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Emotion recognition is a critical problem in Human-Computer Interaction. Numerous techniques were useful to enhance the strength of the emotion recognition systems using electroencephalogram (EEG) signals particularly the problem of spatiotemporal features. Automatic emotion recognition founded on EEG signals has received increasing attention in current years. The human being is blessed inquisitiveness has always wondered how to make machines feel, and, at the same time how a machine can detect emotions. In this paper, we elaborated the difference emotion recognition techniques. An automatic approach to address the emotion recognition problem of EEG signals using fused ResNet-50 and LFCC features and several classifiers. Performance of proposed approach with 10fold cross validation and LOO cross validation. Results show that the model is effective for emotion classification. KNN achieves the best performance in dissimilar classifiers.

EEG, CNN, Pattern Recognition

[1] Elham S.Salama, et.al. , “EEG-Based Emotion Recognition using 3D Convolutional Neural Networks,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 8, 2018.
[2] Laura Piho, et.al., “A mutual information based adaptive windowing of informative EEG for emotion recognition”, IEEE Transactions On Affective Computing 2018.
[3] YangLi, et. al.,, “A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition”, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).
[4] Ningjie Liu,et.al, “Multiple feature fusion for automatic emotion recognition using EEG Signals”, ICASSP 2018, IEEE Explorer 978-1-5386-4658-8.
[5] Bos, D.O. (2006) EEG-based emotion recognition. The influence of Visual and Auditory Stimuli. http://hmi.ewi.utwente.nl/verslagen/capitaselecta/CS-Oude_Bos-Danny.pdf.
[6] Javier Izquierdo - Reyes, et.al., “Emotion Recognition For Semi - Autonomous Vehicles Framework”, International Journal on Interactive Design and Manufacturing (IJIDeM) (2018) 12 : 1447 – 1454. https://doi.org/10.1007/s12008-018-0473-9.
[7] Jingxin Liu,et.al, “Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction”, Concurrency Computat Pract Exper.30:e4446.wileyonlinelibrary.com/journal/cpe 1-13, 2018.
[8] Punam Mahesh Ingale, "The importance of Digital Image Processing and its applications", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.31-32, 2018.
[9] Asha Patil, Kalyani Patil, Kalpesh Lad, "Leaf Disease detection using Image Processing Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.33-36, 2018.

Y. M. Rajput, S. Abdul Hannan, M. Eid Alzahrani, Ramesh R. Manza, Dnyaneshwari D. Patil, "EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques – A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.615-618, 2019.

Smart glasses are wearable computer glasses that display real-time information directly in front of users’ field of vision by using Augmented Reality (AR) techniques. Generally, they can also perform more complex tasks, run some applications, and support Internet connectivity and able to change their optical properties at runtime. There are various companies coming up with new varieties of smart glasses and also improvements are done on the existing ones. Google glasses is also an example of smart glasses. Smart sunglasses are also available, which are programmed to change tint by electronic means are an example of the latter type of smart glasses. In the present paper the author will present a detailed study of smart glasses and its working principle, developments, comparison between different smart glasses product, its benefits, risks, and also its future scope.

Smart glasses, Augmented Reality, Google glasses, Smart sunglasses

[1] Buti Al Delail, Chan Yeob Yeun,” Recent Advances of Smart Glass Application Security and Privacy”,pp.1,December 2015.
[2] Gulshan Kumar, Preeti Sharma,”Google Glasses Impediments”, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 2, October 2014,pp.81
[3] Bjørn Hofmann, Dušan Haustein, and Laurens Landeweerd,” Smart-glasses: exposing and elucidating the ethical issues”,pp.2
[4] Bjørn Hofmann, Dušan Haustein, and Laurens Landeweerd,” Smart-glasses: exposing and elucidating the ethical issues”,pp.2-3
[5] Buti Al Delail, Chan Yeob Yeun,” Recent Advances of Smart Glass Application Security and Privacy”, pp.2, December 2015.
[6] Jing Chen, Yu-Chieh Pai*, Jian-Hong Liu,” SGASDP: Smart Glasses Application Software Development Platform”, INNOV 2017 : The Sixth International Conference on Communications, Computation, Networks and Technologies, pp.57, pp.58-59.
[7] Gulshan Kumar, Preeti Sharma,”Google Glasses Impediments”, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 2, October 2014,pp.81
[8] Bjørn Hofmann, Dušan Haustein, and Laurens Landeweerd,” Smart-glasses: exposing and elucidating the ethical issues”,pp.2, pp.3, pp.4-5.
[9] Gulshan Kumar, Preeti Sharma,”Google Glasses Impediments”, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 2, October 2014,pp.81
[10] Bjørn Hofmann, Dušan Haustein, and Laurens Landeweerd,” Smart-glasses: exposing and elucidating the ethical issues”, pp.4-5.
[11] Student report Ubiquitous computing seminar FS2014 Hermann Schweizer ,” Smart glasses:technology and applications”,pp.2, pp.4-5.
[12] Lik-Hang LEE, and Pan HUI(Fellow, IEEE),” Interaction Methods for Smart Glasses: A survey”,pp.5, pp.7.
[13] Jing Chen, Yu-Chieh Pai*, Jian-Hong Liu,” SGASDP: Smart Glasses Application Software Development Platform”, INNOV 2017 : The Sixth International Conference on Communications, Computation, Networks and Technologies, pp.58-59
[14] Gulshan Kumar, Preeti Sharma,”Google Glasses Impediments”, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 2, October 2014,pp.81
[15] Miss. Shimpali Deshpande, Miss. Geeta Uplenchwar Dr. D.N Chaudhari,”
Google Glass”, International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-s
[16] Bjørn Hofmann, Dušan Haustein, and Laurens Landeweerd,” Smart-glasses: exposing and elucidating the ethical issues”,pp.4-5.
[17] Miss. Shimpali Deshpande, Miss. Geeta Uplenchwar Dr. D.N Chaudhari,”
Google Glass”, International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013, pp.5.
[18] Student report Ubiquitous computing seminar FS2014 Hermann Schweizer ,” Smart glasses:technology and applications”,pp.2, pp.4-5.

Jufishan Boksha, Asoke Nath, "Scope and Challenges in Smart Glasses: A Comprehensive Study on Present Scenario," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.619-626, 2019.

Aggregate function plays very important role in analyzing data of data warehouse. Analysis of such a huge data requires execution of complex queries such as iceberg and OLAP queries which consist of aggregate function. Improving the performance of such a complex query is the challenge in front of the researchers .Presently available iceberg query processing techniques faces the problem of empty bitwise operations, futile queue pushing and require more table scans. The model proposed in this research applies concept of look ahead matching on bitmap index of query attributes. Based on the threshold value the analysis of logical operation is done in advance. If result satisfies threshold condition then only remaining part will be evaluated otherwise it will be prune and declare as fruitless operation. In this way look ahead matching strategy overcome the problem of previous research. This research proposes framework for SUM and COUNT aggregate function.

Aggregate functions(MIN, MAX, SUM, COUNT); Bitwise operations (AND,OR,XOR); Data warehouse(DW); Iceberg query (IBQ); Look Ahead Matching(LAM) strategy

[1] Inmon, William H. Building the data warehouse. Wiley. com,2005.
[2] Kazi, Z., B. Radulovic, D. Radovanovic, and Lj Kazi. "MOLAP data warehouse of a software products servicing Call center." In MIPRO. 2010 Proceedings of the 33rd Inter national Convention, pp. 1283-1287. IEEE, 2010
[3] M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J. D. Ullman,” Computing iceberg queries efficiently” VLDB Conf., pages 299-310, 1998.
[4] Bin He, Hui-I Hsiao, Ziyang Liu, Yu Huang and Yi Chen, “Efficient Iceberg Query Evaluation Using Compressed Bitmap Index”, IEEE Transactions On Knowledge and Data Engineering, vol 24, issue 9, sept 2011, pp.1570-1589
[5] Parth Nagarkar,”Compressed Hierarchical Bitmaps for Efficiently Processing Different Query Workloads”,IEEE International conference on Cloud Engineering ,DOI 10.1109/IC2E.2015.99
[6] C.V.Guru Rao, V. Shankar,”Efficient Iceberg Query Evaluation Using Compressed Bitmap Index by Deferring Bitwise- XOR Operations “978-1-4673-4529-3/12/$31.00c 2012 IEEE
[7] C.V.Guru Rao, V. Shankar, “Computing Iceberg Queries Efficiently Using Bitmap Index Positions” DOI: 10.1190/ICHCI-IEEE.2013.6887811 Publication Year: 2013 ,Page(s): 1 – 6
[8] Vuppu.Shankar, Dr.C.V.Guru Rao,” Cache Based Evaluation of Iceberg Queries”, IEEE International conference on Computer and CommunicationsTechnologies(ICCCT),2014,DOI: 10.1109/ICCCT2.2014.7066694 ,Publication Year: 2014
[9] Rao, V.C.S. , Sammulal, P.,” Efficient iceberg query evaluation using set representation”,India Conference (INDICON), 2014 Annual IEEE DOI: 10.1109/INDICON.2014.7030537 Publication Year: 2014 , Page(s): 1 – 5
[10] K.-Y. Whang, B.T.V. Zanden, and H.M. Taylor, “A Linear-Time Probabilistic Counting Algorithm for Database Applications,” ACM Trans. Database Systems, vol. 15, no. 2, pp. 208-229, 1990
[11] J. Bae and S. Lee, “Partitioning Algorithms for the Computation of Average Iceberg Queries,” Proc. Second Int’l Conf. Data Warehousing and Knowledge Discovery (DaWaK), 2000
[12] K.P. Leela, P.M. Tolani, and J.R. Haritsa, “On Incorporating Iceberg Queries in Query Processors” Proc. Int’l Conf. Database Systems for Advances Applications (DASFAA), pp.431-442, 2004
[13] Ying Mei, Kaifan Ji*, Feng Wang,” A Survey on Bitmap Index Technologies for Large-scale Data Retrieval” 978-1-4799-2808-8/13 $26.00 © 2013
[14] F. Delie`ge and T.B. Pedersen, “Position List Word Aligned Hybrid: Optimizing Space and Performance for Compressed Bitmaps,” Proc. Int’l Conf. Extending Database Technology (EDBT), pp. 228-239, 2010
[15] A. Ferro, R. Giugno, P.L. Puglisi, and A. Pulvirenti, “BitCube: A Bottom-Up Cubing Engineering,”Proc. Int’l Conf. Data Warehousing and Knowledge Discovery (DaWaK), pp. 189-203, 2009
[16] R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Conf. Management of Data, pages 207-216, 1993
[17] W., Perrizo, Peano Count Tree Technology, Technical Report NDSU-CSOR-TR-01-1, 2001

S.N. Zaware-Kale, "Computing SUM and COUNT aggregate functions of Iceberg query using LAM strategy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.627-632, 2019.

Chatbot is a technology which is used to do the interactions between the human and the machine, these chatbots can be used in various fields and have various purpose to do the task, where a human can lag behind due to various reasons. Some of the most recent and advance chatbots are in a voice like IBM Watson, Amazon Alexa, Google Assistance, Microsoft`s Cortana, Apple Siri, wherein text question and answering we have Google Allo, Mitsuku and many more examples lay down. These all chatbots makes life easier to answer the question for different application in no mean of time. As they were being made with different tools and technology like Natural Language Processing (NLP). The technology of machine learning is growing so rapidly that the implementation of chatbots in the organization, and doing the research can improve the efficiency of it and can perform outstandingly in a different sector. The most demanding sector of chatbot is in the business sector where the customer service plays an important role, as they can do the continuous work, and handles the client by 24/7 support. Chatbot virtual assistance gives genuine feedback support to client, where artificial intelligence and machine learning do the backend work done in processing and giving appropriate answer or output to the user.

Chatbot, Machine Learning, A.I, NLP, Neural Network, Deep Learning

[1] Dahiya, Menal. (2017). A Tool of Conversation: Chatbot. INTERNATIONAL JOURNAL OF COMPUTER SCIENCES AND ENGINEERING. 5. 158-161.
[2] Nielson, M. A. (2015). Chapter 3: Improving the way neural networks learn. In Neural Networks and Deep Learning. essay, Determination Press.
[3] Foxsybot. Retrieved from https://www.messenger.com/t/foxsybot
[4] Fundamentals of Deep Learning – Activation Functions and When to Use Them? (OCTOBER 23, 2017). Retrieved from https://www.analyticsvidhya.com/blog/2017/10/fundamentals-deep-learning-activation-functions-when-to-use-them/

[5] World`s data will grow by 50X in next decade, IDC study predicts. (JUN 28, 2011 2:23 PM PT). Retrieved from https://www.computerworld.com/article/2509588/data-center/world-s-data-will-grow-by-50x-in-next-decade--idc-study-predicts.html
[6] How do I accurately understand a neural network diagram?(SEPTEMBER, 2018). Retrieved from https://www.reddit.com/r/learnmachinelearning/comments/9m561w/how_do_i_accurately_understand_a_neural_network/
[7] Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., & Jurafsky, D. (2016). Deep Reinforcement Learning for Dialogue Generation. ArXiv:1606.01541. Retrieved from https://aclweb.org/anthology/D16-1127
[8] Artificial Neural Networks (ANN).( Updated Mar 9, 2018). Retrieved from https://www.investopedia.com/terms/a/artificial-neural-networks-ann.asp
[9] TOP 5 BOTS TO GET YOU FIT . (Oct 10, 2016). Retrieved from https://www.topbots.com/top-5-best-fitness-bots-fitness-apps/
[10] GymBot. Retrieved from https://gymbot.io/
[11] Abdul-Kader, S., & Woods, J. (2015). Survey on Chatbot Design Techniques in Speech Conversation Systems. International Journal of Advanced Computer Science and Applications, 6(7). http://doi.org/10.14569/ijacsa.2015.060712

Jeetu Kumar Gupta, Sohit Agarwal, "An Epitome of Chatbot: A Review Paper," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.633-636, 2019.

Web Page Categorization becomes essential due to the increase in the information on the Internet. As pages on the web are growing regularly and can cover almost all types of information. However finding accurate and useful information from these large amounts of web pages for a user is difficult, so efficient and accurate methods for categorizing this large of information is very necessary. Web page categorization is to categorized web pages into specified categories. It improves the efficiency of search on the web. This paper discusses various methods, approaches & uses of web page categorization.

Web Page Categorization, Web Mining, Web Content Mining, Naive Bayes, KNN, SVM

[1] Blockeel, R. k. " Web Mining Research:A survey". Vol. 2, PP. 1-15, 2000.
[2] R. Jain and Dr. G. N. Purohit,” Page Ranking Algorithms for Web Mining”,International Journal of Computer Applications, ISSN: 0975 – 8887, Vol. 13, No.5, pp. 22–25, 2011.
[3] Xiaoguang Qi and Brian d. Davison, “Web Page Classification: Features and Algorithms” ACM Computing Surveys, Vol. 41, No. 2, Article 12, 2009.
[4]P., R.B. Plastino, A. Zadrozny, B. and L.H. Merschmann, “Categorizing feature selection methods for multi-label classification”, Artificial Intelligence Review, 49(1): 57-78, 2018.
[5] A. Osanyin, O. Oladipupo and Ibukun Afolabi, “A Review on Web Page Classification”, Covenant Journal of Informatics & Communication Technology, Vol. 6, No. 2, Dec. 2018.
[6] S. Dixit, & R. K. Gupta, “Layered Approach to Classify Web Pages using Firefly Feature Selection by Support Vector Machine (SVM)”, International Journal of u-and e-Service, Science and Technology, vol. 8, No. 5, pp. 355-364, 2015.
[7] B. Tang, H. Haibo, M. Paul, ” A Bayesian Classification Approach Using Class-Specific Features for Text Categorization”, IEEE ,2015.
[8] W. A. Awad, ”Machine Learning Algorithms in Web Page Classification”, International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, No. 5, 2012.
[9]T. Joachims, “Text categorization with support vector machines: Learning with many relevant features”, In: Proceedings of European Conference on Machine Learning E, CML, vol. 1398, pp. 137–142, 2000,.
[10] M. B. Revanasiddappa, B. S. Harish, S. V. A. Kumar, ”Meta-cognitive Neural Network based Sequential Learning Framework for Text Categorization”, ICCIDS, 2018.
[11] Liu, C. Wang, W. Tu, G. Xiang, Y. Wang, S. and L, F. “A new Centroid-Based Classification model for text categorization.”, Knowledge-Based Systems, vol. 136, pp. 15-26, 2017.
[12] R., S., V., S.P. “Text categorization by backpropagation network”, International Journal of Computer Applications, vol. 8, No. 6, pp. 1-5, 2010.
[13] C. Chang, M. Kayed, M. R. Girgis and K. F. Shaalan, “A Survey of Web Information Extraction Systems”, in IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1411-1428, Oct. 2006.
[14] K. Donghwa, S. Deokseong, S. Deokseong, C. Suhyoun, K. Pilsung, ”Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec”, 2018.
[15] Dıaz, A. B. Rios, J. H. Barron, T. Y. Guerrero, J. C. Elizondo, ”An automatic document classifier system based on genetic algorithm and taxonomy”, 2018.
[16] J. Hyoungil , K. Youngong , S. Jungyun, ”How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework”, 2016.
[17] Qi Luo, ”Research on Paper Submission Management System by Using Automatic Text Categorization”, Springer International Publishing AG, 2018.
[18] J. Moorey, Eui-Hong (Sam) Han, “Web Page Categorization and Feature Selection Using Association Rule and Principal Component Clustering”, 2010.
[19] S. Roy, P. Shivakumara, N. Jain, V. Khare, A. Dutta, U. P. and Tong Lu, ”Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video” Pattern Recognition”, doi: 10.1016/j.patcog.2018.02.014, 2018.
[20] H. S. Gowda, M. Suhil(B), D.S. Guru, and L. N. Raju, “Semi-supervised Text Categorization Using Recursive K-means Clustering” Recent Trends in Image Processing and Pattern Recognition, Springer, 2016.
[21] A. Qaziaand R.H. Goudar, “An Ontology-based Term Weighting Technique for Web Document Categorization”, Science Direct, Procedia Computer Science vol. 133, pp. 75–81, 2018.
[22] D. L. sanchez, A. G. Arrieta and J. M. Corchado, “Deep neural networks and transfer learning applied to multimedia web mining”, Springer International Publishing AG, 2018.
[23] S. Shinde, J. Prasanna and S. Vanjale, “Web Document Classification using Support Vector Machine”, IEEE, 2017.

Bhavana, Neeraj Raheja, "A Texonomy on Web Page Categorization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.637-641, 2019.

Diabetic Retinopathy is a diabetic complication that affects the eyes and can lead to blindness. The main cause of this condition is the damage to the blood vessels of the light sensitive tissue at the back of the retina. This paper attempts to categorize diabetic retinopathy with its various severity levels using clustering approach. Different Transforms such as Walsh-Hadamard, DCT and DST have been applied to the pre-processed image to extract the features of the image. These extracted features are used for Clustering of those images. The algorithmic performances are measured subjectively and objectively. The normal images were very well classified and distinguishable from the database using the proposed approach.

Diabetic Retinopathy, Severity, DCT, DST, Walsh-Hadamard, Performance Evaluation

[1] Darshit Doshi, Aniket Shenoy, Deep Sidhpura and Dr. Prachi Gharpure,” Diabetic Retinopathy Detection using Deep Convolutional Neural Networks”, International Conference on Computing, Analytics and Security Trends, pp 261-266, IEEE Dec 2016.
[2] Anupama. P, Dr Suvarna Nandyal, “Blood Vessel Segmentation using Hessian Matrix for Diabetic Retinopathy Detection”, Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) IEEE 2017.
[3] Z. A. Omar, M. Hanafi, S. Mashohor, N. F. M. Mahfudz and M. Muna’im,” Automatic diabetic retinopathy detection and classification system”,7th IEEE International Conference on System Engineering and Technology, pp 162-166, 3 October 2017.
[4] Kim Ramasamy & Rajiv Raman & Manish Tandon, “Current State of Care for Diabetic Retinopathy in India”, Curr Diab Rep DOI 10.1007/s11892-013-0388-6 Springer Science and Business Media New York ,2013.
[5] Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM, “Algorithms for digital image processing in diabetic retinopathy”, Computer Med Imaging Graph. 33:608-622, 2009.
[6] Chaitali Desai, Shivani Gupta, Shirgaon, Priyanka, “Diagnosis of Diabetic Retinopathy using CBIR Method”, International Journal of Computer Applications Proceedings on National Conference on Role of Engineers in National Building, 2016, pp. 12-15
[7] Preetika D’Silva, P. Bhuvaneswari, “Content Based Medical Image Retrieval using Artificial Neural Network”, IJSTE - International Journal of Science Technology & Engineering, 2013, Volume 1, Issue 11, ISSN (online).
[8] Nikita Gurudath, Mehmet Celenk, and H. Bryan Riley, Machine Learning Identification of Diabetic Retinopathy from Fundus Images, 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) , pp 1-7, 2014.
[9] K. Argade K.A. Deshmukh, M.M. Narkhede, N.N. Sonawane and S. Jore,” Automatic Detection of Diabetic Retinopathy using Image Processing and Data Mining Techniques.” International Conference on Green Computing and Internet of Things (ICGCoT,) pp 517-521 IEEE 2015.
[10] Z. A. Omar, M. Hanafi, S. Mashohor, N. F. M. Mahfudz and M. Muna’im,” Automatic diabetic retinopathy detection and classification system”,7th IEEE International Conference on System Engineering and Technology, pp 162-166, 3 October 2017.
[11] A. S. Jadhav, Pushpa B. Patil, “Detection of Optic Disc from Retinal Images using Wavelet Transform”, International conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016, pp 178-181.
[12] Karkhanis Apurva Anant, Tushar Ghorpade and Vimla Jethani,” Diabetic Retinopathy Detection through Image Mining for Type 2 Diabetes”, International Conference on Computer Communication and Informatics, Jan. 05 – 07, 2017, Coimbatore, INDIA.
[13] Jyoti D. Labhade, L. K. Chouthmol and Suraj Deshmukh, “Diabetic Retinopathy Detection Using Soft Computing Techniques”, International Conference on Automatic Control and Dynamic Optimization Techniques, pp 175-178, IEEE 2016.
[14] Nikita Kashyap, Dr. Dharmendra Kumar Singh “Colour Histogram Based Image Retrieval Technique for Diabetic Retinopathy Detection”,2017 2nd International Conference for Convergence in Technology (I2CT), pp 799-802.
[15] Nikita Kashyap, Dr. Dharmendra Kumar Singh, Dr. Girish Kumar Singh. “Mobile Phone Based Diabetic Retinopathy Detection System Using ANN-DWT”, 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) GLA University, Mathura, Oct 26-28, 2017, pp 463-467.
[16] Vaishali Suryawanshi, Shilpa Setpal, “Guassian Transformed GLCM Features for Classifying Diabetic Retinopathy”, International Conference on Energy, Communication, Data Analytics and Soft Computing, IEEE 2017, pp 1108-1111.
[17] Yogesh M. Rajput, Ramesh R. Manza, Manjiri B. Patwari, Deepali D Rathod, Prashant L. Borde, Pravin L. Yannavar, “Detection of Non-Proliferative Diabetic Retinopathy Lesions using Wavelet and Classification using K Means Clustering”, 2015 International Conference on Communication Networks (ICCN), pp 981-387.
[18] Md. Jahiruzzaman, A. B. M. and Aowlad Hossain,” Detection and Classification of Diabetic Retinopathy Using K-Means Clustering and Fuzzy Logic”,18th International Conference on Computer and information technology, pp 534-538 December 2015.
[19] Sandra Morales, Kjersti Engan, Valery Naranjo and Adrian Colomer,” Detection of Diabetic Retinopathy and Age Macular Degeneration from Fundus Images through Local Binary Patterns and Random Forests’ 4838-4842 IEEE 2016
[20] Rakshitha T R, Deepashree Devaraj, Prasanna Kumar S.C, “Comparative Study of Imaging Transforms on Diabetic Retinopathy Images”, IEEE International Conference on Recent Trends in Electronics Information Communication Technology, May 20-21, 2016, India, pp 118-122
[21] Shantala Giraddi, Savita Gadwal, Dr. Jagadeesh Pujari, “Abnormality Detection in retinal images using Haar wavelet and First order features”, Abnormality Detection in retinal images using Haar wavelet and First order features, pp 657-661.
[22] Faisal K.K, Deepa C.M, Nisha S.M, Greeshma Gopi, “Study on Diabetic Retinopathy Detection Techniques”, International Journal of Computer Sciences and Engineering, Vol 4, Issue 11, pp 137-140, 2016
[23] Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen A., Voutilainen R., Uusitalo, H., Kälviäinen, H., Pietilä, J., DIARETDB1 diabetic retinopathy database and evaluation protocol, In Proceedings of the 11th Conference on Medical Image Understanding and Analysis (Aberystwyth, Wales, 2007). Accepted for publication

Manjusha Nair, Dhirendra S. Mishra, "Categorization of Diabetic Retinopathy Severity Levels of transformed images using clustering approach.," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.642-648, 2019.

This research has been undertaken to examine the impact of occupational hazards on health of Computer Professionals, The essential data have been collected using Community-Survey method. Additionally books, journals, and websites have been referred for getting certain conclusions from the collected survey samples. In this survey based descriptive research, Opinions of diverse Computer professionals in pune city, Maharashtra are taken to find out the average health of the eyes. This research also tries to find certain patterns on the usage of electronic gadget in a particular age group and their tendency towards getting a particular eye disease.

Carpal Tunnel, Hazards, Health, Syndrome, Repetitive Strain

[1]. Neeta A. Deshpande, Pramod B. Deshmukh, Prateek Thakare, “Occupational Hazards and its Impact on Health of Eyes: A Survey”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.475-480, 2018.
[2]. https://www.fairview.org/patient-education/90498
[3]. https://www.nhs.uk/conditions/repetitive-strain-injury-rsi/
[4]. https://www.healthline.com/health/home-remedies-for-carpal-tunnel
[5]. https://www.ncbi.nlm.nih.gov/pmc/articles
[6]. https://www.mayoclinic.org/diseases-conditions/carpal-tunnel-syndrome/diagnosis-treatment/drc-20355608
[7]. https://www.washingtonpost.com/news/theworldpost/wp/2018/04/25/social-media-addiction/?noredirect=on&utm_term=.2154e4adfddb

Neeta A. Deshpande, Pramod B. Deshmukh, Prateek Thakare, "Treatments for Occupational Hazards: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.649-654, 2019.

FP-Growth algorithm requirements to construct an FP-tree which contains all the datasets. Association rules mining is an imperative technology within DM. FP-Growth algorithm is a conventional algorithm in association rules mining. But the FP-Growth algorithm within mining wants two times to examine database, which reduce the effectiveness of algorithm. During the study of association rules mining with FP-Growth algorithm, we work out enhanced algorithm of FP-Growth algorithm—Painting-Growth algorithm. We compare weighted FP-Growth algorithm with Painting-Growth algorithm. Experimental results explain that Painting-Growth algorithm is faster than the biased FP-Growth algorithm. The presentation of the Painting-Growth algorithm is improved than to of FP-Growth algorithm.

Data Mining, Association rule mining, Fp-growth algorithmrithm, Apriori algorithmrithm

[1] Ian Davidson, “Knowledge Discovery and Data Mining: Challenges and Realities”, ISBN 978- 1-59904-252, Hershey, New York, 2007.
[2] Joseph, Zernik, “Data Mining as a Civic Duty – Online Public Prisoners Registration Systems”, International Journal on Social Media: Monitoring, Measurement, Mining, vol. - 1, no.-1, pp. 84-96, September2010.
[3] J. K. Jain, N. Tiwari and M. Ramaiya, “A Survey: On Association Rule Mining”, International Journal of Engineering Research and Applications (IJERA), vol. 3, no. 1, (2013) January-February, pp. 2065-2069.
[4] D. Kerana Hanirex, K.P.Thooyamani and Khanaa, “performance of association rules for dengue virus type 1 amino acids using an integration of transaction reduction and random sampling algorithmrithm”, IJPSR,2017.
[5] J. K. Jain, N. Tiwari and M. Ramaiya, “A Survey: On Association Rule Mining”, International Journal of Engineering Research and Applications (IJERA), vol. 3, no. 1, (2013) January-February, pp. 2065-2069.
[6] Lior Shabtay, Rami Yaari and Itai Dattner, “A Guided FP-growth algorithmrithm for fast mining of frequent itemsets from big data”, March 20, 2018.
[7] Lior Shabtay, Rami Yaari and Itai Dattner, “A Guided FP-growth algorithmrithm for fast mining of frequent itemsets from big data”, March 20, 2018.
[8] K. Suguna, K. Nandhini, PhD, “Frequent Pattern Mining of Web Log Files Working Principles”, International Journal of Computer Applications (0975 – 8887) Volume 157 – No 3, January 2017.
[9] Neha Goyal and S K Jain,” A Comparative Study of Different Frequent Pattern Mining Algorithmrithm For Uncertain Data: A survey”, International Conference on Computing, Communication and Automation (ICCCA) IEEE, pp: 183-187, 2016 .
[10] Ajinkya Kunjir, Harshal Sawant, Nuzhat F. Shaikh, “A Review on Prediction of Multiple Diseases and Performance Analysis using Data Mining and Visualization Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 155 – No 1, December 2016.
[11] Md. Badi-Uz-Zaman Shajib Md. Samiullah ChowdhuryFarhan Ahmed, Carson K. Leung and Adam G. M. Pazdor,” An Efficient Approach for Mining Frequent Patterns over Uncertain Data Streams”, 28th International Conference on Tools with Artificial Intelligence, IEEE ,pp: 979-983, 2016.

Tanvi Upadhyay, Sushil Chaturvedi, "A Novel Model for Predicting Dengue Disease using Enhanced Weighted FP-Growth," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.655-658, 2019.

The Field of Natural Language Processing NLP is receiving to be one of the dynamic regions in Human-PC association which has seen a study shuffled in both research and philosophy bearing in the previous pair of years. Image retrieval addresses the delinquent of finding that image whose content matches a user’s request from amid a large collection of dataset. The technology of speech browsing is rapidly developing these eras. It is because the convention of cell phones is rising very rapidly, as associated to linked PCs. Speech interface combined browser is a web browser that profits users by via an interactive voice user interface, beneficial to those who have difficulties in seeing and reading a web content. Speaking and Listening are the ordinary approaches of communication and information gathering. Accordingly we are now title to a more speech based technique of surfing rather than working on written approach. A speech browser will proceeds and presents the information in the form of text as well as voice, using speech to text and text to speech conversion to render information. People want to acquire precise and appropriate data at the highest search results in a user approachable manner. Thus there is a necessity of a very effective and efficient ranking algorithm that delivers search results according to user preferences. This paper focuses on this different technique, voice browsing, which speech synthesis and unites speech recognition with improved personalized search that can be highly productive in the future years. In this paper we provide personalization by creating particular search history for individually user on the browser and also concentrated on the search outcomes to get modified according to the user request.

Natural Language Processing, Language Processing, Natural Language Query

[1] Anand Mishra, Karteek Alahari, C. V. Jawahar, “Image Retrieval Using Textual Cues”, 2013 Ieee International Conference On Computer Vision
[2] B. Dinakaran, J. Annapurna, Ch. Aswani Kumar “Interactive Image Retrieval Using Text And Image
Content” Cybernetics and Information Technologies, Volume 10, No 3Sofia, 2010
[3] Karan Sharma, Arun CS Kumar And Suchendra M. Bhandarkar “Action Recognition In Still Images Using Word Embeddings From Natural Language Descriptions”, 2017 Ieee Winter Conference On Applications Of Computer Vision Workshops
[4] Atsuhiro Kojima And Takeshi Tamura ”Natural Language Description Of Human Activities From Video Images Based On Concept Hierarchy Of Actions” International Journal Of Computer Vision 50(2), 171–184, 2002
[5] Avinash N BhuteAnd B.B. Meshram “Text Based Approach for Indexing And Retrieval Of Image And Video: A Review” Advances In Vision Computing: An International Journal (Avc) Vol.1, No.1, March 2014
[6] Anjali Patel, Ashish Patel “A Survey On Tag Based Image Retrieval Using Natural Language Processing” International Journal Of Innovative Research In Technology (IJIRT), Volume 3 Issue 6, November 2016.
[7] Avinash N Bhute, B.B. Meshram “Text Based Approach For Indexing And Retrieval Of Image And Video: A Review “Advances In Vision Computing: An International Journal (Avc) Vol.1, No.1, March 2014.
[8] Sudarshan Awale, S.J.Karale “Information Retrieval From Ontology Using Natural Language Interface” International Journal Of Current Engineering And Scientific Research (Ijcesr), Volume-2, Issue-2, 2015
[9] Garima Singh, Arun Solanki, “An Algorithm To Transform Natural Language Into Sql Queries For Relational Databases” IAEES.
[10] Ruslan Posevkin ,Igor Bessmertny, “Translation Of Natural Language Queries To Structured Data Sources”.

N.L. Palandurkar, S.J. karale, "Review on Image Retrival through Natural Language Query," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.659-664, 2019.

Representational State Transfer (REST) is an architectural style for developing web services and its key constraints are Use of Uniform Interface (UI), client-server based, stateless operations, and Resource caching. It is popular due to its simplicity and builds on the existing systems. Hence, many cloud providers such as Amazon, Google are moving their APIs from Simple Object Access Protocol (SOAP) to REST. Unlike SOAP, RESTful service doesn’t provide standard for security while accessing web services. Hence, we considered the security issues in execution of RESTful web services and proposed a design of a secured model (S-Rest) over RESTful web services with 3-level security services at communication, Application and Management. The proposed architecture enhances the performance of RESTful web application.

Webservices; RESTful; Security issues

[1]. Meiko Jensen, Nils Gruschka, Ralph Herkenhöner, “A survey of attacks on web services”, Computer Science Research and Development, Springer, November 2009.
[2]. Hirsch, Frederick; Kemp, John; Ilkka, Jani. “Mobile Web Services: Architecture and Implementation”, John Wiley & Sons, 2007.
[3]. Richardson, Leonard; Amundsen, Mike, “ RESTful Web APIs”, O`Reilly Media, retrieved 15 September 2015.
[4]. "Web Services Architecture". World Wide Web Consortium. 11 February 2004. 3.1.3 Relationship to the World Wide Web and REST Architectures. Retrieved 29 September 2016.
[5]. Fielding, “Architectural Styles and the Design of Network-based Software Architectures”, Doctoral dissertation. Technical report, University of California, Irvine, 2000.
[6]. Pautasso, O. Zimmermann, and F. Leymann, “RESTful Web Services vs. “Big” Web Services: Making the Right Architectural Decision”, In WWW ’08: Proceeding of the 17th international conference on World Wide Web, pages 805–814, New York, NY, USA, 2008. ACM
[7]. Richardson and S. Ruby, “RESTful Web Services”, O’Reilly, Oct. 2007
[8]. Dharmendra S. Raghuwanshi, M.R.Rajagopalan, “ MS2: Practical data privacy and security framework for data at rest in cloud”, Computer Applications and Information Systems (WCCAIS), 2014 World Congress on 17-19 Jan. 2014.
[9]. Dunglu Peng, Chen Li, Huan Huo, “An extended UsernameToken-based approach for REST-style Web Service Security Authentication”, Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on 8-11 Aug. 2009.
[10]. Hoai Viet Nguyen, Luigi Lo lacono, “REST-ful CoAP Message Authentication, Secure Internet of Things (SIoT)”, 2015 International Workshop on 21-25 Sept. 2015.
[11]. Gabriel Serme, Anderson Santana de Oliveira, Julien Massiera, Yves Roudier, “Enabling Message Security for RESTful Services”, Web Services (ICWS), 2012 IEEE 19th International Conference on 24-29 June 2012

Chatti Subbalakshmi, Rishi Sayal, H. S. Saini, "S-REST: A design of Secured Protocol for Implementation of RESTful Webservices," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.665-669, 2019.