Message And File Transfering Using Key Exchange Protocol Over Secure Network Communication
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.1-5, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.15
Abstract
Authenticated Key Exchange (AKE) protocol allows to communicated each other using a generate session key for suitable and secure communication. The server side will generate a session key after giving approval by the client profile. Clients can view other clients of same group who is online at the time. During the message or file transfer both encryption and decryption takes place automatically using RSA algorithm with a symmetric key. Finally the administrator can also monitoring what all take place in the client side by using remote sensing capability. File transfer take place between the user and client with more security. For the communication purpose here it is using intranet so that it can be easily find out the IP address of the server and client. The proposed system will be give more important to the file transferring, remote sensing. The other speciality of the proposed system is key freshness that takes place periodically. The proposed system will be more secure for file transferring over the network communication.
Key-Words / Index Term
Authenticated Key Exchange (AKE) protocol, Encryption and Decryption,IP address,Group chats,File Transferring, Cyber Monitorning system
References
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[15].K.H.Huang,Y.F.Chung,H.H.Lee,F.Lai and T.S.Chen, “ A Conference Key Agreement Protocol with Fault-Tolerant Capability”, Computer Standard and Interfaces, Vol.31, pp.401-405, Jan 2009.
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Citation
Reshmi Vijayan, Sreedivya R S, "Message And File Transfering Using Key Exchange Protocol Over Secure Network Communication", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.1-5, 2018.
Information Systems in Higher Educational Institutions
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.6-9, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.69
Abstract
Nowadays higher education has been a prime need for all for a better future. As the higher education is globally expanded to a great extend, anybody can attain this from anywhere. But still, the educational institutions have to play a big role to provide the students all the necessary resources on time for them to make their education a quality one. In such a situation, it has been proved that information systems can do wonders. This paper is intended to show how to help an institution in storing, analyzing, monitoring and improving all the academic and co-academic information and status in an efficient way. The major pillars of an education system are the students, their parents, teachers and the management. They are the ones who can point out the actual needs related with an educational institution. If there is an efficient information system that can assist an institution in identifying and evaluating the needs and corresponding solutions, the management will be able to easily get adapted to the new global changes.
Key-Words / Index Term
Quality, Information System, Cloud Data Storage, Real-time Data Processing, Intelligent Information Analysis, Results-based Performance Monitoring
References
[1] Emanuela BUCI, “Analysis and Impact of Information Systems on SMEs in Albania”, In the proceedings of IAC 2017, Vienna, Prague, p. 215, 2017.
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[3] Ian Peers, “Statistical Analysis for Education and Psychology Researchers”, London, pp. 33-248, 1996.
[4] A. Srinivasan, J. Suresh, “Cloud Computing: A Practical Approach for Learning and Implementation”, Pearson, India, pp. 151-152, 2014.
[5] C. Dan Marinescu, “Cloud Computing: Theory and Practice”, Morgan Kaufmann Publishers, United States, pp. 257-261, 2018.
[6] R. Jayaprakash Reddy, “Business Data Processing and Computer Applications”, A P H Publishing Corporation, India, p. 39, 2004.
[7] Michael Berthold, J. David Hand, “Intelligent Data Analysis: An Introduction”, Springer, New York, p. 14, 1999.
[8] Jody Zall Kusek, C. Ray Rist, “Ten Steps to a Results-Based Monitoring and Evaluation System: A Hnadbook for Development Practitioners”, The World Bank, Washington, 2004.
[9] Helen Burnie, “Maintain Business Resources”, Software Publications, Sydney, pp. 23-27, 2003.
Citation
Neethu Tressa, "Information Systems in Higher Educational Institutions", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.6-9, 2018.
Automatic Occlusion Removal System using Optical Flow Method
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.10-16, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.1016
Abstract
We present an automatic occlusion removal methodology for occluded images. The occlusion here considered are the images which contain elements such as grid or fence, the reflection of objects through glass windows and raindrop. The appearance of any object in the space which blocks the complete view of another object or a scene considers as the occlusion. Because of occlusion, we lost the aesthetic beauty of the desired scene. To obtain a background scene without any discrepancies occlusion removal is essential. Occlusion may happen accidentally, and also there are some situations we cannot avoid occlusion. For example, taking photos or videos in a zoo, fence removal is impossible. If the fence obstruction removes from photos, the results became awesome. The Aim is to improve the accuracy of occlusion removal system using an optical flow method. The sequence of frames considers as input to the system. The system automatically detects occlusion. The decomposition of the background component and occlusion components are done using an optical flow method. Finally estimates desired background scene while removing the annoying occlusion. We show results of experiments in the various occluded situation while taking photos or videos.
Key-Words / Index Term
Occlusion, Optical flow, Detection, Decomposition
References
[1] Criminisi, Antonio, Patrik Perez, and Kentaro Toyama, “Region filling and object removal by exemplar-based image inpainting”, IEEE Transaction on image processing 13.9, pp.1200-1212, 2004
[2] Newson, A., Almansa, A., Fradet, M., Gousseau, Y., Perez, P., et al. “ Video inpainting of complex scenes.”, Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2014
[3] Kao, Shannon, “Light Field Occlusion Removal”, Sanford University, 2005
[4] TianfanXue, Michael Rubinstein, Ce Liu, William T Freeman, “ Computational Approach for Obstruction -Free Photography”, ACM Transactions on Graphics, Vol. 34, No. 4, Article 79, 2015.
[5] Park, Minwooi et al. “Image de-fencing”, Asian Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010
[6] Liuy, Yanxi, et al. “Image de-fencing”, Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on IEEE, 2008
[7] Jonna, Sankaraganesh, et al. “A multimodal approach for image de-fencing and depth inpainting”, Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on. IEEE, 2015
[8] Yamashita, Atsushi, Akiyoshi Matsui, and Toru Kaneko. “Fence Removal from multi-focus images.”, Pattern Recognition (ICPR), 2010 20th International Conference on IEEE, 2010
[9] Garg, Kshitiz, and Shree K. Nayar., “Detection and removal of rain from videos”, Computer Vision and Pattern Recognition, 2004. CVPR 2004, Proceedings of 2004, IEEE Computer Society Conference on.Vol.1. IEEE, 2004
[10] Strecha, Christoph, RikFransens, and Luc Van Gool., “Wide-baseline stereo from multiple views: a probabilistic account.”, Computer Vision and Pattern Recognition, 2004. CVPR 2004, Proceedings of 2004, IEEE Computer Society Conference on.Vol. 1. IEEE, 2004
[11] Luan, Xiao, et al., “Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion.”, Pattern Recognition, 47.2 PP. 495-508. 2004
[12] Ashraf Siddique and Seungkyu Lee., “Video Inpainting for Arbitrary Foreground Object Removal”, IEEE Winter Conference on Applications of Computer Vision, 2018
[13] VanshajSikri., “Proposition and Comprehensive Efficiency Evaluation of a Foreground Detection algorithm based on Optical Flow and Canny Edge Detection for Video Surveillance Systems “, IEEE WiSPNET conference. 2016
[14] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. “High accuracy optical flow estimation based on a theory for warping” European Conference on Computer Vision (ECCV), 2004
[15] OpenCV Open source Computer Vision_optical Flow(https://docs.opencv.org/3.3.1/pages.html)
Citation
M.A. Aneesha, K.J. Helen, "Automatic Occlusion Removal System using Optical Flow Method", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.10-16, 2018.
Wearable Device for Fall Detection Using 3-D Accelerometer
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.17-20, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.1720
Abstract
A fall detection device is needed to provide information to paramedics or family members when an elderly is falling. Helping for elderly falling can avoid fatal injuries or loss of life. In order for the falling device comfortably taken by the elderly, this proposed a wearable device that lightweight, using battery for power supply, and a low-energy consumption. proposed device consists of 3-dimensional accelerometer, a communication device and a microcontroller . The sensor meassures accelerations of body movements. Then, the microcontroller identifies position body and a falling from three-axis accelerations. proposed method, that has success detect 75% in fall forward and 95% in fall backward. The proposed device also has a 100% success in providing information on normal activities, such as: standing or sitting, supine, face down, left and right, while the success rate for the e-health device by cooking hack is 92%.
Key-Words / Index Term
Fall Detection, Wearable Device, 3-D Accelerometer
References
[1] M. Peden, K. McGee, and G. Sharma, “The injury chart book: a Graphical overview of the global burden of injuries”, Geneva: World Health Organization, vol. 5, 2002.
[2] K. E. Thomas, J. A. Stevens, K. Sarmiento, and M. M. Wald, “Fallrelated traumatic brain injury deaths and hospitalizations among older adultsunited states”,Journal of safety research, vol. 39, no. 3, pp. 269272,2008.
[3] Causes elderly people to fall, http : // www .agingcare .com / Articles /Fallsin-elderly-people 133953.htm, Accessed: May 18,2015.
[4] B. J. Lee, S. F. Su, and I. Rudas, “Content-independent image Processing based fall detection”, in System Science and Engineering ICSSE), 2011 International Conference on, pp. 654659, IEEE, 2011.
[5] H. W. Tzeng and M. Y. Chen,” Design of fall detection system with floor pressure and infrared image”, in System Science and Engineering (ICSSE), 2010 International Conference on, pp. 131135, IEEE, 2010.
[6] T. Zhang, J. Wang, L. Xu, and P. Liu, Fall detection by wearable sensor and one-class svm algorithm, in Intelligent Computing in Signal Processing and Pattern Recognition, pp. 858863, Springer, 2006.
[7] P. Salgado, P. Alfonso, Fall body detection algorithm based on Tri- Accelerometer sensors, in IEEE International Symposium on Computational Intelligence and Informatics, 2013.
Citation
Nasiya. PM, "Wearable Device for Fall Detection Using 3-D Accelerometer", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.17-20, 2018.
A Proposal of Chatbot for Malayalam
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.21-25, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.2125
Abstract
A chatbot is a conversational agent which interacts with humans via natural languages. Text as well as speech, is used as the input to these systems. We propose a Malayalam chatbot based on a natural language processing with machine learning techniques library on a language-independent platform. The chatbot is a retrieval based model which can converses in Malayalam. Malayalam is a Dravidian language talked over the Indian state of Kerala. Machine learning as well as NLP (Natural Language Processing) approaches are used to analyze user queries and generate responses. We experiment an AIML (Artificial Intelligence Markup Language) based chatbot and a machine learning based chatbot. Among the two bots, the machine learning chatbot performs better. We developed a domain-specific chatbot. It is a commercial product, so we can apply this to any domain
Key-Words / Index Term
Chatbot, Natural Language Processing, Machine Learning, Artificial Intelligence
References
[1] Shawar, Bayan Abu, and Eric Atwell.”Chatbots: are they really useful?." Ldv forum. Vol. 22. No. 1. 2007.
[2] Ranoliya, Bhavika R., Nidhi Raghuwanshi, and Sanjay Singh. "Chatbot for University Related FAQs.", 2017.
[3] Weizenbaum, Joseph. "ELIZA—a computer program for the study of natural language communication between man and machine." Communications of the ACM 9.1, 36-45, 1966, .
[4] Marietto, Maria das Gracas Bruno, et al.”Artificial intelligence markup language: A brief tutorial.”arXiv preprint arXiv:1307.3091, 2013.
[5] S. Chaitrali, Kulkarni, U. Amruta, Bhavsar, Savita Chaitrali S Pingale. "BANK CHATBOT – An Intelligent Assistant System Using NLP and Machine Learning", International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 05, 2017.
[6] Shah, Rishabh, Siddhant Lahoti, and K. Lavanya. "An intelligent chat-bot using natural language processing." International Journal of Engineering Research 6.5 : 281-286. 2017.
[7] Kalaiyarasi, T., Ranjani Parthasarathi, and T. V. Geetha. "Poongkuzhali-an intelligent tamil chatterbot." SIXTH TAMIL INTERNET 2003 CONFERENCE. Vol. 1. sn, 2003.
[8] Abdul-Kader, Sameera A., and John Woods. "Survey on chatbot design techniques in speech conversation systems." International Journal of Advanced Computer Science and Applications 6.7 : 72-80.2015.
[9] E. Loper, and S. Bird, "NLTK: The natural language toolkit." pp. 63-70, 2002.
[10] S. Bird, "NLTK: the natural language toolkit." pp. 69-72, 2006.
[11] A. S. Lokman, and J. M. Zain, "An architectural design of Virtual Dietitian (ViDi) for diabetic patients." pp. 408-411, 2009.
[12] A. M. Galvao, F. A. Barros, A. M. Neves, and G. L. Ramalho, "Persona aiml: An architecture developing chatterbots with personality." pp. 266-1267, 2004.
[13] Mujeeb, Sana, Muhammad Hafeez Javed, and Tayyaba Arshad. "Aquabot: A Diagnostic Chatbot for Achluophobia and Autism." INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 8.9 : 209-216, 2017.
[14] Bani, Balbir Singh, and Ajay Pratap Singh. "College Enquiry Chatbot Using ALICE."
[15] S. J. du Preez, M. Lall and S. Sinha, "An intelligent web-based voice chat bot," EUROCON 2009, EUROCON `09. IEEE, St. Petersburg, 2009.
[16] Wailthare, Sumit, et al. "Artificial Intelligence Based Chat-Bot." Artificial Intelligence 5.03 (2018).
[17] TIWARI, AMEY, RAHUL TALEKAR, and SM PATIL. "College Information Chat Bot System."
[18] Hatwar, Nikita, Ashwini Patil, and Diksha Gondane. "Ai based chatbot." International Journal of Emerging Trends in Engineering and Basic Sciences 3.2 85-87. 2016.
[19] Sarthak V. Doshi “Artificial Intelligence Chabot in Android System using Open Source” Program International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 6, Issue 4, April 2017.
[20] Bayu Setiaji “ Chatbot Using A Knowledge in Database” International Conference on Intelligent System, Modling and Simulation 2016.
Citation
S. Sandhini, R. Binu, R.R Rajeev, M.M Reshma, "A Proposal of Chatbot for Malayalam", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.21-25, 2018.
Implementation and Analysis of k-Barrier Coverage in Wireless Sensor Networks
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.26-31, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.2631
Abstract
Coverage area regarding a particular sensor means the area that can be monitored using a sensor. Barrier coverage enhances the intrusion detection that can be done with a directional sensor. Omni-directional sensors senses the data surrounding it in all directions with a predefined boundary, whereas directional sensors are used to obtain data from a particular direction of area which will be the region of interest. Barrier coverage is a critical issue in most of the border security applications. This paper deals with the protocol used for the implementation of k-barrier coverage. k is a variable which is used to denote the number of times an intruders path is detected across the protected belt area. The region of interest will be highly protected when the value of k is high.
Key-Words / Index Term
Barrier coverage, Directional sensors, Mobile sensors, Path coverage, Bounded belt area
References
[1] Zhibo Wan, Jilong Liao, Qing Cao, k-Barrier Coverage in Hybrid Directional Sensor Networks, 3rd ed. IEEE TRANSACTIONS ON MOBILE COMPUTING
[2] Yahua Zhan Xingming Sun, Baowei Wang K-Barrier Coverage Based on Integer Linear Programming 2012 IEEE International Conference on Parallel and Distributed Systems
[3] Shibo H, IEEE, Xiaowen Gong Curve-Based Deployment- Wireless Sensor Networks Member, IEEE, and Elisa Bertino, Fellow, IEEE
[4] LAN LIU, BINY AND AND GUILIN CHEN Strong Barrier Coverage in Mobile Camera Sensor Networks with Grid-Based Deployment, 3rd ed. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING XX, XXX-XXX (2015)
[5] Anwar Saipulla Benyuan Liu Jie Wang Airdropped Wireless Sensors, 3rd ed. Member, IEEE, and Elisa Bertino, Fellow, IEEE
[6] S.N.Kumar, T.Lai, and A. Arora, StrongBarrier coverage with wire- less sensors, 3rd ed. IEEE, and Elisa Bertino, Fellow
[7] L. Zhang, J. Tang, and W. Zhang, ,“Strong barrier coverage with
directional sensors,” IEEE TRANSACTIONS ON NETWORKING.,VOL. 26,NO. 2, FEBRUARY 2015
[8] Manel Boujelben,Habib Youssef,Rania Mzid and Mohamed Abid ,“ On full-view coverage in camera sensor networks” ”IEEE Transactions On Dependable And Secure Computing,ICSI 2012, Part II, LNCS 7332, pp. 351359, 2012.
[9] C. Shen, W. Cheng, X. Liao, and S. Peng, ,”Barrier coverage with mobile sensors ,” Springer-Verlag Berlin Heidelberg 2012, Vol. 3, No. 1, January 2014.
[10] Junzhao Du, Member, IEEE, Kai Wang, Hui Liu, and Deke Guo, Member, IEEE ”Maximizing the Lifetime of k-Discrete Barrier Coverage using Mobile Sensors”, IEEE Transaction on Sensors Journal
[11] Donghyun Kim, Senior Member, IEEE, Wei Wang, Junggab Son, Member, IEEE, Weili Wu, Senior Member, IEEE, Wonjun Lee, Senior Member, IEEE, Alade O. Tokuta, Member, IEEE ”Maximum Lifetime Combined Barrier-coverage of Weak Static Sensors and Strong Mobile Sensors”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. XX, NO. XX, 2016
[12] Jiming Chen, Senior Member, IEEE, Junkun Li, and Ten H. Lai ”Energy-Efficient Intrusion Detection with a Barrier of Probabilistic Sensors: Global and Local”, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
[13] Junkun Li , Jiming Chen , Shibo He , Tian He , Yu Gu , Youxian Sun , ”On Energy-Efficient Trap Coverage in Wireless Sensor Networks”, ACM Transaction on Computer Networks
Citation
Anagha S. Anand, V. S. Anitha, "Implementation and Analysis of k-Barrier Coverage in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.26-31, 2018.
Bypass Mobile Lock Systems With Gelatin Artificial Fingerprint
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.32-36, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.3236
Abstract
We briefly described how to bypass mobile devices lock system by using fingerprint verification. As the applying method the method of counterfeiting the fingerprint of the mobile device`s owner was chosen (direct attack). The article describes the use of the method of creating a gelatin artificial fingerprints to bypass the locking system of mobile phones. The experiment confirmed the possibility of bypassing fingerprint protection without the need for expensive tools or high-quality fingerprint samples. The artificial fingerprints were tested to unlock iPhone 6 and Meizu m5s phones. To bypass lock system the iPhone 6 with a fake fingerprint we need not more than two unlock attempts. Success rate of bypass iPhone 6 biometric lock system was 70 percent. To unlock Meizu m5s we have to moistening the artificial fingerprint, after that we bypass lock system with the first unlock attempt. Success rate of bypass Meizu m5s biometric lock system was 70 percent.
Key-Words / Index Term
Biometric security systems, Fake artificial fingerprints, Mobile fingerprint readers, Gelatin fingerprint copy
References
[1] Olaf Henniger, Dirk Scheuermann, and Thomas Kniess “On security evaluation of fingerprint recognition systems”, International Biometric Performance Conference (IBPC 2010), March. 2010.
[2] Javier Galbally, Julian Fierrez, and Javier Ortega-Garcia “Vulnerabilities in Biometric Systems: Attacksand Recent Advances in Liveness Detection”, Proc. Spanish Workshop on Biometrics, SWB, 2007.
[3] Umut Uludag, Anil K. Jain “Attacks on Biometric Systems: A Case Study in Fingerprints”, Proceedings of SPIE - The International Society for Optical Engineering, 2004.
[4] Swapnali Mahadik, K Narayanan, D V Bhoir, Darshana Shah "Access Control System using Fingerprint Recognition", International Conference on Advances in Computing Communication and Control, pp. 306-311, 2009.
[5] A. Nagar K. Nandakumar A. K. Jain “Biometric template transformation: a security analysis”, IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2010.
[6] S. Yoon J. Feng A. K. Jain “Altered fingerprints: Analysis and detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34 no. 3 pp. 451-464 2012.
[7] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar “Handbook of Fingerprint Recognition”, Springer, Berlin, Germany, 2009.
[8] “Politician`s fingerprint ‘cloned from photos’ by hacker”, December 2014, http://www.bbc.com/news/technology-30623611.
[9] T. van der Putte and J. Keuning “Biometrical fingerprint recognition: don`t get your fingers burned”, Proceedings of the 4th Working Conference on Smart Card Research and Advanced Applications, pp. 289–303, 2000.
[10] A. Nagar, K. Nandakumar, and A. K. Jain “Biometric template transformation: a security analysis”, Proceedings of the SPIE, Electronic Imaging, Media Forensics and Security II, vol. 7541, San Jose, Calif, USA, January 2010.
[11] Rubal Jain and Chander Kant “Attacks on Biometric Systems: An Overview”, International Journal of Advances in Scientific Research 2015, 1(07), pp. 283-288.
[12] Galbally, J., Fierrez, J., Rodriguez-Gonzalez, J.D., Alonso-Fernandez, F., Ortega-Garcia, J., Tapiador, M. “On the vulnerability of fingerprint verification systems to fake fingerprint attacks”, Proc. of IEEE International Carnahan Conference on Security Technology. Volume 1. (2006) 130–136.
[13] E. Marasco and A. Ross “A Survey on Anti-Spoofing Schemes for Fingerprint Recognition Systems”,ACM Computing Surveys, Vol. 47, No. 2, Article 28, January 2015.
[14] White Paper “Protecting Against Fingerprint Spoofing in Mobile Devices”, Synaptics Incorporated, 2016.
[15] Y. W. Ju B. H. Lee “The implementation of secure mobile biometric system”, International Journal of Bio-Science and Bio-Technology , vol. 5 no. 4 pp. 53-60 2013.
[16] Sanaa Ghouzali, Maryam Lafkih, Wadood Abdul, Mounia Mikram, Mohammed El Haziti, and Driss Aboutajdine “Trace Attack against Biometric Mobile Applications”, Mobile Information Systems, vol. 2016.
[17] Kai Cao and Anil K. Jain “Hacking Mobile Phones Using 2D Printed Fingerprints”, MSU Technical Report MSU-CSE-16-2, 2016.
[18] S. S. Arora, K. Cao, A. K. Jain, and N. G. Paulter “Design and fabrication of 3d fingerprint targets”, IEEE Transactions on Information Forensics and Security, vol. 11, pp. 2284–2297, Oct. 2016.
[19] S. S. Arora, A. K. Jain, and N. G. Paulter “3d whole hand targets: Evaluating slap and contactless fingerprint readers”, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–8, Sept 2016.
[20] S. S. Arora, A. K. Jain, and N. G. Paulter “Gold fingers: 3d targets for evaluating capacitive readers”, IEEE Transactions on Information Forensics and Security, pp. 1–1, Apr. 2017.
[21] Joshua J. Engelsma, Sunpreet S. Arora, Anil K. Jain, Nicholas G. Paulter Jr. “Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations”, IEEE Transactions on Information Forensics and Security, 2017.
[22] Yulong Zhang, Zhaofeng Chen, Hui Xue, and Tao Wei “Fingerprints On Mobile Devices: Abusing and Leaking”, Black Hat Conference, August 7, 2015.
[23] Antonio Bianchi, Yanick Fratantonio, Aravind Machiry, Christopher Kruegel, Giovanni Vigna, Simon Pak Ho Chung, Wenke Lee “Broken Fingers:On the Usage of the Fingerprint API in Android”, Network and Distributed System Security Symposium (NDSS), February 19th, 2018.
[24] Yulong Zhang, Tao Wei “To Swipe or Not to Swipe: A Challenge for Your Fingers”, RSA conference, San Francisco, USA, 2015.
Citation
E.A. Maro, M.M. Kovalchuk, "Bypass Mobile Lock Systems With Gelatin Artificial Fingerprint", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.32-36, 2018.
Know Your Doctor: Topic Modeling and Sentiment Analysis Based Approach To Review Doctor
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.37-42, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.3742
Abstract
Nowadays people tend to search for doctors or firms through business review websites. They naturally opt for doctors that have the very best ratings and an outsized variety of reviews that support those high ratings. Hundreds or perhaps thousands of reviews will be given to the best-rated ones beneath their profiles, and comparing a high rated option to every alternative becomes a tedious task. This paper aims to address this issue by making a summarizer to analyze the doctors review by performing topic modeling using Latent Dirichlet Allocation(LDA) and Word2Vec based sentiment analysis. LDA is a standard Natural Language Processing (NLP) technique to determine topics from a large corpus. Word2vec based sentiment analysis is used to study people`s opinions, attitudes and emotions towards a review. Word2vec is a neural network with two-layer that embeds the text corpus to a set of feature vectors of the words in the corpus. The reviews are taken from Yelp, an online rating website, of doctors across San Francisco. As a result of this study, a snapshot is created for each doctor with most dominant topics and the overall sentiment from their reviews.
Key-Words / Index Term
LDA, NLP, Sentiment Analysis, Topic Modeling, Word2Vec
References
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Citation
K. Kavya, C. Sreejith, "Know Your Doctor: Topic Modeling and Sentiment Analysis Based Approach To Review Doctor", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.37-42, 2018.
Steganalysis -Iterative Rule Learning to Discover Patterns
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.43-47, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.4347
Abstract
In these years, everything is leading to new development and digitization. With these developments in technology, main challenge which exists is the threat of security. Steganography method usually embeds the sensitive messages in visually innocent cover images. The target of steganalysis is to determine the stego images from that of empty images. Every method depending on its hiding capacity of secret data in images place a unique markings or signature in stego images. To find this kind of markings in the images leads us to encorporate a classifier to be made for the purpose of finding the stego images which are usually the outcome of such steganography algorithm. In this, approach involves an evolutionary fuzzy rules to take out the markings of stego images in contrast to those empty images. Thus by using knowledge discovered, appropriate models for steganalysis can be involved and stego images can be found out and evolutionary algorithm can be optimized well. Thus the particular signature of steganographic method can be taken out well and also the kind of method used to produce stego image can be predicted.
Key-Words / Index Term
Steganalysis, Fuzzy rules, Evolutionary Genetic Algorithm, Iterative Rule Learning
References
[1].HediehSajedi, “Steganalysis based on steganography pattern discovery”, journal of information security and applications, Vol. 30, pp. 3-14, 2016.
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Citation
Megala G, Maya Mohan, "Steganalysis -Iterative Rule Learning to Discover Patterns", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.43-47, 2018.
Social Media Sentiment Analysis For Malayalam
Research Paper | Journal Paper
Vol.06 , Issue.06 , pp.48-53, Jul-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si6.4853
Abstract
Sentiment analysis or opinion mining is a Natural Language Processing to find the emotions of public opinion from user generated text. Sentiment Analysis in social media, acquiring large importance today because people use social media platforms to share their views and opinions on relevant topics in the form of movie reviews, product reviews, political discussions etc. The user generated text collected from social media can help machines to summarize and take intelligent decisions in different domains. Sentiment analysis in Malayalam language has a large importance. Malayalam is a low-resource language and it does not possess a standard corpus or a sentiment lexicon. This work presents a machine learning approach to sentiment analysis in Malayalam language using the CRF and SVM. The learning carried out at two levels and the system classify sentences into positive, negative and neutral classes. The work includes creation of a large size annotated corpus as a primary task and then followed by training a sentence level classifier to perform sentiment analysis.
Key-Words / Index Term
Sentiment Analysis, CRF, SVM, NLP
References
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Citation
M. Rahul, R.R. Rajeev, S. Shine, "Social Media Sentiment Analysis For Malayalam", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.48-53, 2018.