Volume-6 , Issue-4 , Apr - 2018 Go Back

The key predicament in the present circumstances is how to categorize the mathematically related keywords from a given text file and store them in one math text file. As the math text file contains only the keywords which are related to mathematics. The math dataset is a collection of huge amount of tested documents and stored in math text file. The dataset is trained with giant amount of text files and the size of dataset increases, training with various text samples. Finally the dataset contains only math-related keywords. The proposed approaches evaluated on the text containing individual formulas and repeated formulas. The two approaches proposed are one is Sequence matcher and another one is Levenshtein Distance, both are used for checking string similarity. The performance of the repossession is premeditated based on dataset of repetitive formulas and formulas appearing once and the time taken for reclamation is also measured.

Levenshtein distance,Sequence matcher

[1] Kai Ma, Siu Cheung Hui and Kuiyu Chang “Feature Extraction and Clustering-based Retrieval for Mathematical Formulas”, pp. 372-377.
[2] Sidath Harshanath Samarasinghe and Siu Cheung Hui “Mathematical Document Retrieval for Problem Solving”, International Conference on Computer Engineering and Technology, pp.583-587,2009.
[3] J. Misutka and L. Galambos, “Mathematical Extension of Full Text Search Engine Indexer”, Proc. 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA 08), , pp. 1-6,April 2008.
[4] B.R. Miller and A. Youssef, “Technical Aspects of the Digital Library of Mathematical Functions”, in Annals of Mathematics and Artificial Intelligence, Springer Netherlands, pp. 121-136, 2003.
[5] H. Zhang, T.B. and M.S. Lin, “An Evolutionary Kmeans Algorithm for Clustering Time Series Data” ,Proc. International Conference on Machine Learning and Cybernetics, pp. 1282-1287, 2004.
[6] R. Munavalli and M.R. MathFind, “A Math-aware Search Engine”, Proc. Annual International ACM SIGIR Conference on Research and development in information retrieval, pp.735-735, 2006.
[7] M. Kohlhase. “Markup for Mathematical Knowledge,” An Open Markup format for Mathematical Documents”, Ver. 1.2, Lecture Notes in Computer Science, , Springer Berlin, pp. 13-23.
[8] G.AppaRao,K.Venkata Rao,PVGD Prasad Reddy and T.Lava Kumar,“An Efficient Procedure for Characteristic mining of Mathematical Formulas from Document”, International Journal of Engineering Science and Technology (IJEST), Vol. 10 No.03,pp152-157, Mar 2018

G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy, "Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.400-404, 2018.

Electronic Trade is the method of cooperating through PC frameworks. For extending the use of online business in making countries, the B2B electronic business is completed for upgrading access to overall markets for firms in making countries. For making country progress in the field of electronic business is fundamental. On opposite, E-business was at first inherent 1990`s for RDBMS databases, as the innovation got overhauled, NoSQL databases become possibly the most important factor. However, here Web application is being planned in that way where no slacking of capacity and speed happens. In this manner, arrangement of NoSQL databases gives the high precision in getting comes about and looking at information. Additionally, Business retailers who need to offer their items over the web, use to give money to E-Trade providers. Because of this, their fluctuated items are being shown to the clients.

web based business, internet business, e-commerce business

[1]. Abhijit Mitra 2013. “E-commerce in India- a review”, International Journal of Marketing, Financial Services and Management Research ISSN 2277- 3622 Vol.2, No. 2, February (2013)
[2]. Alka Raghunath, 2013. “Problem and Prospects of ECommerce “, International Journal of Research and Development - A Management Review (IJRDMR) ISSN (Print): 2319–5479, Volume-2, Issue – 1, 2013 68.
[3]. AustraliaNIC :: https://medium.com/@AustraliaNIC/objectives-and-importance-of-ecommerce-website-design-company-51043a4c2ed2
[4]. Otto, Mark "Say hello to Bootstrap 2.0". Developer Blog. Twitter. Archived from the original on February 23, 2017. Retrieved February 23, 2017.
[5]. Otto, Mark "Bootstrap in A List Apart No. 342". Mark Otto`s blog. Archived from the original on October 28, 2016. Retrieved February 23, 2017.
[6]. "CodeIgniter 3.1.8 Released". codeigniter.com. Retrieved 23 March 2018.

[7]. https://www.codeigniter.com/user_guide/general/
[8]. requirements.html
[9]. Mongodb :: https://www.tutorialspoint.com/mongodb/index.htm
[10]. Md5 :: https://searchsecurity.techtarget.com/definition/MD5
[11]. Acknowledgement ::
[12]. http://1000projects.org/e-commerce-project-preface-acknowledgement.html

Harshit Malik, Arindam Singh, Rajat Gupta, Aditya Gupta, "E-Commerce Web Application," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.404-408, 2018.

Many people can save sensitive data on remote servers, provide data access from the admin to data users. As the stored information can hold important information, before uploading the data to the cloud, the information must be encrypted. If any cloud user wants to retrieve any file then they no need to check every file in the cloud. Data user can utilize keyword-based document retrieval. This paper suggests a technique to retrieve the encrypted information from the data store through multiple keywords. This technique instantaneously maintains active update operations like deleting as well as inserting records. Particularly, TFIDF is preferable for building index as well as generating query. Here we establish a tree-based file index to offer multi keyword ranked search. For encrypting and decrypting the files, AES algorithm is used and simultaneously offer the exact relevance score frequency among encrypted indexed files. In this technique, decryption can be performed before downloading the file from the database. General tests are directed to show the productivity of the proposed scheme.

Multi-Keyword Search, Security, TF-IDF, Indexing, Search Query

[1] K. Ren, C.Wang, Q.Wang et al., “Security challenges for the public cloud,” IEEE Internet Computing, vol. 16, no. 1, pp. 69–73, 2012.
[2] S. Kamara and K. Lauter, “Cryptographic cloud storage,” in Financial Cryptography and Data Security. Springer, 2010, pp. 136–149.
[3] C. Gentry, “A fully homomorphic encryption scheme,” Ph.D. dissertation, Stanford University, 2009.
[4] O. Goldreich and R. Ostrovsky, “Software protection and simulation on oblivious rams,” Journal of the ACM (JACM), vol. 43, no. 3, pp. 431–473, 1996.
[5] D. Boneh, G. Di Crescenzo, R. Ostrovsky, and G. Persiano, “Public key encryption with keyword search,” in Advances in Cryptology- Eurocrypt 2004. Springer, 2004, pp. 506–522.
[6] D. Boneh, E. Kushilevitz, R. Ostrovsky, and W. E. Skeith III, “Public key encryption that allows pir queries,” in Advances in Cryptology-CRYPTO 2007. Springer, 2007, pp. 50–67.
[7] D. X. Song, D. Wagner, and A. Perrig, “Practical techniques for searches on encrypted data,” in Security and Privacy, 2000. S&P 2000. Proceedings. 2000 IEEE Symposium on. IEEE, 2000, pp. 44–55.
[8] C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure ranked keyword search over encrypted cloud data,” Proc. - Int. Conf. Distrib. Comput. Syst., pp. 253–262, 2010.
[9] C. Wang, N. Cao, K. Ren, and W. Lou, “Enabling secure and efficient ranked keyword search over outsourced cloud data,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 8, pp. 1467 1479, 2012.
[10] R. Curtmola, J. Garay, S. Kamara, and R. Ostrovsky, “Searchable symmetric encryption,” Proc. 13th ACM Conf. Comput. Commun. Secur. - CCS ’06, p. 79, 2006.
[11] K. Li, W. Zhang, C. Yang, and N. Yu, “Security Analysis on One-to-Many Order Preserving Encryption-Based Cloud Data Search,” IEEE Trans. Inf. Forensics Secur., vol. 10, no. 9, pp. 1918–1926, 2015.
[12] S. Kamara, C. Papamanthou, and T. Roeder, “Dynamic searchable symmetric encryption,” 2012 ACM Conf. Comput. Commun. Secur.,pp. 965–976, 2012.
[13] B. Wang, S. Member, M. Li, and H. Wang, “Geometric Range Search on Encrypted Spatial Data,” IEEE Trans. Inf. Forensics Secur., vol.11, no. 4, pp. 704–719, 2016.
[14] Q. Tang, “Nothing is for free: Security in searching shared and encrypted data,” IEEE Trans. Inf. Forensics Secur., vol. 9, no. 11, pp.1943–1952, 2014.
[15] R. Popa and N. Zeldovich, “Multi-key searchable encryption,” pp. 1–18, 2013.

L.SOUMYA, B.S.VAMSI KRISHNA, "Multi Keyword Search Within An Encrypted Text Using Tf-Idf Based Trapdoor Function," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.409-414, 2018.

The cordiality business is one of the data-rich enterprises that gets tremendous Volumes of data gushing at high Velocity with extensively Variety, Veracity, and Variability. These properties make the data examination in the cordiality business a big data issue. Meeting the clients` desires is a key factor in the neighborliness business to get a handle on the clients` dependability. To accomplish this objective, advertising experts in this industry effectively search for approaches to use their data in the most ideal way and propel their data scientific arrangements, for example, distinguishing an extraordinary market division clustering and building up a proposal framework. In this paper, we introduce an exhaustive writing audit of existing big data clustering calculations and their favorable circumstances and disservices for different utilize cases. We execute the current big data clustering calculations and give a quantitative correlation of the execution of various clustering calculations for various situations. We additionally display our experiences and proposals with respect to the appropriateness of various big data clustering calculations for various utilize cases. These suggestions will be useful for hoteliers in choosing the proper market division clustering calculation for various clustering datasets to enhance the client encounter and boost the lodging income.

Hospitality, Market Segmentation, Density based Clustering, Neighborhood, Embedded Cluster, Nested Adjacent Cluster

[1] Jorge Luis Cavalcanti Ramos, Ricardo Euller Dantas e Silva, Joao Carlos Sedraz Silva, Rodrigo Lins Rodrigues, Alex Sandro Gomes, “A Comparative Study between Clustering Methods in Educational Data Mining”, Vol. 14, No. 8, PP. 3755 – 3761, 2016.
[2] Pranjal Dubey, Anand Rajavat, “Comparative Study Between Density Based Clustering-Dbscan and Optics”, International Journal of Advanced Computational Engineering and Networking, Vol. 4, No.12, Dec.2016.
[3] C. Pizzuti, D. Talia, “P-AutoClass: scalable parallel clustering for mining large data sets”, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 3, PP. 629 – 641, 2003.
[4] Massimo Brescia, Stefano Cavuoti, Maurizio Paolillo, Giuseppe Longo, Thomas Puzia, “The detection of globular clusters in galaxies as a data mining problem”, Monthly Notices of the Royal Astronomical Society, Vol. 421, No. 2, PP. 1155 – 1165, 2012.
[5] R. J. Dodd, “Data mining in the young open cluster IC 2391”, Monthly Notices of the Royal Astronomical Society, Vol. 355, No. 3, PP. 959 – 972, 2004.
[6] Jian Hou, Huijun Gao, Xuelong Li, “DSets-DBSCAN: A Parameter-Free Clustering Algorithm”, IEEE Transactions on Image Processing, Vol. 25, No. 7, PP. 3182 – 3193, July 2016.
[7] Wenbin Wu, Mugen Peng, “A Data Mining Approach Combining K -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting”, IEEE Internet of Things Journal, Vol. 4, No. 4, PP. 979 – 986, 2017.
[8] Yaling Xun, Jifu Zhang, Xiao Qin, Xujun Zhao, “FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters”, IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 1, PP. 101 – 114, 2017.
[9] Foued Saâdaoui, Pierre R. Bertrand, Gil Boudet, Karine Rouffiac, Frédéric Dutheil, Alain Chamoux, “A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining”, IEEE Transactions on NanoBioscience, Vol. 14, No. 7, PP. 707 – 715, 2015.
[10] Daniele Casagrande, Mario Sassano, Alessandro Astolfi, “Hamiltonian-Based Clustering: Algorithms for Static and Dynamic Clustering in Data Mining and Image Processing”, IEEE Control Systems, Vol. 32, No. 4, PP. 74 – 91, 2012.
[11] Nagaraju S, Manish Kashyap, Mahua Bhattacharya, “A variant of DBSCAN algorithm to find embedded and nested adjacent clusters”, Signal Processing and Integrated Networks (SPIN), Feb. 2016.
[12] Sanjay Kumar Shukla, Manoj Kumar Tiwari, “GA Guided Cluster Based Fuzzy Decision Tree for Reactive Ion Etching Modeling: A Data Mining Approach” IEEE Transactions on Semiconductor Manufacturing, Vol. 25, No. 1, PP. 45 – 56, 2012.
[13] Melanie Po-Leen Ooi, Eric Kwang Joo Joo, Ye Chow Kuang, Serge Demidenko, Lindsay Kleeman, Chris Wei Keong Chan, “Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction”, IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 10, PP. 3300 – 3317, 2011.
[14] Dilhan Perera, Judy Kay, Irena Koprinska, Kalina Yacef, Osmar R. Zaïane, “Clustering and Sequential Pattern Mining of Online Collaborative Learning Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 6, PP. 759 – 772, 2009.
[15] Chun-Hao Chen, Vincent S. Tseng, Tzung-Pei Hong, “Cluster-Based Evaluation in Fuzzy-Genetic Data Mining”, IEEE Transactions on Fuzzy Systems, Vol. 16, No. 1, PP. 249 – 262, 2008.
[16] V.Maniraj, S.Malarvizhi, “A Real Time fraud Rank Identification using Semantic Relation Analysis on Mobile Web Application”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.372-378, 2016.
[17] Xiangyang Li, Nong Ye, “A supervised clustering and classification algorithm for mining data with mixed variables”, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 36, No. 2, PP. 396 – 406, 2006,
[18] V. S. Tseng, Ching-Pin Kao, “Efficiently mining gene expression data via a novel parameterless clustering method”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 2, No. 4, PP. 355 – 365, 2005.

N.Baby Kala, S. Ramya, "A Relative Computable Study of Modern Big Data Clustering Procedures for Fair Division," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.415-423, 2018.

Most of the studies based on tracking individuals, crowd counting, finding the region of motion and crowd detection. Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, public safety, crowd abnormalities, visual surveillance and urban planning. The purpose of crowd density analysis is to calculate the concentration of the crowd in the videos of observers. Pattern recognition technique helps to estimate the crowd detection count and density by using face and detection. The job of detecting a face in the crowd is complicated due to its variability present in human faces including color, pose, expression, position, orientation, and illumination. The counting performance has been steadily improved because of Deep Convolutional Neural Network..

Pattern Recognition, Computer Vision, Crowd Density Estimation, Detection, CNN

[1] Sami Abdulla, Mohsen Saleh, Shahrel Azmin Suandi, Haidi Ibrahim, “Recent survey on crowd density estimation and counting for visual surveillance”, Engineering Application of Artificial Intelligence 41 (2015) pp. 103-114, http://dx.doi.org/10.1016/j.e ngappai.2015.01.0 07
[2] Vishwanath A. Sindagi, Vishal M. Patel, “A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation”, Pattern Recognition Letters, elsevier 2017.
[3] Yingying Zhang, Desen Zhou, Siqin Chen, Shenghau Gao, Yi Ma, “Single-Image Crowd Counting via Multi-Column Convolutional Neural Network”, In CVPR, IEEE, pp. 589-597.
[4] Lingke Zeng, Xiangmin Xu, Bolun Cai, Suo, Qiu, Tong Zhang, “Multi-Scale Convolutional Neural Networks for Crowd Counting”, IEEE 2017.
[5] Di Kang, Zheng Ma, Antoni B. Chan, “Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking”, IEEE 2017.
[6] Ankan Bansal, K S Venkatesh, “People Counting in high Density Crowds from Still Images”, IEEE 2015.
[7] Chuan Wang, Hua Zhang, Liang Yang, Si Liu, Ziaochun Cao, “Deep People Counting in Extremely Dense Crowds”, ACM 2015.
[8] Helbing, D., Brockmann, D., Chadefaux, T., Donnay, K., Blanke, U., Woolley-Meza, O., Moussaid, M., Johansson, A., Krause, J., Schutte, S., et al., 2014. “Saving human lives: what complexity science and information systems can contribute”. J. Stat. Phys., 147.
[9] Zhao, T., Nevatia, R., Wu, B., 2008. “Segmentation and tracking of multiple humans in crowded environments”. IEEE Trans. Pattern Anal. Mach. Intell. 30 (7), pp. 1198-1211.
[10] Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.Q., 2008. “Crowd analysis: a survey. Machine Vision and Applications” 19, pp. 345-357.
[11] Junior, J.C.S.J., Musse, S.R., Jung, C.R., 2010. “ Crowd analysis using computer vision techniques”. IEEE Signal Processing Magazine 27, pp. 66-77.
[12] Li, T., Chang, H., Wang, M., Ni, B., Hong, R., Yan, S., 2015. “Crowded scene analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology” 25, pp. 367-386.
[13] Loy, C.C., Chen, K., Gong, S., Xiang, T., 2013. “Crowd counting and profiling: Methodology and evaluation, in: Modeling, Simulation and Visual Analysis of Crowds”. Springer, pp. 347-382.
[14] Jones, M.J., Snow, D., 2008. “Pedestrian detection using boosted features over many frames”. In: 19th International Conference on Pattern Recognition, 2008. ICPR 2008. IEEE, pp. 14, http://dx.doi.org/10.1109 /ICPR.2008.4761703.
[15] Viola, P., Jones, M.J., Snow, D., 2005. “Detecting pedestrians using patterns of motion and appearance”. Int. J. Comput. Vis. 63 (2), pp. 153-161.
[16] Schapire, R.E., Singer, Y., 1999. “Improved boosting algorithms using confidencerated predictions”. Mach. Learn. 37 (3), pp. 297-336
[17] Leibe, B., Seemann, E., Schiele, B., 2005. “Pedestrian detection in crowded scenes”. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1. IEEE, pp. 878-885, http://dx.doi.org/10.1109/CVPR.2005.272
[18] Lin, S.-F., Chen, J.-Y., Chao, H.-X., 2001. “Estimation of number of people in crowded scenes using perspective transformation”. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 31 (6), pp. 645-654
[19] Lin, S.-F., Lin, C.-D., 2006. “Estimation of the pedestrians on a crosswalk”. In: International Joint Conference SICE-ICASE, 2006. IEEE, pp. 4931-4936, http:// dx.doi.org/10.1109/SICE.2006.314851
[20] Zhao, T., Nevatia, R., 2004. “Tracking multiple humans in complex situations”. IEEE Trans. Pattern Anal. Mach. Intell. 26 (9), 1208-1221.
[21] Rabaud, V., Belongie, S., 2006. “Counting crowded moving objects”. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE, pp. 705-711, http://dx.doi.o rg/10.1109/CVPR.2006.92
[22] Shi, J., Tomasi, C., 1994. “Good features to track”. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94. IEEE, pp. 593-600, http://dx.doi.org/1 0.1109/CVPR.1994.323794
[23] Sidla, O., Lypetskyy, Y., Brandle, N., Seer, S., 2006. “Pedestrian detection and tracking for counting applications in crowded situations”. In: IEEE International Conference on Video and Signal Based Surveillance, 2006. AVSS’06. IEEE, p. 70, http://dx.doi.org/10.1109/AVSS.2006.91.
[24] Idrees, H., Saleemi, I., Seibert, C., Shah, M., 2013. “Multi-source multiscale counting in extremely dense crowd images”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547-2554.
[25] Pham, V.Q., Kozakaya, T., Yamaguchi, O., Okada, R., 2015. “Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation”, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253-3261
[26] Fu, M., Xu, P., Li, X., Liu, Q., Ye, M., Zhu, C., 2015. “Fast crowd density estimation with convolutional neural networks”. Engineering Applications of Artificial Intelligence 43, 81-88.
[27] Zhang, C., Li, H., Wang, X., Yang, X., 2015. “Cross-scene crowd counting via deep convolutional neural networks”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833-841
[28] Shang, C., Ai, H., Bai, B., 2016. “End-to-end crowd counting via joint learning local and global count”, in: Image Processing (ICIP), 2016 IEEE International Conference on, IEEE. pp. 1215-1219
[29] Ankan Bansal and K. S. Venkatesh, 2015. “People Counting in High Density Crowds from Still Images”. International Journal of Computer and Electrical Engineering, pp. 316-324.

Mayur D. Chaudhari, Archana S. Ghotkar, "A Study on Crowd Detection and Density Analysis for Safety Control," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.424-428, 2018.

Most of the recent research has explored the possibility of predicting & analyzing epileptic seizures by using different techniques & methods. Epilepsy is the second most common neurological disorder which affects people of all ages i.e. about 1-2% of the world’s population affected by this major chronic disorder. The Electroencephalogram (EEG) signal is used as a useful tool for the early detection of epileptic seizures in several applications of epilepsy diagnosis. Many techniques have been developed for differentiate the features of seizures present in EEGs. This article reviews the seizure detection techniques & methods reported in last decade/years. However, there are various techniques like Empirical mode decomposition (EMD), wavelet transform, tensors, entropy, chaos theory, and dynamic analysis which are used in the area of epilepsy diagnosis. For better treatment of the patients it is important that the seizures are detected correctly in time. Although efforts have been made for better prediction of the seizures, the translation of current analysis & results to clinical applications is still not possible. We have reviewed a framework of reliable algorithmic seizure prediction studies, discussing each component of the whole block diagram. We have also explored all the processes, from signal acquisition to adequate performance evaluation that should be opted in the designing of an efficient seizure advisory/intervention system. The present review has established that there is a potential for improvement and optimization in the seizure prediction framework.

Epilepsy; Seizure detection algorithm; Signal processing; Feature Extraction; Classification; Performance

[1] World Health Organization, 2011.Available: http://www.who.int/mediacentre/?factsheets/fs999/en
[2] J. Gotman, “Automatic detection of seizures and spikes,” J. Clin. Neurophysiol., 16: 130-140, 1999.
[3] J. C. Sackellares, “Seizure prediction,” Epilepsy Curr., 8: 55-59, 2008.
[4] A. G. Stein, H. G. Eder, D. E. Blum, A. Drachev and R. S. Fisher,“An automated drug delivery system for focal epilepsy,” Epilepsy Res., 39: 103-114, 2000.
[5] Elie Bou Assi, Dang K. Nguyen, Sandy Rihana and Mohamad Sawan, Towards accurate prediction of epileptic seizures: A review, Biomedical Signal Processing and Control 34, 2017.
[6] S.S. Spencer, D.K. Nguyen, R.B. Duckrow, Invasive EEG in presurgicalevaluation of epilepsy, in: The Treatment of Epilepsy, Wiley-Blackwell, 2009,pp. 767–798.
[7] C. Alexandre Teixeira, B. Direito, M. Bandarabadi, M. Le Van Quyen, M.Valderrama, B. Schelter, A. Schulze-Bonhage, V. Navarro, F. Sales, A. Dourado,Epileptic seizure predictors based on computational intelligence techniques:a comparative study with 278 patients, Comput. Methods Progr. Biomed. 114(2014) 324–336.
[8] L. Sörnmo and P. Laguna, Bioelectrical signal processing in cardiac and neurological applications, USA: Elsevier, 2005.
[9] S. Tong and N. V. Thakor (Ed.), Quantitative EEG Analysis Methods and Clinical Applications, Norwood: Artech House, 2009.
[10] M. Bandarabadi, C.A. Teixeira, J. Rasekhi, A. Dourado, Epileptic seizure prediction using relative spectral power features, Clin. Neurophysiol. 126(2015) 237–248.
[11] J. Rasekhi, M.R.K. Mollaei, M. Bandarabadi, C.A. Teixeira, A. Dourado, Preprocessing effects of 22 linear univariate features on the performance ofseizure prediction methods, J. Neurosci. Methods 217 (2013) 9–16.
[12] E. Acar, C. A. Bingol, H. Bingol, R. Bro and B. Yener, “Multiway analysis of epilepsy tensors,” Bioinformatics, 23:i10-i18, 2007.
[13] Y. Park, L. Luo, K.K. Parhi, T. Netoff, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia 52 (2011)1761–1770.
[14] L.Orosco, A.G.Correa and E.Lacier, Review:A survey of performance and techniques for automatic epilepsy detection, Journal of Medical and Biological Engineering, 2013.
[15] A. Eftekhar, W. Juffali, J. El-Imad, T.G. Constandinou, C. Toumazou,Ngram-derived pattern recognition for the detection and prediction of epileptic seizures, PLoS One 9 (2014) e96235.
[16] J. Klatt, H. Feldwisch-Drentrup, M. Ihle, V. Navarro, M. Neufang, C. Teixeira, C.Adam, M. Valderrama, C. Alvarado-Rojas, A. Witon, M. Le Van Quyen, F. Sales,A. Dourado, J. Timmer, A. Schulze-Bonhage, B. Schelter, The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients, Epilepsia 53 (2012) 1669–1676.
[17] M.J. Cook, T.J. O’Brien, S.F. Berkovic, M. Murphy, A. Morokoff, G. Fabinyi, W.D’Souza, R. Yerra, J. Archer, L. Litewka, S. Hosking, P. Lightfoot, V. Ruedebusch,W.D. Sheffield, D. Snyder, K. Leyde, D. Himes, Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients withdrug-resistant epilepsy: a first-in-man study, Lancet Neurol. 12 (2013)563–571.
[18] B.H. Brinkmann, E.E. Patterson, C. Vite, V.M. Vasoli, D. Crepeau, M. Stead, J.J.Howbert, V. Cherkassky, J.B. Wagenaar, B. Litt, G.A. Worrell, Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy, PLoS One 10 (2015).
[19] A. Varsavsky, I., Mareels and M. Cook, Epileptic seizures and the EEG, Boca Raton: CRC Press, 2011.
[20] A. Garcés Correa, E. Laciar, H. D. PatiñoandM. E. Valentinuzzi, “Artifact removal from EEG signals using adaptive filters in cascade,” J. Phys. Conf. Ser., 90: 012081.
[21] I. Güler and I. G. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” J.Neurosci. Methods, 148: 113-121, 2005.
[22] A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Syst. Appl., 32: 1084-1093, 2007.
[23] K. Polat and S. Günes, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform,” Appl. Math. Comput., 187: 1017-1026, 2007.
[24] A. M. Chan, F. T. Sun, E. H. Boto and B. M. Wingeier, “Automated seizure onset detection for accurate onset Time Determination in Intracranial EEG,” Clin. Neurophysiol.119: 2687-2696, 2008.
[25] A. Aarabi, R. Fazel-Rezai and Y. Aghakhani, “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clin. Neurophysiol., 120: 1648-1657, 2009.
[26] R. Meier, H. Dittrich, A. Schulze-Bonhage and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” J. Clin. Neurophysiol., 25: 119-131, 2008.
[27] G. R. Minasyan, J. B. Chatten and R. N. Harner, “Patient-specific early seizure detection from scalp EEG,” J. Clin. Neurophysiol., 27: 163-178, 2010.
[28] Q. Yuan, W. Zhou, S. Li and D. Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Res., 96: 29-38, 2011.
[29] N. Kannathal, M. L. Choob, U. R. Acharya and P.K Sadasivana, “Entropies for detection of epilepsy in EEG,” Comput. Meth. Programs Biomed., 80: 187-194, 2005.
[30] A. J. Gabor, R. R. Leach and F. U. Dowla, “Automated seizure detection using a self-organizing neural network,” Electroencephalogr. Clin. Neurophysiol., 99: 257-266, 1996.
[31] A. T. Tzallas, M. G. Tsipouras and D. I. Fotiadis, “Automaticseizure detection based on time-frequency analysis and artificial neural networks,” Comput. Intell. Neurosci., 80510, 2007.
[32] B. Abibullaev, M. S. Kim and H. D. Seo, “Seizure detection in temporal lobe Epileptic EEGs using the best basis Wavelet functions,” J. Med. Syst., 34: 755-765, 2010.
[33] J. Mitra, J. R. Glover, P. Y. Ktonas, A. T. Kumar, A. Mukherjee, N. B.Karayiannis, J. D. Frost Jr., R. A. Hrachovy and E. M. Mizrahi, “A multi-stage system for the automated detection of epileptic seizures in neonatal EEG,” J. Clin. Neurophysiol., 26: 218-226, 2009.
[34] E. D. Übeyli, “Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks,” Digit. Signal Prog., 19: 134-143, 2009.
[35] H. Adeli and S. Ghosh-Dastidar, Automated EEG-based diagnosis of neurological disorders-inventing the future of neurology, Florida: CRC press, 2010.
[36] G. R. Minasyan, J. B. Chatten and R. N. Harner, “Patient-specific early seizure detection from scalp EEG,” J. Clin. Neurophysiol., 27: 163-178, 2010.
[37] R. Meier, H. Dittrich, A. Schulze-Bonhage and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” J. Clin. Neurophysiol., 25: 119-131, 2008.
[38] J. Gotman, “Automatic recognition of epileptic seizures in the EEG,” Electroencephalogr. Clin. Neurophysiol.54: 530-540, 1982.
[39] J. Gotman, “Automatic seizure detection: Improvements and evaluation,” Electroencephalogr. Clin. Neurophysiol.,76: 317-324, 1990.
[40] U. R. Acharya, S. V. Sree and J. S. Suri, “Automatic detection of epileptic EEG signals using higher order cumulant features,” Int. J. Neural. Syst., 21: 403-414, 2011.
[41] K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, “Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 3824-3827, 2008.
[42] K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, “Application of higher order spectra to identify epileptic EEG,” J. Med. Syst., 35:1563-1571, 2011.
[43] X. Du, S. Dua, U. R. Acharya and K. C. Chua, “Classification of epilepsy using high-order spectra features and principle component analysis,” J. Med. Syst.,36: 1731-1743, 2012.
[44] A. Aarabi, F. Wallois and R. Grebe, “Automated neonatal seizure detection: A multistage classification system through feature selection based on relevance and redundancy analysis,” Clin. Neurophysiol., 117: 328-340, 2006.
[45] L. Orosco, A. Garcés Correa and E. Laciar Leber, “Epileptic seizures detection based on empirical mode decomposition of EEG signals,” in: Prof. Mintaze Kerem Günel (Ed.), Management of Epilepsy-Research, Results and Treatment, 3-20, Austria: IN-TECH Publishing, 2011.
[46] A. J. Gabor, “Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies,” Electroencephalogr. Clin. Neurophysiol.,107: 27-32, 1998.
[47] J. L. Semmlow, Biosignal and biomedical image processing, New York: Marcel Dekker, 2004.
[48] A. Subasi, “Epileptic seizure detection using dynamic wavelet network,” Expert Syst. Appl., 29: 343-355, 2005.
[49] A. Subasi, “Automatic detection of epileptic seizure using dynamic fuzzy neural networks,” Expert Syst. Appl., 31: 320-328, 2006.
[50] Y. U. Khan, O. Farooq and P. Sharma, “Automatic detection of seizure onset in pediatric EEG,” Int. J. Emb. Syst.Appl., 2: 81-89, 2012.
[51] M. E. Saab and J. Gotman, “A system to detect the onset of epileptic seizures in scalp EEG,” Clin. Neurophysiol.,116: 427-442, 2005.
[52] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu,H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A, 454: 903-995, 1998.
[53] P. Grassberger and T. Schrieber, “Nonlinear time sequence analysis,”Int. J. Bifurcation Chaos, 1: 512-547, 1991.
[54] S. M. Pincus, “Approximate entropy (ApEn) as a complexity measure,”Int. J. Bifurcation Chaos, 5: 110-117, 1991.
[55] H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, “A Wavelet-Chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Trans. Biomed. Eng., 54: 205-211, 2007.
[56] N. Moghim, D.W. Corne, Predicting epileptic seizures in advance, PLoS One 9(2014).
[57] M. Bandarabadi, C. Teixeira, B. Direito, A. Dourado, Epileptic seizureprediction based on a bivariate spectral power methodology, in: AnnualInternational Conference of the IEEE Engineering in Medicine and BiologySociety, IEEE, San Diego, CA, 2012, pp. 5943–5946.
[58] H. Peng, L. Fulmi, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans.Pattern Anal. Mach. Intell. 27 (2005) 1226–1238.
[59] B. Direito, F. Ventura, C. Teixeira, A. Dourado, Optimized feature subsets forepileptic seizure prediction studies, Engineering in Medicine and BiologySociety, EMBC, 2011 Annual International Conference of the IEEE (2011)1636–1639.
[60] P. Ataee, A. Yazdani, S.K. Setarehdan, H.A. Noubari, Genetic algorithm for selection of best feature and window length for a discriminate pre-seizure and normal state classification, in: 5th International Symposium on Imageand Signal Processing and Analysis, Istabul Turkey, 2007, pp. 107–112.
[61] C.A. Teixeira, B. Direito, H. Feldwisch-Drentrup, M. Valderrama, R.P. Costa, C.Alvarado-Rojas, S. Nikolopoulos, M. Le Van Quyen, J. Timmer, B. Schelter, A.Dourado, EPILAB: a software package for studies on the prediction of epileptic seizures, J. Neurosci. Methods 200 (2011) 257–271.
[62] N.V. Chawla, N. Japkowicz, A. Kotcz, Editorial: special issue on learning fromimbalanced data sets, SIGKDD Explor. Newslett. 6 (2004) 1–6.
[63] R. Costa, P. Oliveira, G. Rodrigues, B. Leitão, A. Dourado, Epileptic seizureclassification using neural networks with 14 features, in: I. Lovrek, R. Howlett,L. Jain (Eds.), Knowledge-Based Intelligent Information and EngineeringSystems, Springer, Berlin Heidelberg, 2008, pp. 281–288.
[64] P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky, Classification of patterns ofEEG synchronization for seizure prediction, Clin. Neurophysiol. 120 (2009).
[65] L. Chisci, A. Mavino, G. Perferi, M. Sciandrone, C. Anile, G. Colicchio, F.Fuggetta, Real-time epileptic seizure prediction using AR models and supportvector machines, IEEE Trans. Biomed. Eng. 57 (2010) 1124–1132.
[66] C. Teixeira, B. Direito, M. Bandarabadi, A. Dourado, Output regularization ofSVM seizure predictors: Kalman Filter versus the Firing Power method, in:Annual International Conference of the IEEE Engineering in Medicine andBiology Society, IEEE, San Diego, CA, 2012.
[67] F. Mormann, R.G. Andrzejak, C.E. Elger, K. Lehnertz, Seizure prediction: the long and winding road, Brain 130 (2007) 314–333.
[68] F. Mormann, T. Kreuz, C. Rieke, R.G. Andrzejak, A. Kraskov, P. David, C.E. Elger,K. Lehnertz, On the predictability of epileptic seizures, Clin. Neurophysiol. 116(2005) 569–587.
[69] F. Mormann, R. Andrzejak, T. Kreuz, P. David, C.E. Elger, K. Lehnertz,Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients, Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 67 (2003).
[70] F. Mormann, T. Kreuz, R.G. Andrzejak, P. David, K. Lehnertz, C.E. Elger, Epileptic seizures are preceded by a decrease in synchronization, EpilepsyRes. 53 (2003) 173–185.
[71] J.R. Williamson, D.W. Bliss, D.W. Browne, J.T. Narayanan, Seizure prediction using EEG spatiotemporal correlation structure, Epilepsy Behav. 25 (2012)230–238.
[72] S. Sanei and J. Chambers, EEG signal processing, Chichester: Wiley, 2007.
[73] E.B. Assi, M. Sawan, D.K. Nguyen, S. Rihana, A hybrid mRMR-genetic basedselection method for the prediction of epileptic seizures, Biomedical Circuitsand Systems Conference (BioCAS), IEEE (2015) 1–4.
[74] J.J. Niederhauser, R. Esteller, J. Echauz, G. Vachtsevanos, B. Litt, Detection ofseizure precursors from depth-EEG using a sign periodogram transform, IEEETrans. Biomed. Eng. 50 (2003) 449–458.
[75] M. Le Van Quyen, J. Martinerie, M. Baulac, F. Varela, Anticipating epilepticseizures in real time by a non-linear analysis of similarity between EEG recordings, Neuroreport 10 (1999) 2149–2155.
[76] J.J. Howbert, E.E. Patterson, S.M. Stead, B. Brinkmann, V. Vasoli, D. Crepeau,C.H. Vite, B. Sturges, V. Ruedebusch, J. Mavoori, K. Leyde, W.D. Sheffield, B.Litt, G.A. Worrell, Forecasting seizures in dogs with naturally occurringepilepsy, PLoS One 9 (2014) e81920.
[77] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, A.Schulze-Bonhage, J. Timmer, Testing statistical significance of multivariatetime series analysis techniques for epileptic seizure prediction, Chaos 16(2006) 1054–1500, Print.
[78] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, J. Timmer, A.Schulze-Bonhage, Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposedremedies, Epilepsia 47 (2006) 2058–2070.
[79] M.T. Salam, M. Mirzaei, M. Ly, D.K. Nguyen, M. Sawan, An implantable closed loop asynchronous drug delivery system for the treatment of refractory epilepsy, IEEE Trans. Neural Syst. Rehabil. Eng. 20 (2012).
[80] W. Theodore and R. S. Fisher, “Brain stimulation for epilepsy,” Lancet Neurol., 3: 111-118, 2004.
[81] K. Schindler, C. E. Elger and K. Lehnertz, “Changes of EEG synchronization during low-frequency electric stimulation of the seizure onset zone,” Epilepsy Res., 77: 108-119, 2007.
[82] I. Osorio and M. Frei, “Real-time detection, quantification, warning, and control of epileptic seizures: The foundations for a scientific epileptology,” Epilepsy Behav., 16: 391-396, 2009.
[83] L. D. Iasemidis, “Epileptic seizure prediction and control,” IEEE Trans. Biomed. Eng., 50: 549-558, 2003.

Sachin Goel, Rajeev Agarwal, Parag Jain, "Analysis of Techniques and Methods for Automated EEG signal for Epilepsy Diagnosis: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.429-439, 2018.

Deep learning technique is an emerging field of machine learning. In recent years, it has been successfully used in different fields, such as image classification, natural language processing, computer vision, speech reorganization, etc. When compared to the machine learning, deep learning has a high learning ability to extract features of large datasets. Deep learning came into existence in 1971 when Ivakhnenka used group method of data handling algorithm (GMDH) to train 8-layered neural network [1]. This paper focuses on the artificial neural network, learning techniques and optimization methods of deep learning like stochastic gradient descent, batch gradient descent, mini-batch gradient descent and ADAM.

Artificial Neural Network, Deep Learning CNN, RNN, Optimization Methods, Gradient Descent, ADAM, Framework,mageClassification.

[1] A.G. Ivakhnenko, “Polynomial theory of complex systems” IEEE Transaction on System, Man and Cybernetic vol. 1,no 4, pp. 364-378, 1971.
[2] Xuedam Du,Yinghao Cai, Wang, and Leijie Zhang “ Overview of Deep Learning” 31st Youth Academic Annual Conference of Chinese Association of Automation Wuhan China November 11-13-2016.
[3] Siddhartha Sankar Nath, Janynyaseni Kar, Girish Mishra, Sayan Chakraborty , Nilanjan Dey “ A Survey of Image Classification Methods and Techniques ” ICCCICCT 2014.
[4] A. Krizhevsky, I. Sutskever, and G. E. Hinton. “ImageNet Classification with Deep Convolutional Neural Networks”. Neural Information Processing Systems, Nevada, 2012
[5] Henrik Petersson, David Gustafsson and David Bergstroom “ Hyperspectral Image Analysis using Deep Learning- a Review” IEEE 2016.
[6] Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez Ramos and Pascual Campoy “A Review of Deep Learning Methods and Applications for unmanned Aerial Vehicles ” Hindawi Journal of Sensors 2017.
[7] H. Kamitomo and C. Lu, “3-d face recognition method based on optimum 3-d image measurement technology,” Artificial Life and Robotics, vol. 16, no. 4, pp. 551–554, 2012.
[8] Walaa Hussein Ibrrahim, Ahmed AbdelRhman Ahmed Osman, Yusra Ibrahim Mohamad” MRI Image Classification Using Neural Network” ICCEEE, 2013.
[9] S.Kim, B.Park, B.S Song, and S.Yang, “ Deep belief network based statistical feature learning for fingerprint liveness detection, ” Pattern Recog. Lett., vol 77, ,pp. 58-65,2016.
[10] Gang Liu, Liang Xiao, Caiquan Xiong “ Image Classification with deep belief network and improved gradient descent” IEEE 2017.
[11] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradientbased learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
[12] Travis Williams, Robert Li “ Advanced Image Classification using Wavelets and Convolution Neural Network” IEEE 2016.
[13] Narek Abroyan , “ Convolutional and Recurrent Neural Network for real time data classification” The Seventh International Conference on Innovative Computing Technology (INTECH 2017).
[14] Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016, on-line version available at http://www.deeplearningbook.org.
[15] Michael A.Nielsen, “Neural Networks and Deep Learning”, Determination Press, 2015.
[16] Marek Dabrowski, Justyna Gromada, Tomasz Michalik Orange Centrum “A Practical study of neural network –based image classification model trained with transfer learning method” FedCSIS 2016.
[17] C. Lu and X. Tang, “Surpassing human-level face verification performance on lfw with gaussianface,” arXiv preprint arXiv:1404.3840, 2014.
[18] L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and Trends in Signal Processing, vol. 7, no. 3-4, pp. 197–387, 2013.
[19] J. A. Hertz,” Introduction to the theory of neural computation.“ Boulder, USA: Westview Press, 1991.
[20] J. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999.
[21] Y. Xiong and R.Zuo, “ Recognization of geochemical anomalies using a deep autoencoder network,”Computer Geosci-UK, vol.86, pp. 75-82, 2016.
[22] Zejian Shi, Minyong Shi, Chunfang Li,” The prediction of character based on recurrent neural network,” IEEE computer society, Wuhan China, 2017
[23] Taro Ishitakl, Ryolchlro Obukata, Tetsuya Oda, Leonard Baroll, “Application of deep recurrent neural network for prediction of user behavoiur in Tor Network,” 31st International Conference on Advanced Information Networking and Application Workshops, 2017.
[24]Marek Daabrowski, J. Gromada, T. Michalik,” P practical study of neural network-based image classification model trained with transfer learning method,” Federated Conference on Computer Science and Information Systems pp. 49-56, 2016.
[25] B. Wang, K. Yager, D.Yu, Minh Hoai,” X- ray Scattering image classification using deep learning,” IEEE Winter Conference on Application of Computer Science,2017
[26] Nur Anis Mohmon and Norsuzila ya acob,” A review on classification of satellite image using artificial neural network (ANN),” IEEE 5th Control and System Graduate Research Colloquium,2014.
[27] R.Jyothi, Y.K. SundaraKrishna, V. Srinivasa Rao,” Paper Currency recognition for color images based on Artificial Neural Network,” International Conference on Electrical , Electronics and Optimization Techniques ( ICEEOT), 2016.
[28]M.Abadi, A. Agarwal, p. Barham, E. Brevdo, Z.f. Chen, C. C itro,et at.,” Tensorflow : Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv: 1603.04467,2016.
[29]R. Collobert, S.Bengio, and J.Mariethoz, “ Torch: a modular machine learning software library,” Idiap, No.EPFL-REPORT-82802, 2002.
[30]R. AI-Rfou, G.Alain, A.Almahairi el at.,” Theano ; a python framework for fast computation of mathematics expression,” arXiv preprint arXiv : 1605.02688,2016.
[31]http://www.wpclipart.com/medical/anatomy/cells/neuron/neuron.png.html

Amita Khatana, V.K Narang, Vikas Thada , "A REVIEW OF OPTIMIZATION METHODS IN DEEP LEARNING," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.440-447, 2018.

Speech of human beings is the reflection of the state of mind. Proper evaluation of these speech signals into stress types is necessary in order to ensure that the person is in a healthy state of mind. In this work we propose a RBF classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The RBF algorithm assists the system to learn the speech patterns in real time and self-train itself in order to improve the classification accuracy of the overall system. The proposed system is suitable for real time speech and is language and word independent. The human behaviour considers six basic emotions which are happiness, sadness, anger, fear, surprise & disgust. It becomes important to detect emotional state of a person which will be induced by workload, background noise, physical environmental factors (e.g. G-force) & fatigue. Broadly, stress identification becomes a scientific challenge to analyze a human being interaction with environment

RBF, MFCC, Stress Classification, Feature Selection.

[1] Schuller, Bjorn, et al., “Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first Challenge”, Speech Communication 53.9, pp. 1062-1087, 2011.
[2] Anagnostopoulos, Christos-Nikolaos, Theodoros Iliou, and Ioannis Giannoukos, “Features and Classifiers for Emotion Recognition from Speech: A survey from 2000 to 2011”, Artificial Intelligence Review 43.2, pp.155-177, 2015.
[3] Dipti D. Joshi, M. B. Zalte, “Speech Emotion Recognition: A Review”, Journal of Electronics and Communication Engineering (IOSR-JECE) 4.4, pp.34-37, 2013.
[4] Ververidis, Dimitrios, and Constantine Kotropoulos, “Emotional Speech Recognition: Resources, Features, and Methods”, Speech Communication 48.9, pp.1162-1181, 2006.
[5] El Ayadi, Moataz, Mohamed S. Kamel, and Fakhri Karray, “Survey on Speech Emotion Recognition”, Features, classification schemes, and databases, Pattern Recognition 44.3 pp. 572-587,2011.
[6] Scherer, Klaus R., “Vocal Communication of Emotion: A review of research paradigms”, Speech communication 40.1, pp.227-256, 2003.
[7] Vogt, Thurid, Elisabeth Andre, and Johannes Wagner, “Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realization, Affect and emotion in human-computer interaction”, Springer Berlin Heidelberg, pp. 75-91, 2008.
[8] Burkhardt, Felix, et al., “A Database of German Emotional Speech”, INTER-SPEECH, Lisbon, Portugal, vol. 5, pp.1-4, 2005.
[9] Kwon, Oh-Wook, et al, “Emotion Recognition by Speech Signals, INTER-SPEECH, pp.1-4, 2003.
[10] Campbell, N. “Recording and Storing of Speech Data”. In: Proceedings LREC, pp. 12-25, 2002.
[11] Cowie, R., Douglas-Cowie, E., Savvidou, S., McMahon, E., Sawey, M., Schroder, M. Feeltrace, “An Instrument for Recording Perceived Emotion in Real Time”, In: Proceedings of the ISCA Workshop on Speech and Emotion, pp.19-24, 2000.
[12] Devillers, L., Cowie, R., Martin, J.-C., Douglas-Cowie, E., Abrilian, S., McRorie, M.: “Real life emotions in French and English TV video clips: an integrated annotation protocol combining continuous and discrete approaches”, 5th International Conference on Language Resources and Evaluation LREC, Genoa, Italy.2006.
[13] Douglas-Cowie, E., Campbell, N., Cowie, R.P. “Emotional speech: Towards a new generation of databases”, Speech Communication 40(1–2), pp.33-60, 2003.
[14] Douglas-Cowie, E., et al.: “The description of naturally occurring emotional speech”. In: Proceedings of 15th International Congress of Phonetic Sciences, Barcelona, 2003.
[15] http://audacity.sourceforge.net/download.
[16] Broomhead, D. S. Lowe, David “Radial basis functions, multi-variable functional interpolation and adaptive networks”, Technical Report- RSRE 4148, 1988.
[17] Broomhead, D. S. Lowe, David "Multivariable functional interpolation and adaptive networks", Complex Systems 2, pp. 321–355, 1988.
[18] Schwenker, Friedhelm; Kestler, Hans A.; Palm, Gunther, "Three learning phases for radial-basis-function networks", Neural Networks 14, pp. 439–458, 2001.
[19] Park, J., I. W. Sandberg, "Universal Approximation Using Radial-Basis-Function Networks", Neural Computation. 3 (2): pp. 246–257, Retrieved 26 March 2013.
[20] BOGERT, B. P.; HEALY, M. J. R.; TURKEY, J. W.: “The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo Autocovariance, Cross-Cepstrum and Saphe Cracking”, Proceedings of the Symposium on Time Series Analysis, (M. Rosenblatt, Ed) Chapter 15, New York: Wiley, pp.209-243, 1963.

N.P. Dhole, S.N. Kale, "An Application Using Radial Basis Function Classification in Stress Speech Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.448-451, 2018.

Home security has becoming an important issue nowadays. Home security is becoming necessary as the possibilities of thief are increasing day to day. Safety from theft are the most important issue of home security system for people. Smart home security system gives the signals in terms of calling ,SMS, Alarm. However, the GSM based smart home security systems provides intensify security as Whenever there is a motion in front of sensor or if thief try to open the door it directly call the owner or send the SMS In this there are two method for smart home security system. The first system uses calling And SMS system . Whenever there is a motion in front of sensor or if thief try to open the door it directly call the owner or send the SMS. The second method .The second system uses the camera. We can see through camera who is trying to enter the in house.

GSM(Global System For Mobile Communication), Arduino Controller, SMS(Short Message Service)

[1] The-History-of-Home-Security 4th July 2010

[2] V. Karri and J. S. Daniel Lim, Method and Device to Communicate via SMS After a Security Intrusion, 1st International Conference on Sensing Technology, Palmerstone North, New Zealand, (2005) November 21-23.

[3] Y. Zhao and Z. Ye, Low cost GSM/GPRS BASED wireless home security system, IEEE Trans. Consumer Electron, vol. 56, no. 4, (2007) January, pp. 546-567.

[4] Z. Bing, G. Yunhung, L. Bo, Z. Guangwei and T. Tian, Home Video Security Surveillance, Info-Tech and Infonet, 2001,Proceedings,ICII 2001-Beijing. 2001 International Conference, vol. 3, pp. 202-208

[5] M. Meyer, M. Hotter and T. Ohmacht, A new system forVideo-based Detection of moving objects and its integration into digital networks, Security Technology 1996, 30th Annual 1996 International Carnahan Conference, (1996), pp. 105-110.

[6] Mae , Y,; Sasao , N .; INNoue ,K. ; Arai,T.; Person Detection by Mobile Manipulator for Monitoring,SICE 2003 Annual Conference, pages-2801-2806.

[7] “Analysis and Performance of a Low Cost SMS Based Home Security System”, Sheikh Izzal Azid, SushilKumar, International Journal of Smart Home, vol. 5, no. 3, (2011) July.

[8] Prakash Kumar, Pradeep Kumar, “Arduino Based wireless intrusion detection using IR sensor and GSM”, International Journal of Computer Science and Mobile Computing, Vol 2,Issue 5, May, 2013

[9] R. Sharma, K. Kumar, and S. Viq, “DTMF Based Remote Control System,” IEEE International Conference ICIT 2006, pp.2380-2383, December 2006.

[10] Chun-Liang HSU, Sheng-Yuan Yang and Wei-Bin Wu, 2009,“Constructing Intelligent Home- Security System Design With Combining Phone-Net And Bluetooth Mechanism”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, St. John’s University, Taiwan.

Aarti Supe, Kundan P Tajne, Akshay Bakade, Nehal S Mali, Reema Kubal, "Design and Implementation of Smart Home Security System based on GSM Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.452-456, 2018.

The Internet of Things (IoT) is changing human lives by interfacing general inquiries together. A Street light is a wellspring of light on the edge of a street which turns on around night time for the solace of people. A critical preferred standpoint of street lighting joins: avoidance of setbacks and addition in security. Now– a-days Street light have transformed into a basic edge including road wellbeing. A lot of energy is devoured by street lights. So it is essential to spare the power as much as we can. The cost of energy continues growing as wastage of vitality increases. It has ended up being particularly basic for sparing force. Street light checking control is a computerized system proposed to enhance the effectiveness by means of consequently controlling the exchanging of street light. This errand depicts another new answer for street light control system. It comprises of remote innovation.

internet of things, smart city, smart parking system, smart billing,GSM

[1]. B. Abinaya, S. Gurupriya, M. Pooja, “IOT BASED SMART AND ADAPTIVE LIGHTING IN STREET LIGHTS”, IEEE, 2017.
[2]. Parkash, Prabu V, Dandu Rajendra “Internet of Things Based Intelligent Street Lighting System for Smart City” DOI:10.15680/IJIRSET.2016.0505181
[3]. Noriaki Yoshiura “Smart street light system based on IoT” Proceedings of International Conference on Technology and Social Science 2017 KEYNOTE
[4]. J.Arthi, W.Lydiapreethi, B. Gunasundari “IOT BASED SMART LED STREET LIGHTING SYSTEM” © 2017 IJRTI | Volume 2, Issue 4 | ISSN: 2456-3315 IJRTI1704018 International Journal for Research Trends and Innovation (www.ijrti.org)
[5]. Meenu Baby & Aneeshia Johny “INTERNET OF THINGS BASED STREET LIGHTING FOR SMART CITY” International Journal of Latest Trends in Engineering and Technology Special Issue SACAIM 2017, pp. 559-562 e-ISSN:2278-621X.
[6]. Vaishali Gupta, Krutika Thakur, Santosh Surnar, Prof. Ritesh Thakur “IOT Based Smart Street Lights” (IJR) e-ISSN: 2348-6848, p- ISSN: 2348-795X Volume 2, Issue 10, October 2015 Available at http://internationaljournalofresearch.org
[7]. Monika H R, Thanushree B K “INTERNET OF THINGS BASED INTELLIGENT STREET LIGHTING SYSTEM FOR SMART CITY” ©2017 IJEBMS www.ijejournal.org 978-93-84698-33-1
[8]. Anita Gehlot, Rajesh Singh, Raj Gaurav Mishra, Adesh Kumar and Sushabhan Choudhury “IoT and Zigbee based Street Light Monitoring System with LabVIEW” International Journal of Sensor and Its Applications for Control Systems Vol.4, No.2 (2016), pp.1-8 http://dx.doi.org/10.14257/ijsacs.2016.4.2.01 ISSN: 2287-8467 IJSACS
[9]. Snehal Bhosale ,Komal Gaware ,Pradnya Phalke ,Dipali Wadekar, Pallavi Ahire “IOT BASED DYNAMIC CONTROL OF STREET LIGHTS FOR SMART CITY” Volume: 04 Issue: 05 | May -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal.
[10]. D. Prabagaran, L.Prakasam “SMART STREET LIGHT SYSTEM BASED ON IOT” (IRJAET) E - ISSN: 2454-4752 P - ISSN: 2454-4744 VOL 3 ISSUE 3 (2017) PAGES 2175 – 2179.
[11]. SayaliArkade, AkshadaMohite, Shraddha Joshi, RutujaSonawane, VikasPatil, “IoT Based Street Lights For Smart City”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), www.ijraset.com, IC Value: 13.98, ISSN: 2321-9653, Volume 4 Issue XII, December 2016.
[12]. J.Arthi, W.Lydiapreethi, B. Gunasundari, “IOT BASED SMART LED STREET LIGHTING SYSTEM”, International Journal for Research Trends and Innovation (IJRTI1704018) (www.ijrti.org), Volume 2, Issue 4 | ISSN: 2456-3315, 2017.
[13]. ZeeshanKaleem, Ishtiaq Ahmad, Chankil Lee, “Smart and Energy Efficient LED Street Light Control System using ZigBee Network”, 12th International Conference on Frontiers of Information Technology, IEEE, 2014.
[14]. ChetnaBadgaiyan, PalakSehgal, “Smart Street Lighting System”, International Journal of Science and Research (IJSR), ISSN (Online): 2319-7064, Volume 4 Issue 7, www.ijsr.net, July 2015.
[15]. Shreesh Mishra, Shivakant Gupta, Santosh Singh, TripureshTiwari, Anand Mohan, “ARDUINO BASED LED STREET LIGHT AUTO INTENSITY CONTROL SYSTEM”, International Journal of Advanced Research in Engineering Technology & Sciences, ISSN: 2394-2819, Volume 3, Issue-4, April-2016.
[16]. MUSTAFA SAAD, ABDALHALIM FARIJ, AHAMED SALAH and ABDALROOF ABDALJALIL, “Automatic Street Light Control System Using Microcontroller”, Mathematical Methods and Optimization Techniques in Engineering, ISBN: 978-960-474-339-1.
[17]. Parkash, Prabu V, DanduRajendra, “Internet of Things Based Intelligent StreetmLighting System for Smart City”, International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization), Vol. 5, Issue 5, May 2016.
[18]. Archana. G, Aishwarya N, Anitha J “Intelligent Street Light System” International Journal of Recent Advances in Engineering & Technology, Vol-3, Issue-4, 2015.
[19]. AkshayBalachandran, Murali Siva, V. Parthasarathi, Surya and Shriram K. Vasudevan “An Innovation in the Field of Street Lighting System with Cost and Energy Efficiency”Indian Journal of Science and Technology,Vol-8, August 2015.

T.T. Magar, A.R. Babar ,S. Ghatshile, P. Jagtap, Deepak Uplaunkar, "Automatic Switching of Street Light by Considering Intensity of Sunlight and Fault Detection.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.457-460, 2018.