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It is possible to assess the learning behavior in online systems or computed based learning backgrounds using data mining techniques on ‘e-learning session activity log data’ from different learning sessions. This information is very useful to improve the e-learning system better. There is a possibility to identify learners’ performance well before the conduction of an examination. The objective of the research is to find weather it is possible to apply Data Mining techniques on this transformed dataset and to predict some information. The educational dataset is used for analyze and also improve any e-Learning models. This research work proposes an Educational Data Mining (EDM) model which provides good performance with precision, recall and f-score. It shows the predictability of students’ grades by mining the e-learning session log data.

E-learning, Learning Analytics(LA), Technology Enhanced Learning(TEL), Educational Data Mining(EDM).

[1]. M. Vahdat, L. Oneto, D. Anguita, M. Funk, M. Rauterberg.: “A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator”. In: G. Conole et al. (eds.): EC-TEL 2015, LNCS 9307, pp. 352-366. Springer, DOI: 10.1007/978-3-319-24258-3 26,2015
[2]. M. Vahdat, A. Ghio, L. Oneto, D. Anguita, M. Funk, M. Rauterberg, “Advances in learning analytics and educational data mining, in: European Symposium on Artificial Neural Networks”, Computational Intelligence and Machine Learning, 2015.
[3]. Mohamed, N., Sulaiman, R.F., Endut, W.R.: “The use of cyclomatic complexity metrics in programming performance`s assessment”. ProcediaSoc. Beha. Sci. 90, 497-503, 2013
[4]. S. Arumugam, A. Kovalan, A.E.Narayanan " A Study of Easy Educational Data Mining for E-Learning Log Data from complex and large Dataset", IJIET, Volume 11 Issue 1, 39-47, August 2018.
[5]. R. Baker ,"Data Mining for Education". In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevier, 2010
[6]. Jay Gholap,”Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertilityby” Asian Journal of Computer Science and Information Technology,Vol 2,No. 8, 2012
[7]. Merceron and Yacef, "Revisiting interestingness of strong symmetric association rules in educational data", International Workshop on Applying Data Mining in e-Learning (ADML`07), Second European Conference on Technology Enhanced Learning,2007
[8]. García et al ,"Drawbacks and solutions of applying association rule mining in learning management systems", International Workshop on Applying Data Mining in e-Learning (ADML`07), Second European Conference on Technology Enhanced Learning, 2007
[9]. Bravo et al.,"A Problem`-Oriented Method for Supporting AEH Authors through Data Mining" International Workshop on Applying Data Mining in e-Learning (ADML`07), Second European Conference on Technology Enhanced Learning, 2007
[10]. ShafiqAslam and Imran Ashraf , “Data Mining Algorithms and their applications in Education Data Mining", International Journal of Advance Research in Computer Science and Management Studies Volume 2, Issue 7, July 2014
[11]. Amjad Abu Saa, “Educational Data Mining & Students’ Performance Prediction”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016
[12]. Brijesh Kumar Baradwaj, Saurabh Pal , “Mining Educational Data to Analyze Students Performance” International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011
[13]. Mashael Al luhaybi, Allan Tucker and Leila Yousefi ,”The Prediction Of Student Failure Using Classification Methods: A Case study", IPPR, SOENG, DaMi, CSIT, AIS, CSE, CSIP, CCNET - 2018 pp. 79–90, 2018. © CS & IT-CSCP 2018
[14]. DriyaniRajeshinigo, J. Patricia Annie Jebamalar ,”Educational Mining: A Comparative Study of Classification Algorithms Using WEKA”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2017
[15]. Shimaa Abd Elkader AbdElaal, "E-learning using data mining", Chinese-Egyptian Research Journal
[16]. ChandiniLulla, YashAgarwal, SnehalKankariya, PrateekSakaray, PankajaAlappanavar ,”Student Academic Performance Prediction using Machine Learning and Data Mining Techniques”,, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.5, pg. 301-307, May- 2017
[17]. N.Sujatha , K. Prakash, “An Efficient and Scalable Auto Recommender System Based on Users Behavior”, International Journal of Scientific Research in Computer Science Engineering, Vol.6, Issue.6, pp.35-40, December 2018
[18]. Himanshi , Komal Kumar Bhatia, “Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-2, April 2018
[19]. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, 2nd Edition, Morgan Kaufmann Publisher, ELSEVIER, 2006

S. Arumugam, A. Kovalan , A.E. Narayanan, "A Novel Educational Data Mining Model using Classification Algorithm for evaluating Students’ E-learning Performance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.616-624, 2019.

Image Segmentation is an immensely important task in Digital Image Processing. It is used in many fields including Medical Imaging, Machine vision, recognition tasks, etc. Many techniques have been proposed to carry out segmentation. Some of them are K Means, Image segmentation using Arithmetic Mean method, Segmentation via Entropy & histogram, Image segmentation using Maximum Between-cluster Variance and so on. This work proposes a novel Genetic Algorithms based image segmentation technique which may produce better results. A new Fitness function based on Entropy has been introduced. In order to check quality of segmented image, a performance evaluation measure is also presented. The proposed technique has been implemented on various images. Two existing approaches K Means and Arithmetic Mean have been thoroughly studied and implemented on same images. The results of proposed technique are then compared by the results of existing approaches using the introduced performance measure Entropy. Entropy measures the image information content. Greater the entropy, more information can be obtained from image. In comparison to the existing techniques, the proposed approach gives encouraging results.

Image Segmentation, Genetic Algorithms

[1] Shapiro and Stockman, “Computer vision”, Pearson publication, 1st edition, 2001.
[2] P. Umorya1 , R. Singh, “A Comparative Based Review on Image Segmentation of Medical Image and its Technique”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.71-76, 2017.
[3] Pham, Dzung L.; Xu, Chenyang; Prince, Jerry L, "Current Methods in Medical Image Segmentation", Annual Review of Biomedical Engineering, 2000, pp: 315–337.
[4] Mihai Ivanovici Ivanovici, Noël Richard and Dietrich Paulus, “Advanced Color Image Processing and Analysis”, Springer, 2012, pp 219-277.
[5] Mathew, J., & Vijayakumar, R, “Scalable parallel clustering approach for large data using parallel K means and firefly algorithms”, International Conference on High Performance Computing and Applications (ICHPCA) (pp. 1–8). IEEE, 2014.
[6] Kuo, R. J., Syu, Y. J., Chen, Z.-Y., & Tien, F., “Integration of particle swarm optimization and genetic algorithm for dynamic clustering” Information Sciences, 2012, pp.124–140.
[7] Padhy, N.P. and Simon, S.P., “Soft Computing: With Matlab Programming”, Oxford, 2015, ISBN: 9780199455423, 0199455422. Edition: 1st Edition.
[8] Ketna Khanna, Naresh Chauhan and Vedpal, “Genetic Algorithms based Black Box Testing”, International Journal of Emerging Technologies and Innovative Research, 2018, pp. 537- 544.
[9] Dorigo, M. and Krzysztof Socha, “Approximation Algorithms and Metaheuristics”, IRIDIA Institut de Recherches Interdisciplinaires et de Du00b4eveloppements en Intelligence Artiufb01cielle publisher, 2006, pp. 1-25.
[10] Goldberg, D., “Genetic Algorithms in search, Optimization and Machine Learning”, Addison-Wesley publishing co., 1989, pp.1-21.
[11] S. Josephine,” Brain Tumor MRI Image Detection and Segmentation Using Genetic Algorithm”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.358-364, 2018.
[12] Ya Wang, “Improved OTSU and Adaptive Genetic Algorithm for Infrared Image Segmentation”, The 30th Chinese Control and Decision Conference, 2018, pp. 5644-5648.
[13] Pathak S, “Optimized Noisy Image Segmentation Using Genetic Algorithm”, International Conference on Intelligent Computing and Control Systems ICICCS, 15-16 June 2017, Madurai, India, pp. 1311-1316.
[14] Maulik U, “Medical Image Segmentation Using Genetic Algorithms”, IEEE Transactions on Information Technology in Biomedicine ( Volume: 13 , Issue: 2 , March 2009 ), 2009, pp. 166-173.
[15] Yide Ma, Kun Zhan and Zhaobin Wang,“Applications of Pulse-Coupled Neural Networks”, Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg, 2010, pp.1-9.
[16] Brzoza A et. al, “An approach to image segmentation based on shortest paths in graphs”, IEEE, 2017.
[17] Akinina A et. al., “Multiscale Image Segmentation using Normalized Cuts in Image Recognition on Satellite Images”, IEEE, 2018.
[18] Li Y et. Al, “Grab Cut Image Segmentation Based on Image Region”, IEEE, 2018.
[19] Wilhelm T et. al., “Boundary aware image segmentation with unsupervised mixture models”, IEEE, 2017.
[20] Sui H et. al., “Automatic Image Registration Based on Shape Features and Multi-Scale Image Segmentation”, IEEE, 2017.
[21] Zhang R et. al., “The Development of Markov Random Field Theory and Applications on Image Segmentation Algorithm”, IEEE, 2016.
[22] Meinhold R et. al., “Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation”, IEEE, 2016.
[23] Xu W et. al., “Ensemble of active contour based image segmentation”, IEEE, 2017.
[24] Krishna T et. al., “Three Phase Segmentation Algorithm for High Resolution Satellite Images”, IEEE, 2016.
[25] Ahmadi S et. al., “Enhance Support Relation Extraction Accuracy Using Improvement Of Segmentation In RGB-D Images”, IEEE, 2017.
[26] H. T. T. Binh, M. D. Loi and N. T. Thuy, "Improving Image Segmentation Using Genetic Algorithm", 11th International Conference on Machine Learning and Applications, Boca Raton, pp.18-23, 2012
[27] Gao H., “The applications of image segmentation techniques in medical CT images”, IEEE, 2011.
[28] Dhiraj Kumar, M. Tech Thesis, National Institute of Technology, Rourkela, 2009.
[29] Last, M. et. al., “Effective black box testing via genetic algorithms”, Springer-Verlag Berlin Heidelberg, 2006.
[30] Malhotra, R. et. al., “Genetic Algorithms: Concepts, Design for Optimization of Process Controllers”, 2011, Vol. 4, No. 2.
[31] Stender, J., “Introduction to genetic algorithms”, IEEE, 2004.
[32] Fischer, M. and Tonjes, R., “Generating test data for black box testing using Genetic Algorithm”, Conference: Emerging Technologies & Factory Automation (ETFA). IEEE 17th Conference, 2012. [33] Hatem, M., “Solving large problems with heuristic search: general-purpose parallel external-memory search”, Journal of Artificial Intelligence Research 62, 2018.

Ketna Khanna, Naresh Chauhan, "Image Segmentation via Genetic Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.625-630, 2019.

The beneficial nutrients obtained from milk to the human body needs no introduction. But the same is at stake nowadays due to addition of harmful ingredients being added in the milk by the milk suppliers as a result of their ever increasing greed for earning money. The recent advancements in science have helped a lot to rectify and remove such kind of impurity through various analytical techniques that qualitatively and quantitatively measure the impurities. Milk is a highly perishable product thus it is beneficial if this milk is test well in advance rather than its test that is carried out in the laboratory as it is a very time consuming process. With this approach we propose a new system that will quantify the milk parameters electronically. In this project we are developing an automated milk parameter monitoring system, along with maintaining the database of the milk suppliers with their supplied milk`s parameters. This work is carried out by ARM microcontroller, here ARM controller collect data from fat sensor, pH sensor and load cell. The LCD will display this information with farmer’s name, send SMS on weighing is done and finally upload this data on web portal.

Automated milk parameter monitoring, Milk fat, Photometric, pH sensor

[1] Prof. Santosh M. Tambe1, Prof. Mahindra R.Gaikar, Prof. Snehal S. Somwanshi,"The detection of Urea, Sugar, Sodium Chloride, and Measurement of PH Parameter in the Milk",Vol. 5, Issue 3, March 2016.
[2] Dr. K.B.Ramesh1, Khushboo K Gandhi, Pooja Valecha, Shradda Pai K, Sushma M5,"Quantification of Urea in Milk- A review of Existing Methods", International Journal of Engineering Research and General Science Volume 3, Issue 2, March-April, 2015,ISSN 2091-2730.
[3]Kejal Shah1, Rajeshri Kelkar, Amruta Sarda1, M.S.Chavan,"PERFORMANCE AND ANALYSIS OF WATER ADULTERATION IN MILK USING LIGHT SENSING SYSTEM",
International Journal of Pure and Applied Mathematics ,Volume 118 No. 22 2018, 1097-1102
[4] Patel Dilip Kumar,Patel Alkaben,"Financial Performance of Milk Producing CO-Operative society in valsad district",VOL.2,issue 4,April 2014,1-6.
[5] Ms.Humera Quazi,"A Study of Dairy Industry in India".-http://gsw.shikshamandal.org/wp-content/uploads/2015/10/A_STUDY_OF_DAIRY_INDUSTRY_IN_INDIA.pdf
[6] "pH/Temperature meter for milk" - with Application Specific probe.- https://www.mediray.co.nz/media/15606/ds_hanna_ph_ha-hi99162_1p0_0415.pdf
[7] Bhagyashree S.Kunte,Prof.Sanjay Patankar,"A literature review of Indian Dairy Industry",IJMRR,June2015,Vol.5,issue 6,Article No.2,341-350.
[8] "MANUAL OF METHODS OF ANALYSIS OF FOODS FOOD SAFETY AND STANDARDS AUTHORITY OF INDIA MINISTRY OF HEALTH AND FAMILY WELFARE GOVERNMENT OF INDIA NEW DELHI 2015",Vol 5,2465.
[9] Kejal Shah,Rajeshri Kelkar,et.al "Photometric Based Sensor For Fat Detection in Fresh Milk",Vol.3,issue 4,April 2015.
[10] DR.SC.M.SAMER ,"DAIRY FARMS:MECHANIZATION ,AUTOMATION AND ROBOTIZATION".
[11] http://www.reildairy.com/Forms/EMT.aspx
[12] HTTP://WWW.REILDAIRY.COM/PDF/UMA.PDF -RAJASTHAN ELECTRONICS AND INSTRUMENTS LTD.

Mitali Sirdeshpande, Sofiya Shikalgar, Vijeta Vipin, V.K. Bairagi, "Dairy Farming System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.631-635, 2019.

In the computer vision, pattern recognition is a wide area to study. Handwritten digit recognition is an important research topic of pattern recognition. There are various ways to write any digit. To recognize it is a challenging task. This paper shows the effective results of handwritten digit recognition on well-known, reliable handwritten digit database using CNN (Convolutional Neural Network). In the current scenario, the convolution neural network (CNN) shows a remarkable success in most of the computer vision and recognition tasks. CNN is well-known feed-forward architecture important for object recognition. We have tested our work on MNIST database. In this paper we analyzed the accuracy using CNN depending on different parameters like Number of hidden layers, Number of CNN layers, Number of neurons in each layer, Number of iterations and on the optimizer that we are using optimize the result. Aim of this paper is to know how the accuracy varies due to changes in these parameters. Increasing or decreasing the number of parameters leads to change in the performance. These results demonstrate the advantage or effect of different parameters on the result.

CNN(Convolution Neural Network), MNIST(Modified National Institute of Standards and Technology), Deep learning, ANN(Artificial Neural Network)

[1] Chang, Yeong-Hwa, Ping-Lun Chung, and Hung-Wei Lin "Deep learning for object identification in ROS-based mobile robots", In 2018 IEEE International Conference on Applied System Invention (ICASI),pp. 66-69, 2018.
[2] Vidushi, M.Agarwal, “Comprehensive and Comparative Analysis of Neural Network”, International Journal of Computer Application, Vol. 2, Issue 8, pp.126-137, 2018.
[3] C.P.Patidar, Meena Sharma, Varsha Sharda, "Detection of Cross Browser Inconsistency by Comparing Extracted Attributes", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
[4] G. Sharma, A. Kumar, "REVIN: Reduced Energy Virtuous Immune Network for WSN", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.1-8, 2017.
[5] Heusel, Martin, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. "Gans trained by a two time-scale update rule converge to a local nash equilibrium." In Advances in Neural Information Processing Systems, pp. 6626-6637. 2017.
[6] Z.Qiu, B.Z. Allison, J. Jin, Y. Zhang, X. Wang, W. Li, A. Cichocki, "Optimized motor imagery paradigm based on imagining Chinese characters writing movement", IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, Issue. 7, 2017.
[7] D.C. Aishwarya, C.N. Babu, "Prediction of Time Series Data Using GA-BPNN Based Hybrid ANN Model", In 2017 IEEE 7th International Advance Computing Conference (IACC), pp. 848-853, 2017.
[8] D. Wu, W. Zou, X. Li, Y.Zhao, "Kernalised Multi-resolution Convnet for Visual Tracking", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 66-73, 2017.
[9] N. Zhang, W. Karimoune, L. Thompson, H.Dang, "A between-class overlapping coherence-based algorithm in KNN classification", In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on, pp. 572-577, 2017.
[10] P.H. Kassani, A.B.J. Teoh, E. Kim,"Evolutionary-modified fuzzy nearest-neighbor rule for pattern classification", Expert Systems with Applications, Vol. 88, pp. 258-269, 2017.
[11] D. Suryani, P. Doetsch, H. Ney, "On the benefits of convolutional neural network combinations in offline handwriting recognition", In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 193-198, 2016.
[12] Y. Tang, L. Peng, Q. Xu, Y. Wang, A. Furuhata, "Cnn based transfer learning for historical chinese character recognition" In Document Analysis Systems (DAS), 2016 12th IAPR Workshop on, pp. 25-29, 2016.
[13] M. Sharma, K.K. Sharma, "Learning aided mood and dialect recognition using telephonic speech." In Accessibility to Digital World (ICADW), 2016 International Conference on, pp. 163-167, 2016.
[14] Y. Zhu, Y. Jia, L. Wang, "Partial discharge pattern recognition method based on variable predictive model-based class discriminate and partial least squares regression" IET Science, Measurement & Technology 10, Vol. 10, Issue. 7, pp. 737-744, 2016.
[15] F. Faizan, S. Tandon, P. Parashar, P. Sengar. "Vectorized code implementation of Logistic Regression and Artificial Neural Networks to recognize handwritten digit", In Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE International Conference on, pp. 1-5., 2016.
[16] R. Rostaminia, M. Saniei, M. Vakilian, S.S. Mortazavi, V. Parvin, "Accurate power transformer PD pattern recognition via its model." IET Science, Measurement & Technology, Vol. 10, Issue. 7, pp. 745-753, 2016.
[17] F. Zhu, L. Shao, Y. Fang, "Boosted cross-domain dictionary learning for visual categorization", IEEE Intelligent Systems, Vol. 31,Issue. 3, pp. 6-18, 2016.
[18] K. Abhishek, B.B. Misra, "Hybrid Genetic Algorithm and time delay neural network model for Forecasting Traffic flow" In 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 178-183, 2016.
[19] R. F. Alvear-Sandoval , A. R. Figueiras-Vidal, "Does diversity improve deep learning?", In Signal Processing Conference (EUSIPCO), 2015 23rd European, pp. 2496-2500, 2015.
[20] S.K. Sahu, A.K. Pujari, V. Kumar, V.R. Kagita, V. Padmanabhan, "Greedy partitioning based tree structured multiclass SVM for Odia OCR", In Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2015 Fifth National Conference on, pp. 1-4, 2015.
[21] J. Khanapuri, L. Kulkarni, "An efficient fuzzy based approach for color image retrieval with dual tree—Rotated complex wavelet and fuzzy histogram using selective relevance feedback approach ", In Convergence of Technology (I2CT), 2014 International Conference for, pp. 1-8, 2014.
[22] A. Rahaman, M.M. Hasan, M.F. Shuvo, M.A.S. Ovi, M.M. Rahman, "Analysis on handwritten Bangla character recognition using ANN" In Informatics, Electronics & Vision (ICIEV), 2014 International Conference on, pp. 1-5, 2014.
[23] V. Sharma, A.K. Gupta,.” A Comprehensive Study of Fuzzy Logic”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue. 2, pp. 398-403, 2013.
[24] C. Wu, W. Wei, J. Lin, W. Lee, "Speaking effect removal on emotion recognition from facial expressions based on eigenface conversion", IEEE Transactions on Multimedia, Vol. 15, Issue. 8, pp. 1732-1744, 2013.
[25] Y. Reisinger, L. Turner, "Cultural differences between Mandarin-speaking tourists and Australian hosts and their impact on cross-cultural tourist-host interaction", Journal of Business Research, Vol. 42, Issue. 2, pp. 175-187, 1998.
[26] A. Krizhevsky, I. Sutskever, G.E. Hinton, "Imagenet classification with deep convolutional neural networks." In Advances in neural information processing systems, pp. 1097-1105, 2012.
[27] V. Sharma, A. Dev, S. Rai, “A Comprehensive Study of Cellular Automata”,. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue. 10, 2012.
[28] V. Sharma, S. Rai, A. Dev, “A Comprehensive Study of Artificial Neural Network”,. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue. 10, 2012.
[29] K. Dutta, K.K. Sarma, "Multiple feature extraction for RNN-based assamese speech recognition for speech to text conversion application", In Communications, Devices and Intelligent Systems (CODIS), 2012 International Conference on, pp. 600-603, 2012.
[30] S.N. Hajimirja, M.J. Proulx, E. Izquierdo, "Reading users` minds from their eyes: a method for implicit image annotation", IEEE Transactions on Multimedia, Vol. 14, Issue. 3, pp. 805-815, 2012.
[31] X. Li, H. Lu, "Object tracking based on local learning", In Image Processing (ICIP), In 2012 19th IEEE International Conference on, pp. 413-416, 2012.
[32] J. Tanha, M.V. Someren, M.D. Bakker, W. Bouteny, J. Shamoun-Baranesy, H. Afsarmanesh, "Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data", In ICTAI, pp. 690-697. 2012.
[33] S. Ghorpade, J. Ghorpade, S. Mantri, "Pattern recognition using neural networks", International Journal of Computer Science & Information Technology (IJCSIT), Vol. 2, Issue. 6, pp. 92-98, 2010.
[34] S. Rahman, S.M. Naim, A.A. Farooq, M.M. Islam, "Performance of mpeg-7 edge histogram descriptor in face recognition using principal component analysis", In 2010 13th International Conference on Computer and Information Technology (ICCIT), pp. 476-481, 2010.
[35] Q. Zhu, Q. Lao, "Construction of NPR biorthogonal wavelet and its application in edge detection", In 2010 3rd International Congress on Image and Signal Processing, vol. 3, pp. 1049-1053, 2010.
[36] H.C. Tseng, B. Almogahed, "Modular neural networks with applications to pattern profiling problems”, Neurocomputing , Vol. 72,Issue. 10-12, pp. 2093-2100, 2009.
[37] U. Bhattacharya, B.B. Chaudhri, "Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals", IEEE transactions on pattern analysis and machine intelligence, Vol. 31, Issue. 3, pp. 444-457, 2009.
[38] Visakhasart, Sathaporn, O. Chitsobhuk, "Multi-Pipeline Architecture for face recognition on FPGA" In Digital Image Processing, 2009 International Conference on, pp. 152-156, 2009.
[39] J. Naiborhu, S. M. Nababan, R. Saragih, I. Pranoto. "Direct Gradient Descent Control as a Dynamic Feedback Control for Linear System." Bulletin of the Malaysian Mathematical Sciences Society, Vol. 29,Issue. 2, 2006.
[40] K. Shimizu, S. Ito, S. Suzuki, "Tracking control of general nonlinear systems by a direct gradient descent method", Nonlinear Dynamics and Systems Theory, Vol. 5, Issue. 1, pp. 91-105, 2005.
[41] Kussul, Ernst, and Tatiana Baidyk. "Improved method of handwritten digit recognition tested on MNIST database." Image and Vision Computing 22, no. 12 (2004): 971-981.

Vidushi, Manisha Agarwal, "Intelligent Handwritten Digit Recognition Based on Multiple Parameters using CNN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.636-641, 2019.

In recent years, the usage of software has increased radically. The dependence on the software places a huge responsibility on the developers to develop a quality software. Developing a cost effective, high quality software becomes a major challenge. Most of the time the software loses its quality and objective, when the software requirements are not implemented. This limitation happens due to improper requirement prioritization. Therefore it becomes significant to prioritize the software requirements. Prioritizing is the act of evaluating requirements to their importance by stakeholders. Over the years, numbers of prioritization methods, techniques and frame works have been devised. In this paper, a novel method of prioritization of software requirements is formulated using Takagi Sugeno fuzzy logic.

Fuzzy logic, Software requirement prioritization, Takagi Sugeno Fuzzy Inference, Sugeno Fuzzy Logic

[1] M. Asaem, M. Ramzan and A. Jaffar, "Analysis and optimization of software requirements prioritization techniques," International Conference on Information and Emerging Technologies, karachi, Pakistan, June 2010, pp. 1-6.
[2] Perini, A. Susi, A. and Avesani, P. 2013. “A Machine Learning Approach to Software Requirements Prioritization”, IEEE Transactions on Software Engineering., 4(39): 445-460.
[3] J. Azar, R. K. Smith and D. Cordes, "Value-oriented requirements prioritization in a small development organization," IEEE Software, vol.24, no. 1, pp. 32-37, January-February 2007.
[4] Persis Voola, A Vinaya Babu, “Comparison of Requirements Prioritization Techniques Employing Different Scales of Measurement”, ACM SIGSOFT Software Engineering Notes, July 2013 Volume 38 Number 4
[5] Luay Alawneh, “Requirements Prioritization Using Hierarchical Dependencies”, Advances in Intelligent Systems and Computing , 2017, pp. 459-464
[6] Manju Khari and Nikunj Kumar “Comparisons of Techniques of Requirement prioritization,” Journal of Global Research in Computer Science 2013,Volume 4, No. 1, pp.38-43
[7] Saaty, T. L., “The Analytic Hierarchy Process”. Mc-Graw-Hill, 1980
[8] Igor V. Anikin, Igor P. Zinoviev, “Fuzzy Control Based on New Type of Takagi-Sugeno Fuzzy Inference System”, International Siberian Conference on Control and Communications, 2015.

A. Sandanasamy, R. Thamarai Selvi, "A Quality Based Software Requirement Prioritization Using Takagi-Sugeno Neuro Fuzzy Inference," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.642-647, 2019.

Digitalization brings a revolution in technical world and Internet of Things (IoT) plays important role in it. The extension of IoT in various fields leads to Internet of Everything (IoE). IoE connects individuals, processes, information and things in such a way that that the collective, arranged information are steadily used for various significant applications. Further, IoE found as a vision to aspire the innovative techniques in which internet of things, internet of nano things includes. When it is further focused on Internet of Everything, people will find a distributed system with improving limelight on the border during decentralization. Some of the results of IoE are used in digital transformation and business of IoT. While the IoT today fundamentally is drawn from the point of view of association and correspondence is based on conceivable outcomes. At last, the gadgets collect information which are investigated and utilized to control different procedures and power various potential of IoT based used cases. The concept of IoE is very new, it has many things to explore and utilize the power of IoE for better human kind. In this paper, it is tried to portray survey of IoE and its applications in various fields

IoE (Internet of Everything), IoT (Internet of Everything), LoRa (Long Range), LoRaWAN, SigFox, LPWAN(low-power wide area network), decentralization

[1] M. Mirza, M. Ali, P. Excell, R. Picking, “Internet of nano-things, things and everything: future growth trends ”, Future Internet, Vol.10, Issue.8, pp.68, 2018.
[2] P. Sethi, S.R. Sarangi, “Internet of things : Architecture, Protocols and Applications.”, Journal of Electrical and Computer Engineering, 2017.
[3] M. Younis,” Internet of Everything and Everybody: Architecture and service virtualization”, Computer Communications, 2018.
[4] K. A. Rani, A.V. R. Mayuri,”Paper on Basics of Internet of Things”, International Journal of Emerging Trends in Science and Technology Impact Factor, Vol.6, Issue.6,pp.4144-4149, 2016.
[5] K. K. Patel, S. M. Patel,” Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges”, International journal of engineering science and computing,Vol.6, Issue.5, 2016.
[6] S. Mitchell, N.Villa, M. Stewart-Weeks, A. Lange,” The Internet of everything for cities: connecting people, process, data and things to improve the livability of cities and communities”, San Jose: Cisco, 2013.
[7] P. R. Sousa, L. Antunes, R. Martins,”The Present and Future of Privacy-Preserving Computation in Fog Computing”, In Fog Computing in the Internet of Things, Springer, Cham,pp.51-69, 2018.
[8] K. Rose, S. Eldridge, L. Chapin,”The Internet of Things: An Overview-Understanding the Issues and Challenges of a More Connected World”, The Internet Society, 2015.
[9] Nokia,”LTE-M Optimizing LTE for the Internet of Things”, White paper, 2015.
[10] G. Margelis, R. Piechocki, D. Kaleshi, P.Thomas,”Low throughput networks for the IoT:Lessons learned from Industrial implementations”, In Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan,pp.181-186, 2015.
[11] R. S. Sinha, Y.Wei, S.H. Hwang,” A survey on LPWA technology: LoRa and NB-IoT”, Ict Express, pp. 14-21, 2017.
[12] A. Yi. J. Augustin, T. Clausen, W. Townsley,” A study of LoRa: Long range & low power networks for the internet of things”, Sensors, Vol. 16, Issue.9, pp. 1466.
[13] B. Vejlgaard, M. Lauridan, H. Nguyen, I.Z. Kovacs, P. Mogensen, M. Sorensen,”Coverage and Capacity analysis of sigfox, lora, gprs and nb-iot”, In 2017 IEEE 85th vehicular technology conference (VTC Spring), pp.1-5, 2017.

M. Barman, B. Sharma, "An Overview of IoE(Internet of Everything)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.648-651, 2019.

A Wireless Mobile Adhoc network (MANET) is a self-controlled group of multiple nodes establishing a temporary network. MANET nodes have the tendency to change the location from one point to another due to it’s vulnerable and dynamic nature topography, which may invite for several types of cyber-attacks. One of the most dangerous attack is wormhole attack in which two or more malicious nodes could establish a tunnel between them and attract all the neighbours to send the packets though that tunnel by assuring that packet would be delivered to destination through the tunnel’s optimal-route and also assure to deliver all the packets by taking small amount of time delay. Most of the proposed methods to guard against wormhole attack use either clock synchronization or round-trip time technique but our proposed method uses hop-to-hop count technique. The purpose of this research work is to make a solution to detect the most risky and dangerous wormhole attack in the context of mobile Adhoc networks that can drift the normal traffic flow completely. We named this solution as Detection of Collaborative Wormhole Attack in Wireless Mobile Adhoc Network (DC-WAN). An attempt has been done to make an Algorithm to detect Wormhole malicious node(s) based on reactive Adhoc on demand distance vector (AODV) protocol. Several times experiments were conducted on our solution by using NS2 and found that the PDR and Throughput is similar to AODV protocol but the Packet Drop Ratio is fluctuating as time changes.

MANET, Cyber attacks, Wormhole attack, AODV, hop-to-hop count

[1] S. Singh, R. Kansal, “Novel Technique for Detection of Wormhole Attack in MANET”, International Journal of Computer Sciences and Engineering, Vol.6, Issue. 11, pp. 464-468, Nov 2018.
[2] A. Patel and A. Jain, “A study of various Black Hole Attack techniques and IDS in MANET”, International Journal of Advanced Computer Technology, Vol. 4, Issue. 3, pp. 58-62, 2016.
[3] N. Gandhewar, and R. Patel, “Detection and Prevention of Sinkhole Attack on AODV Protocol in Mobile Ad Hoc Network”, In Proceedings of the Fourth International Conference on Computational Intelligence and Communication Networks, IEEE Computer Society, pp. 714-718, 2012.
[4] M. Jahnke, J. T¨olle, S. Lettgen, M. Bussmann, and U. Weddige, “A Robust SNMP Based Infrastructure for Intrusion Detection and Response in Tactical MANETs”, Springer-Verlag Berlin Heidelberg, pp. 164–180, 2006.
[5] S. Muqtar Ahmed and S. Abdul Sattar, “International Journal of Computer Science and Engineering”, Vol. 6, Issue. 11, pp. 77-82, 2018.
[6] S. Upadhyay and A. Bajpai, “Avoiding Wormhole Attack in MANET using Statistical Analysis Approach”, International Journal on Cryptography and Information Security,Vol.2, Issue.1, March 2012.
[7] S.Young Shin, E.H. Hartono Halim, “Wormhole Attacks Detection in MANETs using Routes Redundancy and Time-based Hop Calculation”, International Conference on ICT - ieeexplore.ieee.org IEEE, 2012, India.
[8] M. Rmayti, Y. Begriche, R. Khatoun, “Graph-based wormhole attack detection in mobile ad hoc networks”, 4th International Conference on Mobile and Secure Services, 24-25 Feb. 2018, IEEE Xplore: 12 March 2018, USA.
[9] S. Majumder, D. Bhattacharyya, “Mitigating Wormhole Attack in MANET Using Absolute Deviation Statistical Approach”, 2018, IEEE, India.
[10] T. V. P. Sundararajan, S. M. Ramesh, R. Maheswa, K. R. Deepak, “Biologically inspired artificial intrusion detection system for detecting wormhole attack in MANET”, © Springer Science + Business Media New York 2013, Wireless Networks
May 2014, Volume 20, Issue 4, pp 563–578.
[11] R. Prakash , W. R. Salem and T. Jayasankar “Detection, Prevention and Mitigation of Wormhole Attack in Wireless Adhoc
Network by Coordinator”, Applied Mathematics & Information Sciences, Vol. 12, Issue No. 1, PP. 233-237,2018.
[12] U. K. Singh, J. Patidar and K. C. Phuleriya, “On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks”, International Journal of Scientific Research in Computer Science & Engineering”, Volume-3, Issue-1, pp. 11-16, 2015.

Syed Muqtar Ahmed, Syed Abdul Sattar, "Detection of Collaborative Wormhole Attack in Wireless Mobile Adhoc Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.652-657, 2019.

In data mining k Nearest Neighbor (k NN) classification is one of a widely applied classification algorithm. The k NN is based on Nearest Neighbor (NN) search algorithm. One of the drawbacks in k (where k stands for the number of NN to be selected) NN method is that whenever a query point is given to be classified it has the propensity to search through each and every data point to get the minimum distance for finding the Nearest Neighbors. This increases the computational complexity when a large query set is given. So to reduce this complexity and improve the performance of k NN, a novel classification model called Reduced Distance Computation k Nearest Neighbor RDCkNN model is introduced in this paper. In RDCkNN two processes are combined, first the data is randomized and then an optimum percentage of subset is drawn from the randomized data hence reducing the overall quantum of distance finding tasks. This subset will act as the training point for the query set for performing k NN classification processes. The performance of RDCkNN is compared with standard k NN in terms of number of distance computed and accuracy. The experiments were employed on standard data sets, data sets with missing values and a very large dataset. It was also compared with a number of other well-known classification models in order to validate its efficacy. The results obtained during the experiments done here shows that the proposed model exponentially outperformed standard k NN as well as other classification models.

k NN, Complexity, Distance Computation, randomization, subset.

[1]. P. Nair, N. Khatri, N and I. Kashyap, “A novel technique: ensemble hybrid 1NN model using stacking approach”, International Journal of Information Technology, Springer doi.org/10.1007/s41870-018-0109-0; 2018.
[2]. S. Jabeen Begum, B. Swaathi,”A Survey for identifying Parkinson’s disease by Binary Bat Algorithm”, Computer Science and EngineeringVol.7, Issue.2, pp.17-23, 2019.
[3]. Y. Li, G. Wang, L. Nie, Q. Wang, W. Tan. “Distance metric optimization driven convolutional neural network for age invariant face recognition”. Pattern Recognition; Vol. 75, Issue C, pp 51-62, 2018.
[4]. K. Q. Weinberger, L. K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification”, Journal of Machine Learning Research 10; 207-244; 2009.
[5]. J. Philbin, O. Chum, M. Isard, J. Sivic, A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching”, In: Computer Vision and Pattern Recognition, IEEE Conference on. IEEE; pp 1–8; 2007.
[6]. Z. Zhou, M. Dong, K. Ota, G. Wang, L.T. Yang, “Energy-efficient resource allocation for d2d communications undelaying cloud-RAN-based LTE-A networks”, IEEE Internet of Things Journal; Vol. 3, Issue 3, pp 428-38; 2016.
[7]. L. Fan, X. Lei, N. Yang, T. Q. Duong, G.K. Karagiannidis, ”Secrecy cooperative networks with outdated relay selection over correlated fading channels”, IEEE Transactions on Vehicular Technology, Vol. 66, Issue 8, pp 7599–603, 2017.
[8]. J. Li, Z. Liu, X. Chen, F. Xhafa, X. Tan, D.S Wong, “L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computing”, Knowledge-Based Systems; Vol. 79:18–26; 2015.
[9]. P. He, Z. Deng, C. Gao, X. Wang, J. Li, “Model approach to grammatical evolution: deep-structured analyzing of model and representation” Soft Computing Vo. 21, Issue 18, pp 5413–23, 2017.
[10]. H. Wang, W. Wang, Z. Cui, X. Zhou, J. Zhao, Y. Li, “A new dynamic firefly algorithm for demand estimation of water resources” , Information Sciences; Vol. 438, pp 95–106, 2018.
[11]. H.K.N. Peddinti, J.A.Lavanya, G.K. Chakravarthi, “An Optimal Route Search Using Spatial Keyword Query using Keyword Nearest Neighbor Expansion”, Computer Science and EngineeringVol.4, Issue.3, pp.30-33, 2016
[12]. O.F. Ertugrul, M.E. Tagluk, “A novel version of k nearest neighbor: Dependent nearest neighbor”, Applied Soft Computing, Vol. 55, 480-490, 2017.
[13]. J.L, Bentley, “Multidimensional binary search trees used for associative searching”, Commun. ACM Vol. 18, Issue 9, pp 509-517, 1975.
[14]. H Hu, D. L. Lee, “Range nearest-neighbor query”. IEEE Transactions on Knowledge and Data Engineering; Vol. 18, Issue 1, pp 78–91, 2006.
[15]. Y. Chen, L. Zhou, Y. Tang, J. P. Singh, N. Bouguila, C. Wang, H. Wang, J. Du, “Fast Neighbor Search By Using Revised K-D Tree”, In-formation Sciences Volume 472, pp 145-162, 2019.
[16]. F. Gieseke, J. Heinermann, C.E. Oancea, C. Igel, “Buffer kd trees: Processing massive nearest neighbor queries on GPUS” in Proceedings of the 31st International Conference on Machine Learning, pp. 172–80, 2014.
[17]. S. Bagui, A. K. Mondal, S. Bagui, “Using locality sensitive hashing to improve the KNN algorithm in the mapreduce framework” , Proceedings of the ACMSE Conference Article No. 32; 2018
[18]. K. Ding, C. Huo, B. Fan, C. Pan, “kNN Hashing With Factorized Neighborhood Representation”, IEEE International Conference on Computer Vision (ICCV), pp. 1098-1106; 2015.
[19]. A. Andoni, P. Indyk, “Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions”, Communications of the ACM; Vol. 51, Issue 1, 117–22; 2008.
[20]. J. Han, M. Kamber, J. Pei, “Data Mining Concepts and Techniques, Waltham, MA”, USA: Third edition, Morgan Kaufmann, pp 423-425, 2012.
[21]. C. Lim, J. In, “Randomization in clinical studies”, Korean Journal of Anesthesiol, DOI: https://doi.org/10.4097/kja.19049; 2019.
[22]. K. P Suresh, “An overview of randomization techniques: An unbiased assessment of outcome in clinical research”, Journal of Human Reproductive Sciences Vol. 4 Issue 1, 2011.
[23]. J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. “KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework”, Journal of Multiple-Valued Logic and Soft Computing Vol.17, 255-287, 2011.
[24]. Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, pp 35-40, 2016.

Preeti Nair, Indu Kashyap, "Reduced Distance Computation k Nearest Neighbor Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.658-666, 2019.

This paper proposes a new calibration estimator for estimating the finite population mean in stratified random sampling using a calibration constraint which consider known median of the auxiliary variable. The result has been extended in case of stratified double sampling when median of the auxiliary variable is not known. The efficiency of the proposed estimator has also been compared with the help of simulation study on a real dataset.

Auxiliary information, Calibration estimation, Median, Stratified Sampling, Double sampling

[1]. J.C. Deville, C.E. Sarndal, "Calibration Estimators in Survey Sampling", Journal of the American Statistical Association, Vol.87, No.418, pp.376-382, 1992.
[2]. E.P. Clement, E.I. Enang, "On the efficiency of ratio estimator over the regression estimator", Communications in Statistics-Theory and Methods, Vol.46, No.11, pp.5357-5367, 2017.
[3]. J-M Kim, E.A. Sungur, T-Y. Heo, "Calibration approach estimators in stratified sampling", Statistics and Probability Letters, Vol.77, pp.99-103, 2007.
[4]. N. Koyuncu, C. Kadilar, "Calibration estimator using different distance measures in stratified random sampling", International Journal of Modern Engineering Research (IJMER), Vol.3, No.1, pp.415-419, 2013.
[5]. N. Koyuncu, C. Kadilar, "Calibration weighting in Stratified Random Sampling", Communications in Statistics-Simulation and Computation, Vol.45, No.7, pp.2267-2275, 2016.
[6]. A.M. Mouhamed, A.A. EI-sheikh, H.A. Mohamed, "A new calibration estimator of Stratified random sampling", Applied Mathematical Sciences, Vol.9, No.35, pp.1735-1744, 2015.
[7]. Nidhi, B.V.S. Sisodia, S. Singh, S. K. Singh, "Calibration approach estimation of the mean in stratified sampling and stratified double sampling", Communications in Statistics-Theory and Methods, Vol.46, No.10, pp.4932-4942, 2017.
[8]. S. Singh, R. Arnab, "On Calibration of design weights", METRON-International Journal of Statistics, Vol.LXIX, No.2, pp.185-205, 2011.
[9]. D.S. Tracy, S. Singh, and R. Arnab, "Note on Calibration in Stratified and Double Sampling", Survey Methodology, Vol.29, No.1, pp.99-104, 2003.
[10]. C.E. Sarndal, B. Swensson, J. Wretman, “Model Assisted Survey Sampling”. Springer Verlag, New York, 1992.

Neha Garg, Menakshi Pachori, "Use of Median in Calibration Estimation of the Finite Population Mean in Stratified Sampling," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.667-671, 2019.

Strong intrusion detection is considered as a basic requirement for detecting any cyber attack before the breach yield successful outcomes for intruders along with automated prevention or response to such attacks being the next level of requirement. Although various tools and techniques are available for detecting such activities, intruders are still able to intrude heterogeneous environments successfully across the globe. This research work essentially takes the case of various models suggested in this direction, how they get deployed and what appears insufficient in their functioning making it difficult to be implemented. This research work focuses on developing improved models or improving existing models for designing and deployment of network security frameworks and policies, in which functioning of each component and their interconnectivity is taken towards more sufficiency to yield better actions thereby ensuring best possible level of security for all the system present within environment. The fitness functions for the system and which parameters could be used to decide genes for various network events is discussed along with a method to calculate the overall fitness of various network events.

Attack vector, Artificial Intelligence, Crossover, Cyber attack, Cyber Security, Feedback ingestion, Fitness function threshold, Gene, Genetic Algorithm, Intrusion Detection System, Machine Learning, Model, Mutation, Network Event, Network Packet, Network Security, Network Security Policy Framework, Selection, Self-evolutionary

[1] Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore, "Reinforcement Learning: A Survey", Journal of Artificial Intelligence Research, pp.237-285, 1996.
[2] SU Pu-Rui, LI De-Quan, FENG Deng-Guo, "A Host-Based Anomaly Intrusion Detection Model Based on Genetic Programming", Journal of Software, pp.1120-1126, June 2003.
[3] Pohlheim, Hartmut, "Genetic and Evolutionary Algorithms: Principles, Methods and Algorithms", Genetic and Evolutionary Algorithm Toolbox, 30 October 2003.
[4] Mukkamala R., Chekuri L., Moharrum M., Palley S., "Policy-Based Security Management for Enterprise Systems", International Federation for Information Processing: Research Directions in Data and Applications Security XVIII, Springer, Boston, MA, Vol.144, pp.219-233, 2004.
[5] Wei Li, "Using Genetic Algorithm for Network Intrusion Detection", Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference, Kansas City, Kansas, USA, pp.1-8, 24-27 May 2004.
[6] Jeffrey O. Kephart, William E. Walsh, "An Artificial Intelligence Perspective on Autonomic Computing Policies", Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, Yorktown Heights, NY, USA, pp.3-12, 9 June 2004.
[7] Arosha K. Bandara, Emil Constantin Lupu, J. Moffett, Alessandra Russo, "A Goal-based Approach to Policy Refinement", Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, Yorktown Heights, NY, USA, pp.229-239, 9 June 2004.
[8] Yao Jian, Sun Changping, Sun Hu, Mao Bing, Huang Hao, Xie Li, "Research on Policy-Based Security Management", Computer Applications and Software, March 2005 (in Chinese).
[9] Chuanhuan Yin, Shengfeng Tian, Houkuan Huang, Jun He, "Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection", International Conference on Natural Computation: Advances in Natural Computation, Springer, Berlin, Heidelberg, Vol.3612, pp.323-331, 27-29 August 2005.
[10] Ryen W. White, Joemon M. Jose, Ian Ruthven, "An implicit feedback approach for interactive information retrieval", Information Processing & Management, Vol.42, Issue.1, pp.166-190, January 2006.
[11] Hoi Chan, Thomas Kwok, "A Policy Based management System with Automatic Policy Selection and Creation Capabilities by using Singular Value Decomposition Technique", Seventh IEEE International Workshop on Policies for Distributed Systems and Networks, London, Ont., Canada, pp.4-99, 5-7 June 2006.
[12] Anup Goyal, Chetan Kumar, "GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System", 2007.
[13] A.B.M Alim Al Islam, Md. Ariful Azad, Md. Khurshid Alam, Md. Shamsul Alam, "Security Attack Detection using Genetic Algorithm (GA) in Policy Based Network", International Conference on Information and Communication Technology, Dhaka, Bangladesh, pp.341-347, 7-9 March 2007.
[14] CHEN Xiao-su, WU Jin-hua, NI jun, "Genetic-Feedback Algorithm Based Network Security Policy Framework", International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China, pp.2278-2281, 21-25 September 2007.
[15] Atish Mishra, Prakash Kumar, "Storing Scheme for State Machine based Rule Base of Genetic Feedback Algorithm Based Network Security Policy Framework Depending on Memory Consumption", International Conference on Machine Learning and Computing, pp.349-353, 2009.
[16] Atish Mishra, Arun Kumar Jhapate, Prakash Kumar, "Designing Rule Base for Genetic Feedback Algorithm Based Network Security Policy Framework using State Machine", International Conference on Signal Processing Systems, Singapore, pp.415-417, 15-17 May 2009.
[17] Atish Mishra, Arun Kumar Jhapate, Prakash. Kumar, "Improved Genetic Feedback Algorithm Based Network Security Policy Frame Work", Second International Conference on Future Networks, Sanya, Hainan, China, pp.8-10, 22-24 January 2010.
[18] Suhas B. Chavan, L.M.R.J. Lobo, "Network Security policy framework and Analysis", IJCA Special Issue on Network Security and Cryptography, pp.55-58, 2011.
[19] Srinivasa K.G., "Application of Genetic Algorithms for Detecting Anomaly in Network Intrusion Detection Systems", International Conference on Computer Science and Information Technology: Advances in Computer Science and Information Technology. Networks and Communications, Springer, Berlin, Heidelberg, Vol.84, pp.582-591, 2012.
[20] B. Uppalaiah, K. Anand, B. Narsimha, S. Swaraj, T. Bharat, "Genetic Algorithm Approach to Intrusion Detection System", IJCST, Vol.3, Issue.1, pp.156-160, March 2012.
[21] Louis Mervin Rainey Joseph Lobo, Suhas B. Chavan, "Use of Genetic Algorithm in Network Security", International Journal of Computer Applications, Vol.53, Issue.8, pp.1-7, September 2012.
[22] Khalid Jebari, Mohammed Madiafi, "Selection Methods for Genetic Algorithms", International Journal of Emerging Sciences, Vol.3, Issue.4, pp.333-344, December 2013.

Jay Parag Mehta, Digvijaysinh M. Rathod, "Detection Of Cyber Attack Using Artificial Intelligence Based Genetic Algorithm With Feedback Ingestion," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.672-678, 2019.