Volume-7 , Issue-4 , Apr - 2019 Go Back

The main objective of this project is to identify abnormality in electrical power system and to develop a device for detecting and isolating the same. A fault in the electrical power system is any abnormal high electric current through it. Early detection of fault can help us avoid the damage caused by these abnormal conditions. Fault detection plays an important role in high-cost and safety crucial processes. The circuit is completely controlled by the microcontroller which consistently monitors the voltages of the three phases and if the voltage goes abnormal then the signal is sent to the connected LCD. With the help of transformer, the voltage is sensed; if the voltage level exceeds some particular set value, a signal is sent to microcontroller to disconnect the load. The current response is displayed on the LCD. This detection system here is realized by using microcontroller 89S52. Continuity of power supply in three phase devices is an important concern, as error in this leads to losses in the winding of the devices and may lead to damage of windings. So, there is a need to protect these devices from the abnormalities. In this scheme, we are providing a protection system for devices which not only provide protection from under load but also operate automatically after the supply has come back to normal. Henceforth it finds its application at various medical hospitals, industries, households and places where high protection is needed for saving the costly equipment connected to the main line.

Microcontroller89S52,LCD, Relay, Relay Driver, 555 Timer, Fault Analyis

[1] Ishika Sandilya, Akshada Deshpande, Dimpy Singh, Anupriya Lakra, Annpurna Tandan, Mr. Mousam Sharma, “Three Phase Fault Analysis Using Microcontroller”, International Journal of Management, Technology and Engineering, ISSN NO : 2249-7455, Volume IX, Issue III, MARCH/2019.
[2] Pankaj B. Sondarva, Kishan P. Solanki, Chandpa R. Mulu, Harshad J. Bhakhar, “3 Phase Fault Detection Using Auto Reclosing”, International Journal of Novel Research and Development, Volume 2, Issue 4,ISSN: 2456-4184, April 2017.
[3] M.S. Morey, Amit Ghodmare, Vaibhav Khomane, Amit kumar Singh, Jitendra Dawande, Saifali Iqbal Shaikh, “Microcontroller Based 3 Phase Fault Analysis for Temporary and Permanent Fault”, International Research Journal of Engineering and Technology, ISSN:2395-0056, Volume: 02, Issue: 01, March2015.
[4] Sathish Bakanagari, A. Mahesh Kumar, M. Cheenya, “Three Phase Fault Analysis with Auto Reset for Temporary Fault and Trip for Permanent Fault”, International Journal of Engineering Research and Applications, ISSN: 2248-9622, Volume 3, Issue 6, Nov-Dec 2013, pp. 1082-1086.
[5] Prof. Madhuri Balasaheb Zambre, Vivek Vitthal Waiphale, Bhupali Popat Kumbhar, “Microcontroller Based Protection and Control of Three-Phase Induction Motor”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169, Volume: 3 Issue: 11,November 2015.
[6] Dipali Salave, Priyanka Khade, Jayashree Ghumare, Rupal Khalane, Rahul Nikam, “Microcontroller Based Detection And Protection of Induction Motor”, International Research Journal of Engineering andTechnology, e-ISSN:2395-0056, Volume: 05, Issue: 02, February 2018.
[7] Md. Tanjil Sarker, Md. Anisur Rahman, Md. Timur Rahman, Md. Arafat Sarker, Vidyut Kumar Sarker, Prof. Dr.Zahid Hasan Mahumud, “GSM &Microcontroller Based Three Phase Fault Analysis System”, International Journal of Advancements in Research & Technology ,Volume 6, Issue 1, ISSN 2278-7763,January 2017.
[8] A.R. Sedighi, “A New Model for High Impedance Fault in Electrical Distribution Systems”, International Journal Of Scientific Research In Computer Science And Engineering, Volume 2, Issue 4, ISSN: 2320-7639, August 2014.
[9] Nitin Kumar D, Nithis Kumar T, Senthil Kumar S, Sangavi C, Nivethika G, “Alive Persons in War Field and Hazardous Areas”, International Journal of Scientific Research in Network Security and Communication , Volume 6, Issue 6, ISSN: 2321-3256, December 2018.

Dimpy Singh, Akshada Deshpande, Ishika Sandilya, Anupriya Lakra, Annpurna Tandan, Mousam Sharma, "Three Phase Fault Analysis with Automatic Trip and Reclosing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.711-715, 2019.

Blockchain is a new technology with strong implications for the future of how we exchange information and currency as a globally networked society. It is so new that there is relatively only few academic works done on it, but this is changing quickly. Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the crypto currencies. In this paper, we have compared and compiled various use cases with blockchain technology applications collected from different sources, such as scientific papers, industry expert blogs, Master Theses and research. This paper will help to understand the necessity for development of a detailed blockchain usability model.

Blockchain, Cryptocurrency, Bitcoins

[1] Webster, R. T. Watson, “Analyzing the Past to Prepare for the Future:Writing a Literature Review”., MIS Quarterly 26 (2002) xiii – xxiii
[2] VomBrocke, A. Simons, B. Niehaves, K. Riemer, R. Plattfaut,A. Cleven, J. V. Brocke, K. Reimer, “Reconstructing the Giant: On theImportance of Rigour in Documenting the Literature Search Process”,17th European Conference on Information Systems 2009.
[3] A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder, Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016, 308 p.
[4] Kaspars Zīle, Renāte Strazdiņa, “Blockchain Use Cases and Their Feasibility”, Applied Computer Systems, vol. 23, no. 1, pp. 12–20, 2018. doi: 10.2478/acss-2018-0002
[5] L. S. Sankar, M. Sindhu, and M. Sethumadhavan, “Survey of Consensus Protocols on Blockchain Applications,” in 4th IEEE International Conference on Advanced Computing and Communication Systems. https://doi.org/10.1109/ICACCS.2017.8014672
[6] H. Vranken, “Sustainability of Bitcoin and Blockchains,” Current Opinion in Environmental Sustainability, vol. 28, pp. 1–9, 2017. http://doi.org/10.1016/j.cosust.2017.04.011
[7] L. S. Sankar, M. Sindhu, and M. Sethumadhavan, “Survey of Consensus Protocols on Blockchain Applications,” in 4th IEEE International Conference on Advanced Computing and Communication Systems, 2017. https://doi.org/10.1109/ICACCS.2017.8014672
[8] F. Lamberti, V. Gatteschi, C. Demartini, C. Pranteda, and V. Santamaria, “Blockchain or not Blockchain, That is the Question of the Insurance and Other Sectors,” IT Professional, June 2017. https://doi.org/10.1109/MITP.2017.265110355
[9] Ajay Kumar Bharti, Rashmi Negi, Deepak Kumar Verma, “A Review on Performance Analysis and Improvement of Internet of Things Application”, International Journal of Computer Sciences and Engineering, Vol.-7, Issue-2, pp. 367-371, Feb 2019.
DOI: https://doi.org/10.26438/ijcse/v7i2.367371
[10] B. A. Tama, B. J. Kweka, Y. Park, and K. H. Rhee, “A Critical Review of Blockchain and Its Current Applications,” in International Conference on Electrical Engineering and Computer Science, 2017, pp. 109–113.
https://doi.org/10.1109/ICECOS.2017.8167115
[11] N. Alexopoulos, J. Daubert, M. Muhlhauser, and S. M. Habib, “Beyond the Hype: On Using Blockchains in Trust Management for Authentication,” in 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Aug. 2017, pp.546–553. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.283
[12] S. K. Lo, X. Xu, Y. K. Chiam, and Q. Lu, “Evaluating Suitability of Applying Blockchain,” in IEEE 22nd International Conference on Engineering Complex Computer Systems, 2017, pp. 158–161.
https://doi.org/10.1109/ICECCS.2017.26

Deepak Kumar Verma, Varsha Katheria, Mazhar Khaliq, "Use Cases and Applications of Blockchain Technology in IT Industry," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.716-720, 2019.

Machine Learning Techniques find a myriad of applications in different fields. One such application is the use of these techniques in education. The research in the educational field that involves machine learning techniques is rapidly increasing. Applying machine learning techniques in an educational background aims to discover hidden knowledge and patterns about student’s performance. This work aims to develop student’s academic performance prediction model, among the various students from various departments using machine learning classification methods; K-Nearest Neighbor, Decision Tree, Support Vector Machines, Random Forest, and Gradient Descent Boost Algorithms. Parameters like living area, mother father relation, education and their employment, backlogs, attendance, Internet connection availability and smart phone usage are used. Resultant prediction model can be used to identify student’s performance in the final examination and anticipate the final grade. Thereby, the college management or lecturers can classify students and take an early action to improve their performance. Due to early prediction, solutions can be sought for better results in the final exams.

Educational Data Mining, Machine Learning, Classification, Student Academic Performance

[1] Hashmia Hamsa, Simi Indiradevi, Jubilant J. Kizhakkethottam, Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm, Procedia Technology, Volume 25, 2016, Pages 326-332.
[2] S. S. Athani, S. A. Kodli, M. N. Banavasi and P. G. S. Hiremath, "Student academic performance and social behavior predictor using data mining techniques," 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, 2017, pp. 170-174.
[3] Xiaofeng Ma and Zhurong Zhou. “Student Pass Rates Prediction Using Optimized Support Vector Machine and Decision Tree”, 978-1-5386-4649-6/18/$31.00 ©2018 IEEE.
[4] Huda Al-Shehri, Amani Al-Qarni, Leena Al-Saati, Arwa Batoaq, Haifa Badukhen, Saleh Alrashed, Jamal Alhiyafi and Sunday O. Olatunji. “Student Performance Prediction Using Support Vector Machine and K-Nearest Neighbor”, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).
[5] Pauziah Mohd Arsad, Norlida Buniyamin and Jamalul-lail Ab Manan. “A Neural Network Students’ Performance Prediction Model (NNSPPM)” IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 26-27 November 2013.
[6] Kayah, F. “Discretizing Continuous Features for Naive Bayes and C4. Classifiers”. University of Maryland publications: College Park, MD, USA.
[7] David, L. M. and Carlos E. G. Data Mining to Study Academic Performance of Students of a Tertiary Institute, American Journal of Educational Research,2(9), 2014, pp. 713-726. Doi: 10.12691/education-2-9-3.
[8] Romero, C. and Ventura, S., Educational data mining: A survey from 1995 to 2005, Expert Systems with Applications, 33(1), 2007, pp. 135-146.
[9] Anuradha, C & T, Velmurugan. (2015). A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance. Indian Journal of Science and technology. 8. 974-6846. 10.17485/ijst/2015/v8i15/74555.
[10] P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
[11] https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15
[12] E.Chandra and K. Nandini, ”Predicting Student Performance using Classification Techniques”, Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India, p.no, 83-87.
[13] S. Huang, & N. Fang, Work in Progress - Prediction of Students’ Academic Performance in an Introductory Engineering Course, In 41st ASEE/IEEE Frontiers in Education Conference, (2011), 11–13.

B. Mounika, V. Persis, "A Comparative Study of Machine Learning Algorithms for Student Academic Performance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.721-725, 2019.

Workforce Accumulation System is the latest innovation to give people with the job they are looking for. It is designed to be a cost-effective and time-saving application for searching and acquiring workforce in the nearby area via the use of web application. This study is aimed at providing help to the people who are facing problems about finding the correct labour for the required job in the nearby area. This also aims at providing an easy way to get a job for the labour. The web application will give the complete detail about the job description of the labour and their availability in a particular area. The user can select the job which needs to be done and so the available labour will be displayed. User can now select and contact the labour for getting the job done. It will reduce the manpower required to search labour physically as all that can be done with the web portal.

DotNet, MSSQL server, JavaScript

[1] Prince Singha, Aditya, Kunal Dubey, Jagadeeswararao Palli, “Toolkit for Web Development Based on Web Based Information System”, International Journal of Science Research in Computer Science and Engineering, Vol.6, Issue.5, pp.01-05, 2018.
[2] Carlos Flavian, Raquel Gurrean, Carlos Orus, “Web Design : a key factor for the website success”, Journal of Systems and Information Technology, Vol.11, Issue.2, pp. 386-398, 2009.
[3] Lang M., “Web Based System Development: the influence of disciplinary background on design practices”, Journal of Information and Organizational Sciences, pp.65-77, 2009.
[4] Asha Mandava and Soloman Anthony, “A Review and Analysis of Technologies for Developing Web Applications”, Murray State University Kentucky, Conference Paper - ITERA, 2012.
[5] Grinter, Rebecca E, “Doing Software Development: Occasions for Automation and Formalization”,Fifth Supported Conference on Computer Supported Cooperative Work, pp.173-188, 1997.
[6] M. Patidar, P. Bansal, “Cloud Forensics: An Overall Research Perspective”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.5-10, 2018.
[7] Xiong P., Peyton L. “A Model Driven Penetration test framework for Web Application”, Eighth annual international conference on privacy security and trust, Ottawa pp.173-180, 2010.
[8] Jessica Burdman., “Collaborative Web Development: Strategies and Practices for Web Teams”, Addison-Wesley Pearson Education India, pp. 47-74, 1999.
[9] Peter H Carstensen, Lasse Vogelsang, “Design of Web Based Information System- New Challenges from System Development?”, The 9th European Conference on Information System Bled, Slovenia, pp.536-547, 2001
[10] Isakowitz, Tomas, Michael Bieber, Fabio Vitali, “Web Information Systems”, Communications of the ACM, Vol.41, Issue.7 pp.78-80, 1998.

Deeksha Nanda, Siddhant Mishra, Sumit Kumar, Jaya Shukla, "Workforce Accumulation System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.726-730, 2019.

Many real-world problems have multiple competing objectives and can often be formulated as multi-objective optimisation problems. Multi-objective evolutionary algorithms have proven very effective in obtaining a set of trade-off solutions for such problems. This research seeks Outliers detection is perceptibly different from or inconsistent with the remaining dataset is a major challenge in real-world multi-objective problem. In this paper, the problem of identifying deviation point in a data set that exhibit non-standard behaviour is referred to as outlier. Outlier detection turns out to be a challenging task due to insufficient data in finding features to describe absolute high data. This paper presents a reference point based outlier detection algorithm using multi-objective evolutionary clustering technique(MOODA). In this algorithm, it assigns a deviation degree to each data point using the sum of distances between referential points to detect distant subspaces where outliers may exist. Finally, experimental studies show that our proposed algorithm is more effective in terms of efficiency and accuracy by using UCI dataset.

Outlier detection,Clustering, Multi-objective optimization, Evolutionary algorithrms

[1] M.H.Marghny and A.I. Taloba, “Outlier Detection using Improved Genetic K-means”. 2011. International Journal of Computer Applications. Vol .28, Issue .11, pp.33-36, 2011.
[2] R.Baklouti, M. Mansouri, M. Nounou, H. Nounou, A.B. Hamida, “Iterated robust kernel fuzzy principal component analysis and application to fault detection”, J. Comput. Sci. Vol. 15, pp. 34–49 2016.
[3] W.D. Fisher, T.K. Camp, V.V. Krzhizhanovskaya, “Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection.” J. Comput. Sci, Vol. 20, pp.143–153 ,2017.
[4] M. Sakurada, T. Yairi, Anomaly, “detection using autoencoders with nonlinear dimensionality reduction”, in: Proceedings of the 2014 ACM on Machine Learning for Sensory Data Analysis (MLSDA 2014), pp. 4, 2014
[5] J. T. Andrews, E. J. Morton, L. D. Griffin, “Detecting anomalous data using auto-encoders”, International Journal of Machine Learning and Computing, Vol. 6,Issue.1,pp. 21-26,2016.
[6] S. Wu and S. Wang, “Information-theoretic outlier detection for large-scale categorical data,” IEEE Trans on Knowledge and Data Engineering (TKDE), Vol. 25, Issue. 3, pp. 589–602, 2013.
[7] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection for discrete sequences: A survey,” IEEE Trans on Knowledge and Data Engineering (TKDE), Vol. 24, Issue. 5, pp. 823–839, 2012.
[8] M. Hubert,P.J.Rousseeuw, P. Segaert, “Multivariate functional outlier detection.”, Stat. Methods Appl. Vol. 24,Issue. 2, pp. 177–202,2015.
[9] S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori, “Statistical outlier detection using direct density ratio estimation,” Knowledge and Information Systems”, Vol. 26, Issue. 2, pp. 309–336, 2011.
[10] B. Perozzi, L. Akoglu,P. Iglesias Sánchez,E.Müller, “Focused clustering and outlier detection in large attributed graphs.”, In: Proceedings of the 2014 ACM International Conference on Knowledge Discovery And Data Mining(SIGKDD), pp. 1346–1355, 2014.
[11] H.P.Kriegel, P. Kroger, E. Schubert, A. Zimek, “Outlier detection in arbitrarily oriented subspaces.”, In: Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM), pp. 379–388, 2012.
[12] M.Anusha and J.G.R.Sathiaseelan, “An Improved K-Means Genetic Algorithm for Multi-objective Optimization”, International Journal of Applied Engineering Research,pp. 228-231, 2015.
[13] M.Anusha and J.G.R.Sathiaseelan, “An Empirical Study on Multi-Objective Genetic Algorithms using Clustering Techniques”, International Journal of Advanced Intelligence Paradigms. Vol. 8, Issue. 3, pp. 343-354, 2016.
[14] M.Anusha and J.G.R .Sathiaseelan, “Feature Selection using K-Means Genetic Algorithm for Multi-objective Optimization”, Procedia Computer Science, Vol. 57, pp. 1074-1080,Elsevier B.V., Netherland, 2015
[15] M.Anusha and J.G.R.Sathiaseelan, “An Enhanced K-means Genetic Algorithms for Optimal Clustering”, In the Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp.580-584, 2014.
[16] M.Anusha and J.G.R. Sathiaseelan, “Evolutionary Clustering Algorithm using Criterion-Knowledge-Ranking for Multi-objective Optimization”, Wireless Personal Communication, Springer, Vol.94, pp.2009-2030, Springer, USA. 2017.
[17] M.Anusha and J.G.R. Sathiaseelan, “Multi-objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis”, International Journal Advanced Computer Science Application, Vol.7, pp.485-488, Thesai publishers, UK. 2016.
[18] E.Müller,M.Schiffer,T.Seidl, “Statistical selection of relevant subspace projections for outlier ranking.” In the Proceedings of 2011 IEEE International Conference on Data Engineering (ICDE),pp. 434–445, April 2011.
[19] J.Tang,Z. Chen, A. Fu, D. Cheung, “Enhancing effectiveness of outlier detections for low density patterns.” In the Proceedings of Advances in Knowledge Discovery and Data Mining, pp. 535–548 ,2002.
[20] K. Zhang, M.Hutter, H, Jin, “A new local distance-based outlier detection approach for scattered real-world data.”, In the Proceedings of Advances in Knowledge Discovery and Data Mining, pp. 813–822, 2009.
[21] F. Keller, E. Muller, K. Bohm, “HiCS: high contrast subspaces for density-based outlier ranking.” In the Proceedings of 2012 IEEE International Conference on Data Engineering (ICDE), pp. 1037–1048, 2012.
[22] A. Dukkipati, D. Ghoshdastidar, J. Krishnan, “Mixture modeling with compact support distributions for unsupervised learning”, in the Proceedings of 2016 IEEE International Joint Conference on Neural Networks(IJCNN), pp. 2706– 2713, 2016.
[23] E. Eskin, “Anomaly detection over noisy data using learned probability distributions”,In Proceedings of the International Conference on Machine Learning, Citeseer, 2000.
[24] H.-P. Kriegel, P. Kr¨oger, E. Schubert, A. Zimek, “LoOP: local outlier probabilities”, in: Proceedings of the 2009 ACM Conference on Information and Knowledge Management, pp. 1649–1652, 2009.
[25] B Sch¨olkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson, “Estimating the support of a high-dimensional distribution”, Neural Computation, Vol.13,Issue. 7, pp.1443–1471, 2001.
[26] M. El Azami, C. Lartizien, S. Canu, “Converting svdd scores into probability estimates: Application to outlier detection”, Neurocomputing, Vol. 268, pp. 64–75, 2017.
[27] J. A. Quinn, M. Sugiyama, “A least-squares approach to anomaly detection in static and sequential data”, Pattern Recognition Letters, Vol. 40,pp. 36–40,2014.
[28] M.Anusha, “Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques”, Asian Journal of Computer Science and Technology, Vol.7 Issue.3, pp. 68-71, 2018.

M. Anusha , "Multi-objective Optimization to Detect Outliers with Referential Point using Evolutionary Clustering Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.731-735, 2019.

Nowadays, Digital Image Compression has become an indispensable part for storage and transmission. Compression is necessary for storing and transmitting image because of limited storage space and low bandwidth capacity. Wireless Image Compression is embedding scheme for reduction of image size so that it require less disk space and faster attachment possible in communication. Research issues in Image Compression are in terms of image quality of decompressed image on higher compression ratio and robustness against visual attacks. In this paper, a hybrid technique using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. Hybrid DWT and DCT based Compression technique to obtain increased quality of decompressed image as compared to DCT and DWT individually. The proposed technique is based on embedded process in which DCT is embedded in four level DWT. A good Compression ratio is achieved with increase in PSNR, which shows visually improved quality.

Compression Ratio, Discrete Cosine Transform, Discrete Wavelet Transform, Image Compression, Peak Signal to Noise Ratio, Mean Square Error.

[1] Salam Benchikh, Michael Corinthios, “ A Hybrid image Compression Technique Based on DWT and DCT Transforms” in International Conference on Advanced Infocom Technology 2011, 10.1049/cp.2011.1065.
[2] Krishan Gupta, Mukesh Sharma, Kirti Saneja, “A New Lossless KMK Technique for Image Compression” in International Conference on Reliability, Optimization and Information Technology - ICROIT 2014, India, Feb 6-8 2014, 978-1-4799-2995-5/14.
[3] R. Gonzalez, R. Woods, “Digital Image Processing” in 3rd edition, prentice Hall, New Jersey,2008.
[4] Shubh Lakshmi Agrwal, Deeksha Kumari, Meeta Sharma, Sandeep Kumar Gupta, “Improved image Compression Technique Using IWT-DCT Transformation”, 2nd International Conference in Next Generation Computing technologies (NGCT-2016), India, October,2016, 978-1-5090-3257-0/16.
[5] A.H.M. Jaffar Iqbal Barbhuiya, Tahera Akhtar Laskar, K. Hemachandran, “An Approach for Color Image Compression of JPEG and PNG Images using DCT And DWT” in Sixth International Conference on Computational Intelligence and Communication Networks, 978-1-4799-6929-6/14, 2014 IEEE.
[6] Navneet Kaur Aulakh Er. Yadwinder Kaur, “ Increasing Image Compression Rate using (DWT+DCT) and Steganography” in International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-4, Issue-5), May 2015.
[7] Ankit Chouhan I and M.l Nigam2 “Double Compression of JPEG Image using DCT with Estimated Quality Factor” in 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016), 978-1-4673-8587-9/16, 2016 IEEE.
[8] Ch.Sathi Raju,D.V.Rama Koti Reddy, “On Compression Characteristics of White Band and Narrow Band Images Using Hybrid DCT and DWT” in IEEE SPONSORED 2ND International Conference On Electronics And Communication System (ICECS 2015), 978-1- 4788-7225 -8/15, 2015 IEEE.
[9] Sukant Vats, Geetanj ali Rathee, “An Image-Compression Decomposition Analysis of Sub-Bands using Threshold Implementation” in Third International Conference on Image Infonnation Processing, 978-1-5090-0148-4/15, 2015 IEEE.
[10] Nusrat Ahmed Surobhi, “Amalgamation of SVD to JPEG for Enhanced Performance” in International Conference on Information and Communication Technology-2007.
[11] Kencabeen and Peter Gent,"Image Compression and the Descrete Cosine Transform"Math 45,College of the Redwoods.
[12] R.Praisline Jasmi, B.Preumal, M.Ballikonda, Rajesekaran, “ Comparison of Image Compression Techniques using Huffman Coding, DWT and Fractal Algorithm” in International Conference on Computer Communication and Informatics, Jan-2015, Coimbatore, India.
[13] Pawal Turza and Mariusz Duplaja, “ Hardware Efficient Low Power image Processing System for Wireless Capsule Endoscopy”, IEEE Journal of Bomedical and Health Informatics, Vol.17, No. 6, November-2013.
[14] Yongfei Zhang Haiheng Cao, Hongxu Jiang and Boli, “ Visual Distortion Sensitivity Modelling for Spatially Adaptive Quantization in Remote Sensing Image Compression” in IEEE Geoscience and Remote Sensing Letters, Vol.11,No.4, April 2014.
[15] Seepen Kimand, Namik Choudary,”Hierarchical Prediction and context Adaptive Coding for Lossless Color Image Compression” in IEEE Transactions on Image Processing, Vol.23, No.1, Jan 2014.
[16] D.Sophin Seeli, M.K. Jeyakumar, “ A study on Fractal Image Compression using Soft Computing techniques” in IJCSI, International Journal of Computer Science Issues, Vol.9, Issue 6, No.2, November 2012.
[17] Taha, Mohammad Hasan, Xingqian Wee, “ An Adaptive Fractal Image Compression” in IJCSI, International Journal of Computer Science Issues, Vol.10, Issue 2, No.1, March2013.
[18] Al-Fahaum, Harb B, “ A Combined Fractal and Wavelet Angiography Image Compression Approach” in The Open Medical Imaging Journal, Vol.7, pp 9-18,2013.
[19] Anil Bhagat, Balasaheb Deokate, “Improved Image Quality at Low Bitrates using Wavelet based fractal Image Coder, “ International Journal of Advanced research in Electrical, Electronics and Instrumental Engineering, ISO 3297:2007.
[20] Chen-Lung Hsui, Yu Sheng Huang, Ming Da Chang, Huang Yen Huang, “ Design of An Error Tolerance Scheme for Discrete Wavelet transform in JPEG 2000 Encoder” in Computers, IEEE Transactions, Vol.60,No. 5,pp 628,638, May 2011.
[21] Nusrat Ahmed Surobhi, “Amalgamation of SVD to JPEG for Enhanced Performance” in International Conference on Information and Communication Technology-2007.
[22] Karami A, Yazdi M, Mercier G, “Compression of Hyper Spectral Images using Discrete Wavelet Transform and Tucker Decomposition”, selected topics in Applied Earth Observations and Remote Sensing, IEEE Journal, Vol.5, No.2, pp444, 450, April 2012.

Rashmi Sharma Priyanka, "Digital Image Compression Using Hybrid Technique based on DWT and DCT Transforms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.736-744, 2019.

Pneumonia is an infection of the lungs that is caused by bacteria, viruses, fungi, or parasites and since the health of an individual is proportional to his or her productivity and life span. Therefore, a healthy population leads to a more productive country with a higher life expectancy rate. The aim of this paper is to develop an android mobile expert system for the diagnosis of pneumonia using Imo State University Teaching Hospital as a case study in order to improve the health condition of the people using android smart phone. In this paper we have developed a mobile expert system that can be used to diagnose pneumonia patients by taking various check-up either by patient or it’s assistance that can able to take his /her medical check-up by using medical peripherals and upload the report by its mobile phone to server where expert system could suggest precautionary steps or diagnosis along with patient status. We used PHP as a scripting programming language while MySQL was used as our database. To deal with this problem, a computerized system is needed. Methods used in analyzing and designing of the pneumonia problem is Object Oriented Analysis (OOA) with unified modeling language (UML)

Mobile Phone, Expert System, OOADM, PHP/MySQL, symptoms, Unified Modeling Language

[1] Haskin, Haketo I.N. and Adewale, Arrow (2003). Automation and Easy Accessibility of Medical Services in Nigeria (ICT and Medicine).Nigerian Computer Society National Conference.Pp.175-180 (2009).
[2] Feigenbaum, E, A (1982) Knowledge Engineering in the 1980’s. Dept of Computer Science. Stand ford University, stand ford CA.
[3] Djam X.Y, Wajiga G.M., Kimbi Y.H and Blamah N.V. (2009). A Fuzzy Expert System for themanagement of malaria.An International Journal of Pure and Applied Sciences and Technology. (vol.8), 3 pp.84-108, 2011.
[4] Dedecker, Adeli and Neshat M. (2006). A Fuzzy Expert System for Heart Disease Diagnosis. Proceedings International Multi-conference of Engineering and Computer Scientists; 2010; p 134-139.
[5] Sofia (2013). Introduction to Artificial Intelligence and Expert Systems. Prentice Hall of India Private Limited; 1990.p 326-337.
[6] Webopedia, (2000). Ending of preventable deaths from pneumonia and diarrhea. The Lancet, 2013, (vol.3) 381:1499-506.
[7] Zhoa, Donfack kana A.F, Abdullahi M, Ezugou A.E and Alkali S.A. (2010) .Online System for Diagnosis and Treatment of Malaria. An International Journal for the Application of Wireless and Mobile Computing, 2010.www. abu, edu. ng/._/ 2009-06-22-125106.
[8] Sara M. H. F. Zarandi, M. Zolnoori, M. Moin and H.Heidarnejad (2009). A Fuzzy Rule-Based Expert System for Diagnosing Asthma. An International Journal of Science and Technology. Transaction on Industrial Engineering 2010, (vol.17), (2): 129-142.
[9] Lightstone, Liu L, Johnson H, Cousens S, Perin J, Scott S, Lawn J, Rudan I, Campbell H, Cibulskis R, Li M, Mathers C, and Black R. (2009) Global, regional, and national causes of child mortality database system. An updated systematic analysis for 2010 with time trends since.
[10] Gehani,Brent, Schuetz P.(2006) Antibiotics for non-pneumonic respiratory Tract infections. The Lancet Infectious Diseases, (vol.13:999)

Amanze B.C., Asogwa D.C., Chukwuneke C.I, "An Android Mobile Expert System for the Diagnosi of Pneumonia with Object-Oriented Methodology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.745-758, 2019.

Clustering is an unsupervised classification of patterns into clusters (groups). Image clustering is a system of partitioning image data into clusters on the basis of similarities. It is used in many practical areas like Medical Diagnosis, Military, Remote sensing and etc. It is one type of image indexing where images are categorized into different groups based on their features, such as shape, color, or texture. The purpose of this paper is clustering of visually similar images from the image database using clustering algorithms. The proposed method uses the GLCM (Gray-Level Co-Occurrence Matrix) texture features. The extracted GLCM features are then clustered applying different clustering algorithms such as K-Means, K-Medoids and Improved K-Medoids partitioning clustering techniques. In this work, Corel-1k database is used. This work presents a comparative analysis of various clustering algorithms for image clustering with GLCM feature extraction technique. The experimental outcome of this work shows performance of different clustering algorithms.

Image Clustering, Feature Extraction, K-Means, K-Medoids

[1] Annesha Malakar and Joydeep Mukherjee, “Image Clustering using Color Moments, Histogram, Edge and K-means Clustering”, International Journal of Science and Research (IJSR), Vol.2, No.1, 2013.
[2] Azzam Sleit, Abdel latif Abu dalhoum, Mohammad Qatawneh, Maryam Al-Sharief, Rawa’a Al-Jabaly and Ola Karajeh, “Image Clustering using Color, Texture and Shape Features”, KSII Transactions on Internet and Information Systems, Vol.5, No.1, 2011.
[3] Dong ping Tain, “A Review on Image Feature Extraction and Representation Techniques”, International Journal of Multimedia and Ubiquitous Engineering, Vol.8, No.4, 2013.
[4] Donghua Yu, Guojun Liu, Maozu Guo and Xiaoyan Liu, “An Improved K-medoids Algorithm Based on Step Increasing and Optimizing Medoids”, Expert Systems with Applications, 2017.
[5] Gaurav Mandloi,” A Survey on Feature Extraction Techniques for Color Images”, International Journal of Computer Science and Information Technologies, Vol.5 (3), 2014.
[6] Kannan. A, Dr.V.Mohan, Dr.N.Anbazhagan, “Image Clustering and Retrieval using Image Mining Techniques‖”, IEEE International Conference on Computational Intelligence and Computing Research, 2010.
[7] Khalid Imam Rahmani, Naina Pal and Kamiya Arora, “Clustering of Image Data Using K-Means and Fuzzy K-Means”, International Journal of Advanced Computer Science and Applications, Vol.5, No.7, 2014.
[8] Nanthini. N, M.Vadivukarassi, N. Puviarasan and P. Aruna, “Analysis of Clustering Techniques for Retrieval of Images using Proposed Feature Extraction Method”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, Issue.3, 2017.
[9] Maria Fayez, Soha Safwat and Ehab Hassanein, “Comparative Study of Clustering Medical Images”, SAI Computing Conference 2016.
[10] Patil A. J, C.S.Patil, R.R.Karhe and M.A.Aher, “Comparative Study of Different Clustering Algorithms”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, Issue.7, 2014.
[11] Raghuvira Pratap A, K Suvarna Vani, J Rama Devi and Dr.K Nageswara Rao, “An Efficient Density based Improved K- Medoids Clustering algorithm”, International Journal of Advanced Computer Science and Applications, Vol.2, No.6, 2011.
[12] Rama Kalaivani.E, Suganya. G and Kiruba. J, “Review on K-Means and Fuzzy C Means Clustering Algorithm”, Imperial Journal of Interdisciplinary Research (IJIR), Vol.3, Issue.2, 2017.
[13] SijiT.Mathew and Nachamai M, “Clustering of Brain MRI Image Using Datamining Algorithm”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Vol.3, Issue.4, 2015.
[14] Sriparna Saha, Abhay Kumar Alok and Asif Ekbal, “Brain Image Segmentation using Semi-Supervised Clustering”, Expert Systems with Applications, 2016.
[15] Sukhvir Kaur, “Survey of Different Data Clustering Algorithms”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, pg. 584-588, 2016.
[16] Sukhdev Singh Ghuman, “Clustering Techniques- A Review”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, pg. 524-530, 2016.
[17] Sunil Chowdary, D. Sri Lakshmi Prasanna and P. Sudhakar, “Evaluating and Analyzing Clusters in Data Mining using Different Algorithms”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.2, pg. 86-99, 2014.
[18] Varsha Kundlikar and Meghana Nagori, “Image Mining Using Image Feature”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol.3, Issue 1, 2014.
[19] VikasTondar and Pramod S. Nair, “A Comparative Study on Clustering Algorithms using Image Data”, International Journal of Computer Applications ISSN: 0975 – 8887, Vol.133, No.17, 2016.
[20] Wazarkar S and B.N. Keshavamurthy, “A Survey on Image Data Analysis through Clustering Techniques for Real World Applications”, J. Vis. Commun. Image R, 2018.
[21] Zeynel Cebeci and Figen Yildiz, “Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures”, Journal of Agricultural Informatics, Vol. 6, No. 3, 2015.

M. Kiruthika, S. Sukumaran, "An Improved K-Medoids Partitioning Algorithm for Clustering of Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.759-764, 2019.

The data present in cloud should be provided security because the data is stored on distributed servers or systems. The security issues concerning data are loss of authentication and privacy. In cloud platform security is provided through encryption techniques only during transmission of data. To process the data present on a remote server, the cloud providers need to access the raw data to allow us to perform operations on the data. It can’t provide confidentiality to the data as it is exposed in the form of plain text in operational stage. The disadvantages are the data can’t remain confidential and invisible to cloud service provider and the data can be reused by the cloud service provider. Encryption solves major privacy issues but performing computations, one needs to perform the decryption first. The data privacy issue can be resolved if user is able to carry out computations on encrypted data. Homomorphic Encryption technique enables computing with encrypted data. That means one can perform the operations on this data without converting into the plain text. Data is not in its original form in its most of the operational stages on the cloud. It enables computations on encrypted data. Our idea is to encrypt data before sending it to the cloud, but to execute the calculations; the data should be decrypted every time to work on it. Decryption can be performed directly without encryption where the client is the only holder of the secret key. When the result of any operation is decrypted, it is the same as carrying out the calculations on the raw data.

cloud computing, encryption technique, plain text, decryption and Homomorphic encryption

[1]. Sean Marston and al. “Cloud computing the business perspective”, Volume 51, Issue 1, Pages 176–189, http://www.sciencedirect.com, April 2011.
[2]. Vic (J.R.) Winkler, “Securing the Cloud, Cloud Computer Security, Techniques and Tactics”, Elsevier, 2011.
[3]. Sean Carlin, Kevin Curran, “Cloud Computing Technologies”, International Journal of Cloud Computing and Services Science (IJ-CLOSER), Vol.1, No.2, pp. 59-65, June 2012.
[4]. Pascal Paillier, “Public-key cryptosystems based on composite degree residuosity classes”, In 18th Annual Eurocrypt Conference (EUROCRYPT`99), Prague, Czech Republic, volume 1592, 1999.
[5]. https://www.centos.org/docs/5/html/5.1/Deployment.../s3-openssh-rsa-keys-v2.html
[6]. https://www.ijecs.in/index.php/ijecs/article/download/1527/1410/

P Venkateswarlu, B Manasa, K Srikanth, "An Expensive Study of Homomorphic Encryption to Secure Cloud Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.765-770, 2019.

Treatment for burn injuries depends highly on the type of severity of the burn. Thus, identification of the severity of burns plays a very important role in providing proper treatment to patients suffering from skin burns. With digitization of images using image processing, the treatment becomes easier by properly classifying these skin burn images and identifying the severity of these burns using the some scientific techniques. The color of the skin burn images are represented by Red Green Blue (RGB) histogram. Lot of research has been done in using the RGB histogram to develop different algorithms and methods of classification and also to assess the severity. However, because of the drawbacks in each of the algorithms in one or the other way, there is no single method or algorithm that fits in all the situations. As an alternative, the statistical properties of histograms can be used to assess the severity of burn images by assessing the reliability of burn images, which in turn, helps in providing proper treatment to the patients suffering from burns. The RGB histogram of burn images can be used as a basis for this. The RGB band of burn images have Gaussian distribution and this information can be used in determining the reliability and hence the severity of the burn wounds. Herein, it is intended to assess and analyze the reliability of skin burn images through this Gaussian distribution, using statistical procedure. Some past data obtained through clinical observations have been used for obtaining the same.

Gaussian distribution, Intensity histogram, Reliability analysis, RGB classification, Skin burn

[1] Practical Handbook of Burns Management For National Programme for Prevention, Management and Rehabilitation of Burn Injuries (NPPMRBI) under Ministry of Health and Family Welfare Government of India, pp 17-18, 2015.
[2] Nikoletta Bassiou, Constantine Kotropoulos, “Color image histogram equalization by absolute discounting back-of”, Computer Vision and Image Understanding 107, pp 108–122, 2007.
[3] Hong-yan Li, “Skin Burns Degree Determined by Computer Image Processing Method”, In the proceedings of 2012 International Conference on Medical Physics and Biomedical Engineering, Elsevier Physics Procedia 33, pp 758 – 764, 2012.
[4] Malini Suvarna, Sivakumar, Dr. Kamal Kumar, U C Niranjan, “Diagnosis of Burn Images using Template Matching, k-Nearest Neighbor and Artificial Neural Network”, International Journal of Image Processing (IJIP), Vol. 7, No. 2, pp 191- 202, 2013.
[5] Malini Suvarna, Sivakumar, U C Niranjan, “Classification Methods of Skin Burn Images”, International Journal of Computer Science & Information Technology (IJCSIT) Vol. 5, No. 1, pp 109-118, 2013.
[6] Begon˜a Acha Carmen Serrano Jose´ I. Acha, Laura M. Roa, “Segmentation and classification of burn images by color and texture information”, Journal of Biomedical Optics Vol. 10, No. 3, pp 1-11, 2005.
[7] Hai Son Tran, Thai Hoang Le, Thuy Thanh Nguyen, “The Degree of Skin Burns Images Recognition using Convolutional Neural Network”, Indian Journal of Science and Technology, Vol. 9, No. 45, pp 1-6, 2016.
[8] T.S. Hai, L.M. Triet , L.H. Thai , N.T. Thuy, “Real Time Burning Image Classification Using Support Vector Machine”, EAI Endorsed Transactions on Context-aware Systems and Applications, Vol. 4, No. 12, pp 1-7, 2017.

Somashekhar G. C., H. B. Phaniraju, "Novel Reliability Analysis of Skin Burn Images Obtained Using Image Processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.771-774, 2019.