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In the field of Healthcare, cancer finding is the testing issues and furthermore a considerable lot of the exploration has centered to enhance the performance to get satisfactory outcomes in the specific territory. To analyze a Lung cancer is a troublesome errand in medical research. To beat this testing errand, the numerous analysts use data mining methods were connected to predict the many kind of disease. In this examination we studied and make compression of various classifications to classify and predict the lung cancer illness.

Lung Cancer Detection, Segmentation, Feature Extraction and Classification

[1] http://www.medicalnewstoday.com/info/lung-cancer/ time: 1:33pm date: 7/10/2015
[2] “Lung Cancer Prevention and Early Detection”, Available at:
http://www.cancer.org/acs/groups/cid/documents/webcontent/acspc-039558-pdf.pdf
[3] Dandil E, Cakiroglu M, Eksi Z, Ozkan M, Kurt O.K, Canan A, “Artificial Neural Network-Based Classification System forLung Nodules on Computed Tomography Scans”, Soft Computing and Pattern Recognition (SoCPaR), 2014 6th InternationalConference , Aug 11-14, IEEE2014,DOI:10.1109/SOCPAR.2014.7008037, pp. 382-386.
[4] Smitha P et al., “A review of medical image classification techniques, “International conference on VLSI,
communication & instrumentation (ICVCI) 2011 proceedings published by International journal of computer
applications, 34-38 pages.
[5]Ankit Agrawal, SanchitMisra, En. At Al, A Lung Cancer OutcomeCalculator Using Ensemble Data Mining On Seer Data, Biokdd 2011,
August 2011, San Diego, Ca, Usa.
[6] Amit Bhola, Machine Learning Based Approaches For CancerClassification Using GeneExpression Data, Machine Learning And Applications: An InternationalJournal (Mlaij) Vol.2, December 2015
[7] V. Kirubha, Comparison Of Classification Algorithms In Lung CancerRisk Factor Analysis, International Journal Of Science And Research (Ijsr)Volume 6 Issue 2, February 2017.
[8] K. Jayasurya, G. Fung, S. Yu, C. Dehing-Oberije, D. De Ruysscher, A.Hope, W. De Neve, Y. Lievens, P. Lambin, A. L. A. J. Dekker, ComparisonOf Bayesian Network And Support Vector Machine Models For Two-YearSurvival Prediction In Lung Cancer Patients Treated With Radiotherapy, THe International Journal Of Medical Physics And Research, Vol. 37, No, 4,(2010).
[9] Ada, Early Detection And Prediction Of Lung Cancer Survival UsingNeural Network Classifier, International Journal Of Application Of
Innovation In Engineering Of Management(Ijaiem), Volume 2, Issue 6, June2013
[10] A.Priyanga, S.Prakasam, Ph.D, Effectiveness Of Data Mining – BasedCancer Prediction System (Dmbcps), International Journal Of ComputerApplications , Vol. 83 – No 10, December (2013), Pp. 0975 – 8887.
[11] Thangaraju , Barkavi , Karthikeyan, Mining Lung Cancer Data ForSmokers And Non-Smokers By Using Data Mining Techniques,
International Journal Of Advanced Research In Computer AndCommunication Engineering Vol. 3,No. 7, July (2014).

Rekha Awasthi, Vaibhav Chandrakar, Vijayant Verma, Poonam Gupta, "A Survey on Diagnosis of Lunger Cancer Diseases Using Machine Leanring Approaches," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.371-374, 2019.

Due to the increase in digitalization of sectors like health, banking, e-commerce, education, stock marketing, etc. it is required to share personal as well as sensitive information over the internet. And to protect this information over the Network, various cryptographic algorithms are needed for safe data communication. Mainly, this survey paper consists of two parts. In the first part, we did the comparative analysis of important encryption and decryption algorithms like AES, Blowfish, RSA, DES, 3DES, RC with respect to different attributes such as key size, number of rounds, algorithm structure, block cipher, block size, features, flexibility, security, and attacks. In the second part of this paper, we dealt with the practical implementation of most popular encryption algorithms like AES, Blowfish and RSA over different computer architectures and to check their performance in different Intel processors. The performance is analysed in terms of time taken for encryption and decryption of processes in milliseconds.

AES, Blowfish, RSA, Encryption, Decryption, Intel processor

[1] William Stallings, “Cryptography and Network Security Principles and Practices”, Fourth Edition, Pearson Education, Prentice Hall, 2009.
[2] Jawahar Thakur, Nagesh Kumar, “DES, AES and Blowfish: Symmetric Key Cryptography Algorithms Simulation Based Performance Analysis”, International Journal of Emerging Technology and Advanced Engineering, Volume 1, Issue 2, pp.1-4, 2011.
[3] Gurpreet Singh, Supriya, “A Study of Encryption Algorithms (RSA, DES, 3DES and AES) for Information Security” International Journal of Computer Applications (0975 – 8887) Volume 67– No.19, pp.1-4, 2013.
[4] Akash Kumar Mandal, Chandra Parakash, Mrs. Archana Tiwari, “Performance Evaluation of Cryptographic Algorithms:DES and AES”, 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science, pp. 2, 2012.
[5] Omar G. Abood, Shawkat K. Guirguis, “A Survey on Cryptography Algorithms”, International Journal of Scientific and Research Publications, Volume 8, Issue 7, pp.7-15, 2018.
[6] Muhammad Faheem Mushtaq, Sapiee Jamel, Abdulkadir Hassan Disina, Zahraddeen, A. Pindar, Nur Shafinaz Ahmad Shakir, Mustafa Mat Deris, “A Survey on the Cryptographic Encryption Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, pp.3-6, 2017.
[7] Nidhi Singhal, J.P.S.Raina, “Comparative Analysis of AES and RC4 Algorithms for Better Utilization”, International Journal of Computer Trends and Technology, pp.1-5, 2011.
[8] Tingyuan Nie, Teng Zhang, “A Study of DES and Blowfish Encryption Algorithm”, A Project of Shandong Province Higher Educational Science and Technology Program (No. J09LG10), pp.4, 2009.
[9] Gurjeevan Singh, Ashwani Kumar, & K.S. Sandha, “A Study of New Trends in Blowfish Algorithm”, International Journal of Engineering Research and Application, Vol. 1, Issue 2, pp.5, 2011.

Aditya Sahu, Md Tausif Zafar, Nishi Yadav, "A Survey on Cryptographic Algorithms and their implementation over Advanced Computer Architectures," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.375-383, 2019.

Today’s, Cloud computing is an information & communication technology (ICT) trend that provides pervasive access to shared groups of configurable computing resources and as well as their higher-level services that can be quickly associated with minimal management effort, often over the Internet. Most of the universities are adopting various ICT based services to provide e-learning, e-governance resources to their students and as well as their staff. For efficient management of these split resources, there is a common trend of adoption of cloud computing in universities. This paper presents a standard cloud model for the Bundelkhand University (BU) for the main campus and its affiliating colleges as well. A design strategy is described here to develop University Cloud from existing ICT infrastructure.

Cloud computing, IaaS, SaaS, PaaS, BU, ICT

[1] Mohammed Al-Zoube, “E-Learning on the Cloud” International Arab Journal of e-Technology, Princess Sumaya University for Technology, Jordan Vol. 1, No. 2, June 2009
[2] Kaur, R., & Singh, S. (2015). Exploring the Benefits of Cloud Computing Paradigm in Education Sector. International Journal of Computer Applications, 115(7)
[3] "The NIST-Definition of Cloud Computing". National Institute of Science and Technology. Retrieved 20th May 2012
[4] Geetha.C “C-Campus: Using Cloud computing for learning in Colleges” International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012 ISBN 978-1-4675-2248-9 © 2012 Published by Coimbatore Institute of Information Technology
[5] Cisco (2010)," Cloud computing in higher education :A Guide to Evaluation and Adoption", [online] Available: http://www.cisco.com/web/offer/email/43468/5/cloud computing_in_Higher_Education.pdf
[6] Pocatilu, P. A. (2009 ). Using Cloud Computing For E-Learning Systems
[7] Wyld, D. C. (5 October, 2010). Cloud Computing 101: Universities Are Migrating To The Cloud For Functionality And Savings.

Lalit Kumar Gupta, "Cloud Management System – A Case Study of Bundelkhand University," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.384-387, 2019.

Data Mining and Machine Learning are the emerging research fields that are gaining popularity in many areas including healthcare, education, spam filtering, manufacturing, CRM, fraud detection, intrusion detection, financial banking, customer segmentation, research analysis and many others due to their infinite applications and methodologies to discover the trends and knowledge from voluminous databases in the novel manner. Healthcare industry produces gigantic amount of data related to child immunization, maternal health, family planning, clinical data, health surveys, diagnosis etc. As the process of data collection in health sector increases, the usage of data mining and machine learning techniques for analyzing and decision making also increases. There is one major health issue in health sector i.e. maternal health that needs to be worried about. In this research paper, the maternal health data of the state of Jammu and Kashmir, India has been collected from HMIS portal and Naive Bayes classification algorithm of data mining has been used for the analysis. Various performance measures including Accuracy, Precision, Recall, Kappa, F-measure, AUC and Gini have also been used for calculating the performance.

Data Mining, Machine Learning, Maternal Health, Naïve Bayes

[1] S. Sharmilan and H. T. Chaminda, “Pregnancy Complications Diagnosis using Predictive Data Mining”, In the Proceedings of the 2017 International Conference on Computational Modeling & Simulation (IC2MS), 2017.
[2] S. N. Khandale and K. Kedar, “Analysis of maternal mortality: a retrospective study at tertiary care centre”, International Journal of Reproduction, Contraception, Obstetrics and Gynecology, Vol.6, Issue.4, pp. 1610-1613, 2017.
[3] The Hindustan Times. [Online]. Available: https://www.hindustantimes.com. [Accessed 10 Jan 2019].
[4] W. L. Moreira et al., “An Inference Mechanism using Bayes-based Classifiers in Pregnancy Care", In the Proceedings of the IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 305-309, 2016.
[5] A. K. Singha et al., “Application of Machine Learning in Analysis of Infant Mortality and its Factors”, Working Paper, 2016.
[6] S. Vijayarani and S. Deepa, "Naïve Bayes Classification for Predicting Diseases in Haemoglobin Protein Sequences”, International Journal of Computational Intelligence and Informatics, Vol.3, Issue.4, pp. 278-283, 2014.
[7] S. J. Hickey, "Naive Bayes Classification of Public Health Data with Greedy Feature Selection", Communications of the IIMA, Vol. 13, Issue.2, pp. 87-98, 2013.
[8] C. Sundar, M. Chitradevi and G. Geetharamani, “An Analysis on the Performance of Naive Bayes Probabilistic Model Based Classifier for Cardiotocogram Data Classification”, International Journal on Computational Sciences & Applications, Vol. 3, Issue.1, pp. 17-21, 2013.
[9] S. Saiyed et al., “A Survey on Naive Bayes Based Prediction of Heart Disease Using Risk Factors”, International Journal of Innovative and Emerging Research in Engineering, Vol.3, Issue.2, pp. 111-115, 2016.
[10] A. Kamat, V. Oswal and M. Datar, "Implementation of Classification Algorithms to Predict Mode of Delivery", International Journal of Computer Science and Information Technologies, Vol.6, Issue.5, pp. 4531-4534, 2015.
[11] A. R. Borkar and P. R. Deshmukh, "Naïve Bayes Classifier for Prediction of Swine Flu Disease", International Journal of Advanced Research in Computer Science and Software Engineering, Vol.5, Issue.4, pp. 120-123, 2015.
[12] S. Patel and H. Patel, “Survey of Data Mining Techniques used in Healthcare Domain”, International Journal of Information Sciences and Techniques, Vol.6, Issue.1/2, pp. 53-60, 2016.
[13] S. S. Nikam, “A Comparative Study of Classification Techniques in Data Mining Algorithms”, Oriental Journal of Computer Science & Technology, Vol.8, Issue.1, pp. 13-19, 2015.
[14] J. Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2012.
[15] A. Alemu, Y. Berhanu and M. Mahalkshmi, “Assessment of Breastfeeding practices in Ethiopia using different data mining techniques”, Indian Journal of Computer Science and Engineering, vol.7, Issue.1, pp. 1-6, 2016.
[16] N. Rikhi, "Data Mining and Knowledge Discovery in Database", International Journal of Engineering Trends and Technology, Vol.23, Issue.2, pp. 64-70, 2015.
[17] K. K. Manjusha, K. Sankaranarayanan and P. Seena, "Prediction of Different Dermatological Conditions Using Naïve Bayesian Classification", International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, Issue.1, pp. 864-868, 2014.
[18] T. Shaikh and D. Deshpande, “Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews”, International Journal of Computer Trends and Technology, Vol.36, Issue.4, pp. 225-230, 2016.
[19] B. F. Chimieski and R. D. R. Fagundes, “Association and Classification Data Mining Algorithms Comparison over Medical Datasets”, Journal of Health Informatics, Vol.5, Issue.2, pp. 44-51, 2013.
[20] M. Vuk and T. Curk, “ROC Curve, Lift Chart and Calibration Plot”, Metodoloski Zzvezki, Vol.3, Issue.1, pp. 89-108, 2006.
[21] S. Shastri et al., “Development of a Data Mining Based Model for Classification of Child Immunization Data”, International Journal of Computational Engineering Research, Vol.8, Issue.6, pp. 41-49, 2018.

P. Kour, S. Shastri, A.S. Bhadwal, S. Kumar, K. Singh, M. Kumari, A. Sharma, V. Mansotra, "Classification of Maternal Healthcare Data using Naïve Bayes," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.388-394, 2019.

Use of web applications in our day to day life are increasing, which result in an increase in the amount of sensitive data transmitted over the Internet. Sensitive data can be transmitted securely over the Internet by encrypting them. Among several public key cryptosystems elliptic curve cryptosystem is a recent one. This cryptosystem gives us stronger security with smaller key size, thus making it useful in those devices which have limited memory and power consumption ability. Encoding of text messages into elliptic curve points and decoding encoded points into original plaintext is always challenging in ECC. General approach for message mapping is to encode the characters of a message into the x-Coordinate of a point on an elliptic curve Ep (a, b). Then find out corresponding y value so that (x, y) lies on Ep (a, b). The point is then encrypted and transmitted. Since for every point both x and y values are to be transmitted and for stronger security, value of p is of at least 160 bits in today’s standard, therefore a major concern is to diminish the number of bits used in mapped point (x, y). In this paper we will discuss about a new mapping methodology of a group of alphanumeric characters into an elliptic curve point which reduces the number of bits to be transmitted without compromising the data security.

Elliptic Curve, Mapping algorithm, encoding, decoding, encryption, decryption

[1] Victor Miller, “Uses of Elliptic Curve in Cryptography”, Advances in cryptology-CRYPTO’85, vol-218, SpringerHeidelberg, 1986, pp. 417-426.
[2] Neal Koblitz, “Elliptic Curve Cryptosystems”, Mathematics of Computation, Vol-48, 1987, pp-203-209
[3] Alfred J Menezes, Paul C. van Oorschot, Scott A Vanstone, “A Handbook of applied Cryptography”. CRC Press.
[4] Darrel Hankerson, Alfred Menezes, Scott Vanstone “Guide to Elliptic Curve Cryptography”, Springer Professional Computing, 2004.
[5] William Stallings, “Network Security Essentials: Applications and Standards”, 4th Edition.
[6] F.Amounas and E.H.EI Kinani, “Fast mapping method based on matrix approach for Elliptic Curve cryptography”, International Journal of Information and Network Security 1, 54-59(2012).
[7] Bh P, Chandravati D, Prapoorna Roja P, “Encoding and decoding of a message in the implementation of Elliptic Curve Cryptography using Koblitz’s method.” International Journal on Computer Science and Engineering, 2010; 2(5): 1904-1907.
[8] Aritro Sengupta and Utpal Kumar Ray, “Message mapping and reverse mapping in elliptic curve cryptosystem”, Security Comm. Networks 2016;9:5363:5375.
[9] Oxford University Press, 2019, “which-letters-are-used-most”.
[10] Recommended elliptic curves for federal government use; July 1999.
[11] John Cannon, Wieb Bosma, Claus Fieker, Allan Steel (Editors), “Handbook of Magma Functions”, Version 2.19.

T. K. Ghosh, "A Method of Text Message Mapping in Elliptic Curve Cryptosystems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.395-398, 2019.

Wireless networks are facing variety of attacks nowadays. To prevent from such attacks, a few Intrusion Detection frameworks are being created to distinguish and evacuate the attacks. Intrusion detection frameworks need to manage huge information having duplicate and excess features that require moderate training and testing processes leading to higher resource utilization and poor discovery rate. The performance of the Intrusion detection frameworks depend on the accuracy of the predicted attacks. Various performance parameters are to be considered for determining accuracy of a framework. The whole process is highly dependent on the network features and thus, Feature Classification is a vital issue in intrusion detection process. This paper covers the importance of feature selection, the common feature selection methods and various feature classification approaches that have been used in the field of Intrusion Detection System. The paper has also revised about the different researches that had taken place in the relevant field.

Intrusion detection System, Feature Classification, Feature selection, Wireless Attacks

1. Snort Intrusion Detection System. 2006. Available online: http://www.snort.org, 10 October 2017.
2. Li, Y.; Wang, J.-L.; Tian, Z.-H.; Lu, T.-B.; Chen, Y. Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Comput. Secur., 28, pp. 466–475, 2009.
3. M. Dash, H. Liu, “Feature Selection for Classification,” Intelligent Data Analysis, Elsevier, pp. 131-156, 1997.
4. An Introduction to Feature Selection by Jason Brownlee on October 6, Machine Learning Process, 2014.
5. Hodo, E.; Bellekens, X.; Hamilton, A.; Dubouilh, P.; Iorkyase, E.; Tachtatzis, C.; Atkinson, R. Threat analysis of iot networks using artificial neural network intrusion detection system. In Proceedings of the IEEE International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, Tunisia, pp. 11–13 May 2016.
6. Hodo, E.; Bellekens, X.; Hamilton, A.; Tachtatzis, C.; Atkinson, R. Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey; Cornell University Library: Ithaca, NY, USA, 2017.
7. Brifcani, A.; Issa, A. Intrusion detection and attack classifier based on three techniques: A comparative study. Eng. Technol. J., 29, pp. 368–412, 2011.
8. Roopadevi, E.; Bhuvaneswari, B.; Sahaana, B. Intrusion Detection using Support Vector Machine with Feature Reduction Techniques. Indian J. Sci. 23, pp. 148–156, 2016,.
9. Zhang, J.; Zulkernine, M. A hybrid network intrusion detection technique using random forests.In Proceedings of the IEEE First International Conference on Availability Reliability and Security (ARES’06), Vienna, Austria, 20–22 April 2006.
10. Farid, D.M.; Zhang, L.; Hossain, M.A.; Strachan, R. Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst. Appl., 41, pp.1937–1946, 2014.
11. Koc, L.; Mazzuchi, T.A.; Sarkani, S. A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier. Expert Syst. Appl., 39, pp.13492–13500, 2012.
12. Farid, D.M.; Harbi, N.; Rahman, M.Z. Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection; Cornell University Library: Ithaca, NY, USA, arXiv preprint, 2010.
13. Fahad, A.; Zahir, T.; Ibrahim, K.; Ibrahim, H.; Hussein, A. Toward an efficient and scalable feature selection approach for internet traffic classification. Comput. Netw., 57, pp. 2040–2057, 2013.
14. Al-mamory, S.O.; Jassim, F.S. On the designing of two grains levels network intrusion detection system. Karbala Int. J. Mod. Sci., 1, pp. 15–25, 2015.
15. Yang, J.; Olafsson, S. Optimization-based feature selection with adaptive instance sampling. Comput. Oper. Res., 33, pp. 3088–3106, 2006.
16. Sánchez-Maroño, N.; Alonso-Betanzos, A.; Calvo-Estévez, R.M. A wrapper method for feature selection in multiple classes datasets. In International Work-Conference on Artificial Neural Networks; Springer: Berlin/Heidelberg, Germany, 2009.
17. Sani, R.A.; Ghasemi, A. Learning a new distance metric to improve an svm-clustering based intrusion detection system. In Proceedings of the IEEE International Symposium on Artificial Intelligence and Signal Processing (AISP), Mashhad, Iran, 3–5 March 2015.
18. Sarikaya, R.; Hinton, G.E.; Deoras, A. Application of deep belief networks for natural languageunderstanding. IEEE/ACM Trans. Audio Speech Lang. Process., 22, pp. 778–784, 2014.
19. Gisung Kim and Seungmin Lee, A Novel Hybrid Intrusion Detection Method Integrating Anomaly Detection With Misuse Detection, ELSEVIER, Expert Systems with Applications vol. 41 pp. 1690 – 1700, 2014.
20. Shi-Jinn Horng and Ming-Yang Su, “Novel Intrusion Detection System Based On Hierarchical Clustering and Support Vector Machines”, ELSEVIER, Expert Systems with Applications. pp. 38 306-313, 2011.
21. Mrutyunjaya Panda and Manas Ranjan Patra, “A Comparative Study Of Data Mining Algorithms For Network Intrusion Detection”, First International Conference on Emerging Trends in Engineering and Technology, pp 504-507, IEEE, 2008.
22. Juan Wang, Qiren Yang, Dasen Ren, “An intrusion detection algorithm based on decision tree technology”, In the Proc. of IEEE Asia-Pacific Conference on Information Processing, 2009.
23. Hong Kuan Sok et.al, “Using the ADTree for Feature Reduction through Knowledge Discovery” Instrumentation and Measurement Technology Conference (I2MTC), IEEE International ,pp 1040 – 1044, 2013.
24. Avallaee M, Bagheri E, Lu W, Ghorbani A. “A detailed analysis of the KDD CUP 99 data set”, 2009 IEEE Symposium on Computational intelligence for security and defense applications,,pp 1-6, 2009.
25. Bakar AA, Othman ZA, Hamdan AR, Yusof R, Ismail R: An Agent Based Rough Classifier for Data Mining. Eighth International Conference on Intelligent Systems Design and Applications , vol 1. IEEE Computer Society, Washington, pp.145-151, 2008.
26. Zhu X, Huang Z, Zhoul H: Design of a Multi-agent Based Intelligent Intrusion Detection System. IEEE International Symposium on Pervasive Computing and Applications. IEEE, Amsterdam, pp.290-295, 2006.
27. Fonk C-h, Parr GP, Morrow PJ: Security schemes for Mobile Agent based Network and System Management Framework. J. Networks Syst. Manag. Springer, 19: 232-256, 2011.
28. Mell P, Marks D, McLarnon M: A Denial of service resistant intrusion detection architecture. Comput Networks J Elsevier, Amsterdam, 2000.
29. Mar J, Yeh Y-C, Hsiao I-F: An ANFIS-IDS against Deauthentication DOS Attacks for a WLAN Taichung, 17–20 October 2010. IEEE, Amsterdam, pp. 548-553, 2010.
30. Linda O, Vollmer T, Manic M: Neural network based intrusion detection system for critical infrastructures. In Proceedings of IEEE International Joint Conference on Neural Networks, Georgia. IEEE, Amsterdam, pp.102-109, 2009.
31. Li Y, Wang R, Xu J, Yang G, Zhao B: Intrusion detection method based on fuzzy hidden Markov model. Sixth IEEE International Conference on Fuzzy Systems and Knowledge Discovery, vol 3. IEEE, Piscataway; pp. 470-474, 2009.
32. Tian J, Gu H: Anomaly detection combining one-class SVMs and Particle swarm optimization algorithms, Nonlinear Dynamics. Volume 61. Springer, Berlin; 2010:303-310.
33. Karmore, Preetee K.; Bodkhe, Sonali T, “A Survey on Intrusion in Ad Hoc Networks and its Detection Measures,” Proc. International Journal on Computer Science & Engineering, vol. 3, Issue 5, pp. 1896-1903, 2011.
34. Aakanksha Kori, Harsh Mathur,”A Performance Evaluation of Intrusion Detection system to get better detection rate using ANN Technique”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol.2, Issue 5, pp.217-223, 2017.

Madhavi Dhingra, "Survey on Intrusion Detection System Based on Feature Classification and Selection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.399-403, 2019.

Wireless sensor networks (WSN) is an emerging technology in future. It consists of huge number of sensor nodes which are tiny, cost effective and easily deployable. Sensors execute the functions such as data gathering and data transmission which consequences in energy reduction and these effects the network lifetime. In this paper a brief survey on distributed clustering techniques for wireless sensor network, how to minimizing energy dissipation and maximizing network lifetime among the central concerns when designing applications and protocols for sensor networks. Clustering technique has been proven to be energy-efficient in sensor networks since data routing and relaying are only operated by cluster heads. This paper presents various distributed clustering algorithms based on Dynamic Hyper round policy (DHRP) techniques, HEF clustering, DERC, LCM, EDIT, I-LEACH and DHRP for large-scale WSNs to optimally determine the Energy-efficiency and Scheduling.

Clustering, distributed algorithm, energy-efficiency, scheduling, wireless sensor networks

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[7] A. Chamam, and S. Pierre, “A distributed energy-efficient clustering protocol for wireless sensor networks,” Comput. Electr. Eng., vol. 36, no. 2, pp. 303–312, 2010.
[8] Bo-Chao Cheng, Hsi-Hsun Yeh, and Ping-Hai Hsu, "Schedulability Analysis for Hard Network Lifetime Wireless Sensor Networks With High Energy First Clustering", IEEE Transactions on Reliability, vol. 60, no. 3, SEPTEMBER 2011.
[9] Yichao Jin, Dali Wei, Serdar Vural, Alexander Gluhak and Klaus Moessner, "A Distributed Energy-efficient Re-Clustering Solution for Wireless Sensor Networks", IEEE Globecomm 2011.
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[11] H. Taheri, P. Neamatollahi, O. M. Younis, S. Naghibzadeh, and M. H. Yaghmaee, “An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic,” Ad Hoc Netw., vol. 10, no. 7, pp. 1469–1481, 2012.
[12] A. Wang, D. Yang, and D. Sun, “A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks,” Comput. Electr. Eng., vol. 38, no. 3, pp. 662–671, 2012.
[13] Sheng-Shih Wang and Ze-Ping C, "LCM: A Link-Aware Clustering Mechanism for Energy-Efficient Routing in Wireless Sensor Networks", IEEE sensors journal, vol. 13, no. 2, February 2013.
[14] A. Thakkar and K. Kotecha, “Cluster head election for energy and delay constraint applications of wireless sensor network,” IEEE Sensors J., vol. 14, no. 8, pp. 2658–2664, Aug. 2014.
[15] Xuxun Liu ,"Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review", IEEE sensors journal, vol. 15, no. 10, October 2015.
[16] Kushal B Y and Chitra M, "Cluster Based Routing Protocol to Prolong Network Lifetime through Mobile Sink in WSN", IEEE International Conference On Recent Trends In Electronics Information Communication Technology, May 20-21, 2016.
[17] Peyman Neamatollahi, Mahmoud Naghibzadeh, Saeid Abrishami, and Mohammad-Hossein Yaghmaee, "Distributed Clustering-Task Scheduling for Wireless Sensor Networks Using Dynamic Hyper Round Policy", IEEE transactions on mobile computing, Vol. 17, No 2, February 2018.

Kowsalya. R, B. Rosiline Jeetha, "A Survey on Distributed Clustering Techniques for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.404-409, 2019.

Social media plays a significant role within the propagation of information throughout disasters. This paper essentially contains an investigation identifying with anyway people of Chennai utilized Social media especially Twitter, in light of the nation`s most exceedingly awful flood that had happened recently. The tweets are collected & analysed by various machine learning algorithms like Random Forests, Naive Bayes and call Tree. By comparison the performances of all the three, it had been found that Random Forests is that the best algorithmic rule that may be relied on, throughout a disaster. This paper conjointly targeted the sources of the Twitter messages to explore the foremost influential users of Chennai flood.

Random Forests, Naive Bayes, call Tree

[1] B. Abedin, et al. Characterization of the use of social media in natural disasters: a systematic review. In: Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on 2014; 449–454.
[2] L. Palen, et al. Twitter-based information distribution during the 2009 red river valley flood threat. Bulletin of the American Society for Information Science and Technology 2010; 36(5): 13–17.
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Sabih Ahmad Ansari, Ahmad Talha Siddiqui, "Social Media Data Analytics Framework for Disaster Management," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.410-416, 2019.

The changes in weather and climate conditions have always affected crop cultivation, farming and animal breeding. Measures put in place sometimes fail. Information and cognitive technologies are innovative techniques that can be leveraged to combat these changes by applying precision agriculture. In this paper discussion is on future of precision agriculture which has been proven to work in other countries using machine learning & artificial intelligence. The scope of utilization is focused on medium and large scale farmers with an aim to point out the advantages and disadvantages of the techniques. Previously there has been a slow growth in this sector but from the year 2016 onwards many start ups have been emerging which are yielding high investments. These cognitive technologies have been applied in advanced countries and have resulted in increased yield, growth in GDP, low mortality rates and improved living standards. The same can be applied locally to boost production in the agricultural sector.

precision agriculture, Artificial intelligence, Machine learning, promising solutions

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Victor Mokaya, "Future of Precision Agriculture in India using Machine learning and Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.422-425, 2019.

Automatic question-answering (QA) system is a typical problem in natural language processing task to automatically produce relevant answer to a posed question. This work provides an overview of various techniques and methods employed to solve this typical question-answering problem. The basic idea behind QA system is to support the urge for information. This paper provides a brief review of different types of QA systems and work done so far. It is observed that the lexical gap and semantics with respect to context poses new challenges in question answer system. An attempt is made to provide a review of traditional and deep learning techniques employed for solving the research problem is made in order to bring an insight to research scope in this direction. We provide a proposed framework of question answer system using deep learning approach. The paper also discusses limitation and considerations for the said system.

Question Answer system, knowledge base, deep learning

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K. P. Moholkar, S.H. Patil, "A Question Answer System: A survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.426-432, 2019.