Volume-7 , Issue-5 , May - 2019 Go Back

Facial image analysis is a important and mainstream research point and it incorporates face detection, face recognition, facial expression analysis, and a few other related applications. LBP is a non-parametric descriptor whose point is to proficiently condense the neighborhood structures of images. As of late, it has stirred expanding enthusiasm for some territories of image processing and computer vision, and has demonstrated its viability in various applications, specifically for facial image analysis, including undertakings as assorted as face detection, face recognition, facial expression analysis, statistic classification, and so on. This paper presents a comprehensive overview of Gabor Filter and SVM, Genetic Algorithms and Neural Network and at long last CNN including a few later variations. LBP-based facial image analysis is widely checked on, while its fruitful expansions in managing different errands of facial image analysis are likewise featured.

Facial expressions recognition, LBP, human cognition, emotion model, machine learning

[1] Mehrabian, A. Communication without words. Psychol. Today 1968, 2, 53–56.
[2] Kaulard, K.; Cunningham, D.W.; Bülthoff, H.H.; Wallraven, C. The MPI facial expression Database—A validated database of emotional and conversational facial expressions. PLoS ONE 2012, 7, e32321.
[3] Dornaika, F.; Raducanu, B. Efficient facial expression recognition for human robot interaction. In Proceedings of the 9th InternationalWork-Conference on Artificial Neural Networks on Computational and Ambient Intelligence, San Sebastián, Spain, 20–22 June 2007; pp. 700–708.
[4] Bartneck, C.; Lyons, M.J. HCI and the face: Towards an art of the soluble. In Proceedings of the International Conference on Human-Computer Interaction: Interaction Design and Usability, Beijing, China, 22–27 July 2007; pp. 20–29.
[5] Hickson, S.; Dufour, N.; Sud, A.; Kwatra, V.; Essa, I.A. Eyemotion: Classifying facial expressions in VR using eye-tracking cameras. arXiv 2017, arxiv:1707.07204.
[6] Chen, C.H.; Lee, I.J.; Lin, L.Y. Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Res. Dev. Disabil. 2015, 36, 396–403.
[7] Assari, M.A.; Rahmati, M. Driver drowsiness detection using face expression recognition. In Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia, 16–18 November 2011; pp. 337–341.
[8] Zhan, C.; Li,W.; Ogunbona, P.; Safaei, F. A real-time facial expression recognition system for online games. Int. J. Comput. Games Technol. 2008, 2008.
[9] Mourão, A.; Magalhães, J. Competitive affective gaming: Winning with a smile. In Proceedings of the ACM International Conference on Multimedia, Barcelona, Spain, 21–25 October 2013; pp. 83–92.
[10] Lucey, P.; Cohn, J.F.; Kanade, T.; Saragih, J.; Ambadar, Z.; Matthews, I. The extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In Proceedings of the IEEE Conference on Computer Vision and Pattern RecognitionWorkshops, San Francisco, CA, USA, 13–18 June 2010; pp. 94–101.
[11] Kahou, S.E.; Michalski, V.; Konda, K. Recurrent neural networks for emotion recognition in video. In Proceedings of the ACM on International Conference on Multimodal Interaction, Seattle, WA, USA, 9–13 November 2015; pp. 467–474.
[12] Walecki, R.; Rudovic, O. Deep structured learning for facial expression intensity estimation. Image Vis. Comput. 2017, 259, 143–154.
[13] Kim, D.H.; Baddar,W.; Jang, J.; Ro, Y.M.Multi-objective based Spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Affect. Comput. 2017, PP.
[14] Ekman, P.; Friesen,W.V. Facial Action Coding System: Investigator’s Guide, 1st ed.; Consulting Psychologists Press: Palo Alto, CA, USA, 1978; pp. 1–15, ISBN 9993626619.
[15] Hamm, J.; Kohler, C.G.; Gur, R.C.; Verma, R. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 2011, 200, 237–256.
[16] Jeong, M.; Kwak, S.Y.; Ko, B.C.; Nam, J.Y. Driver facial landmark detection in real driving situation. IEEE Trans. Circuits Syst. Video Technol. 2017, 99, 1–15.
[17] Tao, S.Y.; Martinez, A.M. Compound facial expressions of emotion. Natl. Acad. Sci. 2014, 111, E1454–E1462.
[18] Benitez-Quiroz, C.F.; Srinivasan, R.; Martinez, A.M. EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 5562–5570.
[19] Kolakowaska, A. A review of emotion recognition methods based on keystroke dynamics and mouse movements. In Proceedings of the 6th International Conference on Human System Interaction, Gdansk, Poland, 6–8 June 2013; pp. 548–555.

Tejaswi Satepuri, P. Chandrasekar Reddy, "A Survey on Facial Expression Recognition Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.980-984, 2019.

Web applications play a significant role in today’s digital age. Their uses in our lives have become indispensable. It has made web applications a favorite target for attackers and has increased web security risk. This study focuses on finding structural aspects and vulnerabilities present in India’s 50 websites which were categorized into five categories of 10 most visited sites, i.e., e-commerce, news, entertainment, education, and other scanned as an ordinary user to consider safety assessment of these websites. The knowledge about these sites, such as technologies used and infrastructure they have, the vulnerabilities they possess, has been investigated using penetration tests in this study. As a result of this research, web server information and operating system information from 86% to 66% respectively of the reviewed websites are identified. Medium and low degree vulnerabilities have been present in all scanned websites. Some of them even have High vulnerabilities also. With the vulnerability screening tests, their degree of vulnerabilities graph revealed, and information about the most identified weaknesses was given.

Web Applications, Penetration Testing, Penetration Testing Tools, Weakness Analysis, Web Security

[1] P. Fung, Mitigations of web applications security risks, hong kong: Ph.D dissertation, 2014.
[2] N. Kochare, S. Chalurkar, B.B. Meshram,, “Web Application Vulnerabilities Detection Techniques Survey,” IJCSNS International Journal of Computer Science and Network Security, vol. 13, no. 6, p. 7177, 2013.
[3] C. Polat, Penetration Tests and Security Solutions for Corporate Networks, Dokuz Eylul University Izmir, 2016, pp. 1-182.
[4] Ruse, M.E, Model Checking Techniques for Vulnerability Analysis of Web Applications, Iowa: Iowa State University, 2013.
[5] C. Huang, J. Liu, Y. Fang, Z. Zuo, “A study on Web Security incidents in China by Analyzing Vulnerability disclosure Platforms,” Computer and Security, vol. 58, pp. 47-62, 2016.
[6] D. Stiawan, M. Idris, A. Abdullah, F. Aljaber and R. Budiarto, “Cyber-Attack Penetration Test and Vulnerability Analysis,” International Journal of Online Engineering, vol. 13, no. 1, pp. 125-132, 2017.
[7] S. Sandhya, S. Purkayastha, E. Joshua, A. Deep, “Assessment of website security by penetration testing using Wireshark,” in 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2017.
[8] S. Nixon, Y. Haile, “Analyzing vulnerabilities on WLAN security protocols and enhance its security by using pseudo random MAC address,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS’2017), 2017.
[9] J.H. Bullee, L. Montoya, W. Pieters, M. Junger, P. Hartel, “On the anatomy of social engineering attacks—A literature-based dissection of successful attacks,” Journal of investigative psychology and offender profiling, vol. 15, no. 1, pp. 20-45, 2017.
[10] Y. Wu, G. Feng, R.Y.K Fung, “Comparison of information security decisions under different security and business environments,” Journal of the Operational Research Society, vol. 69, no. 5, pp. 747-761, 2018.
[11] P. Cisar, S.M. Maravi, I. Furstner, “Security Assessment with Kali Linux,” Banki Kozlemenyekl, vol. 1, no. 1, pp. 49-52, 2018.

Irshad Alam, Satwinder Singh, Gurpreet Kaur, "Evaluation of India’s Most Visited Websites in Aspects of Security & Structure," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.985-991, 2019.

Autism Spectrum Disorders (ASDs) are difficult to classify, however it needs with the purpose of physicians have correct training and knowledge. At the instant on the other hand ASD is classified much afterward than is essentially potential. Early on identification of ASD increases the overall mind health of the child. Consequently, in the direction of advantage autism patients through increasing their right to use toward treatments such as early involvement, plan toward begin a robust data mining methods used for autism classification by using detection and feature selection algorithms depending on information from ASD patients. The basic aim of this work is toward choose best optimal features toward conquer the learning problem and to go faster the knowledge ability of autistic children. In order to carryout optimal feature selection process, Firefly Algorithm (FA) is introduced which chooses the features from the ASD screening procedure. In this work, an accurate FA method is introduced for identifying the most important and choosing a best ASD feature subset. The feature selection algorithm is performed depending on the wrapper method, i.e. the FA and the Support Vector Machine (SVM) is developed for ASD classification correspondingly. The proposed SVM classifier might considerably shorten and abbreviate the step of ASD analysis. The results demonstrated that the proposed SVM with FA classifier has provided an improved classification results depending on the chosen features. The experimental results of that FA based feature selection performing better when compared to other algorithms.

Autism Spectrum Disorders (ASDs), Firefly Algorithm (FA), feature selection, Support Vector Machine (SVM), Medical forms, Classification

[1] Autism and Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators, 2014. Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report: Surveillance Summaries, Vol.63, Issue. 2, pp.1-21, 2010.
[2] Iossifov, I, O’roak, BJ, Sanders, SJ, Ronemus, M, Krumm, N, Levy, D, Stessman, HA, Witherspoon, KT, Vives, L Patterson, KE and Smith, JD, “The contribution of de novo coding mutations to autism spectrum disorder”, Nature, Vol.515, Issue. 7526, pp.216–221,2014.
[3] De Rubeis, S, He, X, Goldberg, AP, Poultney, CS, Samocha, K, Cicek, AE, Kou, Y, Liu, L, Fromer, M, Walker, S and Singh, T, “Synaptic, transcriptional and chromatin genes disrupted in autism”, Nature, Vol.515, Issue. 7526, pp. 209–215,2014.
[4] Myers, SM, and Johnson CP, “American Academy of Pediatrics Council on Children With Disabilities”, Management of children with autism spectrum disorders, Pediatrics. Vol.120, Issue.5, pp.1162–1182, 2007.
[5] Daniels, AM and Mandell DS, “Explaining differences in age at autism spectrum disorder diagnosis: a critical review”, Autism, Vol. 18, Issue. 5, pp.583–597, 2014.
[6] Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators, Centers for Disease Control and Prevention (CDC). Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ, Vol.63, Issue. 2, pp.1–21, 2014.
[7] Shattuck, PT and Grosse SD, “Issues related to the diagnosis and treatment of autism spectrum disorders”, Ment Retard Dev Disabil Res Rev, Vol.13, Issue.2, pp.129–135, 2007.
[8] Wallace, KS and Rogers SJ, “Intervening in infancy: implications for autism spectrum disorders”, J Child Psychol Psychiatry, Vol. 51, Issue.12, pp.1300–1320, 2010.
[9] Hsu, HH, Hsieh, CW and Lu, MD, “Hybrid feature selection by combining filters and wrappers”, Expert Systems with Applications, Vol.38, Issue.7, pp.8144-8150, 2011.
[10] Gutlein, M, Frank, E, Hall, M and Karwath, A, “Large-scale attribute selection using wrappers”, IEEE Symposium on Computational Intelligence and Data Mining, 2009 (CIDM`09), pp. 332-339, 2009.
[11] Saeys, Y, Inza, I and Larrañaga, P, “A review of feature selection techniques in bioinformatics”, bioinformatics, Vol.23, Issue.19, pp.2507-2517, 2007.
[12] Chandrashekar, G and Sahin, F, “A survey on feature selection methods”, Computers & Electrical Engineering, Vol.40, Issue. 1, pp.16-28, 2014.
[13] Akyol, K , and Karaci, A, “A Study on Autistic Spectrum Disorder for Children Based on Feature Selection and Fuzzy Rule”, ICELIS, pp. 804-807, year.
[14] Alzubi, R, Ramzan, N and Alzoubi, H, “Hybrid feature selection method for autism spectrum disorder SNPs”, In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) ,pp. 1-7, 2017.
[15] Heinsfeld, AS, Franco, AR, Craddock, RC, Buchweitz, A and Meneguzzi, F, “Identification of autism spectrum disorder using deep learning and the ABIDE dataset”, NeuroImage: Clinical, Vol.17, pp.16-23, 2018.
[16] Kosmicki, JA, Sochat, V, Duda, M and Wall, DP, “Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning”, Translational psychiatry, Vol.5, Issue. 2, pp.e514, 2015.
[17] Bi, XA, Wang, Y, Shu, Q, Sun, Q and Xu, Q, “Classification of autism spectrum disorder using random support vector machine cluster”, Frontiers in genetics, Vol.9, Issue. 18, pp.1-10, 2018.
[18] Cheplygina, V, Tax, DM, Loog, M and Feragen, A, “Network-guided group feature selection for classification of autism spectrum disorder”, In International Workshop on Machine Learning in Medical Imaging, pp. 190-197, 2014.
[19] Thabtah, F, “Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment”, In Proceedings of the 1st International Conference on Medical and Health Informatics 2017 , pp. 1-6, 2017.
[20] X.-S. Yang, “Nature-Inspired Metaheuristic Algorithms”, Luniver Press, 2008.
[21] Zeng, ZQ, Yu, HB, Xu, HR, Xie, YQ and Gao, J, “Fast training support vector machines using parallel sequential minimal optimization”, In 2008 3rd international conference on intelligent system and knowledge engineering , Vol. 1, pp. 997-1001, 2008.
[22] Chang, CC and Lin, CJ, “LIBSVM: a library for support vector machines”, ACM transactions on intelligent systems and technology (TIST), Vol. 2, Issue. 3, pp.27, 2011.
[23] Thabtah, F, “Machine learning in autistic spectrum disorder behavioral research: A review and ways forward”, Informatics for Health and Social Care, pp.1-20, 2018.

R. Rajeswari, R.S. Padma Priya, "Detection With Firefly Algorithm (FA) Based Feature Selection Forautism Spectrum Disorder (ASD) and Machine Learning Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.992-998, 2019.

Grid computing is a form of distributed computing which is used to solve the large-scale scientific problems present in the grid networks. It is the technology of dividing computer networks with different and heterogeneous resources based on distributed computing. Generally, a grid network can be considered as a chain of several big branches, different kinds of microprocessors, thousands of Personal computers and workstations all over the globe. The main aim of grid computing is to apply available computing resources for dense calculations via sites that are distributed geographically without difficulty. Resource scheduling and Resource management play a key role in achieving high utilization of resources in grid computing environments. The allocation of distributed computational resources to user applications is one of the most complicated and difficult tasks in the Grid system. The problem of allocating resources in Grid scheduling requires the definition of a model that allows local and external schedulers to communicate in order to achieve efficient management of the resources themselves. This paper presents a survey of some of the most widely known and recently proposed mechanisms in Grid scheduling algorithms.

Grid Computing, Scheduling, Resource allocation

[1]. S. Vaaheedha Kfatheen, A. Marimuthu, “ETS: An Efficient Task Scheduling Algorithm for Grid Computing”, Advances in Computational Sciences and Technology, Volume 10, Number 10, 2017, pp. 2911-2925.
[2]. G. Murugesan, C. Chellappan, “An Economic-based Resource Management and Scheduling for Grid Computing Applications”, IJCSI International Journal of Computer Science Issues, Volume 7, Issue 2, 2010, pp. 20-25.
[3]. Farhad Soleimanian Gharehchopogh, Majid Ahadi, Isa Maleki, Ramin Habibpour, and Amin Kamalinia, “Analysis of Scheduling Algorithms in Grid Computing Environment”, International Journal of Innovation and Applied Studies, Volume 4, Issue 3, 2013, pp. 560-567.
[4]. Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra, Prachet Bhuyan, “A Survey of Job Scheduling and Resource Management in Grid Computing”, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering, Volume 4, Issue 4, 2010, pp. 736-741.
[5]. Massimiliano Caramia, Stefano Giordani, “Resource Allocation in Grid Computing: An Economic Model”, WSEAS Transactions on Computer Research, Issue 1, Volume 3, 2008, pp. 19-27.
[6]. Omar Dakkak, Suki Arif and Shahrudin Awang Nor, “Resource Allocation Mechanisms in Computational Grid: A Survey”, ARPN Journal of Engineering and Applied Sciences, Volume 10, Issue 15, 2015, pp. 6662-6667.
[7]. Ritu Garg, Awadhesh Kumar Singh, “Adaptive workflow scheduling in grid computing based on dynamic resource availability”, Engineering Science and Technology, an International Journal, 2015, pp. 1-14.
[8]. Boopathi Kumar E, and Thiagarasu V, “Big Data and Its Applications: A Review”, International Journal of Intelligent Computing and Technology (IJICT), Volume 2, Issue 2, 2019, Pages: 1 – 10.
[9]. Sonal Nagariya, Mahindra Mishra, “Resource Scheduling in Grid Computing: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, 2013, pp. 735-739.
[10]. Shiv Prakash, Deo Prakash Vidyarthi, “A Hybrid GABFO Scheduling for Optimal Makespan in Computational Grid”, International Journal of Applied Evolutionary Computation, Volume 5, Issue 3, 2014, pp. 57-83.
[11]. Harshadkumar B. Prajapati, Vipul A. Shah, “Scheduling in Grid Computing Environment”, Fourth International Conference on Advanced Computing & Communication Technologies, IEEE, 2014, pp. 315-324.
[12]. Muhammad Bilal Qureshi, Maryam Mehri Dehnavi, Nasro Min-Allah, Muhammad Shuaib Qureshi, Hameed Hussain, Ilias Rentifis, Nikos Tziritas, Thanasis Loukopoulos, Samee U. Khan, Cheng-Zhong Xu. Albert Y. Zomaya, “Survey on Grid Resource Allocation Mechanisms”, Journal of Grid Computing, Springer, 2014, pp. 1-43.
[13]. Zahra Pooranian, Mohammad Shojafar, Jemal H. Abawajy, Ajith Abraham, “An efficient meta-heuristic algorithm for grid computing”, Journal of Comb Optim, Springer, 2013, pp.1-22.
[14]. Shashi Bhushan Semwal, Amit Das, “Effective Time and Cost Based Task Scheduling In Grid Computing”, International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 5, Sep-Oct 2015, pp. 118-122.

P. Pradeep Kumar, E. Chandra Blessie, "A Survey on Resource Scheduling and Its Applications in Grid Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.999-1003, 2019.

VANET is a wireless communication system established between multiple vehicles moving on the road. The vehicle nodes are present in network but there are some malicious or attacker nodes whose aim is to harm the network. An attacker vehicle node can raise an alert even if there is no crash on the road or it can falsely divert the traffic in wrong direction for their personal interest. In this paper, the new Two Stage Attacked Node Identification Algorithm (TSANI Algorithm) is proposed. This algorithm identifies the attacker nodes and marks then as unauthentic nodes. The performance of new algorithm is analysed and compared with the existing work.

VANET, DTN, MANET, Security, TSANI, Attacks

[1] Geetha Jayakumar, Gopinath Ganapathi, “Reference Point Group Mobility and Random Waypoint Models in Performance Evaluation of MANET Routing Protocols”, Journal of Computer Systems, Networks, and Communications, Volume 2008.
[2] A Peppino Fazio, Floriano De Rango, Cesare Sottile, and Amilcare Francesco Santamaria, “Routing Optimization in Vehicular Networks: A New Approach Based on Multi objective Metrics and Minimum Spanning Tree”, International Journal of Distributed Sensor Networks, Volume 2013, Article ID 598675, 2013
[3] Jochen H. Schiller, “Mobile Communications”, Second Edition, Pearson Education Limited, 2003
[4] R. Yogapriya, A. Subramani, “A Survey on Vulnerabilities, Attacks and Issues in MANET, WSN and VANET”, International Journal of Computer Sciences and Engineering,Vol.-6, Issue-11, Nov - 2018.
[5] [2p] Deepak Rewadkar, Dharmpal Doye, “Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET”, International Journal of Computer Sciences and Engineering, Volume-6, Issue-3, March 2018.
[6] Arif Sari, Onder Onursal, Murat Akkaya, “Review of the Security Issues in Vehicular Ad-hoc Networks (VANET)”, Int. J. Communications, Network and System Sciences, 2015, 8, 552-566, December 2015.
[7] Mainak Ghosh, Anitha Varghese, Arzad A. Kheraniand, Arobinda Gupta, “Distributed Misbehavior Detection in VANETs”, WCNC 2009 proceedings, IEEE 2009.
[8] Arun Kumar, “Enhanced Routing in Delay Tolerant Enabled Vehicular Ad-hoc Networks”, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012.
[9] Vishnu Sharma, Ankur Goyal, “Delay Analysis of Proposed DMN Algorithm in VANET”, International Journal of Computer Sciences and Engineering, Vol.-7, Issue-1, Jan 2019.
[10] Sherali Zeadally, Ray Hunt, Yuh-Shyan Chen, Angela Irwin, Aamir Hassan, “Vehicular ad-hoc networks (VANETS): status, results, and challenges”, Springer Science Business Media, LLC 2010.
[11] RAVNEET KAUR, Nitika Chowdhary, Jyoteesh Malhotra, “Sybil Attacks Detection in Vehicular Ad-hoc Networks”, International Journal of Advanced Research, Volume 3, Issue 6, 1085-1096, 2015.
[12] Uzma Khana, Shikha Agrawal, Sanjay Silakaria, “Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks”, Procedia Computer Science 46, 965 – 972, 2015.
[13] Vimal Bibhu, Kumar Roshan, “Performance Analysis of Black Hole Attack in VANET”, I. J. Computer Network and Information Security, 11, 47-54, 2012.
[14] S. Roselin Mary, M. Maheshwari, M. Thamaraiselvan, “Early Detection Of DOS Attacks In VANET Using Attacked Packet Detection Algorithm (APDA)”, International Conference on Information Communication and Embedded Systems, ICICES 2013.
[15] Mandeep Kaur, Manish Mahajan, “Movement Abnormality Evaluation Model in the Partially Centralized VANETs for Prevention Against Sybil Attack”, I.J. Modern Education and Computer Science, 11, 20-27, 2015.
[16] Sonia, Padmavati, “Performance analysis of Black Hole Attack on Vanet’s Reactive Routing Protocols”, International Journal of Computer Applications (0975 – 8887) Volume 73– No.9, July 2013
[17] Omar Abdel Wahab, Hadi Otrok, Azzam Mourad, “A cooperative watchdog model based on Dempster–Shafer for detecting misbehaving vehicles”, Elsevier, Computer Communications 41, 43–54, 2014.
[18] Gómez Mármol, Félix, and Gregorio Martínez Pérez, "TRIP, a trust and reputation infrastructure-based proposal for vehicular ad-hoc networks", Journal of Network and Computer Applications 35 springer, no. 3, pp- 934-941, 2012.
[19] Harsimrat Kaur, Preeti Bansal, “Efficient Detection & Prevention of Sybil Attack in VANET”, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 9, September 2015.
[20] Khalid Abdel Hafeez, Lian Zhao, Zaiyi Liao, Bobby Ngok-Wah Ma, “A New Broadcast Protocol For Vehicular Ad-hoc Networks Safety Applications”, IEEE Globecom 2010 proceedings, 2010.
[21] Taskeen Zaidi, Shubhang Giri, Shivam Chaurasia, Pragya Srivastava and Rishabh Kapoor, “Malicious Node Detection through AODV in VANET”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), Vol.9, No.2, April 2018.
[22] Archana Harit, N C Barwar , “ Comparative Analysis of Identification of Malicious Node in VANET using FFRDV and ERDV Routing Algorithm”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, Issue 8, August 2016.
[23] Parul Tyagi, Deepak Dembla, “Performance analysis and implementation of proposed mechanism for detection and prevention of security attacks in routing protocols of vehicular ad-hoc network (VANET)”, Egyptian Informatics Journal, 18, 133–139, 2017.

Rajesh Sharma, Ankur Goyal, "Securing Vehicular Ad-hoc Network by Two Stage Attacked Node Identification Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1004-1008, 2019.

In this digital world; security is an important concern towards access control management that can be done through some kind of turing test i.e. CAPTCHA. The term CAPTCHA was introduced for classifying credibility of user whether the intervention has been initiated by human or bots. . CAPTCHA stands for “Completely Automated Public Turing test to tell Computers and Humans Apart”. It is a protection tool that denies junk or bots entries in the way of challenging bots with a problem. CAPTCHA has been represented in various forms such as distorted strings, 3D CAPTCHA, picture recognition CAPTCHA, Gaming CAPTCHA and many more. The conventional CAPTCHA is in the form of distorted string where user will have to recognize the same. The recent approach is gaming CAPTCHA and most of the game may increases the server load towards the browser. The logic behind the gaming CAPTCHA is dealing with dragging & dropping object to the target position, so these CAPTCHAsdo not belong to the hard AI problems. The motive of this paper is to review few existing CAPTCHA techniques that challenges bots claiming better testing in the field of web security.

CAPTCHA, Web Security, Turing Test, Gaming CAPTCHA, 3D CAPTCHA, Picture Recognition CAPTCHA

[1] DRVRGCET, “What is CAPTCHA?”, 2017. [Online].Available:http://drvrgcet.ac.in/blog/what-is-captcha.[Accessed: 03- Mar- 2019]
[2] Lukew, “A Sliding Alternative to CAPTCHA”, 2010.[Online].Available:https://www.lukew.com/ff/entry.asp?1138.[Accessed: 06- Mar- 2019]
[3] Mail Online, “No more wiggly words and annoying fuzzy pictures!”, 2014.[Online].Available:http://www.dailymail.co.uk/sciencetech/article-2859312/No-wiggly-words-annoying-fuzzy-pictures-Google-develops-Captcha-asks-tick-box.html.[Accessed: 06- Mar- 2019]
[4] Webloggerz, “Playthru: Break The CAPTCHA Code Misery with a Game”, 2013.[Online].Available:https://webloggerz.com/playthru-break-the-captcha-code-misery-with-a-game.[Accessed: 07- Mar- 2019]
[5] J. Cui et al., "CAPTCHA design based on moving object recognition problem," The 3rd International Conference on Information Sciences and Interaction Sciences, Chengdu, 2010, pp. 158-162.
[6] J. Cui, J. Mei, X. Wang, D. Zhang and W. Zhang, “A CAPTCHA Implementation Based on 3D Animation”, 2009 International Conference on Multimedia Information Networking and Security, Hubei, 2009, pp. 179-182.
[7] I. F. Ince, Y. B. Salman, M. E. Yildirim and T. Yang, "Execution Time Prediction for 3D Interactive CAPTCHA by Keystroke Level Model," 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, 2009, pp. 1057-1061.
[8] SushmaYalamanchili 1 and Kameswara Rao2: A Framework For Devanagari Script-BasedCAPTCHA, International Journal of Advanced Information Technology (IJAIT) Vol. 1, No. 4, August 2011.
[9] Vipin Kumar and AtulBarve: Dynamic Object and Target based Gaming CAPTCHA forBetter Security Analysis, International Journal of Computer Applications (0975 – 8887)Volume 162 – No 5, March 2017.
[10] R. Aadhirai, P. J. S. Kumar and S. Vishnupriya: Image CAPTCHA: Based on human understanding of real world distances, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), Kharagpur, 2012, pp. 1-6.
[11] S. Gao, M. Mohamed, N. Saxena and C. Zhang: Gaming the game: Defeating a game captcha with efficient and robust hybrid attacks, 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, 2014, pp. 1-6.
[12] S. M. R. S. Beheshti and P. Liatsis, "How humans can help computers to solve an artificial problem?", 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), London, 2015, pp. 291-294.
[13] Cao Lei: Image CAPTCHA technology research based on the mechanism of finger-guessing game, Third International Conference on Cyberspace Technology (CCT 2015), Beijing, 2015, pp. 1-4.
[14] S. A. Kumar, N. R. Kumar, S. Prakash and K. Sangeetha, "Gamification of internet security by next generation CAPTCHAs," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2017, pp. 1-5.

Ayushi Thakur, Shikha Agrawal, Rajeev Pandey, "A Brief Review on Various CAPTCHA Techniques for Enhancement of Web Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1009-1014, 2019.

Off recent level set methods have been used widely in medical image processing. This paper focuses on level set and its variation for MRI brain tissue segmentation. The different tissues are white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) in brain image. It is difficult to differentiate the boundaries for these tissues. A spatial fuzzy c means and level set segmentation methodology are adopted in this paper for brain MRI image segmentation into WM, GM, and CSF. Initially, segmentation is performed by using SFCM and level sets are used on the result of SFCM. The performance of SFCM and level sets is appraised on Brain Web Database where T1, T2, and ρ weighted images are chosen, whose thickness is 5mm with different intensity nonuniformity (RF) and noise. Experimental results demonstrate the supremacy of segmentation precision even on the noisy MRI brain image. The accuracy, sensitivity, and specificity are improved with better segmentation.

Image segmentation, Level set, SFCM

[1] Matineh Shaker, Hamid Soltanian-Zadeh, “Automatic segmentation of brain structures from MRI integrating atlas-based labeling and level set method”, CCECE/CCGEI May 5-7 2008 Niagara Falls. Canada 978-1-4244-1643-1/08/$25.00 2008 IEEE
[2] Dimah Dera, Nidhal Bouaynaya, Hassan M Fathallah-Shaykh, “Level Set Segmentation using Non-Negative Matrix Factorization of Brain MRI Images”, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 978-1-4673-6799-8/15/$31.00 ©2015 IEEE
[3] Malsawm Dawngliana, Daizy Deb, Mousum Handique, Sudipta Roy, “Automatic Brain Tumor Segmentation in MRI: Hybridized Multilevel Thresholding and Level”, 2015 International Symposium on Advanced Computing and Communication (ISACC), 978-1-4673-6708-0/15/$31.00 ©2015 IEEE
[4] Iman Omidvar Tehrani, Subariah Ibrahim, “An Enhanced Fuzzy c-means Medical Segmentation Algorithm”, 2014 International Symposium on Biometrics and Security Technologies (ISBAST), 978-1-4799-6444-4/14/$31.00 ©2014 IEEE.
[5] Ayuni Fateeha Muda, Norhashimah Mohd Saad, Nazreen Waeleh, Abdul Rahim Abdullah, Low Yin Fen, “Integration of Fuzzy C-Means with Correlation Template and Active Contour for Brain Lesion Segmentation in Diffusion-Weighted MRI”, 2015 Third International Conference on Artificial Intelligence, Modelling and Simulation, 978-1-4673-8675-3/15 $31.00 © 2015 IEEE, DOI 10.1109/AIMS.2015.88
[6] Chuang, K.S., et al., “Fuzzy c-means clustering with spatial information for image segmentation”, Computerized Medical Imaging and Graphics, 2006. 30(1): p. 9-15.
[7] S. Osher and J. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988.
[8] V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numer. Math., vol. 66, no. 1, pp. 1–31, Dec. 1993.
[9] R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE Trans. Pattern. Anal. Mach. Intell., vol. 17, no. 2, pp. 158–175, Feb. 1995.
[10] Visu Prateek Agarwal, Manoj Kumar, “Tumor Extraction by Level Set Method using Thershold Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 94 – No 15, May 2014
[11] S. Ho, E. Bullitt, G. Gerig, “Levelset evolution with region competition: automatic 3d segmentation of brain tumors”, in Proc. of Int’l Conf. On Pattern Recognition, 2002, pp. 532–535.
[12] M. Airouche, L. Bentabet, and M. Zelmat, “Image Segmentation Using Active Contour Model and Level Set Method Applied to Detect Oil Spills,” in Proc. World Congress on Engineering 2009, London, July 1 - 3, 2009.
[13] Bing NanLi, CheeKongChui, Stephen Chang, S.H.Ong, “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation,” Computers in Biology and Medicine 41(2011)1–10 0010-4825/$ -see front matter & 2010 Elsevier Ltd.
[14] Zhibin Chen, Tianshuang Qiu, Su Ruan, “Fuzzy Adaptive Level Set Algorithm for Brain Tissue Segmentation”, ICSP2008 Proceedings, 978-1-4244-2179-4/08/$25.00 ©2008 IEEE.
[15] Osher, Fedkiw, “Level Set Methods and Dynamic Implicit Surfaces”, Springer 2003
[16] Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox, “Level Set Evolution Without Re-initialization: A New Variational Formulation”, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),1063-6919/05, © 2005 IEEE

I. M. Kazi, N. S. Zulpe, S. S. Chowhan, U. V. Kulkarni, "MRI Brain tissue Segmentation Using Level set approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1015-1020, 2019.

The purpose of this study was to survey and review the utilization of food delivery app in android phones. 50 questionnaires were distributed to the randomly selected respondents, who were requested to complete the survey. The questionnaire had been taken from the randomly chosen public of all age groups. Based upon that we made a conclusion regarding that survey. Food is of main importance for well being; quality of food is a big million dollar query in this techie world. Technology has almost started in every field of our life, but still in some important areas such as food industry or food serving industries such as hotel, motels and restaurant. Even in the age of technology, the pen paper method is followed by many restaurants for receiving the orders, which in turn wastes a huge amount of time for the customer. Various earlier efforts were done to bring the technology in the field of food serving industries. Based upon the food apps, one can order food for delivery with just a few taps of a phone screen. Seamless is probably the most aptly named piece of mobile software on this list. Not only does the app provide menus from thousands of restaurants and offer exclusive in-app discounts, but it foregoes a delivery fee and allows you to order with just a few clicks. .Each food app had its own advantages and a set of disadvantages. This survey paper tries to analyze the survey of food apps systems and determine the drawbacks of each to overcome them in the future system. This survey improves accuracy for restaurants by saving time, eliminating human errors, getting customers feedback and customer expectation in the food app.

Online Food Ordering,Android Application

[1] Zulkarnain Kedah and Yusof Ismail, A.K.M. Ahasanul Haque & Selim Ahmed “Key Success Factors of Online Food Ordering Services: An Empirical Study” Malaysian Management Review JULY-DECEMBER 2015 Vol. 50 No. 2
[2] Tarun Varma, Krunal Tanna, Harshal Utekar, Monish Verma and Sejal D‟mello “A Survey on Touch Based Food Ordering System in Restaurants” International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 6 Issue: 3.
[3] Mayurkumar Patel “Online Food Order System for Restaurants” School of Computing and Information Systems.
[4] Suryadev Singh Rathore, Mahik Chaudhary “Consumer`s Perception on Online Food Ordering” IJMBS Vol. 8, ISSue 4, oct - Dec 2018.
[5] A. Albayrak. Factors that Influence of Costumers’ Restaurant Selection: The Case of Istanbul. Anatolia: Journal of Tourism Researches, vol. 25 no 2, 2014, pp. 190-201.
[6] C. Feldmann and U. Hamm. Consumers` perceptions and preferences for local food: A review. Food Quality and Preference Journal, vol. 40, pp. 152-164, 2015.
[7] H. Youn and J. H. Kim. Effects of ingredients, names and stories about food origins on perceived authenticity and purchase intentions. International Journal of Hospitality Management, vol. 63, pp 11-21, 2017.
[8] J. Ha, K. Park, K. and J. Park. Which restaurant should I choose? Herd behavior in the restaurant industry. Journal of Foodservice Business Research, vol. 19 no 4, pp. 396-412, 2016.
[9] J. M. Jung, S. Sydnor, S. K. Lee, B. Almanza. A conflict of choice: How consumers choose where to go for dinner. International Journal of Hospitality Management, vol.45, pp. 88-98, 2015. https://doi.org/10.1016/j.ijhm.2014.11.007
[10] L. Lu and D. Gursoy. Does offering an organic food menu help restaurants excel in competition? An examination of diners’ decision-making. International Journal of Hospitality Management,vol. 63, pp. 72-81, 2017. https://doi.org/10.1016/j.ijhm.2017.03.004.
[11] M. Clark and R.C. Wood. Consumer Loyalty in the Restaurant Industry: A Preliminary Exploration of the Issues, International Journal of Contemporary Hospitality Management, vol. 10 no 4, 1998, pp. 139–144.
[12] O. Mhlanga, Dr. T. M. Tichaawa. What are the current factors affecting consumer selection criteria in formal full service restaurants in Port Elizabeth, South Africa. African Journal of Hospitality, Tourism and Leisure, vol. 5 no 2, 2016, pp. 1-11.
[13] R. Lewis. Restaurant advertising: appeals and consumers` intentions, Journal of Advertising Research, vol. 21 no. 5, 1981, pp.69-74.
[14] S. Auty. Consumer choice and segmentation in the restaurant industry. The Service Industries Journal, vol. 12 no. 3, 1992, pp. 324-39.
[15] Y. A. A. Akbar, M. S. S. Alaudeen. Determinant of factors that influence consumer in choosing normal fullservice restaurant: case in Seri Iskandar, Perak. South East Asian Journal of Contemporary Business, Economics and Law, vol. 1, 2012, pp. 137-145.
[16] Resham Shinde, Priyanka Thakare, Neha Dhomne, Sushmita Sarkar, “Design and Implementation of Digital dining in Restaurants using Android”, in International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 1, January 2014.
[17] Shweta Shashikant Tanpure, Priyanka R. Shidankar, Madhura M. Joshi, “Automated Food Ordering System with Real-Time Customer Feedback”, in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 2, February 2013.
[18] Kirti Bhandge, Tejas Shinde, Dheeraj Ingale, Neeraj Solanki, Reshma Totare, “A Proposed System for Touchpad Based Food Ordering System Using Android Application”, in International Journal of Advanced Research in Computer Science & Technology (IJARCST 2015), Vol. 3, Issue 1 (Jan. - Mar. 2015).
[19] Sushmita Sarkar, Resham Shinde, Priyanka Thakare, Neha Dhomne, Ketki Bhakare, “Integration of Touch Technology in Restaurants using Android”, in IJCSMC, Vol. 3, Issue. 2, February 2014, pg.721 – 728.
[20] Varsha Chavan, Priya Jadhav, Snehal Korade and Priyanka Teli, “Implementing Customizable Online Food Ordering System Using Web Based Application”, in International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 4, April 2015.
[21] Vikas Mullemwar, Vaibhav Virdande, Madhura Bannore, Ashwini Awari, Raviprakash Shriwas, "Electronic Menu card For Restaurants”, in International Journal of Research in Engineering and Technology.
[22] Ashutosh Bhargave, Niranjan Jadhav, Apurva Joshi, Prachi Oke, Prof. Mr. S. R Lahane, “Digital Ordering System for Restaurant Using Android”, in International Journal of Scientific and Research Publications, Volume 3, Issue 4, April 2013.
[23] Nibras Othman Abdul Wahid (2014). "Improve the Performance of the Work of the Restaurant Using PC Touch Screen", in Computer Science Systems.

S. Durairaj, G. Gopinath, "Analytical Survey on Online Food Delivery Applications of Android Platform from a Service Perspective," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1021-1025, 2019.

Presently,malicious program is a genuine danger. It is created to harm the PC framework and some of them are spread over the associated framework in the system or web association. Researchersare taking extraordinary endeavors to deliver hostile to malware framework with viable malware location techniques to ensure PC framework. As of late extraordinary analysts have proposed malware discovery framework utilizing information mining and AI techniques to identify referred to just as obscure malwares. In this paper, a brief investigation has been led on the current condition of malware disease and work done to improve the malware identification frameworks.In the current work the technique considers the behavior analysis of the user and also of the application as well. For the behavior analysis decision tree is being used. By which different patterns are been drawn of the past considered activities and also for the current activity as well. The data matching and the prediction of the malicious activity is done using the ANN algorithm and also ANN works for the dataset training in which the patterns drawn with respect to the past history is training to work for the prediction process. Begins from pattern generation for which decision tree is being utilized next stage, is tied in with preparing of informational collection for which ANN is being utilized and furthermore ANN works for pattern generationfor productive malware identification.

Attack, ANN (Artificial Neural Network), Malware, Analysis, web, IDS

[1] R. Durgam and R.V.Krishnaiah, “Online Intrusion Alert Aggregation with Generative Data Stream Modeling”, International Journal of Scientific Research in Computer Science &Engineering, Vol.1 , Issue.5 , pp.23-23, Sep-2013.
[2] E. Cooke, F. Jahanian, and D. McPherson, “The Zombie Roundup: Understanding, Detecting, and Disrupting Botnets”, In Proceedings of the USENIX SRUTI Workshop, pp. 39–44, 2005.
[3] M. Casado and M. Freedman, “Peering Through the Shroud: The Effect of Edge Opacity on IP-Based Client Identification”, In Proceedings of the 4th Networked Systems Design and Implementation, April 2007.
[4] C. Low, “Understanding Wireless attacks &detection”, GIAC Security Essentials Certification (GSEC) Practical Assignmen -SANS Institute InfoSec Reading Room,13 April 2005.
[5] A.Saracino, D.Sgandurra, G. Dini, and Fabio Martinelli, “MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention”, IEEE Transactions On Dependable And Secure Computing, Vol. 15, No. 1,pp. 83-97, January/February 2018
[6] T. Y. Win, H. Tianfield, Q. Mair “Detection of Malware and Kernel-level Rootkits in Cloud Computing Environments” IEEE, 2015.
[7] X. Han, J. Sun, W. Qu3, Xuanxia Yao “Distributed Malware Detection based on Binary File Features in Cloud Computing Environment” IEEE, 2014.
[8] A. K. Marnerides, M. R. Watson, N. H. Shirazi, A.Mauthe, and D. Hutchison “Malware Analysis in Cloud Computing: Network and System Characteristics”, 2013.
[9] E. Raftopoulos and XenofontasDimitropoulos “Aquality metric for IDS signatures: in the wild the size matters” EURASIP Journal on Information Security 2013.
[10] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, IJSRNSC, Vol.5 , Issue.6 , pp.5-8, Dec-2017.
[11] L. Caviglione, M. Gaggero, Jean-François Lalande, W. Mazurczyk, Senior Member, IEEE,andMarcinUrba´nski “Seeing the Unseen: Revealing Mobile Malware Hidden Communications via Energy Consumption and Artificial Intelligence”, IEEE Transactions On Information Forensics And Security, vol. 11, No. 4, April 2016.

Sweta Khatana, Anurag Jain, "Malware Detection Using the Behavioral Analysis of the Web based Applications and User," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1026-1031, 2019.

This millennium is all about the copious data available across the globe and the important question of the user is that “Is our data safe and secured?” This paper deals with the generation of keys to be employed in encryption algorithms using our biometric system. The biometric used in this paper is the fingerprint and this concept is used to safeguard the voluminous user data transferred to and from the cloud storages. The process starts from acquiring the fingerprint from the user, preprocess the fingerprints, extract the important features, convert the features into bit matrix form, and generate the thousands of keys for a particular user from the biometric acquired from them. Most of the end user faces immense difficulty to recollect long and complex cryptographic keys. Hence, the research fraternities across the globe is exploring easier ways to use the biometric features of the end users instead of utilizing long passwords or passcode to discover tough cryptographic keys. The main objective of this paper is to integrate the fingerprint of the user to produce the security key to be used in cryptosystem especially in the cloud storage.

Biometric, Security, Cryptokeys, Fingerprints, Minutiae, Cloud security

[1] M Baca and K. Rabuzin, “Biometrics in Network Security”, in Proceedings of the XXVIII International Convention MIPRO 2005, pp. 205-210 , Rijeka,2005.
[2] Beng.A, Jin Teoh and Kar-Ann Toh, "Secure biometrickey generation with biometric helper”, in proceedings of 3rd IEEE Conference on Industrial Electronics and Applications, pp.2145-2150, Singapore, June 2008.
[3] Chen, B. and Chandran, V., "Biometric Based Cryptographic Key Generation from Faces", in proceedings of 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 394 - 401, December 2007
[4] Christian Rathgeb, Andreas Uh, “A survey on biometric cryptosystems and cancelable biometrics”, EURASIP Journal on Information Security 2011.
[5]FengHao, Ross Anderson, John Daugman, (2005) “Combining cryptography with biometrics effectively”, Technical Report No. 640, UCAM-CL-TR-640, ISSN 1476-2986.
[6] Gang Zheng, Wanqing Li and Ce Zhan, "Cryptographic Key Generation from Biometric Data Using Lattice Mapping", in Proceedings of the 18th International Conference on Pattern Recognition, vol.4, pp. 513 - 516,2006.
[7] Jagadeesan.A, T. Thillaikkarasi, K. Duraiswamy, “Cryptographic Key Generation from Multiple Biometric Modalities: Fusing Minutiae with Iris Feature”, International Journal of Computer Applications (0975 – 8887) Vol. 2 – No.6, pp. 16-26, June 2010
[8] Muhammad Khurram Khan and Jiashu Zhang, "Multimodal face and fingerprint biometrics authentication on space-limited tokens", Neurocomputing, vol. 71, pp. 3026-3031, August 2008.
[9] S. Vitabile, V. Conti, M. Collotta, G. Scatà, S. Andolina, A. Gentile, F. Sorbello, "A Real-Time Network Architecture for Biometric Data Delivery in Ambient Intelligent", Journal of Ambient Intelligence and Humanized Computing (AIHC), (in press), © Springer-Verlag Editor, 2012
[10] SP.Venkatachalam, P.MuthuKannan, V.Palanisamy, “Combining Cryptography with Biometrics for Enhanced Security”, International Conference on Control, Automation, Communication and Energy Conservation, INCACEC 2009, pp. 1-6, ISBN: 978-1-4244-4789-3

M. Akila, S. Ravichandran, "Generating Biometric Keys for the Cryptosystem Using the Minutiae Features Present In the Fingerprint," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1032-1037, 2019.