Volume-7 , Issue-12 , Dec - 2019 Go Back

Paralysis is the loss of voluntary muscle control. Many people cannot move a single part of their body. Even though the paralyzed people are cognitively aware of their surroundings, they have no means of communication. The paralyzed people have lost their ability to talk, type, etc. These victims affected by Locked-In Syndrome have their thoughts and ideas trapped inside of them. Usually people with paralysis have total control over their eye movement. Therefore this project aims to build a real time interactive eye blink system that allows paralyzed people to easily express themselves. Some people who are affected with Amyotrophic Lateral Sclerosis (ALS) and Tetraplegia can also express their emotions (Eg: smile) and hence these expressions can be used as commands for performing some helpful tasks. We aim to show this by implementing sentiment-based music system to play music by retrieving their current mood via video streams.

Formatting real time interactive eye blink system, Locked-In Syndrome, Amyotrophic Lateral Sclerosis (ALS), Tetraplegia, sentiment-based music system and video streams

[1] Christopher and Dana Reeve Foundation: christopherreeve-stats about paralysis.
[2] MedicineNet :stats for locked in syndrome
[3] SpinalCord.com: spinal cord injury statistics
[4] IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions 2017 on “An Intelligent Music Player based on Emotion Recognition”.
[5] 2012 Fourth International Conference on “Digital Home”.
[6] 2nd IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions 2017 on “An Intelligent Music Player based on Emotion Recognition”.
[7] IEEE International Conference on Computer Vision and Information Security (CVIS) “A Novel Method for Eye Tracking and Blink Detection in video frames”.
[8] Research gate Conference Paper for “Automatic Eye Blink Detection Using Consumer Web Cameras”.
[9] CS 231M Project Paper Spring Quarter at Stanford University for “Real Time Blink Detection For Burst Capture”.
[10] 21st Computer Vision Winter Workshop, Luka Cehovin, RokMandeljc, VitomirStruc on “Real-Time Eye Blink Detection using Facial Landmarks”.
[11] 9th International Conference on Computational Intelligence and Communication Networks on “Human computer interaction by eye blinking on real time”.
[12] International Conference on Electronics, Communication and Aerospace Technology ICECA 2017 on “face detection and tracking”.
[13] Institute of Biomedical Engineering, National Central University on “Implementation of an Eye-blink-based Communication Aid for People with Severe Disabilities”.
[14] Institute of Biomedical Engineering, National Central University on “Implementation of an Eye-blink-based Communication Aid for People with Severe Disabilities”.
[15] International Conference on “Identifying Accuracy of Social Tags by Using Clustering Representations of Song Lyrics”.

Sameer G Mathad, Pruthvi L R, Sanjana C S, B Akash, Deepthi V S, "Detection of Facial Micro Expressions and Textual-Tracking for Paralyzed Using Computer Vision," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.62-66, 2019.

The goal of segmentation is to alter amendment the illustration of a image into one thing that`s additional meaningful and easier to research. During this segmentation methodology, the particular portion of a image is highlighted keep with the matter printed. During this paper, we`ve got an inclination to examine the performance of various algorithms for various footage. Medical image method wishes continuous enhancements in terms of techniques and applications to help improve quality of services in health care business. Here during this paper totally different approaches of medical image segmentation are classified at the side of their sub fields and sub strategies. Recent techniques planned in every class also will be mentioned followed by a comparison of those strategies.

Medical image segmentation, Thresholding, Region growing, Classifiers, Clustering, Compression

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Shaik Salma Begum, D. Rajya Lakshmi, "A Review of Current Methods in Medical Image Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.67-73, 2019.

Big Data refers to the huge amount of data in terabytes and petabytes. These kinds of data is collected from different heterogeneous sources like defence, government data, health industry, banking sector, social media, sensor data, public data, transaction data, etc. And these data is yet growing exponentially with time. Big Data can be structured, semi-structured and unstructured by nature and handling such kind of data through traditional data management system has become very complex. Also there are a number of applications available in today’s era which requires high speed data transmission capacity for the storing and computing large amount of data. This requirement can be fulfilled using Big Data as a Service. To handle the large amount of unstructured data the organizations use Big data as a service to free up the organizational resources by taking the advantages of predictive analytics skills of an outside service provider including storage and computing services, rather than hiring in-house staff. In this paper we have given the overview of Big Data as a service, its advantages, application areas and challenges faced by industry.

Component Big Data, Big Data as a service, cloud computing

[1] ISO/IEC JTC 1. Information technology Big Data, Preliminary Report 2014. ISO/IEC 2015.
[2] J. Srinivas, K. Reddy, and A. Qyser, “Cloud Computing Basics,” Build. Infrastruct. Cloud Secur., vol. 1, no. September 2011, pp. 3–22, 2014.
[3] Eli Colins, “Bringing Big Data System to the Cloud”, IEEE Cloud Computing, 2014.
[4] ISO/IEC 17789 , “ Information Technology- Cloud Computing-Reference Architecture” ISO/IEC 2014.10.
[5] Mohammad Sajid, Zahid Raja, “Cloud Computing: Issues and Challenges”, International Conference on Cloud, Big Data and Trust, 2013.11.
[6] Z. Zheng, J. Zhu, and M. R. Lyu, "Service-generated big data and big data-as-A-service: An overview," pp. 403-410, 2013.
[7] Yunkon Kim,Yong –Hyun Kim,Ga-Won Lee, Eui-Nam Huh, “Survey of Big Data as a Service Type”, 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), 2015.
[8] “Big data-as-a-service: A market and technology perspective,” EMC Solution Group, Tech. Rep., 2012.
[9]M. Rouse, “Definition of big data Analytics”, http://searchbusinessanalytics.techtarget.com/definition/big-dataanalytics, January 2012.
[10]“Why big data analytics as a service?” http://www.analyticsasaservice.org/why-big-data-analytics-as-aservice/ ,August 2012.
[11] Siham Yousfi, Dalila Chiadmi, “Big Data-as-a-Service Solution For Building Graph Social Networks”, IEEE, 2015.
[12] Xiaokang Wang, Laurence T. Yang, Huazhong Liu and M. Jamal Deen, “A Big Data-as-a-Service Framework: State-of-the-Art and Perspectives”, IEEE TRANSACTIONS ON BIG DATA, VOL. 4, NO. 3, JULY-SEPTEMBER 2018.
[13] Konstantinos Vassakis, Emmanuel Petrakis and Ioannis Kopanakis, “Big Data Analytics: Applications, Prospects and Challenges”, Springer International Publishing, 2018.
[14] Ye, F., Wang, Z., Zhou, F., Wang, Y., Zhou, Y.: Cloud-based big data mining and analyzing services platform integrating R. In: Proceedings of the 2013 International Conference on Advanced Cloud and Big Data (CBD), Nanjing, pp. 147–151 (2013)
[15] http://www.bigdatavalue.eu/
[16] Georgios Skourletopoulos, Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla, Ciprian Dobre, Spyros Panagiotakis and Evangelos Pallis, “Big Data and Cloud Computing: A Survey of the State-of-the-Art and Research Challenges”, Springer International Publishing Switzerland 2017.
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[19] The 2 types of data strategies every company needs. In: Harvard Business Review, 01 May 2017. https://hbr.org/2017/05/whats-your-data-strategy. Accessed 18 Jun 2017
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[22] V. Markl, “Breaking the chains: On declarative data analysis and data independence in the big data era,” Proc. of VLDB Endowment, vol. 7, no. 13, pp. 1730–1733, August 2014.
[23] P. Russom, Big Data Analytics, TDWI best practices report, TDWI Research, 2014, http://www.iso.org/iso/home/news index/ news archive/news.htm?refid=Ref1821.
[24] R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao, “Toward efficient and privacy-preserving computing in big data era,” IEEE Network, vol. 28, no. 4, pp. 46–50, July 2014.
[25] D. Wu, M. J. Greer, D. W. Rosen, and D. Schaefer, “Cloud manufacturing: Strategic vision and state-of-the-art,” Journal of Manufacturing Systems, vol. 32, no. 4, pp. 564–579, 2013.
[26] K. E. Martin, “Ethical issues in the big data industry,” MIS Quarterly Executive, vol. 14, p. 2, 2015.
[27] K. A. Salleh and L. Janczewski, “Adoption of big data solutions: A study on its security determinants using sec-toe framework,” in CONF-IRM 2016 Proceedings, 2016. [Online]. Available: http://aisel.aisnet.org/conrm2016/66
[28] N. Rahman, “Factors affecting big data technology adoption,” http://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1099, 2016.
[30] IDC, “Six patterns of big data and analytics adoption,” 3 2016, http://www.oracle.com/us/technologies/big-data/six-patterns-bigdata-infographic-2956541.pdf

Santosh Kumar Sharma, Ajay Pratap, Harsh Dev, "Challenges against Big Data as a Service: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.74-78, 2019.

In this paper, MR Brain image segmentation technique based on Modified Gaussian Kernelized Fuzzy C- Means (MGKFCM) clustering approach has been presented. Moreover, in FCM the cluster centroids are selected in a random manner, which may affect the process sometime. Hence, In this proposed method, instead of selecting the cluster centres in a random manner, Histogram technique along with K- Means clustering was used. In general, the MR images are suffered by noise, intensity inhomogeneity and Partial Volume Effect (PVE), primarily the noise has been removed by applying median filtering process. The Fuzzy C-Means (FCM) clustering technique has been proposed to deal with the problem of PVE. The intensity inhomogeneity problem has been handled by modifying the Objective function of the standard Fuzzy C- Means by applying a Gaussian radial basis function with the additive bias field. The result analysis has been carried out with the addition of impulsive and Gaussian noise.

Histon formation, K-means clustering, Modified Gaussian Kernelized FCM, Magnetic Resonance (MR) Brain Image, Noise Analysis

[1] Nara M, Portela, George D.C, Cavalcanti, and Tsang Ing Ren, “Semi-Supervised Clustering for MR Brain Image Segmentation”, Expert Systems with Applications, Vol. 41, Issue 4, Part 1, pp. 1492–1497, 2014.
[2] Sriparna Saha , Abhay Kumar Alok, and Asif Ekbal, “Brain Image Segmentation using Semi-Supervised Clustering”, Expert Systems with Applications, Vol. 52, No.1, pp. 50-63, 2016.
[3] Xuan ji, Quan-sen sun, and De-shen Xia, “A Framework with Modified Fast FCM for Brain MR Images Segmentation”, pattern recognition, Vol. 44, pp. 999-1013, 2011.
[4] Thamaraichelvi B and Yamuna G, “A Complexity Reduced FCM Based Segmentation Technique for Brain MRI Image Classification”, Journal of Medical Imaging and Health Informatics, Vol. 5, pp. 202-209, 2015.
[5] Llia Lazli1, 2, lilia.lazli.1@ens.etsmtl.ca, Mounir Boukadoum1, boukadoum.mounir@uqam.ca, “Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy – Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease”, International Journal of Networked and Distributed Computing, Volume 6, Issue 2, April, Pages 63 – 77. 2018.
[6] Nameirakpam Dhanachandra, Khumanthem Manglem, Yambem and Jina Chanu, “Image Segmentation Using K -Means Clustering Algorithm and Subtractive Clustering Algorithm”, Procedia Computer Science, Vol. 54, pp. 764-771. 2015.
[7] Thamaraichelvi B. and Yamuna G, “Gaussian kernel- based FCM segmentation of brain MRI with BPNN classification”, Int. J. Biomedical Engineering and Technology, Vol.20, No.2, 116-131, 2016.
[8] Ahemed, M.N. Yamany, S.M., Mohamed, N., Farag, A.A. and Moriarty, T.A.”Modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data”, IEEE transactions on Medical Imaging, Vol. 21, pp.193–199, 2002.
[9] Kannan, S.R., Sathya, A., Ramathilagam, S. and Devi, R “Novel Segmentation Algorithm in Segmenting Medical Images”, The Journal of Systems and Software, Vol. 83, No. 12, pp. 2487–2495, 2010.
[10] Halder A1, Talukdar NA2, “Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI”, Magnetic Resonance Imaging., Oct 62: 129-151, 2019.
[11] Yanyan He, M. Yousuff Hussaini, Jianwei Ma, Behrang Shafei, Gabriele Steidl, “A New Fuzzy C-Means method with total Variation Regularization for Segmentation of Images with Noisy and Incomplete data”, Pattern Recognition, Vol. 45, No.9, pp.3463–3471. 2012.
[12] D. Sherlin, D. Murugan, “A Case Study on Brain Tumor Segmentation Using Content based Imaging”,Research Paper , Journal (IJSRNSC), Vol.6 , Issue.3 , pp.1-5, Jun-2018.
[13] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, “A Review: Design and Development of Novel Techniques for Clustering and Classification of Data”, , Review Paper , Journal Paper (IJSRCSE), Vol.06 , Special Issue.0.1 , pp.19-22, Jan-2018.
[14] Punam Mahesh Ingale, “The importance of Digital Image Processing and its applications”, Review Paper , Journal Paper (IJSRCSE), Vol.06 , Special Issue.01 , pp.31-32, Jan-2018.
[15] A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan, “SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, Research Paper, Journal (IJSRNSC), Vol.6 , Issue.3 , pp.13-17, Jun-2018.

B. Thamaraichelvi, "Histon based Combined Clustering Approach for Brain Tissue Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.79-86, 2019.

Nowadays, achieving good results for the text classification of the multilingual scripts in arbitrary images in the videos is the most challenging task for the researchers. Most of the people depends on the internet and the digital world that makes difficult task to understand the multilingual script in various domain. Motivated from this, we proposed a text classification model for multilingual-word-scripts in video frames extracted from the videos which contains South Indian Multilingual Scripts namely, English, Tamil, Kannada, Malayalam and Telugu. Six-layer convolution neural network model has been used to classify the text to their respective classes. In this work we have castoff 600 word images from each script and total of 3000 word images that is extracted as the word images from the video frames for our experimentation. Our proposed model is proficient in accomplishing decent classification results when compared to existing conventional methods such as KNN and SVM classifier.

Deep Neural Networks, Text Classification, Multilingual Scripts

[1] L Pang, S Zhu and C W Ngo., “Deep Multimodal Learning for Affective Analysis and Retrieval”, IEEE Transactions, Multimedia, Vol. 17, pp. 2008-2020, 2015.
[2] M M Rathore, A Paul, A Ahmad and S Rho., “Urban planning and Building Smart Cities based on Internet Things using Big Data Analytics”, Computer Networks, pp. 63-80, 2016.
[3] P B Pati and A G Ramakrishana., “OCR in Indian Scripts: A Survey”, Journal of IETE Technical Review, pp. 217-227, 2015.
[4] D Ghosh, T Dube and A P Shivaprasad., “Script Recognition –Review”, IEEE Transactions, pp. 2142-2161, PAMI 2010.
[5] T Young, D Hazarika and S Poria., “Recent Trends in Deep Learning based onNatural language processing”, IEEE Computational Intelligence Magazine, Vol 13, Issue 3, pp 55-75, 2018.
[6] A K Bhunia, A Konwer, A K Bhunia, A Bhowmick, P P Roy and U Pal., “Script Identification in natural scene image and video frames using attention based Convolutional-LSTM network”, Pattern Recognition, Elsevier, Vol 85, pp. 172-184, 2019.
[7] W Li, P Liu, Q Zhang and W Liu., “An Improved Approach for Text Sentiment Classification Based on Deep Neural Network via a Sentiment Attention Mechanism”, Journal of Future Internet, 11940, 2019.
[8] M Z Amin and N Nadeem., “Convolution Neural Network: Text Classification Model for Open Domain Question Answering System”, Computer Science, Information Retrieval, 2019.
[9] M Hughes, I Li, S Kotoulas and T Suzumura., “Medical Text Classification Using Convolution Neural Networks”, Studies in Health Technology and Informatics, Vol 235, pp. 246-250, 2017.
[10] A Hassan and A Mahmood., “Efficient Deep Learning Model for Text Classification based on Recurrent and Convolutional Layers”, 2017 16th IEEE International conference on Machine Learning and Applications (ICMLA), pp. 1108-1113, 2017.
[11] K S Raghunandan, P Shivakumara, G H Kumar, U Pal, and T Lu., “Sharpness and Contrast Features for Word-Wise Video Type Classification”, 2017 4trh IAPR Asian Conference on Pattern Recognition (ACPR) pp. 103-108, 2017.
[12] J Mei, L Dai, B Shi and X Bai., “Scene Text Script Identification with Convolutional Recurrent Neural Networks”, 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 4053-4058, 2016.
[13] S Roy, P Shivakumara, U Pal, T Lu and C L Tan., “New tampered features for Scene and Caption text Classification in Video Frames”,2016 15th International Conference of Frontiers in Handwriting Recognition (ICFHR), pp. 36-41, 2016.
[14] N Sharma, P Shivakumara, U Pal, M Blumenstein and C L tan., “Piece-wise Linearity based Method for Text Frame Classification in Video”, Pattern Recognition, Elsevier, Vol 48, pp. 862-881, 2015.
[15] P P Yeotikar and P R Deshmukh., “Script Identification of Text Words from Multilingual Document”, International Journal of Computer Applications, pp. 22-29, X-PLORE 2013.
[16] D Duong, T Ba Dinh, T Dinh and D Duc., “Sports Video Classification using Bag of Words Model”, Intelligent Information and database Systems, ACIIDS, Springer, Vol. 7198, pp. 316-325,2012.
[17] S Haboubi, S Maddouri and H Amiri., “Word Classification in Bilingual Printed Documents”, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 502-506, 2012.
[18] P Shivakumara, A Dutta, T Q Phan, C L Tan and U Pal., “A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification”, Pattern Recognition, Elsevier, Vol 44, Issue 8, pp. 1671-1683, 2011.
[19] S Chanda, S Pal, K Frankle and U Pal., “Two-stage Approach for word-wise script Identification”, 2009 10th International Conference on Document Analysis and Recognition, pp. 926-930, 2009.
[20] W Zhang, T Yoshida and X Tang., “Text Classification based on multi-word with Support Vector Machine ”, Knowledge Based Systems, Elsevier, Vol 21, Issue 8, pp. 879-886, 2008.
[21] P B Pati and A G Ramakrishana., “Word Level Muli-Script Identification”, Pttern Recognition Letters, Vol 29, Issue 9, pp. 1218-1229, 2008.
[22] S Jaeger, H Ma and D Doermann., “ Identifying Script on Word-Level with Informational Confidence”, Eighth International Conference on Document Analysis and Recognition (ICDAR’05), Vol 1, pp. 416-420, 2005.
[23] A S Banu, P Vasuki, S M M Roomi and A Y Khan., “Sar Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurment”, International Journal of Scientific Research in Network Security and Communictions (IJSRNSC), Vol 6, Issue 3, pp 13-17, 2018.
[24] N S Lele., “Image Classification using Convolution Neural Network”, International Journal of Scientific Research inComputer Science and Engineering (IJSRCSE), Vol 6, Issue 3, pp 22-26, 2018.
[25] M S Hossain, M Al-Hammadi and G Muhammad., “Automatic fruit Classification using Deep Learning for Industrial applications” In IEEE Transactions on Industrial Informatics, 2015, pp.1027-1034.
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Sunil C., Chethan H.K., Raghunandan K.S., G. Hemantha Kumar, "Multilingual-Word-Script Classification in Text Video Frames," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.87-92, 2019.

Cloud Computing is a term used to describe both a platform and type of application. Cloud Computing differs from traditional computing paradigms as it is scalable, can be encapsulated as an abstract entity which provides different level of services to the clients, driven by economies of scale and the services are dynamically configurable. Data stored in third party storage systems like the cloud might not be secure since confidentiality and integrity of data are not guaranteed. Though cloud computing provides cost-effective storage services. Hence, many organizations and users may not be willing to use the cloud services to store their data in the cloud until certain security guarantees are made. In this paper, a solution to the problem of securely storing the client’s data by maintaining the confidentiality and integrity of the data within the cloud is developed. The proposed protocols are developed which ensure that the client’s data is stored only on trusted storage servers, replicated only on trusted storage servers, and guarantee that the data owners and other privileged users of that data access the data securely.

Cloud Computing Trusted Storage and Security

[1] Muhammad Aufeef Chauhan and Muhammad Ali Babar, “Migrating Service-Oriented System to Cloud Computing: An Experience Report”, 2011 IEEE 4th International Conference on Cloud Computing, pp 404-411.
[2] Louridas, P., Up in the Air: Moving Your Applications to the Cloud. Software, IEEE, 2010. 27(4): p. 6-11.
[3] Ali Babar, M., Chauhan, M. A., A Tale of Migration to Cloud Computing for Sharing Experiences and Observations, SECLOUD workshop, Collocated with ICSE 2011, Hawaii, USA.
[4] Rajkumar Buyyaa, Chee Shin Yeoa, , Srikumar Venugopala, James Broberga, and Ivona Brandicc, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility”, Future Generation Computer Systems, Volume 25, Issue 6, June 2009, Pp 599-616.
[5] K. Keahey and T. Freeman, “Science Clouds: Early Experiences in Cloud Computing for Scientific Applications,” in proceedings of Cloud Computing and Its Applications 2008, Chicago, IL. 2008.
[6] Junjie Peng, Xuejun Zhang, and Zhou Lei, Bofeng Zhang, Wu Zhang, Qing Li, “Comparison of Several Cloud Computing Platforms”, Second International Symposium on Information Science and Engineering, IEEE 2009, pp 23-27.
[7] Ommeren, E. V., Duivestein, S., deVadoss, J,Reijnen, C. & Gunvaldson, E. Collaboration in the Cloud. Microsoft and Sogeti, Bariet, Ruinen, theNetherlands, 2009.
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Thade Lakshmidevi, "Secure Protocols for Developed Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.93-98, 2019.

Digital Crime is a wrongdoing which includes the utilization of computerized innovations in commission of offense, coordinated to registering and correspondence advances. The cutting edge methods that are multiplying towards the utilization of web movement brings about making misuse, defenselessness making a reasonable path for exchanging secret information to submit an offense through illicit action. The movement includes like assaulting on Information focus Data System, burglary, youngster sex entertainment assembled pictures, online exchange misrepresentation, web deal extortion and furthermore organization in web vindictive exercises, for example, infection, worm and outsider maltreatment like phishing, email tricks and so on. The all-inclusive methodology of system like web at all dimensions of system needs to recoup from perpetrating illicit action in everywhere throughout the world and to stop the criminal nature by ensuring unlawful movement by upholding distinctive dimension of firewall setting inside its disconnected control for each country so as to screen and anticipate violations did in the internet. System security controls are utilized to avoid the entrance of programmers in systems which incorporates firewall, virtual private systems and encryption calculations. Out of these, the virtual private system assumes an indispensable job in keeping programmers from getting to the systems. Virtual Private Network (VPN) furnishes end clients with an approach to secretly get to data on their system over an open system foundation, for example, the web.

cyber-crime, cyber criminals, cyber-crime hackers, protectedcateogories, cyber stalking

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Marripelli Koteshwar, Bipin Bihari Jaya Singh, "Survey Report on Cyber Crimes and Cyber Criminals Get Protected from Cyber Crimes: Review Paper," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.99-109, 2019.

Electronic commerce is a way of trading in different types of products, gadgets and services. It offers a wide variety of people’s comments and opinions. Sentimental analysis also known as opinion mining is a method of understanding people’s attitudes, sentiments, feelings expressed in those written comments. This is an e-commerce web application tackles with sentence level sentimental analysis which fetches and classify the polarity of reviews as positive, negative and neutral. All those reviews are ranked based on this polarity that helps the user to make a smart choice among good, bad and worst products. This aims to reduce the time and effort of the user in searching for specific product on e-commerce website.

Login, API, Rating System

[1] S.Ullah, T.Alauddin, H. Zaman, “Developing an E-commerce website”, Microelectronics, Computing and Communications (MicroCom),2016 International Conference on 23-25 Jan. 2016
[2] D. Jayvant, P. Sunita “,” Product Review By Sentiment Analysis”, International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 3 Issue 5 may, Page No. 6202-6205 2014
[3] X. Fang , J. Zhan, “Sentiment analysis using product review data”, Journal of Big Data (2015) 2:5 DOI 10.1186/s40537-015-0015-2
[4] A. Kumar,T.M.Sebastian, “Sentiment Analysis on Twitter”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 3, July 2012 ISSN (Online): 1694-0814,2012

P. Rathee, "E-commerce Product Analysis Using Sentimental Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.110-114, 2019.

Phishing is one of the most severe threats to internet security. It utilizes spotted websites to rob users’ passwords and online identities. Generally, phishers use spotted emails or instant messages to attract users to phishing websites. In order to detect phishing attacks in the network, Deep Neural Network (DNN) was introduced. However, the computational complexity of DNN-based phishing attack detection is high because of using irrelevant and redundant features in DNN. So, DNN with Stacked Denoise AutoEncoder (DNN-SDAE) was proposed which reconstructed input features by removing irrelevant and redundant features. Then, the softmax activation function was processed the reconstructed features detect the phishing attack. In this paper, DNN with Ensembling SDAE (DNN-ESDAE) is proposed to reduce the complexity of SDAE and enhance the phishing attack detection accuracy. Initially, Uniform Resource Locator (URL)-based features, Hyper-Text Markup Language (HTML)-based features and domain-based features are extracted by using feature extractor. Then, individual type of features is processed in different SDAE which reconstruct input features. After the ensembling of three SDAE using negative correlation learning, the best selective ensembling is chosen using Shuffled Frog Leaping Optimization Algorithm (SFLOA). Finally, majority voting is employed to combine the results of three SDAE. The experiment is conducted to prove the effectiveness of DNN-ESDAE in terms of accuracy, precision, recall, and f-measure.

Phishing attack detection, Deep Neural Network, Ensembling Stacked Denoise AutoEncoder, Shuffled Frog Leaping Optimization Algorithm, majority voting

[1] K. Parsons, M. Butavicius, P. Delfabbro, M. Lillie, “Predicting susceptibility to social influence in phishing emails”, International Journal of Human-Computer Studies, Vol.128, pp.17-26, 2019.
[2] M. Shukla, S. Sharma, “Analysis of efficient classification algorithm for detection of phishing site”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.5, Issue.3, pp. 136-141, 2017.
[3] T. Chin, K. Xiong, C. Hu, “Phishlimiter: A Phishing Detection and Mitigation Approach Using Software-Defined Networking”, IEEE Access, Vol.6, pp.42516-42531, 2018.
[4] K. Sumathi, V. Sujatha, “Deep learning based-phishing attack detection”, International Journal of Recent Technology and Engineering (IJRTE), Vol.8, Issue.3, pp.8428-8432, 2019.
[5] H.K. Soni, “Machine learning- A new paradigm of AI”, International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.3, pp.31-32, 2019.
[6] K. Sumathi, V. Sujatha, “Deep neural network with stacked denoise auto encoder for phishing detection”, International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE), Vol.3, Issue.2, pp.114-124, 2019.
[7] I.R.A. Hamid, J. Abawajy, “Hybrid feature selection for phishing email detection”, In the Proceedings of 2011 International Conference on Algorithms and Architectures for Parallel Processing Springer, Berlin, pp.266-275, 2011.
[8] G.A. Montazer, S. ArabYarmohammadi, “Detection of phishing attacks in Iranian e-banking using a fuzzy–rough hybrid system”, Applied Soft Computing, Vol.35, pp.482-492, 2015.
[9] G. Sonowal, K.S. Kuppusamy, “PhiDMA–a phishing detection model with multi-filter approach”, Journal of King Saud University-Computer and Information Sciences, Vol.32, pp.99-112, 2017.
[10] S. Smadi, N. Aslam, L. Zhang, “Detection of online phishing email using dynamic evolving neural network based on reinforcement learning”, Decision Support Systems, Vol.107, pp.88-102, 2018.
[11] P. Yang, G. Zhao, P. Zeng, “Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning”, IEEE Access, Vol.7, pp.15196-15209, 2019.
[12] M. Chatterjee, A.S. Namin, “Detecting Phishing Websites through Deep Reinforcement Learning”, In the proceedings of 2019 IEEE Conference on Annual Computer Software and Applications Conference (COMPSAC), Vol.2, pp.227-232, 2019.
[13] F. Toolan, J. Carthy, “Feature selection for spam and phishing detection”, IEEE eCrime Researchers Summit, pp.1-12, 2010.
[14] S. Garera, N. Provos, M. Chew, A.D. Rubin, “A framework for detection and measurement of phishing attacks”, In the Proceedings of the 2007 ACM workshop on Recurring malcode, pp.1-8, 2007.
[15] M. Lichman, UCI machine learning repository, 2013.

K. Sumathi, V. Sujatha, "Ensembling of Stacked Denoise Autoencoder for Phishing Attack Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.115-121, 2019.

The parallelized person cannot establish the communication with the normal person. For this process implement the wearable glove. While the implementation of through wearable glove which helps to communicate with normal persons. The main objectives of this project are to detect the position of fingers and give command to control the home appliances. It consists of a glove in which flex sensor and accelerometer sensor are attached to an electronic conditioning circuit. The glove can have several other applications such as :( 1) The recognition of sign language, (2) To control the electrical parameters. These progressions focus on studying and implement a system for measuring the finger position of one hand. It can be used in rehabilitation intention and here we concentrate on biomedical application which exhibit coordination among parallelized patient and doctor. So this is a simple and moderate system.

Flex sensor, Graphical User Interface, Rehabilitation, Sensorized glove, Wireless communication

[1] International Journal of Engineering Research and General Science Volume 3, Issue 3, Part-2, May-June, 2015 ISSN 2091-2730Sensorized Glove for Rehabilitation Purpose Priti D. Nandnikar, Dr. Manoj S. Nagmode.
[2] Syed Faiz Ahmed, Syed Muhammad Baber Ali and Sh. Saqib Munawwar Qureshi ―Electronic Speaking Glove for Speechless Patients, A Tongue to a Dumb‖ IEEE Conference on Sustainable Utilization and Development in Engineering and Technology,20 21 November 2010.
[3] N. P. Bhatti, A. Baqai, B. S. Chowdhry, M. A. Unar, "Electronic Hand Glove for Speech Impaired and Paralyzed Patients", EIR Magazine, May 2009, pp. 59-63, Karachi, Pakistan
[4] B. B. Edin, L. Ascari, L. Beccai, S. Roccella, J. J. Cabibihan, M. C. Carrozza, "Bio-Inspired Sensorization of a Biomechatronic Robot Hand for the Grasp-and-Lift Task", Brain Research Bulletin, Volume 75, Issue 6, 15 April 2008, pp. 785-795.
[5] M. Wald, "Captioning for Deaf and Hard of Hearing People by Editing Automatic Speech Recognition in Real Time", Proceedings of 10th International Conference on Computers Helping People with Special Needs ICCHP 2006, LNCS 4061, pp. 683-690.
[6] L. K. Simone, E. Elovic, U. Kalambur, D. Kamper, "A Low Cost Method to Measure Finger Flexion in Individuals with Reduced Hand and Finger Range of Motion", 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2004 (IEMBS `04), Volume 2, 2004, pp. 4791-4794.
[7] Jingdong Zhao, Li Jiang, Shicai Shi, Hegao Cai, Hong Liu, G. Hirzinger, "A Five-fingered Underactuated Prosthetic Hand System", Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, June 2006, pp. 1453-1458.
[8] Aparna P, Mohana priya P, Usha Rani T, Jeba Jaculin B, Pradeep Raja B, “Development of an Assistive Aid for Speech Impaired”, International Journal of Scientific and Research Publications, Volume 2, Issue 3, March 2012.
[9] Charlotte Baker Shenk & Dennis Cokely,American Sign Language, A teacher’s resource text on Grammar and Culture, Clerc Books Gallaudet University Press, Washington D.C., 1981.
[10] Jamal Haydar, Bayan Dalal, Shahed Hussainy, Lina El Khansa, Walid Fahs Faculty of Engineering, Islamic University of Lebanon Khaldeh, Lebanon submiteed report on “ASL Fingerspelling Translator Glove”.
[11] Tirthankar Dasgupta, Sambit Shukla, Sandeep Kumar, Synny Diwakar, Anupam Basu submitted report on “A Multilingual Multimedia Indian Sign Language Dictionary Tool”.
[12] Sourangsu Bandopadhyay, “Sign language glove with voice synthesizer” Department of Electronics & Communication Engineering, RCC Institute of Information Technology, West Bengal University of Technology,June-2012.
[13]Sign language recognition using sensor gloves Mehdi,S.A. FAST-Nat. Univ. of Comput. & Emerging Sci., Lahore, Pakistan Khan Y.N.
[14] Development and evaluation of a low-cost sensor glove for Assessment of human finger movements. Journal of Neuroscience Methods, Volume 178, Issue1, 30 March 2009, Pages 138-147 Reinhard Gentner, Joseph Classen.
[15] Hernandez-Rebollar, J. L. Kyriakopoulos, N.Lindeman R.W, “A new instrumented approach for translating American Sign Language into sound and text”,Proceedings of the Sixth IEEE International Conference, 2004
[16] Mehdi S.A., Khan Y. N., “Sign language recognition using sensor gloves”, Proceedings of the 9th International Conference, Volume:5, IEEE Conference Publications, 2002.
[17] Kunal Kadam, Rucha Ganu, Ankita Bhosekar, Prof. S. D. Joshi, “American Sign Language Interpreter”, Proceedings of the IEEE Fourth International Conference on Technology for Education, 2012
[18] Satjakarn Vutinuntakasame, “An Assistive Body Sensor Network Glove for Speech- and Hearing- Impaired Disabilities”, Proceedings of the IEEE Computer Society International Conference on Body Sensor Networks, 2011.
[19] Warsuzarina Mat Jubadi, Siti Faridatul Aisyah Mohd Sahak(2009) Heartbeat Monitoring Alert via SMS, IEEE Symposium on Industrial Electronics and Applications.
[20] Sunil L. Rahane1, Prof. Ramesh S. Pawase.(2015) A Healthcare Monitoring System Using Wireless Sensor Network with GSM; International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4, Issue 7, Dept. of Electronics & Telecommunication, Amrutvahini College of Engineering, Sangamner, Maharashtra, India.

T. Thirumalai, S. Ramkumar, M. Selva kumar, K. ArunGanesh M.E., "Wearable Multi-Sensor Gesture Recognition for Paralysis Patients," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.122-127, 2019.