICCS 2009), LNCS 5544, (pp. 123–132), Springer.
[11] Nadeau D., Turney P. Matwin S. 2006,"Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity", Proc. 19th Canadian Conf. Artificial Intelligence.
[12] Zornitsa Kozareva, "Bootstrapping Named Entity Recognition with Automatically Generated Gazetteer Lists" ,Proceedings of 11th Conference of European chapter of ACL,2006
[13] Etzioni O., Cafarella M., Downey D., Popescu A.M., Shaked T., Soderland S., Weld D.S. AND Yates A. 2005, "Unsupervised named-entity extraction from the Web: An experimental study", Artificial Intelligence, 165, pp. 91–134.
[14] Thelen M. AND Riloff E. 2002," A bootstrapping method for learning semantic lexicons using extraction pattern contexts", Conference on Empirical Methods in natural Language Processing (EMNLP 2002).
[15] Bikel, D. M., Schwartz, R., Weischedel, R. M. (1999),"An algorithm that learns what’s in a name. Machine Learning", 34, 211–231. doi:10.1023/A:1007558221122 .
[16] Collins M. AND Singer Y. 1999,"Unsupervised models for named entity classification", Proc. EMNLP,pp.100-110.
[17] Talukdar, P., Brants, T., Liberman, M., & Pereira, F. 2006,"A context pattern induction method for named entity extraction" ,In proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-2006), pp. 141–148.

Mukta S. Takalikar, Manali M.Kshirsagar, Kavita R. Singh, "Pattern based Named Entity Recognition using context features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.365-368, 2018.

Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information, precisely in large text, in a short span of time. In this paper, the proposed method uses sentence ranking of a topic-specific document to generate automatic summary. The method is based on the concept of extractive summary, in which the summary of a document is obtained by scoring, ranking and selecting the highest ranked sentences of the document. Initially, the text is pre-processed by tagging the document and selecting adjectives, nouns and verbs, and then the text is analysed and sentences most similar to all is ranked and selected for generation of summary. Experiments on these methods were conducted to compare the results on sentence ranking. The algorithm proposed was tested on different documents and has given accuracy of about 80% when compared to summarization tools available online.

Text Summarization, data mining, extraction-based summarization, sentence ranking

VI. REFERENCES:
[1] N. Andhale, L. Bewoor (Vishwakarma Institute of Information Technology, Pune), “An Overview of Text Summarization Techniques”, International Journal of Scientific Research Engineering & Technology (IJSRET), Volume 6, Issue 3, 2017, pp. 146-150
[2] S. Akter et. al, “An Extractive Text Summarization Technique for Bengali Document(s) using K-means Clustering Algorithm.”, American Journal of Engineering Research (AJER) , Volume-6, Issue-1, pp-226-239
[3] J. Tian, M. Cao, J. Liu, X. Sun, Z. Hai, “Ranking Sentences in Scientific Literatures”, In the proceedings of11th International Conference on Semantics, Knowledge and Grids, USA, pg . 275, 2015
[4] P. Gupta, R. Tiwari and N. Robert, “Sentiment Analysis and Text Summarization of Online Reviews: A Survey”, In the proceedings of International Conference on Communication and Signal Processing, pg. 241-245, 2016, India.
[5] M Indu,, Kavitha K V, “Review on text summarization evaluation methods”, In the proceedings ofInternational Conference on Research Advances in Integrated Navigation Systems, pg. 2016, India

[6] N.Moratanch, S.Chitrakala, “A Survey on Extractive Text Summarization”, IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017) , India.
[7] K. Chen, S. Liu, B. Chen, H. Wang, E. Jan, W. Hsu, “Extractive Broadcast News Summarization Leveraging Recurrent Neural Network Language Modeling Techniques”, IEEE / ACM Transactions On Audio, Speech, And Language Processing, Vol.23, No.8, pp. 1322-1334, 2015
[8] Y. Meena, P. Deolia, D. Gopalani, “Optimal Features Set For Extractive Automatic Text Summarization”, Fifth International Conference on Advanced Computing & Communication Technologies, pp. 35, India, 2015
[9] Y. Zhang, M. Joo Er, M. Pratama, “Extractive Document Summarization Based on Convolutional Neural Networks”, 42nd Annual Conference of the IEEE Industrial Electronics Society, USA, 2016
[10] J. Zhang, P. Fung, “Learning Deep Rhetorical Structure For Extractive Speech Summarization”, IEEE International Conference on Acoustics, Speech and Signal Processing,, pp.5302-5305, USA, 2010

A.A. Shrivastava, A.S. Bagora, R. Damdoo, "Extractive Technique for Text Summarization based on Ranking Scheme," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.369-373, 2018.

Bunch investigation is utilized to order comparative protests under same gathering. It is a standout amongst the most critical data mining techniques. In any case, it neglects to perform well for big data because of enormous time many-sided quality. For such situations parallelization is a superior approach. MapReduce is a prevalent programming model which empowers parallel handling in an appropriated domain. Be that as it may, a large portion of the clustering calculations are not "normally parallelizable" for example Genetic Algorithms. This is thus, because of the successive idea of Genetic Algorithms. This paper acquaints a system with parallelize GA based clustering by expanding hadoop MapReduce. An examination of proposed way to deal with assess execution picks up regarding a consecutive calculation is displayed. The investigation depends on a genuine huge data set.

Big Data, Clustering, Davies-Bouldin Index, Distributed processing, Hadoop MapReduce , Heuristics, Parallel Genetic Algorithm

[1] R.T.Ng, Jiawei Han, “CLARANS: a method for clustering objects for spatial data mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 5, PP. 1003 – 1016, 2002.
[2] G.Biswas, J.B.Weinberg, D.H.Fisher, “ITERATE: a conceptual clustering algorithm for data mining, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 28, No. 2, PP. 219 – 230, 1998.
[3] Yan Yang, Hao Wang, “Multi-view clustering: A survey”, Big Data Mining and Analytics, Vol. 1, No. 2, PP. 83 – 107, 2018.
[4] Ruizhi Wu, Guangchun Luo, Qinli Yang, Junming Shao, “Learning Individual Moving Preference and Social Interaction for Location Prediction”, IEEE Access, Vol. 6, PP. 10675 – 10687, 2018.
[5] K.U. Malar, D. Ragupathi, G.M. Prabhu, “The Hadoop Dispersed File system: Balancing Movability And Performance”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.166-177, 2014.
[6] Qiqi Zhu, Yanfei Zhong, Siqi Wu, Liangpei Zhang, Deren Li, “Scene Classification Based on the Sparse Homogeneous–Heterogeneous Topic Feature Model”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 5, PP. 2689 – 2703, 2018.
[7] Guangwei Shi, Liying Ren, Zhongchen Miao, Jian Gao, Yanzhe Che, Jidong Lu, “Discovering the Trading Pattern of Financial Market Participants: Comparison of Two Co-Clustering Methods”, IEEE Access, Vol. 6, PP. 14431 – 14438, 2018.
[8] Jianzhou Wang, Fanyong Zhang, Feng Liu, Jianjun Ma, “Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series”, IET Renewable Power Generation, Vol. 10, No. 3, PP. 287 – 298, 2016.
[9] Fen Miao, Nan Fu, Yuan-Ting Zhang, Xiao-Rong Ding, Xi Hong, Qingyun He, Ye Li, “A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques”, IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 6, PP. 1730 – 1740, 2017.
[10] Mauro De Sanctis, Igor Bisio, Giuseppe Araniti, “Data mining algorithms for communication networks control: concepts, survey and guidelines”, IEEE Network, Vol. 30, No. 1, PP. 24 – 29, 2016.
[11] Daniele Casagrande, Mario Sassano, Alessandro Astolfi, “Hamiltonian-Based Clustering: Algorithms for Static and Dynamic Clustering in Data Mining and Image Processing”, IEEE Control Systems, Vol. 32, No. 4, PP. 74 – 91, 2012.
[12] Xiangyang Li, Nong Ye, “A supervised clustering and classification algorithm for mining data with mixed variables”, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 36, No. 2, PP. 396 – 406, 2006.
[13] Yuan He, Cheng Wang, Changjun Jiang, “Mining Coherent Topics With Pre-Learned Interest Knowledge in Twitter”, IEEE Access, Vol. 5, PP. 10515 – 10525, 2017.
[14] Feng Zhang, Timwah Luk, “A Data Mining Algorithm for Monitoring PCB Assembly Quality”, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 30, No. 4, PP. 299 – 305, 2007.
[15] Byron Graham, Raymond Bond, Michael Quinn, Maurice Mulvenna, “Using Data Mining to Predict Hospital Admissions From the Emergency Department”, IEEE Access, Vol. 6, PP. 10458 – 10469, 2018.
[16] A.Bernstein, F.Provost, S.Hill, “Toward intelligent assistance for a data mining process: an ontology-based approach for cost-sensitive classification”, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 4, PP. 503 – 518, 2005.
[17] Chun-Hao Chen, Vincent S.Tseng, Tzung-Pei Hong, “Cluster-Based Evaluation in Fuzzy-Genetic Data Mining”, IEEE Transactions on Fuzzy Systems, Vol. 16, No. 1, PP. 249 – 262, 2008.
[18] Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-Lung Wu, “Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, PP. 252 – 265, 2008.
[19] D.A.Keim, C.Panse, M.Sips, S.C.North, “Visual data mining in large geospatial point sets”, IEEE Computer Graphics and Applications, Vol. 24, No. 5, PP. 36 – 44, 2004.

S. Ramya, N. Subha, "Examination of Clustering Techniques using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.374-378, 2018.

Automated Library data tracking system by smartphone is the android based application containing the facility for the student and the staff of the library to track and transact the book and library system. These application has the functionality to register the student on the library database according the branch and enrolment number. In this system the student can make the book issue and return easily by using the QR code. Each and every student has the unique QR code instead of paper ID card. The book also has the QR code by scanning these QR codes the librarian can issue or return the book. Application also has the facility for student to search for the book availability and college department information with teacher information. The staff can also use the application to track the book data and make the entry for new books to the library. And can also make the entry for the college department staff. All the information about the staff, student and book transaction is stored and managed by the centralized database over the internet. All the system is fully online. The website middle ware is also designed to handle and manage all request by the staff, student and book by the admin.

QR Code, Centralised Database, Android Application, Library Automation

[1] A. Tripathi & A. Srivastava, “Online Library Management System” IOSR Journal of Engineering (IOSRJEN), Vol. 2 Issue 2, Feb.2012.
[2] S. Sivasubramanian, S. Sivasankaran, S. Thiru Nirai Senthil,“A Proposed Android Based Mobile Application to Monitor Works at Remote Sites”, 3, IJSR International Journal of Science and Research ISSN (Online): 2319- 7064 Volume 3 Issue 2, February 2014
[3] P. Pillai, “Android Application for Library Automaion”, International Journal of Technical Research in Applications, Vol.4, Issue.2, pp.72-74, 2016.
[4] Semantic, ―Transformational smart cities: cyber security and resilience‖, 2010.
[5] W. Jackson`s (2011) "Android Appsfor Absolute Beginners" Apress Publications.
[6] “Features”, Android Studio 2.3 user guide. Android Studio., https://developer.android.com/studio/features.html
[7] J. "JavaJeff" Friesen`s (2010) "Learn Java for Android Development" - ApressPublications.
[8] L. Darcey and S. Conder (2010) "Sams Teach yourself Android Application Development" -Sams Publications.
[9] Q. Huang, ”Mobile Services in University Libraries in China”, Library of Huzhou Teachers College Huzhou, China.
[10] M. Newmon, Dr. V. Sengar, “Engineering College Library”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013

A.P. Khan, Y.B. Patil, P.R. Patil, M.S. Nagarale, R.V. Patil, "Automated Library Data Tracking System By Smartphone," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.379-382, 2018.

In light of the touchy development of data that we are choking in, while we are starving for learning, mining data and data from generous databases has been seen as a key research point. In this way, Due to the gigantic size of data that exists in the databases and distribution centers and in light of the fact that these data are big, dynamic and change as often as possible it is troublesome and costly to do mining for visit examples and association rule starting with no outside help. In light of such an intrigue, this paper proposes a Dynamic Algorithm for Association Rules Mining in Big Data that is equipped for finding successive thing sets progressively and creating association rules from the thing sets by utilizing gathered learning put away in a database table, this table will be adjusted as often as possible when the framework runs each time and the new estimation of the table will be the aftereffect of preparing new embedded data added to the consequences of beforehand handled data. The proposed arrangement is executed utilizing C#.net and SQL server. The outcomes contrasted and the Apriori calculation. It was presume that Apriori calculation indicated preferable outcomes over the proposed calculation in the underlying runs, then again the proposed dynamic calculation gave comes about close to Apriori calculation on visit runs that utilization modest number of exchanges yet the proposed dynamic calculation took less handling time than Apriori calculation by 63.95% on the regular runs that utilization big number of exchanges.

Big data, Apriori algorithm, Association rules, Data mining

[1] Golnar Assadat Afzali, Shahriar Mohammadi, “Privacy preserving big data mining: association rule hiding using fuzzy logic approach”, IET Information Security, 2018, Vol. 12, No. 1, PP. 15 – 24.
[2] Gehao Sheng, Huijuan Hou, Xiuchen Jiang, Yufeng Chen, “A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model”, IEEE Transactions on Smart Grid, Vol. 9, No. 2, PP. 695 – 702, 2018.
[3] Saurav Mallik, Anirban Mukhopadhyay, Ujjwal Maulik*, “RANWAR: Rank-Based Weighted Association Rule Mining From Gene Expression and Methylation Data”, IEEE Transactions on NanoBioscience, Vol. 14, No. 1, PP. 59 – 66, 2015.
[4] Qin Ding, Qiang Ding, William Perrizo, “PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 38, No. 6, PP. 1513 – 1524, 2008.
[5] M. Kantarcioglu, C. Clifton, “Privacy-preserving distributed mining of association rules on horizontally partitioned data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 9, PP. 1026 – 1037, 2004.
[6] F. Coenen, P. Leng, S. Ahmed, “Data structure for association rule mining: T-trees and P-trees”, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 6, PP. 774 – 778, 2004.
[7] D.Ragupathi, N.Jayaveeran, “The Design & Implementation of Transportation Procedure using Migration Techiques”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.273-278, 2017.
[8] Eui-Hong Han, G. Karypis, V. Kumar, “Scalable parallel data mining for association rules”, IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 3, PP. 337 – 352, 2000.
[9] José María Luna, Alberto Cano, Mykola Pechenizkiy, Sebastián Ventura, “Speeding-Up Association Rule Mining With Inverted Index Compression”, IEEE Transactions on Cybernetics, Vol. 46, No. 12, PP. 3059 – 3072, 2016.
[10] Chin-Chen Chang, Chih-Yang Lin, “Perfect Hashing Schemes for Mining Association Rules”, The Computer Journal, Vol. 48, No. 2, PP. 168 – 179, 2005.
[11] Ruizhi Wu, Guangchun Luo, Qinli Yang, Junming Shao, “Learning Individual Moving Preference and Social Interaction for Location Prediction”, IEEE Access, Vol. 6, PP. 10675 – 10687, 2018.
[12] Xu He, Fan Min, William Zhu, “Comparison of Discretization Approaches for Granular Association Rule Mining”, Canadian Journal of Electrical and Computer Engineering, Vol. 37, No. 3, PP. 157 – 167, 2014.

N.Subha, N.Baby Kala, "A PLANNED VIBRANT PROCEDURE FOR ASSOCIATION RULES IN BIG DATA," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.383-388, 2018.

With the consistent and exponential increment of the quantity of clients and the span of their information, information deduplication turns out to be increasingly a need for distributed storage suppliers. By putting away a one of a kind duplicate of copy information, cloud suppliers significantly diminish their capacity and information exchange costs. These immense volumes of information require some down to earth stages for the capacity, handling and accessibility and cloud innovation offers every one of the possibilities to satisfy these necessities. Information deduplication is alluded to as a procedure offered to distributed storage suppliers (CSPs) to dispense with the copy information and keep just a solitary one of a kind duplicate of it for storage room sparing reason.In this paper, we display a plan that allows an all the more fine-grained exchange off. The instinct is that outsourced information may require distinctive levels of assurance, contingent upon how mainstream it is: content shared by numerous clients.We show an originalfelt that isolates data according to their reputation. In light of this thought, we outline an encryption arrange for that ensures semantic security for obnoxious information and gives weaker security and better putting away and transmission restrict benefits for eminent information. Subsequently, information de-duplication can be able for standard information, while semantically secure encryptionguarantees unsavory substance. We can use the backup recover system at the time of blocking and also analyze frequent login access system.

Cloud storage, Chunks, Similarity matching, Data security, Backup Recovery

[1]. X. Zhang, C. Liu, S. Nepal and J. Chen, “An Efficient Quasiidentifier Index based Approach for Privacy Preservation over Incremental Data Sets on Cloud,” Journal of Computer and System Sciences (JCSS), 79(5): 542-555, 2013.
[2]. X. Zhang, T. Yang, C. Liu and J. Chen, “A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization using Systems, in MapReduce on Cloud,” IEEE Transactions on Parallel and Distributed, 25(2): 363-373, 2014.
[3]. N. Laptev, K. Zeng and C. Zaniolo, “Very fast estimation for result and accuracy of big data analytics: The EARL system,” Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE), pp. 1296-1299, 2013.
[4]. T. Condie, P. Mineiro, N. Polyzotis and M. Weimer, “Machine learning on Big Data,” Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE), pp. 1242-1244, 2013.
[5]. Aboulnaga and S. Babu, “Workload management for Big Data analytics,” Proceedings of the 29th IEEE International Conference on Data Engineering (ICDE), pp. 1249, 2013
[6]. M. Abadi, D. Boneh, I. Mironov, A. Raghunathan, and G. Segev,“Message-locked encryption for lock-dependent messages,” in Advancesin Cryptology - CRYPTO 2013 - 33rd Annual Cryptology Conference,Santa Barbara, CA, USA, August 18-22, 2013. Proceedings, PartI, 2013, pp. 374–391.
[7]. L. Wang, J. Zhan, W. Shi and Y. Liang, “In cloud, can scientific communities benefit from the economies of scale?” IEEE Transactions on Parallel and Distributed Systems 23(2): 296-303, 2012.
[8]. B. Li, E. Mazur, Y. Diao, A. McGregor and P. Shenoy, “A platform for scalable one-pass analytics using mapreduce,” in: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD`11), 2011, pp. 985-996.
[9]. R. Kienzler, R. Bruggmann, A. Ranganathan and N. Tatbul, “Stream as you go: The case for incremental data access and processing in the cloud,” IEEE ICDE International Workshop on Data Management in the Cloud (DMC`12), 2012
[10]. C. Olston, G. Chiou, L. Chitnis, F. Liu, Y. Han, M. Larsson, A. Neumann, V.B.N. Rao, V. Sankarasubramanian, S. Seth, C. Tian, T. ZiCornell and X. Wang, “Nova: Continuous pig/hadoop workflows,” Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD`11), pp. 1081-1090, 2011.
[11]. K.H. Lee, Y.J. Lee, H. Choi, Y.D. Chung and B. Moon, “Parallel data processing with mapreduce: A survey,” ACM SIGMOD Record 40(4): 11-20, 2012.
[12]. T. Jiang, X. Chen, Q. Wu, J. Ma, W. Susilo, and W. Lou, “Secure andefficient cloud data Deduplication with randomized tag,” IEEE Trans.Information Forensics and Security, vol. PP, no. 99,
[13]. Y. Zhou, D. Feng, W. Xia, M. Fu, F. Huang, Y. Zhang, and C. Li, “Secdep: A user-aware efficient fine-grained secure Deduplication scheme with multi-level key management,” in IEEE 31st Symposium on Mass Storage Systems and Technologies, MSST 2015, Santa Clara, CA, USA, May 30 - June 5, 2015, 2015, pp. 1–14.
[14]. Z. Yan, W. Ding, X. Yu, H. Zhu, and R. H. Deng, “Deduplication on encrypted big data in cloud,” IEEE Trans. Big Data, vol. 2, no. 2, pp. 138–150, 2016.
[15]. “Prism (surveillance program),” https://www.theguardian.com/us-news/ prism.
[16]. R. Bhaskar, S. Guha, S. Laxman, and P. Naldurg, “Verito: A practical system for transparency and accountability in virtual economies,” in 20th Annual Network and Distributed System Security Symposium, NDSS 2013, San Diego, California, USA, February 24-27, 2013, 2013.
[17]. D. Boyd, K. Crawford, S. Shaikh, and V. Ravishankar, “Six provocations for big data,” Six-provocations-for-Big-Data1.pdf.
[18]. X. Yang, R. Lu, H. Liang, and X. Tang, “SFPM: A secure and fine-grained privacy-preserving matching protocol for mobile social networking,” Big Data Research, vol. 3, pp. 2–9, 2016.
[19]. A. K. Mohan, E. Cutrell, and B. Parthasarathy, “Instituting credibility, accountability and transparency in local service delivery?: helpline and aasthi in karnataka, india,” in International conference on information and communication technologies and development, ICTD 2013, Cape Town, South Africa, December 7-10, 2013, Volume 1: Papers, 2013, pp. 238 247.
[20]. S Saravanan, V Venkatachalam, Improving map reduce task scheduling and micro-partitioning mechanism for mobile cloud multimedia services [J]. Int J of AdvIntell Paradigms 8(2), 156–167 (2016)
[21]. S Saravanan, V Venkatachalam,“Advance Map Reduce Task Scheduling algorithm using mobile cloud multimedia services architecture” IEEE Digital Explore,pp21-25,2014.
[22]. S.Saravanan, Arivarasan.”An efficientranked keyword search for effective utilization of outsourced cloud data” Journal of Global Research in Computer Science, Vol4(4), pp:8-12
[23]. S.Swathi “Preemptive Virtual Machine Scheduling Using CLOUDSIM Tool”, International Journal of Advances in Engineering, 2015, 1(3), 323 -327 ISSN: 2394-9260, pp:323-327.
[24]. S Saravanan, V Venkatachalam, Then Malligai "Optimization of SLA violation in cloud computing A artificial bee colony"2015, 1(3), 323 -327 ISSN: 2394-9260, pp:410-414.
[25]. M.Murugesan, A.Kalaiyarasi, “Secure Data Compression Scheme in Cloud Environments With Backup Recovery Scheme”, International Journal of Pure and Applied Mathematics, issue Feb. 2018, pp467-471.
[26]. Dr.P.Santhi, “Privacy Preserving and consistency check of Data Store in Cloud using Attribute based Encryption”, International Journal of Engineering Development and Research, issue 2017.

M.Murugesan, A. Kalaiyarasi, "AN EFFICIENT DEDUPLICATION MECHANISM FOR BIG DATA ANALYSIS IN CLOUD ENVIRONMENTS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.389-395, 2018.

High average-utility itemsets mining (HAUIM) is a key data mining task, which aims at discovering high average-utility itemsets (HAUIs) by taking itemset length into account in transactional databases. Most of these algorithms only consider a single minimum utility threshold for identifying the HAUIs. In this paper, we address this issue by introducing two phase algorithm with pruning strategy in which the task of mining HAUIs is done with multiple minimum average utility thresholds , where the user may assign a distinct minimum average-utility threshold to each item or itemset.

Frequent itemsets ,minimum supports, utility mining,high utility mining

[1] Agarwal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective.IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)
[2] Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in largedatabases. In: International Conference on Very Large Data Bases, pp. 487–499 (1994)
[3] Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: IEEE International Conference on Data Mining, pp. 19–26 (2003)
[4] Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V.S.: FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T.,Christiansen, H., Cubero, J.-C., Ra´s, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502,pp. 83–92. Springer, Heidelberg (2014)
[5] Hong, T.P., Lee, C.H., Wang, S.L.: Effective utility mining with the measure of average utility. Expert Syst. Appl. 38(7), 8259–8265 (2011)
[6] Kiran, R.U., Reddy, P.K.: Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: ACM International Conference on Extending Database Technology, pp. 11–20 (2011)
[7] Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports.In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)
[8] Liu, Y., Liao, W., Choudhary, A.K.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)
[9] Lin, C.-W., Hong, T.-P., Lu, W.-H.: Efficiently mining high average utility itemsets with a tree structure. In: Nguyen, N.T., Le, M.T., ´Swi _ atek, J. (eds.) ACIIDS 2010.LNCS, vol. 5990, pp. 131–139. Springer, Heidelberg (2010)
[10] Lin, J.C.W., Gan, W., Fournier-Viger, P., Hong, T.P.: Mining high-utility itemsets with multiple minimum utility thresholds. In: International C* Conference on Computer Science and Software Engineering, pp. 9–17 (2015)
[11] Lan, G.C., Hong, T.P., Tseng, V.S.: A projection-based approach for discovering high average-utility itemsets. J. Inf. Sci. Eng. 28(1), 193–209 (2012)
[12] Lan, G.C., Hong, T.P., Tseng, V.S.: Efficiently mining high average-utility itemsets with an improved upper-bound strategy. Int. J. Inf. Technol. Decis. Making 11(5),1009–1030 (2012)
[13] Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In:ACM International Conference on Information and Knowledge Management, pp.55–64 (2012)
[14] Lu, T., Vo, B., Nguyen, H.T., Hong, T.-P.: A new method for mining high average utility itemsets. In: Saeed, K., Sn´aˇsel, V. (eds.) CISIM 2014. LNCS, vol. 8838, pp.33–42. Springer, Heidelberg (2014)
[15] Ryang, H., Yun, U., Ryu, K.: Discovering high utility itemsets with multiple minimum supports. Intell. Data Anal. 18(6), 1027–1047 (2014)
[16] Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: SIAM International Conference on Data Mining, pp.211–225 (2004)
[17] Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626 (2006)

Neha Agrawal, Amit Sariya, "Discovering high average utility itemsets with multiple minimum supports," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.396-399, 2018.