Volume-8 , Issue-6 , Jun - 2020 Go Back

Nowadays, there is a very adverse impact on economic, cultural, social and almost all fields in the world because of Covid-19. The Covid-19 term is described as -`CO` for corona, `VI` for virus, and `D` for disease. It is an infectious disease caused by severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Since December 2019, corona-virus disease (COVID-19) has out-broke from the country China. Till now, more than 78, 23, 289 people are infected and more than 4 Lakhs of deaths have been caused worldwide. Unfortunately, the number of infections and deaths are still increasing rapidly which has put the world in a different state. Artificial Intelligence can play a key role to infection forecasting in national and provincial levels in many countries. The objective of this study is to use machine learning methods to forecast the number of cases for the next 2 weeks, i.e. till 30th June 2020. The data was collected from 22nd January to 15th June 2020 by nationally recognized sources. The data file contains the cumulative count of confirmed, death and recovered cases of COVID-19 from different countries from the date 22nd January 2020.In this study, the outbreak of this disease has been analyzed for India till 15th June 2020 and predictions have been made for the number of cases for the next two weeks.

Covid-19, Corona, Corona Virus, Machine Learning, Forecasting, Artificial Intelligence, time series forecasting

[1] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.19-22, 2018
[2] Hemant Kumar Soni, "Machine Learning – A New Paradigm of AI," International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.3, pp.31-32, 2019
[3] Amogha A.K., "Load Forecasting Algorithms with Simulation & Coding," International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.15-20, 2019
[4] K. Krishna Rani Samal, Korra Sathya Babu, Santosh Kumar Das, Abhirup Acharaya , ?Time Series based Air Pollution Forecasting using SARIMA and Prophet Model? , In the Proceedings of ITCC 2019: International Conference on Information Technology and Computer Communications, pp 80-85, 2019.
[5] Upendra Kumar Tiwari & Rizwan Khan, ? Role of Machine Learning to Predict the Outbreak of Covid-19 in India?, Journal of Xi`an University of Architecture & Technology, Vol.12, Issue.4, pp. 2663-2669, 2020.
[6] Herlawati ,?COVID-19 Spread Pattern Using Support Vector Regression?, PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal , Vol.8, Issue.1, pp. 67-74, 2020
[7] Dutta,Shawni , Samir Kumar Bandyopadhyay ,Tai-Hoon kim , ?CNN-LSTM Model for Verifying Predictions of Covid-19 Cases?, Asian Journal of Computer Science and Information Technology, Vol.5, Issue.4, pp. 25-32, 2020
[8] Rustam, Furqan & Reshi, Aijaz & Mehmood, Arif & Ullah, Dr. Saleem & On, Byungwon & Aslam, Waqar & Choi, Gyu Sang, ?COVID-19 Future Forecasting Using Supervised Machine Learning Models?, in IEEE Access, Vol. 8, pp. 101489-101499, 2020
[9] R. Ranjan, ?Predictions for COVID-19 outbreak in India using Epidemiological models? medRxiv 10.1101/2020.04.02.20051466, 2020
[10] Simon James Fong, Gloria Li, Nilanjan Dey, Rub?n Gonz?lez Crespo, Enrique Herrera-Viedma, ? Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak?, International Journal of Interactive Multimedia and Artificial Intelligence, Vol.6, Issue.1, pp. 132-140 , 2020
[11] Petropoulos F, Makridakis S, ?Forecasting the novel coronavirus COVID-19? , PLOS ONE journal , March 31, 2020. https://doi.org/10.1371/journal.pone.0231236 , 2020
[12] Zheng N, Du S, Wang J, Zhang H, Cui W, Kang Z, et al. , ?Predicting COVID-19 in China Using Hybrid AI Model?, IEEE Trans Cybern, https://doi.org/10.1109/TCYB.2020.2990162, 2020
[13] Heni Bouhamed, "Covid-19 Cases and Recovery Previsions with Deep Learning Nested Sequence Prediction Models with Long Short-Term Memory (LSTM) Architecture," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.10-15, 2020

Saroj S. Date, Sachin N. Deshmukh, "Forecasting novel COVID-19 confirmed cases in India using Machine Learning Methods," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.57-62, 2020.

Filtering is an essential part of any signal processing system. This involves estimation of a signal degraded, in most cases, by additive random noise. Several filtering techniques have been proposed where linear processing techniques have been the method of choice for many years because of their simplicity. Most of these techniques, however, assume a Gaussian model for the statistical characteristics of the underlying process and try to optimize the parameters of a system for this model. Nonlinear techniques have recently assumed significance as they are able to suppress noise to preserve important signal elements such as edges and fine details and eliminate degradations occurring during signal formation or transmission through nonlinear channels. A detailed literature survey has been done here to compare these conventional image filters with a iterative approach. This paper includes an analysis about the significance of iteration based approach for image filtering.

Image Processing, Filters, Denoising

[1]. Anas, R.; Elhadi, H.A.; Ali, E.S.; ?Impact of Edge Detection Algorithms in Medical Image Processing?, World Scientific News, vol: 118, 2019, pp: 129-143
[2]. Arabinda Dash and Sujaya Kumar Sathua, ?High Density Noise Removal By Using Cascading Algorithms?, Fifth International Conference on Advanced Computing & Communication Technologies 2015, doi: 10.1109/ACCT.2015.100
[3]. Bangug, C.M.; Fajardo, A.C.; Medina, R.P.; ?A Modified Median Filtering Algorithm (MMFA)?, International MultiConference of Engineers and Computer Scientists, 2019, pp: 377-380
[4]. Channappayya, S.S.; Bovik, A.C.; Caramanis, C.; Heath, R.W. ?Design of linear equalizers optimized for the structural similarity index?, IEEE Trans Image Process, vol: 17, No: 6, 2008
[5]. Erkan, U.; Thanh, D.N.H.; Hieu, L.M.; Enginoglu, S.; ?An Iterative Mean Filter for Image Denoising?, IEEE, vol: 7, 2019, pp: 167847-167849
[6]. G. Balasubramanian, A. Chilambuchelvan, S. Vijayan and G. Gowrison, ?An extremely fast adaptive highperformance filter to remove salt and pepper noise using overlapping medians in images?, The Imaging Science Journal, 64:5, pp. 241-252, doi: 10.1080/13682199.2016.1168144
[7]. Garg, S.; Vijay, R.; Urooj, S.; ?Statistical Approach to Compare Image Denoising Techniques in Medical MR Images?, Procedia Computer Science, vol: 152, 2019, pp: 367-374
[8]. Kaur, N.; Verma, P.; Verma, G.N. ?A Novel Hybrid Image Denoising Technique based on Trilateral Filtering and Gaussian Condition Random Field Model?, International Research Journal of Engineering and Technology, vol: 6, 2019, pp: 2124-2129
[9]. K. S. Srinivasan and D. Ebenezer, ?A New Fast and Efficient Decision-Based Algorithm for Removal of High Density Impulse Noises,? IEEE Signal Processing Letters, Vol. 14, NO. 3, March 2007, doi: 10.1109/LSP.2006.884018.
[10]. Madhu S. Nair, K. Revathy, and Rao Tatavarti, ?Removal of Salt-and Pepper Noise in Images: A New DecisionBased Algorithm?, Proceedings of the International MultiConference of Engineers and Computer Scientists 2008 Vol I, IMECS 2008, 19-21 March, 2008, Hong Kong, doi:
[11]. Md Tabish Raza and Suraj Sawant , ?High Density Salt and Pepper Noise Removal Through Decision Based Partial Trimmed Global Mean Filter?, 2012 Nirma University International Conference on Engineering (NUiCONE), doi: 10.1109/NUICONE.2012.6493236
[12]. Muhammad Mizanur Rahmani, M. Abdullah-AI-Wadud and Chrysanthe Prezal, ?A Decision-based Filter for Removing Salt-and-Pepper Noise?, IEEE/OSA/IAPR International Conference on Infonnatics, Electronics & Vision 2012, doi: 10.1109/ICIEV.2012.6317513
[13]. Murugeswari, P.; Manimegalai, D.; ?Noise Reduction in Color image using Interval Type-2 Fuzzy Filter (IT2FF)?, International Journal of Engineering Science and Technology, Vol. 3 No. 2, 2011, pp: 1334-1338
[14]. Mu-Hsien Hsieh , Fan-Chieh Cheng, Mon-Chau Shie, Shanq-Jang Ruan, ?Fast and efficient median filter for removing 1?99% levels of salt-and-pepper noise in images?, Engineering Applications of Artificial Intelligence 26(2013), pp. 1333-1338, doi: 10.1016/j.engappai.2012.10.012
[15]. Naragund, M.N.; Jagadale, B.N.; Priya, B.S.; Hegde, V.; ?An Efficient Image Denoising Method based on Bilateral filter Model and Neighshrink SURE?, International Journal of Recent Technology and Engineering, vol: 8, 2019, pp: 8470-8475
[16]. Oszust, M.; ?No-Reference Image Quality Assessment with Local Gradient Orientations?, Symmetry, vol:11, No: 1, 2019
[17]. Ranjitha, S.; Hiremath S. G.; ?High Density Impulse Noise Removal and Edge Detection in SAR Images based on Frequency and Spatial Domain Filtering?, International Journal of Engineering and Advanced Technology, vol: 8, 2019, pp: 643-648
[18]. Satyabrata Biswall and Nilamani Bhoi, ?A new filter for removal of salt and pepper noise?, 2013 International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPR], doi: 10.1109/ICSIPR.2013.6497990
[19]. Sudhakar, M.; Meena, M.J.; ?Underwater Image Enhancement using Conventional Techniques with Quality Metrics?, International Journal of Innovative Technology and Exploring Engineering, vol: 8, 2019, pp: 241-249
[20]. S. Esakkirajan, T. Veerakumar, Adabala N. Subramanyam, and C. H. PremChand, ?Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter?, IEEE Signal Processing Letters, Vol. 18, No. 5, May 2011, doi: 10.1109/LSP.2011.2122333
[21]. Xu, Q.; Zhang, Q.; Hu, D.; Liu, J.; ?Removal of Salt and Pepper Noise in Corrupted Image Based on Multilevel Weighted Graphs and IGOWA Operator?, Mathematical Problems in Engineering, vol: 2018, 2018, pp: 1-12
[22]. Zeng, J.; Liu, Z.; ?Type-2 fuzzy Gaussian mixture models?, Pattern Recognit, 2008.

Manmeet Kaur, Kamaljeet Kaur Mangat, "A Review & Analysis of Image Filters for Impulse Noise," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.63-68, 2020.

In present-day time, most of the associations are making use of web services for improved services to their clients. With the upturn in count of web users, there is a considerable hike in the web attacks. Study indicates that more than 80% of the web applications are vulnerable to cross-site scripting (XSS) attacks. XSS is one of the fatal attacks & it has been practiced over the maximum number of well-known search engines and social sites. At the same time, there are large number of attacks on web applications that are getting popular among attackers. Attacks like injection vulnerabilities such as SQL Injection, Cross site Scripting, Cross site Request Forgery (CSRF) are very common and threatening to the modern web applications. In this paper, we have considered XSS attacks, its prediction and detection different kind of methods applied to repel these attacks with their corresponding limitations. Additionally, we have discussed the proposed approach for defying XSS attack using intrusion detection system. For using IDS cyber-attacks detection system along with KF(knowledge flow model) model approach for prediction cross scripting attack. Finally, the result of vulnerability scanners are shown and analysed before and after the implementation of known XSS security trials.

Cross-Site-Scripting, XSS, Attacks, Web application, cyber-attacks, IDS system. Kf Model, network security, prediction, detection, network attacks, etc.

[1] N. Niu E. Stroulia M. El-Ramly: Understanding Web usage for dynamic Web-site adaptation: a case study. 2002 Proceedings. Fourth International Workshop on Web Site Evolution.
[2] K. Pranathi, S. Kranthi, Dr.A.Srisaila, P. Madhavilatha: Attacks on web Application Caused by Cross Site Scripting: 2018 2nd international conference on electronics, Communication and Aerospace Technology.
[3] Twana Assad TAHA, Murat Karabatak: A proposed approach for preventing Cross Site Scripting: 2018 6th International Symposium on Digital Forensic and Security (ISDFS)
[4] V.K Malviya, S.Saurav: On security issues in web applications through cross-site scripting: 2013 20th Asia Pacific Software Engineering Conference (AtiPSEC), Bangkok, 2013, pp.583-588
[5] MohitDayal, Nanhay Singh, Ram Shringar Raw: A comprehensive Inspecon of Cross Site Scripting Attack. International Conference on Computing, Communication, and Automation (ICCCA2016)
[6] Francois Mouton; Mercia M. Malan; Louise Leenen; H.S. Venter: Social engineering attack framework. 2014 Information Security for South Africa .
[7] Florian Kerschbaum 2007. Simple Cross-Site Attack Prevention: 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007.
[8] Imran Yusof, Al-SakibPathan: Preventing Persistent Cross-Site Scripting (XSS) Attack By Applying Pattern Filtering Approach.
[9] https://www.netsparker.com/blog/websecurity/dom-based-cross-site-scriptingvulnerability/.
[10] https://www.veracode.com/directory/owasp-top10.
[11].Abusaimeh, H. and Shkoukani, M. (2012). Survey of Web Application and Internet Security Threats. International Journal of Computer Science and Network Security. Vol 12, Issue 12, 67-76.
[12] Internet Security Threat Report, Symantec, vol.22, retrieved from: https://www.symantec.com/content/dam/symantec/docs/reports/istr-22-2017-en.pdf .
[13] WhiteHat Website Security Statistics Report,2014. retrieved from https://www.whitehatsec.com/.
[14] Web Application Attack Report,2015. Imperva. Retrieved from http://www.imperva.com/ .
[15] The Ten Most critical Web Application Security Risks, 2010. Open Web Application Security Project Top 10. Retrieved from http://www.owasp.org/.
[16] The Ten Most critical Web Application Security Risks, 2013. Open Web Application Security Project Top 10. Retrieved from http://www.owasp.org/.

Marripelli Koteshwar, Bipin Bihari Jaya Singh, "Prediction and detection of cross scripting attack XSS in web application using intrusion detection system IDS: Novel approach," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.69-72, 2020.

DevOps is a new concept that consolidates development and operations team to intently incorporate individuals, procedures and innovation for automated end to end delivery and deployment of software. DevOps Engineers have start to finish obligation of the Application (Software) directly from gathering the prerequisite to improvement, to development, to testing, to application deployment lastly checking and assembling input from the end clients, again implement the changes as per end client requirements. This paper presents DevOps concept, how it has been evolved from traditional methods, technologies involved such as CI/CD pipeline, project gating and DevOps tools that automates the software development cycle.

DevOps, CI/CD, Jenkins, Git, Docker, ZUUL, Artifactory, CI/CD Visualization Dashboard, JIRA, Valgrind

[1] M. Httermann, ?DevOps for developers?, Apress Publisher, 2012.
[2] Jay Shah, Dushyant Dubaria and Prof. John Widhalm, ?A Survey of DevOps tools for Networking?, IEEE, 2018.
[3] ?2015 State of DevOps Report?, Available at: https://puppetlabs.com/2015- devops-report.
[4] BASS, L., WEBER, I., and ZHU, L., ?DevOps: A Software Architect`s Perspective?, Addison-Wesley Professional Publisher, 2015.
[5] FITZGERALD, B. and STOL, K.-J., ?Continuous Software Engineering: A Roadmap and Agenda?, the Journal of Systems and Software, 2017.
[6] Chellamalla Mamatha, S C V S L S Ravi Kiran, ?Implementation of DevOps Architecture in the project development and deployment with help of tools?, ISROSET, Vol.6, Issue.2, pp.87-95, 2018.
[7] HUMBLE, J., ?Continuous Delivery vs Continuous Deployment?, Available at: https://continuousdelivery.com/2010/08/continuous-delivery-vs-continuousdeployment/ [Last accessed: 1 March 2016].
[8] LUKE, E. and PRINCE, S., 2016. No One Agrees How to Define CI or CD. Available at: https://blog.snap-ci.com/blog/2016/07/26/continuous-deliveryintegration-devops-research/ [Last accessed: 1 August 2016]
[9] THIELE, A., 2014. Continuous Delivery: An Easy Must-Have for Agile Development, Available at: https://blog.inf.ed.ac.uk/sapm/2014/02/04/continuous-delivery-an-easy-musthave-for-agile-development/ [Last accessed: 10 July 2016].
[10] WEBER, I., NEPAL, S., and ZHU, L., ?Developing Dependable and Secure Cloud Applications?, IEEE Internet Computing 20, 3, 74-79, 2016.
[11] DINGS?YR, T. and LASSENIUS, C., ?Emerging themes in agile software development: Introduction to the special section on continuous value delivery?. Information and Software Technology 77, 56-60,2016.
[12] MOONEY, M., ?Continuous Deployment For Practical People?, https://www.airpair.com/continuous-deployment/posts/continuousdeployment-for-practical-people.
[13] REED, J.P., ?The business case for continuous delivery?, Available at https://www.atlassian.com/continuous-delivery/business-case-for-continuousdelivery, [Last accessed: 12 July 2016].
[14] FORD, N., ?Continuous Delivery for Architects?, Available at: http://nealford.com/downloads/Continuous_Delivery_for_Architects_Neal_Fo rd.pdf [Last accessed: 20 October 2016].
[15] Valentina Armenise, ?Continuous Delivery with Jenkins?, IEEE/ACM 3rd International Workshop on Release Engineering, 2015.
[16] Nikita Seth,Rishi Khare, ?ACI ( Automated Continuous Integration ) using Jenkins: Key for Successful Embedded Software Development?, Proceeding of the RAECS UIET Panjab University ,Chandigarh, 21-22nd December 2015.
[17] Mojtaba Shahin, M. Ali Babar, Liming Zhu, ?Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices?, IEEE, Received February 16, 2017, accepted March 16, 2017, date of publication March 22, 2017, date of current version April 24, 2017.
[18] Pulasthi Perera, Roshali Silva, Indika Perera, ?Improve Software Quality through Practicing DevOps?, International Conference on Advances in ICT for Emerging Regions (ICTer): 013 ? 018, 2017.
[19] Jay Shah, Dushyant Dubaria, Prof. John Widhalm, ?A Survey of DevOps tools for Networking?, IEEE, 2018.
[20] Hessa Alfraihi and Kevin Lano,?The Integration of Agile Development and Model Driven Development - A Systematic Literature Review?, In Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017). SCITEPRESS, 451?458, 2017.
[21] Gerald Schermann, J?rgen Cito, Philipp Leitner, Uwe Zduny, Harald C. Gall, ?An Empirical Study on Principles and Practices of Continuous Delivery and Deployment?, PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.1889v1 | CC-BY 4.0 Open Access | rec: 22 Mar 2016, publ: 22 Mar 2016.
[22] Tony Savor, Mitchell Douglas,Michael Gentili, ?Continuous Deployment at Facebook and OANDA?, IEEE/ACM 38th IEEE International Conference on Software Engineering Companion, 2016.
[24] Y. Sundman., ?Continuous Delivery vs Continuous Deployment?, 2013 [Online] Available: http://blog. crisp.se/2013/02/05/yassalsundman/continuous-delivery-vs-continuousdeployment
[25] Alexander Eck, Falk Uebernickel, and Walter Brenner, ?Fit For Continuous Integration: How Organizations Assimilate An Agile Practice,? 2014.
[26] Amit Deshpande and Dirk Riehle, ?Continuous Integration in Open Source Software Development,? 2008.
[27] Daniel St?hl and Jan Bosch, ?Experienced Benefits of Continuous Integration in Industry Software Product Development: A Case Study,? 2015.

Pallavi Deshwal, Poonam Ghuli, "DevOps: Concept, Technology and Tools," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.73-78, 2020.

Sender sends the lock of an encrypted binary number of a locker crisp value from a fuzzy membership table which is defined on ASCII values of uppercase alphabets and membership function. The receiver then converts the binary number into a fuzzy number and then it is de-fuzzified using a suitable formula. In terms of area costs, computing resources, web services, and full capabilities, Smart Grid are considered to be the enhancements over the existing grid system. The future domain of the missions in the region is described by smart energy, smart grids, intelligent homes, and clever cities as the ambitious flagship programmer of the Indian digital initiative. More than 100 intelligent cities in India are expected for ICT-led solutions with big data analytics. The exponential growth of smart grids has posed several safety dangers, cyber threats, and data protection as a nation`s security. Smart grids, made up of numerous networks, intelligent control, access control, and power grid equipment, are more likely to be exposed to network security and cyber-attacks potentially interrupting delivery in a city. The paper discusses the software-oriented binary grid system and problems associated with the intelligent grid system and discusses the use of smart grid smart hardware using the Advanced Encryption Standard (AES). To ensure that AES can be employed in the intelligent grid and communication infrastructure. The Xilinx ISE 14.2 software has designed the AES encryption and decryption unit and is synthesized in SPARTAN-3E FPGA to test for certain cases.

Unintelligible text, Linear Recurrence, Unprotected AES implementation, fuzzy set, membership function, crisp number, smart grid communication, ASCII values, cryptography

[1] P. Amudha, A.C. Charles Sagayaraj A, C.ShanthaSheela,?An application of Graph theory in Cryptography?, International Journal of Pure and Applied Mathematics, 119(13), 375-383, 2018.
[2] Anita Pal, National Institute of Technology Durgapur West Bengal-713209, India.
[3] K.Ganeshkumar, D.arivazhagan, et.al,`New Cryptography Algorithm with Fuzzy logic for Effective Data Communication`. International journal of Science and tech. vol 9(48), DOI: 10.17485 /ijst/2016/v9i48/108970, Dec 2016.
[4] Kamilahabdullah. Sumarni Abu Baskar, Nor Hanimakamis, and Harialimais, `RSA cryptosystem with Fuzzy set Theory for encryption and decryption`, AIP conference Proceeding 190, 030001(2017), volume 950, Issue 10.063/1.5012147.
[5] M. Muthumeenakshi, T. Archana, P. Muralikrishna,?Fuzzy Application in Secured Data Transmission?, International Journal of Pure and Applied Mathematics, 116(3), 711-715, 2017.
[6] Ravindumadanayake, et.al, `Advanced Encryption Algorithm Using Fuzzy Logic?, International Journal of Computer networks ((ICICN 2012) IPCSIT vol 27(2012) IACSIT Press), Singapore.
[7] S.Shara et. al. on "RSA algorithm using modified subset sum Cryptosystem,? in computer and commTech. (India, 2011) pp. 457-461.
[8] L.A.Zadehand R.RYager et al. (John Wiley, New York, 1987). ?Fuzzy Sets and Applications :?
[9] L.A Zadeh, information And control, vol. 8, 338- 353(1965)
[10] Bari A, Jiang J, Saad W, Jaekel A (2014) Challenges in the smart grid applications: an overview. Ataul Hindawi Publishing Cor- poration. Int J Distrib Sensor Netw 974682:11. https://doi.org/10. 1155/2014/974682.
[11] Fadel E, Gungor VC, Nassef L, Nadine A, Abbas Malik MG (2015) ?A survey on wireless sensor networks for smart grid.? Comput Netw Elsevier 71:22?33
[12] Wang W, Lu Z (2013) ?Cyber security in the smart grid: survey and challenges.? Comput Netw 57:1344?1371.
[13] Guo X, Liu Z, Xing J, Fan W, Zou X (2006) ?Optimized AES crypto design for wireless sensor networks with balanced S-box architecture:? In Proceedings of International Conference on Informatics and Control Technology (ICT 2006). pp 203?208.
[14] Deshpande AM, Deshpande MS, Kayatanavar DN (2009) ?FPGA implementation of AES encryption and decryption.? In: Interna- tional conference on control, automation, communication and energy conservation (INCACEC 2009). IEEE, pp 1?6.
[15] Hodjat A, Verbauwhede I (2006) ?Area-throughput trade-offs for fully pipelined 30 to 70 Gbits/s AES processors.? IEEE Trans Comput 55:366?372.
[16] Daemen J, Rijmen V (2013), ?The design of Rijndael: AES-the advanced encryption standard.? Springer, Berlin.
[17] Daemen J, Rijmen V (2005) Rijndael/aes. In: van Tilborg HCA (ed) ?Encyclopedia of Cryptography and Security.? Springer, US, pp 520?524.
[18] Hammad I, Sankary KE, Masry EE (2010) ?High-speed AES encryptor with efficient merging techniques.? IEEE Embed Syst Lett 2(3):67?71.
[19] Dyken JV, Delgado-Frias JG (2010) ?FPGA schemes for mini- mizing the power-throughput trade-off in executing the advanced encryption standard algorithm.? J Syst Architect 56(2?3):116?123.
[20] Sklavos N, Papakonstinou A, Koufopavlou STO (2002) Low- power implementation of an encryption/decryption system with asynchronous techniques. VLSI Design 15(1):455?468.
[21] Priya SS, Karthigaikumar P, Siva Mangai NM. et al. (2017) ?Wireless personal communication.? 94: 2259. doi: https://doi.org/ 10.1007/s11277-016-3385-7.
[22] Good T, Benaissa M (2006) ?Very small FPGA application-specific instruction processor for AES.? IEEE Trans Circ Syst I Regul Pap 53(7):1477?1486.
[23] Zhang X, Parhi KK (2004) ?High-Speed VLSI architectures for the AES algorithm.? IEEE Trans Very Large Scale Integr VLSI Syst 12(9):957?967.
[24] Li, Chaoyun, and Bart Preneel. "Improved Interpolation Attacks on Cryptographic Primitives of Low Algebraic Degree." International Conference on Selected Areas in Cryptography. Springer, Cham, 2019.
[25] Shabbir Hassan, Prof. M. U. Bokhari, presented a paper entitled "Lightweight Cryptography: A Review", Recent Trends in Mathematical and Computational Science (NCRTMCS), January 2015, pp-78.
[26] Shabbir Hassan and Mohammad Ubaidullah Bokhari. "Computing in Cryptography." 2016 3rd International Conference on Computing for SustainableGlobal Development (INDIACom). IEEE, 2016. ISSN 0973-7529; ISBN 978-93-80544-20-5.
[27] Shabbir Hassan, M.U. Bokhari and Md. Zeyauddin. "Radio Frequency Identification Tag: A Review.? 2017 4th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2017. ISSN 0973-7529; ISBN 978-93-80544-24-3
[28] Bokhari, M. U., and Shabbir Hassan. "A comparative study on lightweight cryptography." Cyber Security. Springer, Singapore, Cyber Security, Advancesin Intelligent Systems and Computing 729. 2018. 69-79. https://doi.org/10.1007/978-981-10-8536-9_8
[29] Hassan, Shabbir and Mohammad Ubaidullah Bokhari, (2019), "Analysis and Design of LFSR Based Cryptographic Algorithm." Journal of Advances and Scholarly Researches in Allied Education (JASRAE), ISSN 2230-7540, Vol. 16, Issue No. 9, June-2019.
[30] Hassan, Shabbir and Mohammad Ubaidullah Bokhari, ?Design of Pseudo Random Number Generator using Linear Feedback Shift Register.? International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 ? 8958, Volume-9 Issue-2, December, 2019.
[31] Prof. M. U. Bokhari, Shabbir Hassan, 2020, ?Design of a Lightweight Stream Cipher: BOKHARI? 256, International Journal of Engineering Research & Technology (IJERT) Volume 09, Issue 03 (March 2020).
[32] Shabbir Hassan. ?The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security.? International Journal of Computer Applications 176(20):6-13, May 2020.

Shabbir Hassan, "Dual Secure Cryptographic Measures by Two-Phase Locking Protocol," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.79-85, 2020.

Information Technology is an enabler that brings flexibility to the business processes. Major factors to the successful adoption of Information Technology for Small and Medium Enterprises (SMEs) are the IT benefits and organizational culture, the selection and implementation of IT, and technical support. The paper discusses the selection and implementation of Open Source Software (OSS) and the technical support options, knowing which gives the SMEs better visibility at the IT adoption process. The paper also presents the implementation of selected OSS projects that can fulfill the common IT requirements of an SME, like Web server: to host and serve the web applications, Mail server: to facilitate the email messaging functionality within and outside of the SME, File server: to provide a space where the employees of an SME can store their files, and Backup server to copy the files, applications and/or databases of an SME and restore them in case of data loss.

SME, Open Source Software, Information Technology, IT selection, IT implementation, IT Support

[1] Ayyagari, M., Demirg??-Kunt, A. and Maksimovic, V., 2017. SME finance. The World Bank.
[2] Fink, D., 1998. Guidelines for the successful adoption of information technology in small and medium enterprises. International journal of information management, 18(4), pp.243-253.
[3] Banham, H.C., 2010. External environmental analysis for small and medium enterprises (SMEs). Journal of Business & Economics Research (JBER), 8(10).
[4] Nguyen, T.H., Newby, M. and Macaulay, M.J., 2015. Information technology adoption in small business: Confirmation of a proposed framework. Journal of Small Business Management, 53(1), pp.207-227.
[5] Sangani, N.K. and Vijayakumar, B., 2012. Cyber security scenarios and control for small and medium enterprises. Informatica Economica, 16(2), p.58.
[6] Boulanger, A., 2005. Open-source versus proprietary software: Is one more reliable and secure than the other? IBM Systems Journal, 44(2), pp.239-248.
[7] Nagy, D., Yassin, A.M. and Bhattacherjee, A., 2010. Organizational adoption of open source software: barriers and remedies. Communications of the ACM, 53(3), pp.148-151.
[8] Lakhani, K.R. and Wolf, R.G., 2003. Why hackers do what they do: Understanding motivation and effort in free/open source software projects.
[9] Lakhani, K.R. and Von Hippel, E., 2004. How open source software works: ?free? user-to-user assistance. In Produktentwicklung mit virtuellen Communities (pp. 303-339). Gabler Verlag.

Ahmad Reshad, Shweta Sinha, Komal, "Open Source Software Solution for Small and Medium Enterprises," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.86-90, 2020.

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science , intelligence science , and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis,formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages ,store and manipulates large amount of data at the desirable speed and time. In particular, big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

Data Science Big Data, Data Analytics, Epicycles, Business Intelligence(BI)

[1]Roger D. Peng and Elizabeth Matsu, The Art of Data Science, A Guide for Anyone Who Works with Data,Lean publishing book,2015 - 2016 Skybrude Consulting, LLC.
[2] LONGBING, University of Technology Sydney, Australia,Data Science: A Comprehensive Overview , ACM Computing Surveys, Vol. 50, No. 3, Article 43, Publication date: June 2017.
[3]JavaT Point, Data Science Tutorial for beginners,javapoint.com/data science.
[4]EMC Academic Alliance University , Data science and big data analytics ,Discovering, Analyzing, visualizing and presenting data, EMC education services(EMC2)
[5] T. H. Davenport and D. J. Patil, ?Data Scientist: The Sexiest Job of the 21st Century,? Harvard Business Review, October 2012.
[6] J. Manyika, M. Chiu, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H.Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, 2011.
[7] J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein and C. Welton, MAD Skills:New Analysis Practices for Big Data, Watertown, MA 2009.
[8] S. Todd, ?Data Science and Big Data Curriculum? [Online].Available:http://stevetodd.typepad.com/my_weblog/data-science-and-big-datacurriculum/.
[9] D. R. John Gantz, ?The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,? IDC, 2013.
[10] Blog,industries? using big data,solutions of big data
[11] Quora,Future Scope of Data Science .

Akella Subhadra, "The Art of Data Science & Big Data Analytics Inspecting & transforming data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.91-100, 2020.

Cancer has been one of the biggest issues today, The early diagnosis of cancer remains complicated for doctors. When developing novel methods of cancer detection and prevention, It is particularly important to identify genetic and environmental factors. This paper presents the novel approach based on the selection of hybrid features that reduces the dimensionality of features significantly. This paper suggests an efficient Relief and PCA approach that is used on the dataset of cervical cancer. Further, the obtained score is taken as the input for the classification, mechanism. The 3 different classification techniques have been applied. The experiment is conducted on MATLAB. Moreover, The threshold value is experimentally shown to significantly affect the selection of appropriate features. On the basis of many accuracy parameters including accuracy or recall, the experimental result is compared.

Hybrid Feature Selection, Chronic Disease Datasets, PCA, classification techniques, Disease Diagnosis, Relief

[1] K.Arutchelvan, Dr.R.Periyasamy, ?Cancer Prediction System Using Datamining Techniques? International Research Journal of Engineering and Technology (IRJET) Volume: 02 Issue: 08 | Nov-2015
[2] Ritu Chauhan ?Data clustering method for Discovering clusters in spatial cancer databases? International Journal of Computer Applications (0975-8887) Volume 10-No.6, November 2010. [
[3] Dechang Chen ?Developing Prognostic Systems of Cancer Patients by Ensemble Clustering? Hindawi publishing corporation, Journal of Biomedicine and Biotechnology Volume 2009, Article Id 632786.
[4] S M Halawani ?A study of digital mammograms by using clustering algorithms? Journal of Scientific & Industrial Research Vol. 71, September 2012, pp. 594-600.
[5] Ada and Rajneet Kaur ?Using Some Data Mining Techniques to Predict the Survival Year of Lung Cancer Patient? International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 2, Issue. 4, April 2013, pg.1 ? 6, ISSN 2320?088X
[6] V.Krishnaiah ?Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques? International Journal of Computer Science and Information Technologies, Vol. 4 (1) 2013, 39 ? 45 www.ijcsit.Com ISSN: 0975-9646
[7] Charles Edeki ?Comparative Study of Data Mining and Statistical Learning Techniques for Prediction of Cancer Survivability? Mediterranean journal of Social Sciences Vol 3 (14) November 2012, ISSN: 2039-9340.
[8] Zakaria Suliman zubi ?Improves Treatment Programs of Lung Cancer using Data Mining Techniques? Journal of Software Engineering and Applications, February 2014, 7, 69-77
[9] Labeed K Abdulgafoor ?Detection of Brain Tumor using Modified K-Means Algorithm and SVM? International Journal of Computer Applications (0975 ? 8887) National Conference on Recent Trends in Computer Applications NCRTCA 2013
[10] A. Sahar ?Predicting the Serverity of Breast Masses with Data Mining Methods? International Journal of Computer Science Issues, Vol. 10, Issues 2, No 2, March 2013 ISSN (Print):1694-0814| ISSN (Online):1694-0784 www.IJCSI.org
[11] Rajashree Dash ?A hybridized K-means clustering approach for high dimensional dataset? International Journal of Engineering, Science and Technology Vol. 2, No. 2, 2010, pp. 59-66.
[12] B Khalid, N Abdelwahab. “A Comparative Study of Various Data Mining Techniques: Statistics, Decision Trees and Neural Networks”, International Journal of Computer Applications Technology and Research, Volume-5, Issue-03, pp (172 – 175), 2016.
[13] S Mahajan, "Convergence of IT and Data Mining with other technologies ", International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-04, pp (31-37), Aug 2013

Priyanka Rajpoot, Mahesh Parmar, "Cervical Cancer prediction based on Hybrid Feature Selection Model and Classification Algorithm," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.101-105, 2020.

In this paper, the method for detection of neovascularization from fundus retinal image is presented. Neovascularization is the type of proliferative diabetic retinopathy and it is characterized by new, fragile retina vessels. It poses high risk for sudden vision loss. To avoid this risky situation, an early detection, proper treatment and diagnosis is essential. Therefore, we cannot underestimate the significance of accurate and timely detection of NV. We propose a method to detect NV which is based on automatic image processing that involves vessel segmentation using K-means, Vessel morphology, texture based features extraction and classification of images with support vector machine(SVM) and we achieved an average accuracy of 99 % on the selected test set

Feature Extraction, K-means clustering, morphological image processing, Neovascularization, Support vector machine

[1] M. D. Abr?amoff, M. K. Garvin, and M. Sonka, ?Retinal imaging and picture analysis,? IEEE Reviews in Medicine Engineering, vol. 3, pp. 169?208, 2010.
[2] K. A. Goatman, A. D. Fleming, S. Philip, G. J. Williams, J. A. Olson, and P. F. Clear, ?New color fundus picture detection vessels?? IEEE transactions on medical imaging, vol. 30, no. 4, pp. 972?9, 2011.
[3] R. Welikala, M. Fraz, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, T. Williamson, and S. Barman, ?Genetic algorithmic program primarily based on various features combined with two classification for the machine driven detection of proliferative diabetic retinopathy,?processed Medical Imaging and Graphics, vol. 43, pp.64-77, 2015.
[4] R. Welikala, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, T. Williamson,and S. Barman, ?Automated detection and dual classification of the proliferative diabetic retinopathy? Computer Methods and programs in biomedicine, vol. 114, no. 3, pp. 247-261,2014
[5] S. S. A. Hassan, D. B. L. Bong, and M. Premsenthil, ?Detection of Neovascularization in Diabetic Retinopathy,? Journal of Digital maging, vol. 25, no. 3, pp. 437?444, 2012.
[6] C. Agurto, H. Yu, V. Murray, M. S. Pattichis, S. Barriga, W. Bauman, and P. Soliz, ?Detection of neovascularization at intervals the optic disc follow associate AM-FM illustration, granulometry, and vessel segmentation?, Annual International Conference of the IEEE Engineering in and Biology Society, vol.2012, pp. 4946-9,2012.
[7] Q. Li, ?Neovascularity detection based upon fractal analysis and texture analysis with diabetic retinopathy interaction effects,? PLoS ONE, vol. 8, no. 12, 2013.
[8] S. Eswari and S. Rajeswari, ?Recent vessels detection method in retinal fundus images A Survey,? in In Computing Technologies and Intelligent Data Engineering (ICCTIDE), International Conference on, pp. 1?6, 2016.
[9] K. Saranya, B. Ramasubramanian, and S. Kaja Mohideen, ?A novel approach for the detection of new vessels in the retinal images for screening Diabetic Retinopathy,? 2012 International Conference on Communication and Signal Processing, pp. 57?61, 2012.
[10] S. D. Kasurde and S.N.Randive, ?An Automatic Detection of Proliferative Diabetic Retinopathy,? in Energy Systems and Applications, 2015 International Conference on, pp. 86?90, 2015.
[11] B. Ramasubramanian and G. Anitha, ? An Effective Approach to Detect New Vessels in Diabetic Retinopathy images,? International Journal of Engineering and Innovative Technology(IJEIT), vol. 2, no. 3, pp. 240-244, 2012.
[12] G. Gupta, S. Kulasekaran, K. Ram, M. Sivaprakasam, M. N. Joshi and R. Gandhi, ?Local Neovascularization Characterisation and Identification of retinal retinopathy in proliferative diabetic fundus Images, ?Medical Imaging and Graphics Computerised, vol. 55, 55 Pp. 132? 124, 2017.
[13] ?Automatic Neovascularization Identification of Optic Disc Region based on Machine Learning? IEEE Transaction.
[14] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
[15] T. Kauppi, V. Kalesnykiene, J.-k. Kamarainen, L. Lensu, and I. Sorri, ?DIARETDB0 : Evaluation Database and Methodology for Diabetic Retinopathy Algorithms,? Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland.,pp. 1?17, 2006.
[16] David A. Clausi, ?An analysis of the co-occurrence texture statistics based on gray levels?, Can. J. Remote Sensing, vol. 28, no. 1, pp. 45-62, 2002.
[17] M.S.Miri, and A. Mahloojifar ?A Review of Contrast to Determine Retinal Image Enhancement Techniques ?ICSIPA (2009) 90 ? 94.
[18] M.Rajesh Babu, BVNR Siva Kumar. Morphological process and SVM classification of diabetic retinopathy diagnosis. September 2016 , IJIRT ,Volume 3 Issue 4 ,ISSN: 2349- 6002.
[19] J.A. Hartigan and M.A. Wong, ?Algorithm AS 136: A algorithm of k-means clustering ,? Applied Statistics, vol. 28, pp. 100-108, 1979.
[20] Kaggle,?Diabetic Retinopathy Detection Challenge.?https://www.kaggle.com/c/diabetic-retinopathydetection. Accessed: 2016-07-05.
[21] Vijay Kumar, Priyanka Gupta, ?Importance of Statistical Measures in Digital Image Processing,? International Journal of Emerging Technology and Advanced Engineering, ISBN: 22502459, vol. 2(8), August 2012.

Neha Tamboli, G.S. Malande, "Proliferative Diabetic Retinopathy Detection Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.106-111, 2020.

Pandemics are cruel and devastating. No business, area and affair can be left unaffected or least affected. The entire world is shaken by a pandemic caused by new virus covid-19 since December-2019. This paper describes the broad impact of COVID-19 on educational systems and how the entire learning system is perturbed by the preventive steps taken to curb the spread of the pandemic. Moreover, the evolution of online tools as to continue the teaching learning process has also been illustrated. The study involves use of MOOCS, Moodle and online teaching platforms while conducting academic activities in our Institute from March-June 2020.

COVID-19, Pandemic, Moodle-LMS, MOOCS, Google Meet, Google Classroom

[1] L. Bender, ?Key Messages and Actions for COVID-19 Prevention and Control in Schools?, Retrieved from https://www.unicef.org/romania/documents/key-messages-and-actions-covid-19-prevention-and-control-schools, 2020.
[2] L. Meng, F. Hua, Z. Bian, ?Coronavirus Disease 2019 (COVID-19): Emerging and Future Challenges for Dental and Oral Medicine?, Journal of Dental Research, 2020.
[3] A.S. Abdulamir, R.R. Hafidh, ?The Possible Immunological Pathways for the Variable Immunopathogenesis of COVID-19Infections among Healthy Adults, Elderly and Children?, Electronic Journal of General Medicine, Vol. 17, Issue 4, em202,2020. https://doi.org/10.29333/ejgm/7850
[4] D. Gondauri, E. Mikautadze, M. Batiashvili, ?Research on COVID-19 Virus Spreading Statistics based on the Examples of the Cases from Different Countries?, Electronic Journal of General Medicine, Vol. 17, Issue 4, em209, 2020. https://doi.org/10.29333/ejgm/7869
[5] M.L. Holshue, C. DeBolt, S. Lindquist, K.H. Lofy, J. Wiesman, ?First case of 2019 novel coronavirus in the United States?, N Engl J Med [epub ahead of print 31 Jan 2020] in press,2020. https://doi.org/10.1056/NEJMoa2001191
[6] W. Bao, ?COVID-19 and online teaching in higher education: A case study of Peking University?, Hum Behav & Emerg Tech., 2020, Vol. 1, Issue 3, 2020. https://doi.org/10.1002/hbe2.191
[7] E.J. Sintema, ?Effect of COVID-19 on the Performance of Grade 12 Students: Implications for STEM Education?, Eurasia Journal of Mathematics, Science and Technology Education, Vol. 16, Issue 7, em1851, 2020. https://doi.org/10.29333/ejmste/7893
[8] Z. Yan, ?Unprecedented pandemic, unprecedented shift, and unprecedented opportunity?, Hum Behav & Emerg Tech, 2020,1-3. https://doi.org/10.1002/hbe2.192
[9]"COVID-19 Educational Disruption and Response", UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse, 2020.
[10] T. Frieden, "Lessons from Ebola: The secret of successful epidemic response", CNN. https://edition.cnn.com/2020/03/11/health/coronavirus-lessons-from-ebola/index.html, 2020.
[11] A. Zumla, W. Yew, D.S. Hui, ?Emerging Respiratory Infections in the 21st Century, An Issue of Infectious Disease Clinics?, Vol. 24, Elsevier Health Sciences. pp. 614. ISBN 978-1-4557-0038-7,2010.
[12] S. Cauchemez, N.M. Ferguson, C. Wachtel, A. Tegnell, G. Saour, B. Duncan, A. Nicoll, "Closure of schools during influenza pandemic", The Lancet, Infectious Diseases, Vol 9, Issue 8, pp.473?81,2009. Doi:10.1016/S1473-3099(09)70176-8. PMC 7106429. PMID 19628172
[13] De Looper, Christian, "Google will begin shutting down the classic Hangouts app in October", DigitalTrends.com. Archived from the original on August 4, 2019. Retrieved September 5, 2019
[14] Boland, Hannah. "Google launches free version of Meet in bid to topple Zoom". The Telegraph. Retrieved 5 May 2020
[15] Kahn, Jordan, "Google Classroom now available to all Apps for Education users, adds collaboration features", August 12, 2014. 9to5Google. Retrieved April 28, 2017.
[16]https://spoken-tutorial.org/statistics/academic-center/8652/acropolis-institute-of-technology-and-research/

Kamal K. Sethi, Praveen Bhanodia, "A Case Study of Online Learning Tools to Mitigate the Impact of Covid-19 Pandemic on Education System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.112-115, 2020.