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Cancer is a most prevailing problem in the society now days. Generally cancer specifically Breast cancer is a major problem in women. On among three cases of cancer is a Breast cancer. There are many factors that affect the cancer. All these factors and the symptoms in the patient can be recorded using hardware and software. Now days, due to advancement in technology data of patient is recorded and processed by using analytical method. Data mining provides various methods to process this data effectively and efficiently. This processed data can be proven very useful in earlier detection of diseases. The earlier detection of these symptoms can be proven helpful to save life of a patient. In our research, original data on Breast cancer from Winconsin has been taken. This data set has 10 attribute and 699 instances. In this study, a comparative model has been developed that compare performance of various data mining technique on the dataset. The study reveals that BayesNet is the best classifier that correctly predicts cancer survivability in the patient. Further, KStar is the fastest algorithm that takes lowest computation time for the classification. In the next step dimensionality reduction using gain ratio is performed to find out most dominant factors causing Breast cancer.

Data Mining, Breast Cancer, Bayesian, SVM, Decision Tree, Regression Model

[1]. H. Trevor, T. Robert, and F. Jerome, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., vol. 2. Springer: New York, 2009, pp. 32-36.
[2]. N.T.Nghe, P. Janecek, and P. Haddawy, “A comparative analysis of techniques for predicting academic performance", ASEE/IEEE Frontiers in Education Conference, pp. T2G7-T2G12, 2007.
[3]. M. Lichman, UCI Machine Learning Repository, http://www.cs.waikato.ac.nz/ml/weka,2013
[4]. J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. University of Illinois at Urbana-Champaign Elsevier San Francisco, 2009, pp. 285-306
[5]. Bellaachia, Abdelghani, and Erhan Guven, "Predicting breast cancer survivability using data mining techniques", Age, Vol. 58, Issue 13, 2006, pp. 10-110
[6]. Anunciacao Orlando, Gomes C. Bruno, Vinga Susana, Gaspar Jorge, Oliveira L. Arlindo and Rueff Jose, “A Data Mining approach for detection of high-risk Breast Cancer groups,” Advances in Soft Computing, vol. 74, pp. 43-51, 2010.
[7]. Shelly Gupta, Dharminder Kumar,Anand Sharma, “DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS “
Vol. 2 No. 2 Apr-May 2011
[8]. Ahmad LG*, Eshlaghy AT, Poorebrahimi A, Ebrahimi M and Razavi AR “Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence” Health and Medical Informatics 2013, 4:2
[9]. Htet Thazin Tike Thein1 and Khin Mo Mo Tun “An Approach For Breast Cancer Diagnosis Classification Using Neural Network” Advanced Computing: An International Journal (ACIJ), Vol.6, No.1, January 2015
[10]. Uma Ojha, Savita Goel, ”A study on prediction of breast cancer recurrence using data mining techniques” Cloud Computing, Data Science & Engineering - Confluence, 2017 , Noida Inida.
[11]. K. Saravanapriya and J. Bagyamani, “Performance analysis of Classification Algorithms on Diabetes DataSet” . International Journal of Computer Science and Engineering (IJCSE), Vol. 5, Issue-9, pp 15-20, sept-2017

R. Garg, V. Mongia, "Dimensionality Reduction and Comparison of Classification Models for Breast Cancer Prognosis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.308-312, 2018.

Use of biometrics system growing rapidly due to increase in demands for rigorous security in applications like national identity cards, border security, government benefits, network or computer login, time and attendance management, criminal investigation, passport control and access control. This paper is a review paper presenting introduction to biometrics technology comprising of its working, classification and operating modes. Different biometric modalities and related research work by researchers is also presented in this paper. Performance metrics for biometric system is also presented. This paper helps researchers to know the overall working of biometric system and research areas associated with different modalities in the field of biometrics.

Biometrics, Finger Knuckle Print (FKP), Ocular Biometrics, verification, identification, False Acceptance,False Rejection, Equal Error Rate

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[3]A. Kumar and Y. Zhou, “ Human Identification using Knuckle Codes”, BTAs Proceedings of third IEEE International Conference on Biometrics: Theory, Applications and Systems, 147-152,2009.
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[8] A. K. Jain and J. Feng, “ Latent Palmprint Matching”, IEEE Transactions on Pattern Analysis Machine Intelligence, Vol 31, no. 6, 1032-1047,2009.
[9]G. Michael, O. Kah, T. Connie , Andrew T, Beng J, “Touchless Palmprint Biometrics”, Image And Vision Computing, Elsevier, Vol. 26, no. 12,1551-1560,2008.
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[14] A. K. Jain, P.J. Flynn, A. Ross, “Handbook of Biometrics”, Springer-Verlag, USA, 2007.
[15] S. Neware, K. Mehta, A.S. Zadgaonkar, “Finger Knuckle Identification using RLF and Dynamic Time Warping”, International Journal of Computer Applications (0975 – 8887), Volume 119 , No.3,2015.
[16] S. Neware, K. Mehta, A.S. Zadgaonkar, “Finger Knuckle Print Identification using Gabor Features”, International Journal of Computer Applications (0975 – 8887) ,Volume 98, No. 16,2014.
[17] K. Mehta, S. Neware, A.S. Zadgaonkar, “Finger Knuckle Feature Extraction using Radon like Features”, International Journal of Computer Science & Communication, ISSN 0973-7391,Volume 5, 134-137,2014.
[18] S. Neware, K. Mehta, A.S. Zadgaonkar, “Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier”, International Journal of Computer Applications, (0975 – 8887) Volume 70, No. 9,2013
[19] S. Neware, K. Mehta, A.S. Zadgaonkar, “Finger Knuckle Surface Biometrics”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, No. 12,2012.
[20]R. Jiang et al. (eds.), “Biometric Security and Privacy”, Signal Processing for Security Technologies, Springer International Publishing Switzerland 2017.
[21] Weijun Tao, “Gait Analysis Using Wearable Sensors”, Sensors 12(2), 2012,
[22] R.Lemoney, “Wearable and wireless gait analysis platforms: Smartphones and portable media devices”, Wireless MEMS Networks and application, Pages 129–152, 2017
[23] H Tabatabaee, A Milani-Fard, H Jafariani , “A novel human identifier system using retina image and fuzzy clustering approach”, Proceedings of the 2nd IEEE International conference, 2006.
[24] M.Faundes, “On-line signature recognition based on vq-dtw”, pattern recognition, volume 40, issue 3, pages 981-992, 2007.
[25]J. Daugman, “New Methods in Iris Recognition”, IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 37, no. 5, 2007.
[26] A. Kumar, D. Zhang, “Hand-Geometry Recognition Using Entropy-Based Discretization”, IEEE transactions on information forensics and security, vol. 2, no. 2, 2007.
[27] Z Chen, W Huang, Z Lv , “Towards a face recognition method based on uncorrelated discriminant sparse preserving projection”, Multimedia Tools and Applications, Springer, 2017.
[28] J.Yang L Luo, J Qian, Y Tai, F Zhang, “ Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes”, IEEE transactions , 2017.
[30] T.Yang, V. Govindaraju, “A minutia-based partial fingerprint recognition system”, Pattern Recognition 38, 1672 – 1684, 2005.
[31] A. Abhyankar, S. Schuckers, “Integrating a wavelet based perspiration liveness check with fingerprint recognition”, Pattern Recognition, Elsevier, 2008.
[32] U. Park, A. Ross, A. K. Jain, “Periocular Biometrics in the Visible Spectrum: A Feasibility Study, Theory, Applications and Systems”, BTAS 09, Washington DC, September 2009.
[33] G. Santos, H. Proenca, “Periocular biometrics: An emerging technology for unconstrained scenarios”, Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013

Shubhangi Neware, "Biometrics System: An Overview," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.313-319, 2018.

Internet of things plays a vital role in the human life. Raspberry Pi is one of the widely used IoT based module. IoT includes information delivery as important property aspect. In this paper importance of sensor reading is highlighted. Sensor readings are important values on which further values may depend. Like IoT appliances used in chemical , pharmaceutical or agricultural industries. In such industries, sensor reading values are important. This paper proposed the importance of sensor value delivery and its impact. IoT based water plantation is considered to explain how sensor values are used to perform certain tasks using raspberry pi module.

IoT,sensor,Raspberrypi,security

[ 1] Zhang, Zhi-Kai, et al. [2014] IoT security: ongoing challenges and research opportunities. 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications. IEEE
[ 2] Jing, Qi, et al. [2014] Security of the internet of things: Perspectives and challenges, Wireless Networks 20.8 Springer : 2481-2501.
[ 3] VS. Rasal, SU. Rasal, ST. Shelar “A Cryptographicaly Imposed DCP-ABE-M Scheme with Attribute Based Proxy Re-Encryption and Keyword Search in Untrusted Public Cloud”, The IIOAB Journal, Vol.8, Issue.2, pp.21-26, Feb 2017
[ 4] VT. Mulik, K. Saritha, SU. Rasal, “Privacy Preserving Through Mediator in Decentralized Ciphertext policy Attribute Based Encryption”, IJRET: International Journal of Research in Engineering and Technology, Vol.5, Issue.6, pp. 535-540, 2016
[ 5] Stallings, William. [2006] Cryptography and network security: principles and practices. Pearson Education India.
[ 6] SU. Rasal , R. Redhu , VS. Rasal , ST. Shelar “Improving Source Code Encryption using Proposed Cipher Logic”, IJCSE: International Journal of Computer Sciences and Engineering, Vol.5(4), Apr 2017, E-ISSN: 2347-2693.
[ 7] Suraj U. Rasal , Raghav Agarwal , Varsha S Rasal , Shraddha T. Shelar, “IOT Appliance Access Structure using ABE Based OTP Technique” The IIOAB Journal, Vol. 7 , Suppl 1, 180–186 , Sept 2016
[ 8] Yao Xuanxia, Zhi Chen, and Ye Tian. [2015] A lightweight attribute-based encryption scheme for the Internet of Things." Future Generation Computer Systems 49 Elsevier: 104-112.
[ 9] Touati, Lyes, Yacine Challal, and Abdelmadjid Bouabdallah. [2014] C-cp-abe: Cooperative ciphertext policy attributebased encryption for the internet of things, Advanced Networking Distributed Systems and Applications (INDS), 2014 International Conference on. IEEE.
[ 10] SU. Rasal, ST. Shelar, VS. Rasal, “Securing Internet Banking Using Multiple Attributes Scheme And OTP”, The IIOAB Journal, Vol.7, Issue.10, pp.26-30, 2016.
[ 11] S. Rasal, S. Relan, K. Saxena, “OTP Processing using UABE & DABE with Session management”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.6, Issue.5, pp.57-59, 2016.
[ 12] VS. Rasal, SU. Rasal, AK. Joseph, NK. Joseph “Amelioration of Decentralized Cipher Text Policy Attribute Based Encryption with Mediator technique by adding Salt”; IJARCS: International Journal of Advanced Research in Computer Science, Volume 8, Issue. 5, PP. 1709-1713, May – June 2017
[ 13] Suo, Hui, et al. [2012] Security in the internet of things: a review, Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on. IEEE Vol. 3.
[ 14] Sicari Sabrina et al. [2015] Security, privacy and trust in Internet of Things: The road ahead." Computer Networks 76 Elsevier 146-164.
[ 15] Roman, Rodrigo, Jianying Zhou, and Javier Lopez. [2013] On the features and challenges of security and privacy in distributed internet of things, Computer Networks 57.10 Elsevier: 2266-2279.

Kratika Gupta, Ashwani Kumar, Suraj Rasal, Varsha S. Rasal, "Importance of Sensor Readings and Its Secured Delivery in Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.320-325, 2018.

Thyroid hormones delivered by the thyroid organ help control of the body`s digestion. The thyroid, a butterfly-formed organ situated in the human neck and ace organ of digestion. At the point when thyroid doesn`t work, it can influence each part of human wellbeing, particularly heaviness, causative or directing to gloominess and uneasiness, liveliness levels, and cardiac issues. Assortments of strategies have been suggested for thyroid illness. Healing of thyroid infection is simple, but the treatment taken by the greater part of the patients ceaselessly like blood pressure and diabetic patients. The principle goal is to build up a prototype intelligent thyroid Prediction System utilizing Big data and information mining displaying strategies. This framework can find and concentrate concealed information (examples and relationships) related to the thyroid ailment from a chronicled thyroid database. It can answer complex inquiries for diagnosing thyroid and consequently help medicinal services specialists to settle on wise clinical choices which conventional choice emotionally supportive networks. By giving compelling medicines, it likewise diminishes treatment costs. The social insurance industry gathers tremendous measures of enormous information which, shockingly, are not mined. Medicinal determination is viewed as an essential undertaking that should be executed precisely and capably. The computerization of this framework would be to a great degree worthwhile. Accordingly, a medicinal diagnosis system like the thyroid prediction framework would probably be exceedingly useful.

Hormones, clinical, Hypo Thyroid, Treatment, patients, Risk Prediction

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[4]. Bahn, R., Burch, H, Cooper, D, et al. Hyperthyroidism and Other Causes of Thyrotoxicosis: Management Guidelines of the American Thyroid Association and American Association of Clinical Endocrinologists. Endocrine Practice. Vol 17 No. 3 May/June 2011.
[5]. Braverman, L, Cooper D. Werner & Ingbar`s the Thyroid, 10th Edition. WLL/Wolters Kluwer; 2012.
[6]. Dr. Rishitha Banu et. Al., “Predicting thyroid disease using data mining Technique” International Journal of Modern Trends in Engineering and Research on 11 October 2016, pg: 666-670.
[7]. Senthilkumar et al., “ Classification of Multi-dimensional Thyroid Dataset Using Data Mining Techniques: Comparison Study” Advances in Natural and Applied Sciences, 9(6) Special 2015, Pages: 24-28.
[8] Rasitha Banu, Baviya “A study on Thyroid disease using Data Mining Technique”, IJTRA Journal, aug 2015.
[9] Banu, et al “Predicting Thyroid Disease using Linear Discriminant Analysis (LDA) Data Mining Technique”, Communications on Applied Electronics (CAE) – ISSN : 2394-4714 Foundation of Computer Science FCS, New York, USA Volume 4– No12, January 2016.
[10] Ebru turanoglu-beka R et al., “Classification of Thyroid Disease by Using Data Mining Models: A Comparison of Decision Tree Algorithms”, Oxford Journal of Intelligent Decision and Data Science, PP: 13-28, 2016.

K. Vijayalakshmi, S. Dheeraj, B.S.S. Deepthi, "Intelligent Thyroid prediction system using Big data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.326-331, 2018.

A project is a combination of several interrelated activities which must be performed in a certain order of its completion. To meet tight deadlines in software projects, managers need to understand key reservations about the scheduling techniques and how to use a schedule risk analysis to provide information crucial to a project’s success. This paper describes an application of simulation which simulates the duration of the activities for analyzing schedule risk and providing reliable estimates of time. Monte Carlo Simulation Methods are mostly used for analyzing schedule risk. In this the random numbers are generated to simulate the software project number of times. The primary objective of the simulation is to find out the effect of uncertainties on the schedule of project completion. The designed simulator SRAES for a live website Filmtribe uncovered the critical paths and risky activities in the project and also provided the risk indices of those risky activities. The simulator also calculated the Project Completion Time in less than 1 minute which can take months to calculate analytically. Therefore, it is concluded that Monte Carlo Simulation is an important technique for risk analysis and estimation of scheduling in any type of software projects.

Schedule Risk Analysis, Monte Carlo Simulation, Estimation

[1] Miler, J. & Górski, J. 2001. “Implementing risk management in software projects”, 3rd national conference on software engineering, Otwack, Poland.
[2] Xing-xia, W. and Jian-wen, H. 2009. “Risk Analysis of Construction Schedule Based on Monte Carlo Simulation”. Computer Network and Multimedia Technology, IEEE, Wuhan, pp. 1 – 4
[3] Sharma, S.D. 2009. Operations Research, 15th Ed. Reprint, Kedar Nath and Ram Nath, Meerut.
[4] Hira, D. S. 2001. System Simulation, S. Chand & Company Pvt. Ltd, New Delhi.
[5] Subhas C. Misra, Vinod Kumar and Uma kumar (2006), “Different Techniques for Risk Management in Software Engineering: A Review”, ASAC Banff, Alberta.
[6] Sharma, I. et al. 2011. “Schedule Risk Analysis Simulator using Beta Distribution”. International Journal of Computer Science and Engineering (IJCSE), Vol. 3, No. 6, pp.2408-2414.
[7] Deo, N. 2003. System Simulation with Digital Computer, Prentice-Hall of India, New Delhi.
[8] Pressman, R. S. 2006. Software Engineering - A Practitioner’s Approach, fifth edition, McGraw-Hill.

Yogita Bindra, Rajesh Garg, Navneet Kaur, "Risk Ananlysis and Estimation of Scheduling of Software Project – Using Stochastic Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.332-335, 2018.

As people are getting smarter so are the things. The Internet of Things (IoT) is a system of connecting mechanical and digital machines, animals or people with interrelated computing devices to provide an ability to collect process and transfer data over a network without requiring human interaction. While the thought comes up for smart cities there is a requirement for smart agriculture. As an example, this project presents a Smart weighing system which can be used for agriculture automation. The idea of Smart weighing system is for the farmer where they can weight their goods by placing it in crate. The Smart weighing system thus thought is an improvement of normal weighing machine by elevating it to be smart using sensors and logics. Smart weighing machine is a new idea of implementation which makes a normal weighing machine smart using sensors for weighing goods and sending message to keep track of the container using GSM modem.

IoT, Smart Agriculture, Weighing System, Cloud Computing

[1] Ruhin Mary Saji, Drishya Gopakumar, Harish Kumar, K N Mohammed sayed, Lakshmi S (2016) “A Survey on Garbage Management in cities using IOT”, International Journal of Engineering and Computer Science, Vol.5, Issue.11, ISSN:2319-7242, pp.18749-18754,
[2] Kanchan Mahajan, Prof.J.S.Chitode (2014), “Waste Bin monitoring system using Integrated Technologies” International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Issue.7, ISSN: 2319-8753,pp.14953-14957.
[3] Prakash, Prabhu V (2016) “IOT based waste management for smart city”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue.2, DOI: 10.15680/2016.0402029,pp.1267-1274.
[4] Shyamala S C, Kunjan Sindhe, Viswanth Muddy, Chitra C N (2016), “Smart waste management system”, International Journal of Scientific evelopment and Research, Vol.1, Issue.9, ISSN:2455-2631,pp.224-230.

P.M. Sonsare, "IOT based Smart weighing system for Crate in Agriculture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.336-341, 2018.

Much attention in recent years has been attracted by the process exploring useful information from a large amount of textual data produced by microblogging services such as Twitter. A very important preprocessing step is to convert natural language texts of microblog text mining into proper numerical representations. The short-length characteristics of microblog texts result in using the term frequency vectors to represent microblog texts and it will cause “sparse data” problem. Finding proper representations for microblog texts is a challenging issue.In the previous paper, they applied deep networks so that they can map the high-dimensional representations to low-dimensional representations.The retweet and hashtags have been used as the semantic similarity. They used two types of approaches which includes modifying the training data and modifying the training objective. They have also shown that deep models perform better than traditional methods such as latent Dirichlet allocation topic model and latent semantic analysis.

Microbloging, Accessibility, Sentiment Classfication, Latent Semantic Analysis

[1] Lei Xu, Chunxiao Jiang,“Microblog Dimensionality Reduction—A Deep Learning Approach,” Ieee Transactions On Knowledge And Data Engineering, Vol. 28, No. 7, July 2016.

[2] Zhi-Qiang Xian , “Sentiment Analysis of Chinese Micro-blog Using Vector Space Model,” APSIPA,2014.

[3] Amit mittal , “Social Networking text Classification in Big Data Environment,” IJlEET, 2016

[4] X. Yan and H. Zhao, “Chinese microblog topic detection based on the latent semantic analysis and structural property,” J. Netw., vol. 8, pp. 917–9233, no. 4, 2013.

[5] D. Ramage, S. T. Dumais, and D. J. Liebling, “Characterizing microblogs with topic models,” in Proc. 4th Int. Conf. Weblogs Social Media, pp. 130–137, 2010.

[6] O. Jin, N. N. Liu, K. Zhao, Y. Yu, and Q. Yang, “Transferring topical knowledge from auxiliary long texts for short text clustering,” in Proc. 20th ACM Int. Conf. Inf. Knowl. Manag., pp. 775–784, 2011.

[7] Q. Diao, J. Jiang, F. Zhu, and E.-P. Lim, “Finding bursty topics from microblogs,” in Proc. 50th Annu. Meet. Assoc. Comput. Linguistics: Long Papers-Vol. 1. , pp. 536–544, 2012.

[8] M. A. Ranzato and M. Szummer, “Semi-supervised learning of compact document representations with deep networks,” in Proc. 25th Int. Conf. Mach. Learning, pp. 792–799, 2008.

[9] G. Hinton and R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Sci., vol. 313, no. 5786, pp. 504–507, Jul. 2006.

[10] R. Salakhutdinov and G. Hinton, “Semantic hashing,” Int. J. Approx. Reasoning, vol. 50, no. 7, pp. 969–978, Jul. 2009.

[11] M. A. Ranzato and M. Szummer, “Semi-supervised learning of compact document representations with deep networks,” in Proc. 25th Int. Conf. Mach. Learning, pp. 792–799, 2008.

[12] S. Zhou, Q. Chen, and X. Wang, “Active deep learning method for semi-supervised sentiment classification,” Neurocomputing, vol. 120, pp. 536–546, 2013.

[13] M. R. Min, L. Maaten, Z. Yuan, A. J. Bonner, and Z. Zhang, “Deep supervised t-distributed embedding,” in Proc. 27th Int. Conf. Mach. Learn. , pp. 791–798, 2010.

[14] D. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learning Res., vol. 3, pp. 993–1022, 2001.

[15] T. K. Landauer, P. W. Foltz, and D. Laham, “An introduction to latent semantic analysis,” Discourse Processes, vol. 25, pp. 259–284, 1998.

[16] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” CoRR, vol. abs/1301.3781, 2013.

[17] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proc. 49th Annu. Meet. Assoc. Comput. Linguistics: Human Language Technol.-Volume 1., pp. 142–150,2011.

[18] J. Tang, X. Wang, H. Gao, X. Hu, and H. Liu, “Enriching short text representation in microblog for clustering,” Frontiers Comput. Sci., vol. 6, no. 1, pp. 88–101, 2012.

[19] Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, “Sharing clusters among related groups: Hierarchical dirichlet processes,” in Proc. Int. Conf. Neural Information Processing Syst, pp. 1385– 1392, 2004.

[20] C. E. Grant, C. P. George, C. Jenneisch, and J. N. Wilson, “Online topic modeling for real-time twitter search,” in Proc. Text Retrieval Conf. , pp. 1–9, 2011.

[21] X. Wang, F. Zhu, J. Jiang, and S. Li, “Real time event detection in twitter,” in Proc. 14th Int. Conf. Web-Age Inf. Manag., pp. 502–513, 2013.

[22] D. Yu and L. Deng, “Deep learning and its applications to signal and information processing,” IEEE Signal Process. Mag., vol. 28, no. 1, pp. 145–154, Jan. 2011.

[23] Y. Bengio, A. C. Courville, and P. Vincent, “Unsupervised feature learning and deep learning: A review and new perspectives,”CoRR, vol. abs/1206.5538, 2012.

[24] R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proc. 25th Int. Conf. Mach. Learning, pp. 160–167,2008.

[25] J. P. Turian, L.-A. Ratinov, and Y. Bengio, “Word representations:A simple and general method for semi-supervised learning,” in Proc. 48th Annu. Meet. Assoc. Comput. Linguistics, pp. 384–394, 2010.

M.S. Masram, T. Diwan, "Microblog Dimensionality Reduction With Semantic Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.342-346, 2018.

Natural language understanding (NLU) task deals with use of computer software to understand human text or speech in the form of sentences. IE is the integral component of this task. IE extracts information about desired entities from diverse resources and stored it in machine readable format for future processing. IE systems developed so far uses either supervised or unsupervised approach for information extraction. Distant supervision, Open information extraction and Joint prediction are few more techniques which claims to improve IE system performance. This paper is an attempt to give comparative analysis of these advanced approached and the need of combination of these techniques for further enhancement. To conclude, few application areas were identified like machine reading which can be benefited from this combined approach.

Information Extraction, Open Information Extraction, Distant Supervision, Joint Prediction

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[3] Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni. : Open Information Extraction from the Web. In IJCAI.volume 7, pages 2670–2676, 2007
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[7] Mausam, Schmitz, M., Bart, R., Soderland, S. Open Language Learning for Information Extraction. In Proceedings of EMNLP, 2012.
[8] L.D. Corro, R. Gemulla, ClausIE: Clause-Based Open Information Extraction. In Proceedings of WWW, 2013
[9] Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2, ACL ’09, pp. 1003–1011. Association for Computational Linguistics, Stroudsburg,2009
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[12] Roth, D., Yih, W.: Global inference for entity and relation identification via a linear programming formulation. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge, 2007
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[14] Finkel, J.R., Manning, C.D., Ng, A.Y.: Solving the problem of cascading errors: approximate bayesian inference for linguistic annotation pipelines. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pp. 618–626. Association for Computational Linguistics, Stroudsburg (2006)
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Ashwini V. Zadgaonkar, "Natural Language Understanding Using Open Information Extraction Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.347-350, 2018.

As medical imaging move towards digital imaging, medical information compression play major role in tele radiology application development. A DICOM standard work as an interface to send data from vendor independent equipment (Picture archiving and Communication System) to PDA based tele radiology system. With the help of DICOM file format, radiologist can view images in different file format. For excessing, viewing and examining patient information and images, information transmission and compression are key issues in case of such platform usage. This Paper served information of available compression techniques like Lossy and Lossless, JPEG, JPEG-LS, JPEG2000. The propose paper also analyze and compare different lossy and lossless techniques based of domain, principle and methods used. This paper contributed information related to Image Hierarchical Coding and there types like Bit Planes, Tree Structure, Laplacian Pyramid, Gaussian Pyramid. The paper also include comparison of different Wavelet family and Wavelet Transform based on various parameters.

Digital Imaging and Communication in Medicine, Personal Digital Assistant, Joint Photographic Experts Group

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[3] Michele Larobina, Loredana Murino, “Medical Image File Formats” Journal of Digital Imaging, April 2014, Volume 27, Issue 2, pp 200–206
[4] Dandu Ravi Verma, “ Managing DICOM Images: Tips and tricks for the radiology and imaging”, Journal of Digital Imaging, 2012 , Volume : 22 , Issue : 1, Page : 4-13, doi: 10.4103/0971-3026.95396
[5] Nitin S. Ujgare, Swati P. Baviskar; “Conversion of DICOM Image in to JPEG, BMP and PNG Image Format” International Journal of Computer Applications (0975 – 8887) Volume 62– No.11, January 2013
[6] N. Faccioli, S. Perandini, a. Comai, M. D’ Onofrio, R.Ozzimucelli; “ Proper use of common image file format in handling radiological image”, La radiological Medica, April 2009, Volume 114, issue3, PP 484 – 495
[7] Mrinal Kr. Mandal; “Digital Image Compression Techniques” Chapter Multimedia Signals and Systems, 2003, Volume 716, The Springer International Series in Engineering and Computer Science pp 169-202, 978-1-4615-0265-4
[8] K. Funahashi, H. Kikuchi, and S. Muramatsu, “Progressive
Biplane coding for lossless image compression,” IEICE
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Jun. 2008.
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T.N. Baraskar, V.R. Mankar, "A survey and analytical approach on image compression for DICOM Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.351-356, 2018.

Agriculture and farming are the key components and they contribute to the maximum in the income of any country. Farmers cannot depend on the rainfall for the crops cultivation. So, watering and monitoring of the crops becomes a critical issue. Less watering or more watering to the plants can be a serious issue and it may lead to the less yield of the crop. Also, farmers cannot be at the irrigation land all the time. So, we are proposing a design for automatic monitoring of the crops so that the system automatically understands the need of water to the plants and acts respectively. Also during heavy rains, we are proposing to drain out the excess water in the fields so that it will not affect the plants yield. We are also proposing to apply artificial intelligence to the irrigation system. By this the plants will be continuously monitored for any diseases affecting the crops and any changes in the quality of the crops which will be immediately notified to the farmer. Also planning to detect the budding weeds around the crops. In this paper the survey of different papers on automated irrigation system has been presented with their advantages, limitations and their future scope.

Automated Irrigation, Arduino, Sensors, Artificial Intelligence, crop quality, Weeds

[1] M.Usha Rani, “Web Based Service to Monitor Automatic Irrigation System for the Agriculture Field Using Sensors”, International Conference on Advances in Electrical Engineering (ICAEE 2014), Vellore, India, 2014.
[2] Junaidy B Sanger, “Reliable Data Delivery mechanism on Irrigation Monitoring System”, IEEE Conference on Electronics and Communication System(ICACSIS 2014), Indonesia, pp.53-56, 2014.
[3] Chandan Kumar Sahu, “A Low Cost Smart Irrigation Control System”, IEEE Sponsored 2nd Internatıonal Conference On Electronıcs And Communıcatıon System(ICECS 2015), Sambalpur, India, pp.1146-1152, 2015.
[4] Nikhil Agrawal, “Smart Drip Irrigation System using Raspberry pi and Arduino”, International Conference on Computing, Communication and Automation(ICCCA 2015), Noida, India, pp.928-932, 2015.
[5] Stefan Koprda, “Proposal of the irrigation system using low-cost Arduino system as part of a smart home”, IEEE 13th International Symposium on Intelligent Systems and Informatics, Subotica, Serbia, pp.229-233, 2015.
[6] A.N Arvindan, “Experimental Investigation Of Remote Control Via Android Smart Phone Of Arduino-Based Automated Irrigation System Using Moisture Sensor”, 3rd International Conference on Electrical Energy Systems, Chennai, India, pp.168-174, 2016.
[7] Matti Satish Kumar, “Monitoring moisture of soil using low cost homemade Soil Moisture Sensor and Arduino Uno”,3rd International Conference on Advanced Computing and Communication System(ICACCS-2016), Coimbatore, India, 2016.
[8] Pushkar Singh, “Arduino based smart irrigation Using Water Flow Sensor, Soil Moisture Sensor, Temperature Sensor and ESP8266 Wifi Module”, Humanitarian Technology Conference (R10-HTC), 2016 IEEE Region 10, Tezpur, India, 2016.
[9] Manish B, “Agricultural Environment Sensing Application using wireless sensor networks for the automated drip irrigation system”, International Journal of Computer Sciences And Engineering IJCSE, Vol:4, Issue:7, 2016.

Srishti Jain, Vani K S , "A Survey of the Automated Irrigation Systems and the Proposal to Make the Irrigation System Intelligent," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.357-360, 2018.