Volume-07 , Special Issue-01 , Jan - 2019 Go Back

Agriculture plays a vital role in India . Most of the people in India are involved for cultivation. This paper presents different techniques of data mining used in Agriculture sector. Several methodologies has been used in data mining techniques such as Neural Network, K-Means, Fuzzy-set , Bayesian Network, K-nearest neighbor, Decision tree analysis etc.

Data Mining Techniques, Agriculture

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[3]. Zhang G.Peter,” . Nerual Network for data mining”, Georgia State University,Department of managerial Science, gpzhang@gsu.edu
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[5]. Amrender Kumer ,”Forecasting of crops using data mining techniques”, Indian Agriculture statistic research Institute,Newdelhi-110012.
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Biswajit Mondal, "A Study on Data mining techniques used in Agriculture", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.109-112, 2019.

Today’s economy becomes more and more knowledge based, and education is a dynamic process of gaining and updating knowledge for individual to mass people throughout the globe. Education now a day’s required not only for the growing up and carrier oriented student but also for the qualified person who are in a very crucial position, for regular refreshing their present knowledge. To meet these requirements, the latest technology should be utilised for all round development of the present e- education system. So learning anytime anywhere, any one, and for anything, is required to be available through some mobile devices using efficient secured cloud computing integrated network. All these user requirements force to modify the present network architecture with the currently innovated 5G technology. In this paper we explore the mobile education scenario with 5G technology under design and implementation aspect.

OFDM Orthogonal Frequency Division Multiple Access, TDMA Time Division Multiple Access, VR Virtual Reality, AR Augmented Reality, CCN Content Centric Network, ICNInformation Centric Network, NREN- National Research and Education Network,GEANTPan European Network,eduroamworld wide education roaming for research and education, RATRemote Access Troja

[1] www.bsnl.o.in dated 08/10/2018
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[6] Andrew S.Tanenbaum,Computer Networks4th Edition, Prentice Hall of India,2003.
[7] BruiceSchneier,Applied Cryptography 2ndEdition, Jhon Wiley & Sons INC, UK.
[8] https://www.uc3m.es dated 10/10/2018, Interview with AruroAzcorra, Professor UC3M

Pradip Kumar Samanta, "Impact of 5G Technology in Efficent M-Edcation", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.113-117, 2019.

The Blockchain concept permits the simultaneous working of multiple stakeholders of business by eliminating the intermediary. It makes the transaction possible even if the stakeholders do not trust each other. Many companies including the security exchanges have developed blockchain based applications in last two years to make payments internally within the company as well as externally to trade with outside world. However, there are many who are still hesitating in adopting the system. Security and monitoring mechanism to administer blockchain are major issues of concern among the regulators and adopters of the technology. This paper is an attempt to explore the various dimensions of blockchain technology in context of financial sector. The technology is continuously developing and hence a critical understanding is required to reap its all the benefits.

Distributed Ledger, Decentralized network, Cryptocurrency, Bitcoin, KYC, Identity, Block, Transaction, Fraud, Security

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[3] Cheng, R., & Song, D. (2018, January 29). Smart Contracts. Retrieved October 2, 2018, from https://berkeley-blockchain.github.io: https://berkeley-blockchain.github.io/cs294-144-s18/assets/docs/02-smartcontracts-jan-29-2018-v2.pdf
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[6] DragLet. (2018). Smart Contract Application Examples and Use Cases. Retrieved October 16, 2018, from https://www.draglet.com: https://www.draglet.com/blockchain-services/smart-contracts/use-cases/
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[12] PWC. (2018). Blockchain in financial services. Retrieved September 5, 2018, from https://www.pwc.com: https://www.pwc.com/us/en/industries/financial-services/research-institute/top-issues/blockchain.html
[13] Smolenaers, J. (2016, November 29). Blockchain – loyalty and rewards. Retrieved October 16, 2018, from https://www2.deloitte.com: https://www2.deloitte.com/nl/nl/pages/financial-services/articles/blockchain-technology-loyalty-and-rewards.html
[14] Szabo, N. (1996). Smart Contracts: Building Blocks for Digital Markets. Retrieved October 11, 2018, from http://www.fon.hum.uva.nl: http://www.fon.hum.uva.nl/rob/Courses/InformationInSpeech/CDROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/smart_contracts_2.html

Madhu Agnihotri Nee Agarwal, "Blockchain Technology for Financial Sector – An Overview", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.118-122, 2019.

Now-a-days ICT (Information and Communication Technology) is so much improved by using various network technologies so that any type of information can be send and received very easily. This information may be belongs to banking system, government oriented system or E-learning system. Security is highly needed to protect it from any unauthorized person. For security four things are required: confidentiality, integrity, authentication and non-repudiation. When a sender wants to send an electronic document to the receiver, an attacker can get it and modify it and send the altered document to that receiver. Digital Signature is applied to avoid such situations. Properly applied Digital Signature gives confident to the receiver that the document is reliable and was sent by the original sender. Thus Digital Signature provides not only the confidentiality and integrity of the document but also provides non-repudiation so that the signature can’t be denied by the signer. In this paper authors have discussed about the comparative study of different cryptographic Digital Signature algorithms such as RSA, DSA, ECDSA, GOST and ElGamal to achieve a better security for authenticity and integrity in E-Learning system during upload lecture notes between Teacher and Admin.

ICT, Confidentiality, Integrity, Authentication, Non-repudiation, Digital Signature, RSA, DSA, ECDSA, GOST, ElGamal

[1] A.Kahate, “Cryptography and Network Security”, McGraw-Hill Publisher, India, pp.7-9,197-199, 2010.
[2] B. Schneier, “Applied Cryptography”, Wiley Publisher, India, pp. 476-478,495-496, 2007.
[3] W. Stallings, “Cryptography and Network Security Principles and Practices”, Pearson Publisher, India, pp.398-400, 2012.
[4] A. Roy, S. Karforma, “A Survey on digital signatures and its applications”, Journal of Computer and Information Technology Vol. 03 , No. 1 & 2, Pp- 45-69, August 2012.
[5] A. Ghosh, S. Karforma,” Object Oriented Modeling of DSA for Authentication of Student in E-Learning”, International Journal of Science and Research, Vol. 03, No. 07, pp. 2293-2297, July 2014.
[6] M. Kaur, N.Kaur, B. Singh, “Comparative Study of Different Cryptographic Algorithms”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 4, pp. 352-354, May 2017.
[7] C. Endrodi, “Efficiency Analysis and Comparison of Public Key Algorithms”, Presented in Conference of PhD Students in Computer Science, July 2002.
[8] A. Sarkar, “An Overview of Cryptographic Algorithms and Security Challenges in Big Data”, ACCENTS Transaction on Information Security, Vol. 1, pp. 7-14, 2016.
[9] R. Haddaji, R. Ouni, S. Bouaziz, A. Mtibba, “Comparison Digital Signature Algorithm and Authentication Schemes for H.264 Compressed Video”, International Journal of Advanced Computer Science and Applications, Vol.7, No. 9, pp.357-363, 2016.
[10] A. Khalique, K. Singh, S. Sood, “Implementation of Elliptic Curve Digital Signature Algorithm”, International Journal of Computer Applications, Vol. 2, No. 2, pp. 21-27, May 2010.
[11] A. I. Ali, “Comparison and Evaluation of Digital Signature Schemes Employed in NDN Network”, International Journal of Embedded Systems and Applications, Vol. 5, No. 2, pp. 15-29, June 2015.
[12] M. Michels, D. Naccache, H. Petersen, “GOST 34.10- A Brief Overview of Russia’s DSA”, Published in Computers and Security, Vol. 15, No. 8, pp. 725-732, 1996.
[13] E. R. Weippl, ”Security in E-Learning”, Springer Publisher, India, 2005
[14] D. Johnson, A. Menezes, S. Vanstone, “The Elliptic Curve Digital Signature Algorithm (ECDSA)”, International Journal of Information Security, Vol. 1, No. 1, pp. 36-63, 2001.
[15] S. Karforma, S. Banerjee , “Object Oriented modeling of ElGamal Digital Signature for authentication of study material in E-learning system”, IJARSE, Vol..4, No.2, pp. 455-460 February 2015.
[16] S. Singh, Md. S. Iqbal, A. jaiswal, “Survey on Techniques developed using Digital Signature: Public Key Cryptography”, International Journal of Computer Applications, Vol. 117, No. 16 pp. 1-4, May 2015.
[17] A. H. Lone, M. Uddin, “Common Attacks on RSA and its Variants with Possible Countermeasures”, International Journal of Emerging Research in Management & Technology, Vol. 5, No. 5, pp. 65-70, May 2016.
[18] M. Repka, M. Varchola, M. Drutarovsky, “ Improving CPA Attack Against DSA and ECDSA”, Journal of Electrical Engineering, Vol. 06, No. 03, pp. 159-163, 2015.
[19] J. Schmidt, M. Medwed, “A Fault Attack on ECDSA”, In the Proceedings of the 2009 workshop on Fault Diagnosis and Tolerance in Cryptography, Lausanne, Switzerland, pp. 93-99,2009.
[20] N. Courtois, M. Misztal, “1st Differential Attack on Full 32-Round GOST”, International Conference on Information and Communications Security, Beijing, China, pp. 216-227, 2011.
[21] X. Li, X. Shen, H. Chen, “ElGamal Digital Signature Algorithm of Adding a Random Number ”, Journal of Networks, Vol. 06, No. 05, pp. 774-782, May 2011.

A. Ghosh, S. Karforma, "Authentication of Study Material in E-Learning using Digital Signature Algorithms", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.123-127, 2019.

Confidence Based Learning (CBL) is an innovative technique for teaching and learning mechanism including training on hands-on-practice. The technique revolves around majorly three phases: diagnosis, prescribe and learning. In this paper the authors propose a technique that prescribe a customized learning content in terms of Learning Object (LO) where it identifies the deficiencies from the 2-dimensional assessment. The proposed system also takes care of the dependencies in terms of content as well as pre-requisites as required. The system identifies the average increment of level of confidence and knowledge while prescribing the contents. The system however has certain limitation where despite best effort in customizing content, the trend of the learner is towards downward. In such extreme cases human intervention may be required.

CBL, Content Modelling, Content Prescription

[1] R. Chatterjee, S. Mukherjee, R. Dasgupta, “Design of an LMS for Confidence Based Learning”, In the Proceedings of the 5th International Technology Education and Development Conference (INTED 2011), Valencia, Spain, pp.619-626, 2011.
[2] S. S. Jacobs, “Confidence-Weighting as a Scoring Technique”, Annual Convention of American Educational Research Association, Mineapolis, Minnesoata, USA, March 1970.
[3] T. M. Adams, Gary W. Ewen, “The importance of Confidence in improving Educational Outcomes” 25-Annual Conference on Distance Teaching and Learning, University of Winsconsin, Madison, Wisconsin, USA, 2009.
http://www.uwex.edu/disted/conference/Resource_library/proceedings/0 9_20559.pdf. Date of access (DOA): April 15, 2016.
[4] S. Nath, R. Chatterjee, “Deficiency diagnosis technique for Confidence Based Learning” 6th International Conference on Education and New Learning Technologies (EDULEARN14), Barcelona, Spain. pp. 6507-6514, 2014
[5] D. A. Barr, J. R. Burke, “Using confidence-based marking in a laboratory setting: A tool for student self-assessment and learning”, The Journal of Chiropractic Education, Vol. 27, Issue. 1, pp- 21-26, 2013.
http://www.journalchiroed.com/doi/full/10.7899/JCE-12-018. Date of Access (DOA): July 5, 2016.
[6] R. Chatterjee, J.K. Mandal, "Two dimensional assessment technique for CBL", IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE-2016), Bangkok, Thailand pp.40-43, 2016.
[7] S. Maxwell, J Mucklow, “e-learning Initiatives to Support”, British Journal for Clinical Pharmacology, Vol.74, Issue.4, pp.621-631, 2012
[8] V. Shute, B. Towle, “Adpative E-learning”, Educational Pschyologist, Vol.38, Issue.2, pp.105-114, 2003
[9] G.J. Hwang, H.Y. Sung, C.M. Huang, I. Huang, C.C. Tsai, “Development of aPersonalized Educational Computer Game Based on Students’ Learning Styles”, Educational Technology Research and Development, Vol.60, Issue.4, pp.623-638, 2012
[10] R. Chatterjee, J.K. Mandal, " A Novel Learning Object Framework for Confidence Based", IEEE International Conference on Information Science and Communication Technologies (ICISCT-2016), Tashkent, Uzbekistan pp.1-6, 2016. .
[11] R. Chatterjee, S.P. Kar, J.K. Mandal, " An Intelligent Mining Technique for CBL", IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE-2017), Hong Kong , Hong Kong pp.303-306, 2017.

Rajeev Chatterjee, Sadhu Prasad Kar, Jyotsna Kumar Mandal3, "An Intelligent Prescription of Content Modelling for A Typical Learner", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.128-131, 2019.

In this paper a corelated bit plane based steganographic technique has been proposed. The image is sliced into bit planes. A weighted matrix is created corresponding to each bit plane. The entries in the weighted matrix is made based on the position of bit plane and the values are corresponding to ones in bit plane matrix. The correlation of original image and all the weighted matrices corresponding to all bit planes are calculated and bit planes corresponding to two minimum correlation coefficients are selected for embedding. The secret image is converted into binary string. A window is swept over the selected bit planes in row major non-overlapping fashion and secret bits are embedded into these windows in diagonal fashion. The proposed method achieved better image quality on embedding

Steganography, Correlation

[1]. K. Gaurav, U. Ghanekar,” Image steganography based on Canny edge detection”,dilation operator
and hybrid coding. Journal of Information Security and Applications Volume 41,pp:41-51
[2]. S.K Ghosal, J.K Mandal,” On the use of the Stirling Transform in image steganography”, Journal of Information Security and Applications,2018,Doi: https://doi.org/10.1016/j.jisa.2018.04.003.
[3]. S Kumar, A. Singh, M. Kumar,” Information hiding with adaptive steganography based on novel fuzzy edge identification”, Defence Technology,2018, doi: 10.1016/j.dt.2018.08.003
[4]. C-F. Lee, C-C. Chang, X. Xie, K. Mao, R-H. Shi,” An Adaptive High-Fidelity Steganographic Scheme Using Edge Detection and Hybrid Hamming Codes”, Displays,2018, doi: https://doi.org/ 10.1016/j.displa.2018.06.001
[5]. Nguyen, T.D., Arch-int, S. & Arch-int N., “An adaptive multi bit-plane image steganography using block data-hiding”, N. Multimed Tools Appl ,2018, Vol 75 issue 14, pp. 8319–8345.
[6]. Standard Image database, USC University of Southern California, http://sipi.usc.edu/database/, doa:16, October 2018
[7]. M. Y. Valandar, P. Ayubi., M. J Barani. “ A new transform domain steganography based on modified logistic chaotic map for color images”, Journal of Information Security and Applications, 2017, vol. 34 pp. 142-151.
[8]. P. Maniriho., T. Ahmad,” Information Hiding Scheme for Digital Images Using Difference Expansion and Modulus Function”, Journal of King Saud University - Computer And Information Sciences.,2018,doi:https://doi.org/10.1016/j.jksuci.2018.01.011
[9]. H.R Kanan,. And B. Nazeri,” A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm”, Expert Systems with Applications,vol. 41, issue 14,pp. 6123–6130.
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Sujit Das, Jyotsna Kumar Mandal, Arundhati Bhowal, "Bit Plane Based Image Authentication in Spatial Domain", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.132-137, 2019.

Cloud computing is the new emerging technology and its real trends toward massively scalable various computing services. Cloud computing services are used widely to foster the business volume of an organization or industry based on delivery and consumption of everything from storage to computing management services with minimum effort. Regarding to this importance, the service providers of this technology needs to address many issues related to the cloud computing environment security issues like privacy, authentication and integrity issues etc. Many researchers worked on these issues and provide the possible mechanisms to resolve the issues. However our paper represents a critical study on authentication issues in cloud computing environment.

Cloud Computing, Authentication Issues, Research Issues and Challenges

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[2] National Institute of Standards and Technology, U.S. Department of Commerce,”NIST Cloud Computing Program “ sp 500-322.
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[14] S. Singla, J. Sigh, “ Cloud Data Security using Authentication and Encryption Technique “ ,Global Journal of Computer Science and Technology , vol.13,Issue .3, Ver. 1.0, 2013.
[15] R. Gajula,Dr. A.M.Qyser, N. Rajender,” Combining Two Factor Authentication and Public Key Encryption to Ensure the Authentication in Cloud Computing”,International Journal of Recent Trends in Engineering & Research, pp.118-121,2017.
[16] S.A. Adam, A. Yousif, M.B. Bashir, “ Multilevel Authentication Scheme for Cloud Computing “,International Journal of Grid and Distributed Computing, Vol. 9, No. 9 , pp.205-212,2016.
[17] B. Sumitra, C.R.Pethuru, M.Misbahuddin, “A Survey of Cloud Authentication Attacks and Solutions Approaches”, International Journal of Innovative Research in Computer and Communication Engineering “ Vol.2, Issue .10, pp.6245-6253, 2014.

Dulal Kumbhakar, Sunil Karforma, "A Study on Authentication Issues in Cloud Computing Environment", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.138-142, 2019.

Nowadays it is believed that Brain tumor is one of the most harmful diseases that may lead to serious cancer. Major issue of the treatment of brain tumor is early detection of it before leading to malignant stage. More importantly early diagnosis of brain tumors plays an important role in improving further treatment possibilities and thus increases the survival rate of the patients. Here in this study, we have developed a system that can accurately detect tumor from brain Magnetic Resonance Imaging (MRI) images. To do this we have prepared a laboratory made moderate size database collecting various types of brain Magnetic Resonance Imaging images. In this experiment the brain MRI image has been preprocessed first, then the image has been separated into tumor or non-tumor portion of the image using deep neural net.

Brain Tumor, MRI, CNN, Anisotropic Diffusion

[1] Nabanita Basu, Sanjay Nag, Indra Kanta Maitra and Samir K. Bandyopadhyay, ”Artefact removal and edge detection from medical image”, European Journal of Biomedical And Pharmaceutical Sciences, Ssn 2349-8870, Volume: 3, Issue: 4, 493-502, 2016
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[3] Rafel C. Gonzalez, Rechard E. Woods, “Digital Image Processing”, Prentice-Hall, 3rd Edition, 2008
[4] Manoj K Kowear and Sourabh Yadev, “Brain tumor detection and segmentation using histogram thresholding”, International Journal of engineering and Advanced Technology, April 2012.
[5] Rajesh C. patil, A.S. Bhalchandra, “Brain tumor extraction from MRI images Using MAT Lab”, IJECSCSE, ISSN: 2277-9477, Volume 2, issue1.
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[7] M.Karuna, Ankita Joshi, “Automatic detection and severity analysis of brain tumors using gui in matlab” IJRET: International Journal of Research in Engineering and Technology, ISSN: 2319-1163, Volume: 02 Issue: 10, Oct-2013
[8] R. B. Dubey, M. Hanmandlu, Shantaram Vasikarla, “Evaluation of three methods for MRI brain tumor segmentation”, IEEE computer society, ITNG.2011.92
[9] S. Roy and S.K. Bandyopadhyay, ―Detection and Quantification of Brain Tumor from MRI of Brain and it’s Symmetric Analysis,‖ International Journal of Information and Communication Technology Research, KY, USA, June 2012.
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Debjyoti Ghosh, Utpal Roy, "Brain Tumor Detection from MRI Image Using Deep Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.142-149, 2019.

This study investigates the feasibility of identifying age group, gender, handedness and number of hand(s) used of a user by measuring the typing pattern on a computer keyboard which has good impact on keystroke dynamics biometric user authentication system. Fuzzy-Rough Nearest Neighbour (FRNN) with the help of Vaguely Quantified Rough Set (VQRS) machine learning method was used to develop the model based on the collected typing pattern and evaluated the effectiveness of the classifier in this domain. Multiple benchmark datasets have been used to validate the proposed model in order to check the robustness of the proposed approach. The obtained results indicate that age group, gender, handedness, and a number of hand(s) used can be predicted by the way user type on a computer keyboard for a single predefined text. It is also observed that incorporation of such soft biometric traits as extra features with primary keystroke dynamics characteristics can be used to enhance the performance of keystroke dynamics systems pretending to be used in future at low cost. The model is developed with a limited number of samples collected from a small group of participants in a controlled environment. However, this model will be further trained and evaluated by some extra features which are easily available in each smartphone such as gyroscope and acceleration information. Identifying such traits are important issues in digital forensics, age-based access control, targeted advertisement and auto profiling of the users. It adopts a suitable method to be used on the desktop computer as well as a smartphone.

Keystroke Dynamics (KD), Soft Biometric, Fuzzy Rough NN (FRNN), Vaguely Quantified Rough Set (VQRS)Introduction

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Soumen Roy, Utpal Roy, D. D. Sinha, "Feasibility of Predicting Soft Biometric Traits Based on Keystroke Dynamics Characteristics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.150-157, 2019.

Negotiation is a long standing process during procurement. Traditional negotiation needs a complete knowledge about the business process and its legacy systems. Depending upon the size, volume and the nature of the business the criticality of the negotiation process varies. For an efficient negotiation of a large and medium size enterprise historical information’s plays a vital role. A Data Warehouse along with the OLAP measures provide that support. Further the introduction of Internet the entire negotiation process is changed. The traditional transaction process is replaced by the automated transactions. The real market place is now become a virtual market place and opens new avenues for enterprises to do smart business through electronic agents. The agents are multifaceted, intelligent and autonomous and can be made enriched with the enhancement of business logics. In this paper we propose a generic framework on e-procurement process with the help of a case study in steel industry.

E-procurement, Negotiating Agents, Agent’s Knowledge base, Data Warehouse, OLAP Measure

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R. Karmakar, B. B. Sarkar, N. Chaki, "A Data Warehouse Application Framework for Negotiation in Procurement", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.158-164, 2019.