Volume-7 , Issue-9 , Sep - 2019 Go Back

The concept of Smart Campus Near Field Communication (NFC) is a current growing process. We know that education is one of the basic need of every individual now-a-days. Running collage with all student and parents as well as faculty with complete communication on one single platform will be a boon for today’s security. the main advantage of a Smart Campus NFC technology is that it provides advance technology to make the campus life easier. By using this technology, we can easily enter data while accessing any class room or equipment in the campus. The Smart Campus Near Field Communication (NFC) is mostly used by people education and is time efficient. This is a motivation for the university to act in a smart way, today’s society will have the opportunity to lead a collage or students, parents and each other in the faculty of communication. The Smart Campus system is focus on a solution such as security and collage management to address the university with Cutting- Edge Technologies called Internet of Things(IoT) and NFC technologies.

Internet of Things (IoT), NFC Technology, Arduino, Cloud Computing

[1] Smart Campus an Android using IoT and NFC “Shyam Ambilkar1”, “ShivkumarHegonde1”,” Rutuja Therade”1, “Surbhi Lingamwar1” [last accesed:10-08-19]
[2] NFC technology literature “Ed” [last accessed:11-08-19]
[3] NFC-enabled Attendance System Interface “Mahinderjit Singh” [last accessed:11-08-19]
[4] Registration system of cloud campus “ Tetsuya Shigeyasu” [last accessed:12-08-19]
[5] Near-Field Communication Technology and Its Impact in Smart University and Digital Library: Comprehensive Study “Doaa Abdel-Gaber Abdel-Aleem Ali” [last accessed:13-08-19]
[6] Applications and Future of Near Field Communication “Rajiv” [last accessed:14-08-19]
[7] NFC design for attendance system in the university “Marisa Karsen”,” Yohannes Kurniawan”,” Cadelina Cassandra” and “Hanny Juwitasary” [last accessed:14-08-19].
[8] Smart Wireless Attendance Monitoring Using NFC “Puja Rani1”, “Oshin2” [last accessed:15-08-19] Building a smart campus to support ubiquitous learning“Y.Atif”, “SujithSamuelMathew”,“Abderrahmane Lakas” [last accessed:16-08-2014].
[9] Constructing Smart Campus Based on the Cloud Computing Platform and the Internet of Things Published by “Atlantis Press, Paris, France. the authors 1576” [last accessed:17-08-2019].
[10] Smart campus features, technologies, and applicationsreview “Wardani Muhamad”, “Novianto Budi Kurniawan”, “Suhardi Suhardi”[Last accessed:18-08-19]

Vishal S. Patil, Tejaswini S. Borkar, Suraj S. Bhute, Gauri J. Chauhan, Aparna P. Morey, "A Review paper on Smart Campus using NFC technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.108-111, 2019.

The crop cultivation plays a very important role within the agriculture. Presently, the loss of food is principally because of infected crops, that reflexively reduce the assembly rate, productivity per unit space and reduction in quality of economic part of the crops, as a result of the 70-80 per cent blackout in yield of crops is because of diseases caused by varied micro-organisms like bacterium, virus and fungi. The detection of unwellness on the plant could be a vital to stop loss of yield and also the quality of agricultural turn out. The symptoms will be ascertained on the components of the plants like leaf, stem, lesions, fruits and roots that area unit developed because of bound organic phenomenon and abiotic factors. The leaf shows the symptoms by modification in color, spots and gall like formation thereon. This identification or detection of the unwellness is completed by manual observation and infectious agent detection which may consume longer and should prove pricey. In agriculture analysis of automatic plant disease detection is crucial analysis topic because it could prove advantages in observant massive fields of crops, and therefore mechanically observe symptoms of unwellness as shortly as they seem on plant leaves. The digital image process could be a technique used for improvement of the image.

Disease detection; Productivity; Economic part; Image processing; Spots

[1]. Gurjar, Ajay A., and Viraj A. Gulhane. "Disease detection on cotton leaves by eigenfeature regularization and extraction technique." International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE) 1, no. 1 (2012): 1.
[2]. Bhong, Vijay S., and B. V. Pawar. "Study and Analysis of Cotton Leaf Disease Detection Using Image Processing." International Journal of Advanced Research in Science, Engineering and Technology 3, no. 2 (2016).
[3]. Dandawate, Yogesh, and Radha Kokare. "An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective." In 2015 International conference on advances in computing, communications and informatics (ICACCI), pp. 794-799. IEEE, 2015.
[4]. Ying, Geng, Li Miao, Yuan Yuan, and Hu Zelin. "A study on the method of image pre-processing for recognition of crop diseases." In 2009 International Conference on Advanced Computer Control, pp. 202-206. IEEE, 2009.
[5]. Ramakrishnan, M. "Groundnut leaf disease detection and classification by using back probagation algorithm." In 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 0964-0968. IEEE, 2015.
[6]. Sannakki, Sanjeev S., Vijay S. Rajpurohit, V. B. Nargund, and Pallavi Kulkarni. "Diagnosis and classification of grape leaf diseases using neural networks." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1-5. IEEE, 2013.
[7]. Phadikar, Santanu, and Jaya Sil. "Rice disease identification using pattern recognition techniques." In 2008 11th International Conference on Computer and Information Technology, pp. 420-423. IEEE, 2008.

Jyoti, Prince Kumar, "A Brief Review on Plant Disease Detection Using Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.112-114, 2019.

This Paper thinks of the utilizations of NLP (Natural Language Processing) methods for identifying the `phony news`, that is, deceiving news stories that originates from the non-respectable sources. Counterfeit news recognition is a basic yet testing issue in Natural Language Processing (NLP). The fast ascent of person to person communication stages has not just yielded an immense increment in data availability however has additionally quickened the spread of phony news. Given the gigantic measure of Web content, programmed counterfeit news recognition is a pragmatic NLP issue required by all online substance suppliers. This paper displays an overview on phony news discovery. Our overview presents the difficulties of programmed counterfeit news identification. We methodically survey the datasets and NLP arrangements that have been created for this task. We additionally talk about the breaking points of these datasets and issue plans, our bits of knowledge, and suggested arrangements. The fundamental target is to distinguish the phony news, which is a great content characterization issue with a straight forward recommendation. It is expected to manufacture a model that can separate between "Genuine" news and "Phony" news.

Natural Language Processing, Fake news detection, Data Mining, Machine Learning, Dataset

[1] M.Balmas, "Communication Research", SAGE, Vol.41, Issue.3, pp.430-454, 2014.
[2] A. Vlachos, S.Riedel, “Fact checking: Task definition and dataset construction”, In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 18–22, 2014.
[3] V. Rubin, T. Vashchilko,” Identification of truth and deception in text: Application of vector space model to rhetorical structure theory”, In Proceedings of the Workshop on Computational Approaches to Deception Detection, pp. 97–106, 2012.
[4] H. Rashkin, E. Choi, J. Jang, S. Volkova, Y. Choi, “Truth of varying shades: Analyzing language in fake news and political fact-checking”, In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937,2017.
[5] N. Ruchansky, S. Seo, Y. Liu,” A hybrid deep model for fake news detection”, In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806, 2017.
[6] H. Karimi, P. Roy, S.Sadiya, J. Tang, ”Multi-source multi-class fake news detection”, In Proceedings of the 27th International Conference on Computational Linguistics, Location , pp. 1546–1557, 2018.
[7] A. Kirilin and M. Strube, “Exploiting a speaker’s credibility to detect fake news”, In Proceedings of Data Science, Journalism & Media workshop at KDD (DSJM18), 2018.
[8] S. Bhattacharjee, A. Talukder, B. Venkatram Balantrapu, "Active learning based news veracity detection with feature weighting and deep-shallow fusion", In the Proceedings of the 2017 IEEE International Conference on Big Data , Boston, USA, pp.556-565, 2017.
[9] N. Mehala, "Fake News Detection: A Survey" ,International Journal of Computer Sciences and Engineering , Vol.-7, Special Issue-16, pp.81-87, 2019.
[10] O. Ayankemi, “A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.57-63, 2018.
[11] V.Kapoor,”A New Cryptography Algorithm with an Integrated Scheme to Improve Data Security”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue-2, pp.39-46, 2013.

K.D. Patel , "Fake News Detection on Natural Language Processing: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.115-121, 2019.

With technological advancements many small to large, simple to complex activities are automated. Growth of Artificial Intelligent techniques has eased the way we would look to solve the real world problems. One such area which has recently gained lot of attention is the facial analytics. It involves extracting features such as face expressions, gender, age etc. Gender information plays a vital role in areas such as human computer interaction, crime detection, gender preferences, facial biometrics for digital payments etc. This paper proposes an improved Convolutional Neural Network (CNN) framework for real time gender classification from facial images. A pretrained model Visual Geometry Group “VGGNet16” is used. It loads image datasets consisting of male and female images and trains consistently for 16 hours. Haar Cascade classifier is used to classify images based on facial traits. The proposed architecture exhibits a much reduced design complexity as compared to other CNN solutions applied in pattern recognition. A recognition accuracy of 90% was achieved with this method.

CNN, Face Images, Gender Recognition

[1] D. Gupta, “Architecture of Convolutional Neural Networks (CNNs) demystified”, Analytics Vidhya, June 29, 2017.
[2] P. Smith, C. Chen, “Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation”, In the Proceedings of the 2018 IEEE International Conference on Big Data, Seatle USA,pp. 2564-2571,2018
[3] S. Choudhary, M.Agarwal, M. Jailia, “Design Framework for Facial Gender Recognition Using MCNN”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Vol.8 Issue-3, pp. 209-213,2019
[4] O. Arriaga ,M.Valdenegro-Toro, P.G. Ploger, “Real-time Con tional Neural Networks for emotion and gender classification”, In the Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, pp. 221-226, 2019
[5] L. F. d. Araujo Zeni and C. Rosito Jung, "Real-Time Gender Detection in the Wild Using Deep Neural Networks," In the Proceedings of 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, pp. 118-125, 2018.
[6] N. M. Khalifa , M.H. N. Taha , A.E. Hassanien, H. N. E. T. Mohamed “Deep Iris: Deep Learning for Gender Classification Through Iris Patterns”, ACTA INFORM MED. Vol.27, Issue2, pp. 96-102,2019. doi: 10.5455/aim.2019.27.96-102
[7] A. V. Savchenko “Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet “,PeerJ Comput. Sci, June, pp.1-26, 2019. DOI 10.7717/peerj-cs.197
[8] R.Ranjan, A. Bansal, J. Zheng, H. Xu, J.Gleason, B. Lu, A. anduri, J. Chen, C. D. Castillo, R. Chellappa “ A Fast and Accurate System for Face Detection, Identification, and Verification”, Journal of Latex Class Files, Vol. 14, no. 8, pp.1-16, 2015

Taran Rishit Undru, CVNS Anuradha, "A Real Time Gender Recognition System Using Facial Images and CNN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.122-126, 2019.

Progressive Web Application has emerged to be the top contender to alternative native application development. Despite the fact that the development practices for traditional applications have matured and become systemic by providing templates, cross platform development still remains a prominent topic. Developing an application with different architectural structure is redundant and comparing these on different platforms becomes demanding. PWA provides a solution to these problems by having to write a single codebase and providing similar features to all platforms with browser support. In this research paper we analyse the foundations of PWA, its features and core functionalities. A detailed investigation of the architectural structure of a PWA application and its benefit over its counterparts has been viewed. A case study analysing web applications on different platform is carried out.

Progressive Web Applications, Web View, Native Applications, Performance Testing, Service Worker

[1] I. Malavolta, G. Procaccianti, P. Noorland, and P.Vukmirovi, “Assessing the Impact of Service Workers on the Energy Efficiency of Progressive Web Apps,” pp. 52–62, 2017.

[2] I. Malavolta, “Beyond Native Apps: Web Technologies to the Rescue! (Keynote).” ACM Mobile!’16, Amsterdam, Netherlands, pp. 5–6, 2016.
[3] T. Leadership and W. Paper, “Native, web or hybrid mobile-app development.”

[4] S. K. Gudla, J. K. Sahoo, A. Singh, J. Bose, and N. Ahamed, “A Systematic Framework to Optimize Launch Times of Web Apps,” in International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. , 2017, pp. 785–786.

[5] I. Malavolta, G. Procaccianti, P. Noorland, and P. Vukmirovi, “Assessing the Impact of Service Workers on the Energy Efficiency of Progressive Web Apps,” 2017.
[6] A. I. Wasserman, “Software Engineering Issues for Mobile Application Development,” in FoSER 2010, 2010, pp. 397–400.

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[7] H. Takabi, J. B. D. Joshi, and G.-J. Ahn, “Security and Privacy Challenges in Cloud Computing Environments,” IEEE Secure. Priv. Mag., vol. 8, no. 6, pp. 24–31, 2010.
[8] N. Koch, P. Fraternali, and M. (Eds.), “Lecture Notes in Computer Science: Web Engineering,” in 4th International Conference, ICWE 2004 Munich, Germany, July 26-30, 2004 Proceedings, 2004.

[9] G. S. T. Koziokas, Panagiotis T., Nikolaos D. Tselikas, “Usability Testing of Mobile Applications: Web vs. . . Hybrid Apps,” in PCI 2017, September 28–30, 2017, pp. 9–10.
[10] H. Muccini, D. Informatica, A. Di Francesco, D. In-formatica, P. Esposito, and D. Informatica, “Software Testing of Mobile Applications: Challenges and Future Research Directions,” pp. 29–35, 2012.

[11] I. Malavolta, S. Ruberto, T. Soru, and V. Terragni, “End Users ’ Perception of Hybrid Mobile Apps in the Google Play Store,” in Mobile Services (MS), 2015 IEEE International Conference on , 2015.

[12] Ian Warren ; Andrew Meads ; Satish Srirama ; Thi-ranjith Weerasinghe ; Carlos Paniagua, “Push Notification Mechanisms for Pervasive Smartphone Applications,” IEEE Pervasive Compute. , vol. 13, no. 2, pp. 61–71, 2014.

[13] Satish Narayana Srirama, “Mobile web and cloud services enabling Internet of Things,” CSI Trans. ICT, vol. 5, no. 1, pp. 109–117, 2017.

[14] K. Behl, G. Raj, “Architectural Pattern of Progressive Web and Background Synchronization”, in the International Conference on Advances in Computing and Communication Engineering (ICACCE-2018) Paris, France, pp. 366-371, 2018.
[15] T. A. Majchrzak, A.B. Hansen, T.M. Grønli, “ProgressiveWeb Apps: the Definite Approach to Cross-Platform Development?” In the Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 5735-5744, 2018.

Arush Agarwal, Akhil Dixit, "Progressive Web Applications: Architectural Structure and Service Worker Asset Caching," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.127-139, 2019.

VANET provides the information in the dynamically changing environment in the rapidly changing topological environment so as to provide the seamless flow of data between the vehicles in the in ad-hoc environment. Basically VANET is the subset of MANET. For effective routing from the source vehicle to destination vehicle in the dynamic environment the broadcast protocol must withstand this dynamically changing topology of the network. The mobility of network in urban scenarios is highly variable so the protocol must be able to adapt changes as per the topology of VANET. Also the delay in network communication can cause loss of data packets which may be very dangerous from the driver’s point of view in VANET. The loss of data packets may occur due to large amount involved in the communication or another reason may the different types of attacks such as DDoS attack or eavesdropping attacks which will reduce the efficiency and security of the network to a great extent and harm the communication model. This paper focuses on the detection of the attacks such DDoS and Eavesdropping attacks and its effect to the protocol parameter such routing efficiency and mean delay. After detection of these attacks a novel solution is adopted from improving routing efficiency and mean delay of the protocol so as to make network reliable and more secure.

VANET, DADCQ, Attacks, Broadcaststrom

[1] S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, “The BroadcastStorm Problem in a Mobile Ad Hoc Network,”Proc. ACM/IEEE MobiCom,pp. 151-162, 1999.
[2] M.J. Slavik and I. Mahgoub, “Statistical Broadcast Protocol Design for Unreliable Channels in Wireless Ad-Hoc Networks,” Proc.IEEE GlobeCom,Dec. 2010.
[3] W. Lou and J. Wu, “Toward Broadcast Reliability in Mobile Ad Hoc Networks with Double Coverage,”IEEE Trans. Mobile Computing,vol. 6, no. 2, pp. 148-163, Feb. 2007.
[4] Y.-C. Tseng, S.-Y. Ni, and E.-Y. Shih, “Adaptive Approaches to Relieving Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network,”IEEE Trans. Computers, vol. 52, no. 5, pp. 545-557, May 2003.
[5] S. Biswas, R. Tatchikou, and F. Dion, “Vehicle-to-Vehicle Wireless Communication Protocols for Enhancing Highway Traffic Safety,”IEEE Comm. Magazine,vol. 44, no. 1, pp. 74-82, Jan. 2006.
[6] F. Ye, R. Yim, J. Guo, J. Zhang, and S. Roy, “Prioritized Broadcast Contention Control in VANET,”Proc. IEEE Int’l Conf. Comm.(ICC),pp. 1-5, May 2010.
[7] Y. Bi, L. Cai, X. Shen, and H. Zhao, “A Cross Layer Broadcast Protocol for Multihop Emergency Message Dissemination in InterVehicle Communication,”Proc. IEEE Int’l Conf. Comm. (ICC),pp. 1-5, May 2010.
[8] J. Cartigny, D. Simplot, and J. Carle, “Stochastic Flooding
Broadcast Protocols in Mobile Wireless Networks,” technical
report, Universite ´ des Sciences et Technologies de Lille 1, http://citeseer.ist.psu.edu/525199.html, May 2002.
[9] X.-Y. Li, K. Moaveninejad, and O. Frieder, “Regional Gossip Routing for Wireless Ad Hoc Networks,”Mobile Network Applications,vol. 10, nos. 1/2, pp. 61-77, 2005.
[10] S. al Humoud, L. Mackenzie, and J. Abdulai, “NeighbourhoodAware Counter-Based Broadcast Scheme for Wireless Ad Hoc Networks,”Proc. IEEE GlobeCom Workshops,pp. 1-6, 2008.
[11] A.Mohammed,M.Ould-Khaoua,L.Mackenzie,andJ.-D.
Abdulai, “Dynamic Probabilistic Counter-Based Broadcasting in Mobile Ad Hoc Networks,”Proc. Second Int’l Conf. Adaptive Science Technology (ICAST ’09),pp. 120-127, 2009.
[12] M. Slavik and I. Mahgoub, “Stochastic Broadcast for VANET,”Proc. Consumer Comm. and Networking Conf.,Jan. 2010.
[13] O. Tonguz, N. Wisitpongphan, J. Parikh, F. Bai, P. Mudalige, and V. Sadekar, “On the Broadcast Storm Problem in Ad Hoc Wireless Networks,”Proc. Third Int’l Conf. Broadband Comm., Networks and Systems (BROADNETS ’06)pp. 1-11, Oct. 2006.
[14] O. Tonguz, N. Wisitpongphan, F. Bai, P. Mudalige, and V. Sadekar, “Broadcasting in VANET,”Proc. Mobile Networking for Vehicular Environments,pp. 7-12, May 2007.
[15] N. Wisitpongphan, O. Tonguz, J. Parikh, P. Mudalige, F. Bai, and V. Sadekar, “Broadcast Storm Mitigation Techniques in Vehicular Ad Hoc Networks,”IEEE Wireless Comm.,vol. 14, no. 6, pp. 84-94, Dec. 2007.
[16] O. Tonguz, N. Wisitpongphan, and F. Bai, “DV-CAST: A Distributed Vehicular Broadcast Protocol for Vehicular Ad Hoc Networks,”IEEE Wireless Comm., vol. 17, no. 2, pp. 47-57, Apr. 2010.
[17] W. Viriyasitavat, F. Bai, and O. Tonguz, “UV-CAST: An Urban Vehicular Broadcast Protocol,”Proc. IEEE Vehicular Networking Conf. (VNC),pp. 25-32, Dec. 2010.
[18] P. Kyasanur, R. Choudhury, and I. Gupta, “Smart Gossip: An Adaptive Gossip-Based Broadcasting Service for Sensor Networks,”Proc. IEEE Int’l Conf. Mobile Adhoc and Sensor Systems, pp. 91-100, 2006.
[19] B. Bako, F. Kargl, E. Schoch, and M. Weber, “Advanced Adaptive Gossiping Using 2-Hop Neighborhood Information,”Proc. IEEE GlobeCom,pp. 1-6, Nov. 2008.
[20] T. Osafune, L. Lin, and M. Lenardi, “Multi-Hop Vehicular Broadcast (MHVB),” Proc. Sixth Int’l Conf. ITS Telecomm., pp. 757-760, June 2006.
[21] M. Mariyasagayam, T. Osafune, and M. Lenardi, “Enhanced Multi-Hop Vehicular Broadcast (MHVB) for Active Safety Applications,”Proc. Seventh Int’l Conf. ITS Telecomm. (ITST ’07),pp. 1-6, June 2007.
[22] Michael Slavik, Student Member , IEEE, and Imad Mahgoub, Senior Member , IEEE, “Spatial Distribution and Channel Quality Adaptive Protocol f or Multihop Wireless Broadcast Routing in VANET”,IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 12, NO. 4, APRIL 2013.
[23]S.RoselinMary,M.Maheshwari,M.Thamaraiselvan,”Early Detection of DOS Attacks in VANET Using Attacked Packet Detection Algorithm (APDA)”

Dharmaveer P. Choudhari, Sanjay S. Dorle, "DADCQ Protocol and Attacks in VANET - A Literature Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.140-147, 2019.

Database is considered as an essential element in any organization. Many Databases contains columns, dedicated to hold information about fingerprint. Fingerprint can be stored in two ways. We can either store the path of the fingerprint image inside the database or store the fingerprint template itself as binary stream. Sometimes, database is overloaded with many requests on fingerprint column and this degrades the overall performance of the Database. To solve this problem, we take the advantage of Fingerprint conversion to text which relieves the battleneck problem and offers better performance. SQL Server is used to show the difference of Database throughput in both situation, fingerprint as image and fingerprint as text.

fingerprint conversion, fingerprint presentation, minutiae point, Database performance

[1] Haonan Su, Dong Zheng, and Hinghui Zhang. “An Efficient and Secure Deduplication Scheme Based on Rabin Fingerprinting in Cloud Storage”. IEEE international Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2017.
[2] Wahid Zafar, Tasweer Ahmad, and Muhammad Hassan. “Minutiae Based Fingerprint Matching Techniques”. 2014.
[3] Chiung Ching Ho, and C.Eswaran. “Consolidation of Fingerprint Databases: Challenges and Solutions in the Malaysian Context”. International Journal of Computer Information Systems and Industrial Management Applications. Vol 5 . pp. 373-382 , 2013.
[4] Ankita Mehta, and Sandeep Dhariwal. "Design & Implementation of Features based Fingerprint Image Matching System". Vol .2, International Journal of Multidisciplinary and Current Research. 2014.
[5] D Maltoni, D Maio, and S Parbhakar. "Handbook of Fingerprint Recognition". Springer publisher, New York. pp174, 2003, ISBN no 0-387-95431-7
[6] J Kilian, "Advanced in Cryptology - Crypto", Springer publisher, USA, pp447, 2001, ISBN no 0302-9743.
[7] Nashwan Yahya Ali1, Dr. V M Thakare "MECHANISM OF FINGERPRINT CONVERSION TO TEXT". IJCSE, 2017.

Nashwan Yahya Ali, "Fingerprint usage as a Text and Its Performance on Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.148-151, 2019.

Nowadays, there has been a sudden surge in thefts from ATMs. Though ATM cum debit cards provides us with a faster access to our money, there lies a big security issue with such cards. The cards are skimmed by using skimming devices and the information from these cards are often stolen when one tries to perform transactions at an ATM. This information is then misused by the people performing such thefts. This paper will shed light upon the above matter. In this paper, we will be discussing the very basics of an ATM cum debit card and an ATM. Then we will talk about the method in which such thefts take place which we can refer to as skimming. In addition to that, we will elaborate on a technique that could possibly be used to prevent such thefts from taking place. The paper will elaborate on the measures to be taken to implement such a mechanism and the scope of this mechanism.

ATM, skimming, debit card, theft, PIN, bank server, transaction, OTP

[1] Khalifa, Salem S.M, and Kamarudin Saadan, “The Formal Design Model of an Automatic Teller Machine (ATM).” Lecture Notes on Information Theory, vol. 1, no. 1, Mar. 2013.
[2] Mrunal A. Mahajan, “An approach for securing Swiping Machine transactions”, Journal Paper (IJSRCSE), Volume 06 , Special Issue.01 , pp.68-72, Jan-2018.
[3] Omari, Richard Kwaku Bamfo, “An assessment of the use of Automated Teller Machine (A.T.M) of Barclays Bank Ghana Limited Akim Oda Branch”, Institute Of Distance Learning, Kwame Nkrumah University of Science and Technology, September 2012.
[4] Mukesh Sharma and Shailendra Jha, “Digital Data Stealing from ATM using Data Skimmers: Challenge to the Forensic Examiner”, Journal of Forensic Sciences and Criminal Investigation, Volume 1, Issue 4, January 2017.
[5] Dhanush J.Nair and Sunny Nahar.
“ATM Transaction : A New Time Based Approach Research paper”, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 6, June 2015.
[6] Yekini N.A., Itegboje A.O., Oyeyinka I.K. and Akinwole A.K., “Automated Biometric Voice-Based Access Control in Automatic Teller Machine (ATM)”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No.6, 2012.
[7] Kavita Hooda, “ATM Security”, International Journal of Scientific and Research Publications, Volume 6, Issue 4, April 2016.

Asoke Nath, Sourya Saha, Sarthak Gupta, Nilarghya Das, "Enhancing Security of ATM Transactions via Debit Cards," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.152-157, 2019.

The paper analyzes many learning disabilities prevalent in male and female sample student population which describes how the significance of the problems can be used to pinpoint the existence of educational problems related to health. A learning problem or disability can be seen as spanning a continuum from mild to severe. We will use the term “learning disability” to define the milder educational problems. The severity of the learning problem depends on the number and severity of processes affected. Statistics show that a large number of students are academically inhibited that they have trouble in holding their professional status also in later years. Many students all over the world suffer from some form of learning disability like arithmetic difficulties, verbal disability, memory retention disorders etc. Personal characteristics like introvert nature, inferiority complex, attention deficiency etc reduce their academic progresss. The social factors trigger these problems that they do not fit into their peer groups. They also exhibit learning disabilities due to biological factors like parents to sibling disorders, chromosomal disorders etc. It can also be due to psychological factors like lack of self confidence, lack of motivation, not adaptability etc. To model this problem we use Chi-square variate to find the independence of two attributes male students and female students category which forms two groups with the different number of students affected by the different disability causing factors. We assume the null hypothesis that the disabilities of two categories of students are independent of number of students affected by different factors. The calculated Chi-square value is less than the table value at 5 % level of significance. Hence the null hypothesis can be accepted. We can therefore conclude that male and female categories of students in the sample population taken are independent of the disability causing factors in each level.

learning disabilities, null hypothesis, significance level, hereditary factors, Personal traits, Psychological factors, Social factors.

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[9] Dr.R.Jamuna "Data Analytics and Predictions in Cell Phone usage" , International Journal in IT & Engineering (IJITE),Volume 6 Issue 1, page 22-27 ,January 2018 ISSN: 2321-1776 Impact Factor: 6.341, International Journal in IT & Engineering.

R. Jamuna, "Modeling the Learning Disabilities in Student Population," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.158-161, 2019.

In this postulation, some progress area based division approaches have created to perform picture division for grayscale and shading pictures. PC vision and picture getting applications, picture division is a significant pre-handling step. The fundamental objective of the division procedure is the partition of forefront area from foundation district. In light of the yield of the division result, division can be ordered as worldwide division or neighborhood division. The worldwide division goes for complete detachment of the article from the foundation while the neighborhood division partitions the picture into constituent locales. For accomplishing division, various calculations are created by different specialists. The division approaches are application explicit and don`t function admirably for both grayscale and shading picture division. For any picture comprising of frontal area and foundation, some change districts exist between the forefront and foundation areas. Powerful extraction of change district prompts a superior division result. In this manner, the doctoral postulation plans to proficient and viable change area approaches for picture division for both grayscale and shading pictures.

Image Segmentation, ME, FPR, FNR

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Rukmani Kushwaha, Devkant Sen, Abhishek Bhatt, "Automated Cancer Diagnosis Identification System using Image Segmentation and Threshold Filter," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.162-166, 2019.