Volume-7 , Issue-2 , Feb - 2019 Go Back

The HSES Knowledge portal (www.hseshiksha.in) is designed and developed for the student, teacher and others who are teaching, learning and guiding in the higher secondary education sector of the India. In this portal, syllabus, eBooks, question papers and video lectures are ported for proving the study materials. Data is collected during the registration of new members and filling of feedback forms which is done online. A user can register anytime and anywhere all over India. Registered users can be filling the feedback form. Counting of users by locale, stream, board, gender, medium and state is difficult manually. It is possible with Microsoft Excel but time taking. Locale, stream, board, gender, and medium wise counting are possible through the counters which remove counting mistakes and saves time. In this portal, Counting of users have performed in this portal automatically which is the very challenging task. Invention of counting system for registered members of the HSES Knowledge Portal is beneficial for counting of users at locale, stream, board, gender and medium wise. It may aware green computing because persons take a lot of time to do it.

Counting by Locale, Counting by Stream, Counting by Board, Counting by Gender, Counting by Medium

[1] D.V. Subramanian and A. Geetha, “Evaluation Strategy for Ranking and Rating of Knowledge Sharing Portal Usability”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, pp.395-400, January 2012
[2] H.M.R. Al-Zegaier and S. M. Barakat, “Mobile Knowledge Portals: A new way of Accessing Corporate Knowledge”, American Academic & Scholarly Research Journal Vol. 4, No. 4, pp.42-49, July 2012
[3] N.R.M. Suradi, H. Subramaniam, M. Hassan, and S. F. Omar, “Development of Knowledge Portal using Open Source Tools: A Case Study of FIIT, UNISEL”, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering Vol:4, No:2, pp. 94-97, 2010
[4] I. Kondratova and I. Goldfarb, “Knowledge portal as a new paradigm for scientific publishing and collaboration”, ITcon Vol. 9, pp.161-174, 2004
[5] Z. Baracskai and J. Velencei, “Knowledge on Knowledge in Knowledge Portal”, 26th Int. Conf. Information Technology Interfaces ITI 2004, 3-7, June 2004
[6] I.T. Hawryszkiewycz, “Customizable Knowledge Portals for Teaching”, Informing Science, InSITE - “Where Parallels Intersect” pp.705-713, June 2002
[7] C. M. Jansen, V. Bach and H. Österle, “Knowledge Portals: Using the Internet to Enable Business Transformation”, INET conference Proceedings, pp. 77-81, 2010
[8] A. A. Faleh, I. J. Hani, H. B. Khaled, “Building a Knowledge Repository: Linking Jordanian Universities E-library in an Integrated Database System”, International Journal of Business and Management, Vol. 6, No. 4, Pp:129-133, April 2011

S. K. Miri, N. Sahu, "HSES Knowledge Portal: Invention of Counting System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.769-775, 2019.

Token Based Authentication is one of the basic mechanism of login that will be required and used in most of the web applications. The token is prima focus in the whole functionality of the product which can not be decrypted by users.

Security,Authentication,Authorization

[1] Mrunal A. Mahajan, “An approach for securing SWIPING MACHINE transactions” IJSRCSE, Vol.06 , Special Issue.01 , pp.68-72, Jan-2018
[2] Priyang Bhatt, Bhasker Thaker, Neel Shah, “A Survey on Developing Secure IoT Products”, Isroset-Journal Vol.6 , Issue.5 , pp.41-44, Oct-2018
[3] Jim Stabile, Robert Pang, Mala Anand
Oracle Corporation, “An Authentication Model for a Web Application Server” Sixth International World Wide Web Conference : POS749
[4] OASIS Security Services TC, “Security Assertion Markup Language (SAML) V2.0 Technical Overview” sstc-saml-tech-overview-2.0
[5] Ramanpreet Singh Lamba, “OAUTH – “A NEW ERA IN IDENTITY MANAGEMENT” AND ITS APPLICATIONS” White Paper, External Document, Infosys Limited
[6] Muhamad Haekal, Eliyani, “Token-based authentication using JSON Web Token on SIKASIR RESTful Web Service” ISBN: 978-1-5090-1648-8
[7] Timothy Claeys, Franck Rousseau, Bernard Tourancheau “Securing Complex IoT Platforms with Token Based Access Control and Authenticated Key Establishment” hal-01596135

Akshay Hegade, Sindhu K. G, "Token Based Authentication Using IOT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.776-779, 2019.

Search Engine Optimization is a methodology of techniques that are used to increase the traffic of the website through the search results. It is also a method of finding the worth of the pages on the website. The main purpose of the online presence of the website is to get viewed by all over the world. The website which scores a high rank in the SEO results makes number of people to visit the site. Google provides the most user-friendly features for indexing the website. In this project, a simple website is developed and this website is indexed in Google. Various methods are used for the search engine process. Section I contains the introduction of this project, In Section II, the explanation of the project is included. In Section III, the various processes of SEO are included and Section IV contains the Conclusion.

Mice planning, Description, Content, On Page Optimization, Off Page Optimization

[1] Evans M. P. Analyzing Google rankings through search engine Optimization data, Internet Research Vol.17, No.1, 2007
[2] S. Mugherjee “ A Probablistic model of optimal searching of the deep web “ 2003
[3] Zhen Liu and Philippe Nain,” Optimization issues in Web search Engines”, IBM Research 2006.
[4] Zgang.J, Dimitroff.A “The impact of webpage content characteristics on web page visibility in search engine results” Information Processing and management 2005.
[5] G.Kumar, N.Duhan, A.K.Sharma “Page Ranking Based on Number of visits of links of the Web Page” Computer and Communication Technology (ICCCT) 2nd Conference on IEEE, 2011.

D. Anushri, M. Nandhini, T. Shanmugapriya, "Website Development and Search Engine Optimization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.780-782, 2019.

Text recognition is a technique that recognizes text from the paper document in the desired format (such as .doc or .txt). The text recognition process involves several steps, including pre-processing, segmentation, feature extraction, classification, and post-processing. The preprocessing is performed as a binarized image to convert a grayscale image, and noise is reduced on the input image of the basic operation performed by removing the noise of the image signal. The segmentation phase is used to segment the image given online and segment each character of the segmentation line. Feature extraction is to compute the characteristics of the image document. This document describes techniques for converting the textual content of a paper document into a machine-readable format. This paper analyzes and compares the technical challenges, methods, and performance of text detection and recognition studies in color images. It summarizes the basic issues and lists the factors that should be considered when addressing them. The prior art is classified as step-by-step or integrated and highlights sub-problems including text localization, verification, segmentation and identification of text. This survey provides a basic comparison and analysis of the scope and challenges in the field of text recognition.

Classification, Datamining, Segmentation, Text recognition

[1] C. Patel and A. Desai, “Segmentation of text lines into words for Gujarati handwritten text,” Proc. 2010 Int. Conf. Signal Image Process. ICSIP 2010, pp. 130–134, 2010.
[2] C. Patel and A. Desai, “Zone identification for Gujarati handwritten word,” Proc. - 2nd Int. Conf. Emerg. Appl. Inf. Technol. EAIT 2011, pp. 194–197, 2011.
[3] C. Patel and A. Desai, “Gujarati Handwritten Character Recognition Using Hybrid Method Based on Binary Tree-Classifier And K-Nearest Neighbour,” Int. J. Eng. Res. Technol., vol. 2, no. 6, pp. 2337–2345, 2013.
[4] A. Desai, “Segmentation of Characters from old Typewritten Documents using Radon Transform,” Int. J. Comput. Appl., vol. 37, no. 9, pp. 10–15, 2012.
[5] A. A. Desai, “Handwritten Gujarati Numeral Optical Character Recognition using Hybrid Feature Extraction Technique,” Int. Conf. Image Process. Comput. Vision, Pattern Recognition, IPCV, 2010.
[6] A. A. Desai, “Gujarati handwritten numeral optical character reorganization through neural network,” J. Pattern Recognit., vol. 43, no. 7, pp. 2582–2589, 2010.
[7] A. a. Desai, “Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space,” CSI Trans. ICT, vol. 2, no. January, pp. 235–241, 2015.
[8] Mayil S. and Vanitha M, “A Survey on privacy Preserving Data Mining Techniques”, International Journal of Computer Science and Information Technologies. Vol.5 (5), pp. 6054-6056. ISSN: 0975-9646, 2014.
[9] M. Maloo, K. V Kale, and I. Technology, “Support Vector Machine Based Gujarati Numeral Recognition,” Int. J. Comput. Sci. Eng. ({IJCSE}), {ISSN} 0975-3397, vol. 3, no. 7, pp. 2595–2600, 2011.
[10] M. B. Mendapara and M. M. Goswami, “Stroke identification in Gujarati text using directional feature,” Proceeding IEEE Int. Conf. Green Comput. Commun. Electr. Eng. ICGCCEE 2014, 2014.
[11] N. Rave and S. K. Mitra, “Feature extraction based on stroke orientation estimation technique for handwritten numeral,” in Eighth International Conference on Advances in Pattern Recognition (ICAPR), 2015.
[12] Manimaran R. and Vanitha M, “An Efficient Study on Usage of Data Mining Techniques for Predicting Diabetes”, International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol.3 (20), pp.268-272 ISSN: 2394-3785, 2016.
[13] A. N. Vyas and M. M. Goswami, “Classification of handwritten Gujarati numerals,” 2015 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2015, pp. 1231–1237, 2015.
[14] Y. M. Prutha and S. G. Anuradha, “Morphological Image Processing Approach of Vehicle Detection for Real-Time Traffic Analysis,” Int. J. Comput. Sci. Int. J. Comput. Sci. Eng., vol. 3, no. 5, pp. 88–92, 2014.
[15] M. A. Abuzaraida, A. M. Zeki, and A. M. Zeki, “Online recognition system for handwritten hindi digits based on matching alignment algorithm,” in International Conference on Advanced Computer Science Applications and Technologies, ACSAT 2014, 2014, pp. 168–171.
[16] S. Belhe, C. Paulzagade, A. Deshmukh, S. Jetley, and K. Mehrotra, “Hindi handwritten word recognition using HMM and symbol tree,” Proceeding Work. Doc. Anal. Recognit. - DAR ’12, p. 9, 2012.
[17] S. Joseph and A. Hameed, “Online handwritten malayalam character recognition using LIBSVM in Matlab,” in National Conference on Communication, Signal Processing and Networking, NCCSN 2014, 2015, pp. 1–5.
[18] A. Arora and A. M. Namboodiri, “A hybrid model for recognition of online handwriting in Indian scripts,” in International Conference on Frontiers in Handwriting Recognition, ICFHR 2010, 2010, pp. 433–438.
[19] K. P. Primekumar and S. M. Idiculla, “On-line Malayalam Handwritten Character Recognition using HMM and SVM,” Int. Conf. Signal Process. , Image Process. Pattern Recognit. [ ICSIPR], pp. 1–5, 2013.
[20] A. Sampath, C. Tripti, and V. Govindaru, “Online Handwritten Character Recognition for Malayalam,” ACM Int. Conf. Proceeding Ser., pp. 661–664, 2012.
[21] G. S. Reddy, P. Sharma, S. R. M. Prasanna, C. Mahanta, and L. N. Sharma, “Combined online and offline assamese handwritten numeral recognizer,” in National Conference on Communications, NCC 2012, 2012.
[22] A. Bharath and S. Madhvanath, “HMM-based lexicon-driven and lexicon-free word recognition for online handwritten indic scripts,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 670–682, 2012.

U.Karthikeyan, M. Vanitha, "A Study on Text Recognition using Image Processing with Datamining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.783-787, 2019.

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that is focused on enabling computers to understand and process human languages, to get computers closer to a human level understanding of language. The main emphasis in the task of text generation is to generate semantically and syntactically sound, coherent and meaning full text. At a high level. the techniques has been to train end to end neural network models consisting of an encoder model to produce a hidden representation of text, followed by a decoder model to generate the target. For the task of text generation, various techniques and models are used. Various algorithms which are used to generate text are discussed in the following subsections. In the field of Text Generation, researcher’s main focus is on Hidden Markov Model(HMM) and Long Short Term Memory (LSTM) units which are used to generate sequential text. This paper also discusses limitations of Hidden Markov Model as well as richness of Long Short Term Memory units.

Natural Language Processing,HMM,RNN,ANN,LSTM

[1] A. Graves, "Generating Sequences with Recurrent Neural Networks," Computing Research Repository- CoRR ArXiv, 2014.
[2] J. B. C. E. Zachary C. Lipton, "A Critical Review of Recurrent Neyral Networks for Sequence Learning," Computer Research Repository- arXiv, 2015.
[3] Z. L. H. L. C. Baotian Hu, "Convolutional Neural Network Architectures for Matching Natural Language Sentences," Neural Information Processing Systems Foundation, 2014.
[4] G. R. H. T. Ruli Manurang, "Using genetic algorithms to create meaningful poetic text," Journal of Experimental & Theoritical Artificial Intelligence , vol. 24, pp. 43-64, 2013.
[5] P. T. ,. H. T. T. R. A. Eric Malmi, "DopeLearning : A Computational Approach to Rap Lyrics Generation," Knowledge Discovery and Data Mining,Association for Computer Machinery , pp. 13-17, 2016.
[6] Q. Z. Y. C. Jia Wei, "Poet-based Poetry Generation: Controlling Personal Style with Recurrent Neural Networks," 2018 workshop on computing, Networking and Communications(CNC), 2018.
[7] H. O. M. M. Naoko Tosa, "Hitch Haiku : An Interactive Supporting System for Composing Haiku Poem," International Fedaration for Information Processing, pp. 209-216, 2008.
[8] christopher c. olah’s blog.

Lichi Upadhyay, M.I. Hasan, P.S. Patel, "Demystifying Text Generation approaches," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.788-791, 2019.

lectroencephalography (EEG) is a straightforward technique which gives thought regarding the potential produced on the outside of the mind which helps in understanding the usefulness of the cerebrum. EEG signals play a vital job in recognizing the human feelings. In feeling appraisal using EEG flags, the time span of EEG motions in given number of channels, enthusiastic upgrades, and recurrence groups, nature of statistical feature extraction techniques and highlights important job. In this paper, new highlights are removed using Discrete Wavelet Transform (DWT) and further the feelings are arranged using EEG signs of 10 subjects is gathered and using 24 anodes from the standard 10-20 Electrode Placement System which is set over the whole scalp. Feature Extraction is performed by using DWT and the Decomposition of EEG signals is separated for 8 levels using "db4" wavelet. The feature extracted signs are then grouped using Artificial Neural Network (ANN) and the neural framework which can be compared at for feeling passionate states classification.

Electroencephalogram (EEG), Discrete wavelet transform, Feature extraction, Artificial Neural Network (ANN), Daubechies Wavelet

[1] Murugappan M, Ramachandran N, Sazali Y. Classification of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering. 2010 Apr 28; 3(04):390.
[2] M. A. Khalilzadeh, S. M. Homam, S. A. Hosseini and V. Niazmand,“Qualitative and Quantitative Evaluation of Brain Activity in Emotional Stress”, Iranian Journal of Neurology, vol.8 (28), pp. 605-618, 2010.
[3] K. Schaaff and T. Schultz, “Towards an EEG-Based Emotion Recognizer for Humanoid Robots”, 18th IEEE International Symposium on Robot and Human Interactive Communication, Toyama, Japan. 2009: pp. 792-796.
[4] Mingyang Li, Wanzhong Chen, Tao Zhang “Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble”, Biomedical Signal Processing and Control 31 (2017), 357–365.
[5] Jasmin Kevric Abdulhamit Subasi “Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system”, Biomedical Signal Processing and Control 31 (2017), 398–406
[6] Gilsang Yoo, Sanghyun Seo , Sungdae Hong Hyeoncheol Kim “Emotion extraction based on multi bio-signal using back-propagation neural network” ,Springer Science , Business Media ,New York ,2016
[7] Gyanendra K. Verma, Uma Shanker Tiwary, “A Review Multimodal fusion framework: A Multiresolution approach for emotion classification and recognition from physiological signals”, Indian Institute of Information Technology Allahabad, 2014, India
[8] Suwicha Jirayucharoensak,Setha Pan-Ngum ,Pasin Israsena,“Research Article -EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation, Hindawi Publishing Corporation Scientific World Journal ,2014.
[9] Amjed S. Al-Fahoum, Ausilah A. Al-Fraihat, “A Review Article-Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains”, Hindawi Publishing Corporation,ISRN Neuroscience, Volume 2014.
[10] N. Jatupaiboon, S. Pan-ngum, and P. Israsena, “Real-time EEG based happiness detection system,” Hindawi Publishing Corporation, The Scientific World Journal, vol. 2013, Article ID618649, 2013
[11] Umut Orhan , Mahmut Hekim , Mahmut Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model ” , Expert Systems with Applications 38 (2011) ,13475–13481
[12] Abdulhamit Subasi, M. Ismail Gursoy “EEG signal classification using PCA, ICA, LDA and support vector machines”, Expert Systems with Applications 37 (2010), 8659–8666
[13] Neelam Rout “Analysis and Classification Technique Based On ANN for EEG Signals, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (4), 5103-5105, ISSN: 0975-9646, 2014.
[14] Guler, I., & Ubeyli, E. D. “Adaptive neuro-fuzzy inference system for classification”, Journal of Neuroscience Methods, 148, 113–121, 2005
[15] Alkan, A., Koklukaya, E., & Subasi, A., “Automatic seizure detection in EEG using logistic regression and artificial neural network “Journal of Neuroscience, Methods, 148, 167–176.2005
[16] Ubeyli, E. D. “Combined neural network model employing wavelet coefficients for EEG signals classification” Digital Signal Processing, 19, 297–308.2009a
[17] Subasi, A. “EEG signal classification using wavelet feature extraction and a mixture of expert model”, Expert Systems with Applications, 2007.
[18] Hazarika, N., Chen, J. Z., Tsoi, A. C., & Sergejew, A.“Classification of EEG signals using the wavelet transform. Signal Processing, 59(1), 61–72., 19
[19] XW Wang, D Nie, and BL Lu, ‘‘EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines”, Neural Information Processing, Lecture Notes in Computer Science. Springer vol. 7062, pp. 734-743, 2011.
[20] Panagiotis C, Petrantonakis, and LeontiosHadjileontiadis,"Emotion Recognition from brain signals using Hybrid Adaptive Filtering and Higher order Crossings Analysis, “IEEE Ttransactions on affective computing, vol. 1, no. 2, pp.81 – 97,December 2010

Krishna kumar N J, Balakrishna R, "EEG Feature extraction using DaubechiesWavelet and Classification using Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.792-799, 2019.

E-Learning has made learning easy with most of the courses floated online for convenient 24X7 learning at learners ease. With virtual learning environment, learning behavior of the online learner has become one of the significant factor. To facilitate fast learning on virtual platform, there is need to analyze online learning pattern of learner. Once the pattern are mined personalized learning environment can be created for the learner as per his/her learning behavior, which will make online learning interesting faster. For finding learning pattern artificial intelligence can be a good tool. Proposed work classifies the learning behavior of the learners with application of artificial neural networks. Proposed work used two types of students test data, one where test was conducted on Moodle server with objective questions and negative marking and second was descriptive test in pen paper mode. First test was conducted to analyze fundamental concepts and their applications in problem solving and second test was to check the innovative thinking ability of students. Three artificial neural networks were trained to classify students in to each three categories based on their number of attempts in the test. All the three models classified the students accurately with negligible mean square error.

Learning behavior, Artificial Neural Network, Classification, Supervised learning

[1] Nick Z. Zacharis, “Predicting Student Academic Performance In Blended Learning Using Artificial Neural Networks”, International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 5, September 2016
[2] Samy Abu Naser, Ihab Zaqout, Mahmoud Abu Ghosh, Rasha Atallah and Eman Alajrami, “Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology”, International Journal of Hybrid Information Technology, Vol.8, No.2 (2015), pp.221-228
[3] Mason, C., Twomey, J., Wright, D. et al. “Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression”, Research in Higher Education, Volume 59, Issue 3, pp 382–400, May 2018, Springer
[4] González, J.M.B. & DesJardins, S.L., “Artificial Neural Networks: A New Approach to Predicting Application Behavior”, Research in Higher Education, Volume 43, Issue 2, pp 235–258, (2002) , Springer
[5] Galbraith, C.S., Merrill, G.B. & Kline, D.M. “Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses”, Research in Higher Education, Volume 53, Issue 3, pp 353–374,(2012), Springer
[6] .Sergey Zagoruyko, Nikos Komodakis, “Paying More Attention To Attention: Improving The Performance Of Convolutional Neural Networks Via Attention Transfer”, Published as a conference paper at ICLR 2017, pg. 1-13, https://arxiv.org/abs/1612.03928v3
[7] Ahmed Hamza Osman, “An Evaluation Model Of Teaching Assistant Using Artificial Neural Network”, VAWKUM Transactions on Computer Sciences, Volume 11, Number 2, November- December , 2016
[8] Ying Cui, Mark Gierl & Qi Guo, “Statistical classification for cognitive diagnostic assessment: an artificial neural network approach”, Educational Psychology Vol. 36, Iss. 6, 2016
[9] Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Miltiadis D. Lytras , Farhat Abbas , Jalal S. Alowibdi, “Predicting Student Performance using Advanced Learning Analytics”, Proceedings of the 26th International Conference on World Wide Web Companion, Pages 415-421, Perth, Australia — April 03 - 07, 2017
[10] Mike Holmes , Annabel Latham , Keeley Crockett, James D. O’Shea, “Near real-time comprehension classification with artificial neural networks: decoding e-Learner non-verbal behaviour”, IEEE Transactions on Learning Technologies ( Volume: PP, Issue: 99 ), 20 September 2017
[11] Erdinç Kolay , Taner Tunç, Erol Eğrioğlu , “Classification with Some Artificial Neural Network Classifiers Trained a Modified Particle Swarm Optimization”, American Journal of Intelligent Systems 2016, 6(3): 59-65.
[12] .Nor Liyana Mohd Shuib, Ahmad Shukri Mohd Noor,
Haruna Chiroma, and Tutut Herawan, “Elman Neural Network Trained by using Artificial Bee Colony for the Classification of Learning Style based on Students Preferences”, Appl. Math. Inf. Sci. 11, No. 5, 1269-1278 (2017) 1269.
[13] N.Sujatha, K. Prakash, “An Efficient and Scalable Auto Recommender System Based on Users Behavior”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.35-40, December (2018)
[14] A.Jenita Jebamalar, “Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools”, IJSRNSC, Volume-6, Issue-6, December 2018, pp 14-18.

K. S. Oza, R.K. Kamat, P.G. Naik, "Student Learning Behavior: an Artificial Neural Network approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.800-804, 2019.

Automatic Image enhancement is one of the major concerns in the digital image processing. There are various methods to enhancement the image. Noise removal is one of the best followed approaches that results in better image. We are using fuzzy and histogram techniques to achieve it. In fuzzy based automatic image enhancement by noise avoidance using histogram technique, we have studied various fuzzy enhancement methods as wavelets that has different issues, and studied histogram hyperbolization. Fuzzy based noise avoidance technique makes use of noise cheating and correction and removal of grain noise. The results clearly show that, the proposed technique overcomes the existing limitations and removes the noise using encoder and decoder fuzzy mechanism thereby increasing automatically the image view using histograms.

Hyperbolization, Fuzzy, image enhancement, noise, histogram

[1] B.Shweta Gayakwad, and S. S. Ravishankar, "Image enhancement by histogram specification", International Journal of Recent Advances in Engineering & Technology, Vol.2, No.4, 2014.
[2] Jaspreet Kaur, and Amandeep Kaur, "Image Contrast Enhancement method based on Fuzzy Logic and Histogram Equalization", International Research Journal of Engineering and Technology, Vol.3, No.5, 2016.
[3] G.N.Vivekananda, and P. Chenna Reddy, “A Congestion Avoidance Mechanism in Multimedia Transmission over MANET using Multi-streaming”, Multimedia Tools and Applications, Feb. 2019.
[4] Mittal, Neetu, "Automatic Contrast Enhancement of Low Contrast Images using MATLAB", International Journal of Advanced Research in Computer Science, Vol.3, No.1, 2012.
[5] G.N.Vivekananda, and P. Chenna Reddy, “Efficient video transmission technique using clustering and optimization algorithms in MANETs”, International Journal of Advanced Intelligence Paradigms, 2018.
[6] Akash Kumar Bhagat, S. P. Deshpande, "Various Image Enhancement Methods – A Survey", IOSR Journal of Computer Engineering, pp. 63-66, 2017.
[7] Kim, Yeong-Taeg, "Contrast enhancement using brightness preserving bi-histogram equalization", IEEE transactions on Consumer Electronics, Vol.43, No.1, 1997.
[8] ZhiYu Chen, "Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic metho", IEEE transactions on image processing, Vol.15, No.8, 2006.
[9] ZhiYu Chen, "Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement-part II: the variations”, IEEE Transactions on Image Processing, Vol.15, No.8, 2006.
[10] Faraj, Noor Kasim, and Loay Kadom Abood,"Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)”, Iraqi Journal of Physics, Vol.16, No.37, 2018.
[11] Khan, Mohd Farhan, Ekram Khan, and Z. A. Abbasi, "Multi segment histogram equalization for brightness preserving contrast enhancement", Advances in Computer Science, Engineering & Applications. Springer, Berlin, Heidelberg, 2012.
[12] Raman Maini, and Himanshu Aggarwal, "A comprehensive review of image enhancement techniques", arXiv preprint arXiv:1003.4053, 2010.
[13] B.Suresh, U. Poojitha, and P. Vasanthi, "Enhancement of the image by using Histogram Modification and High-pass Filtering Mask." IJRCCT, Vol.4, No.2 , 2015.
[14] Swetha Kanuparthy, "Implementation and Analysis of Image Contrast Enhancement using Gaussian Mixture Model", IJRCCT, Vol. 4, No.7, 2015.
[15] Mohsen Abdoli, “Gaussian mixture model-based contrast enhancement", IET image processing, Vol. 9, No.7, 2015.
[16] Dibya Jyoti Bora, "Importance of image enhancement techniques in color image segmentation: a comprehensive and comparative study", arXiv preprint arXiv:1708.05081, 2017.
[17] Saeed Anwar, Chongyi Li, and Fatih Porikli. "Deep Underwater Image Enhancement", arXiv preprint arXiv:1807.03528, 2018.
[18] C.Anusha, and L. Laxmi, "Image Enhancement Approach for Digital Applications Based On Generalized Histogram Equalization Model", International Journal Of Engineering And Computer Science, Vol. 5, No.9, 2016.
[19] Sukhjinder Singh, R. K. Bansal, and Savina Bansal, "Medical Image Enhancement Using Histogram Processing Techniques Followed by Median Filter", 2000.
[20] Garima Yadav, Saurabh Maheshwari, and Anjali Agarwal, "Foggy image enhancement using contrast limited adaptive histogram equalization of digitally filtered image: Performance improvement", Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014.
[21] Iqbal, Kashif, "Underwater Image Enhancement Using an Integrated Colour Model", IAENG International Journal of Computer Science, Vol.34, No.2, 2007.
[22] Hosein Abedikia, Hamed Deznaby, Hiva Mahmodinezhad, and Morteza Nobahari, "Presentation of Robust Method in Image Contrast Enhancement Using Particle Swarm Optimization", International Advances in Engineering and Technology, Vol.19, pp.524-533, July 2013.
[23] S.O.Nirmala, T. D. Dongale, and R. K. Kamat, "Review on image enhancement techniques: FPGA implementation perspective", International Journal of Electronics Communication and Computer Technology, Vol.2, No.6, 2012.
[24] Sebastiano Battiato, "Automatic image enhancement by content dependent exposure correction", EURASIP Journal on Applied Signal Processing, 2004.
[25] Sandeep Kumar, and Puneet Verma, "Comparison of Different Enhanced Image Denoising with Multiple Histogram Techniques", Threshold, Vol.15, No.16, 2008.
[26] Sandeep Singh, and Sandeep Sharma, "A Survey of Image Enhancement Techniques", published on International Journal of Computer Science (IIJCS) Vol. 2, pp.1-5, 2014.
[27] Mahima Dayal, “Gaussian Mixture Model Based Contrast Enhancement”, Dissertation 2016.
[28] Mohammad Abdullah-Al-Wadud, "A dynamic histogram equalization for image contrast enhancement", IEEE Transactions on Consumer Electronics, Vol.53, No.2, 2007.
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V.Murali, T. Venkateswarlu, "Automatic Image Enhancement by Noise Avoidance using Fuzzy and Histogram techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.805-811, 2019.

This paper presents an overview of developing Convolutional Neural Network using Genetic Algorithm. CNNs have been quite popular for image recognition and classification problems, but developing and training a CNN is a time-consuming and computationally costly and complex process. In this paper we discuss and review various GA based methods used for automatically generating and developing CNN networks and optimizing their networks for various pattern recognition problems and various tasks on image datasets. This paper looks at how using genetic approach for developing a network reduces its computational complexity compared to the traditional methods and increases the efficiency and accuracy of the network while also making the training process easier. We look at the genetic encoding used to generate a network and perform its evolution. A general survey of developing CNNs using GA is presented in order to understand the improvement in performance achieved through the given method. We look at the relative performance of CNNs developed through genetic approach and make a general comparison with the ones produced manually.

Convolutional Neural Network (CNN), Genetic Algorithm (GA), Neural Networks, Pattern Recognition, Image Classification, Structure Learning, Deep Learning

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Mudasir Ali Lone, Mohammad Islam, "A Brief Overview of Developing Convolutional Neural Network Using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.812-818, 2019.

Vehicle Ad-hoc Network (VANET) is an emerging technology that ensures road safety by enabling wireless communications. VANETs have drawn greater attention due to their significant attractive features such as dynamic connectivity, self-management and no centralized administration. However, due to the high mobility and the large scale of the network lead to dynamic topology. The rapid and continuous changing topology causes frequent disconnections of the communication links, which results in an increased overhead of the communication protocols. To resolve such problems, many of the clustering algorithms have been proposed for providing an efficient communication among vehicles. In this paper, we are most concerned about to explore the different clustering algorithms for improving routing stability and reliability even with dynamic mobility and dynamic topology.

Clustering, VANET, MANET

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Rajendra S. Hande, Akkalakshmi Muddana, "A Survey on Stable and Efficient Data Dissemination clustering algorithms for VANETs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.819-823, 2019.