Volume-7 , Issue-6 , Jun - 2019 Go Back

In recent studies, it is found that a fault prediction technique plays an important role especially in software development. Software fault prediction implies a decent investment in better style in future system to avoid building a fault prone modules. Faulty modules are expected using data mining techniques such as various classifiers which are used to classify faulty or non faulty modules. Many researchers have been produces different approaches for predicting fault in the software. In this paper it is found that various fault prediction techniques have been used and also found out the way to judge the performance of fault prediction methodologies in recent year. The main objective of survey is to identify best prediction techniques for detecting fault in early stage, and also determine the problem area in software fault prediction methodology which provides improvement in software development system. This paper presents the survey on fault prediction using data mining techniques which will helpful for further research in field of software fault prediction.

Software fault prediction, Data Mining, Prediction techniques

[1] Naik, K., & Tripathy, “Software Testing and Quality Assurance”, John Wiley & Sons, Inc, pp. (2008).
[2] Wangshu Liu, Shulong Liu, Qing Gu, Member, IEEE, Jiaqiang Chen, Xiang Chen, Member, IEEE, and Daoxu Chen, Member, IEEE , “Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault Prediction” in IEEE TRANSACTIONS ON RELIABILITY Volume: PP Year: 2015.
[3] Yasutaka Kamei, Akito Monden, Shuji Morisaki, Ken-ichi Matsumoto, “A Hybrid faulty module Prediction using Association Rule Mining and Logistic Regression Analysis”, Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and measurement, Pages 279-281.
[4] Catal. C, Sevim. U and Diri. B, “Software fault prediction of unlabeled program modules”, London,UK., WCE 2009, July 1-3
[5] Shanthini. A Chandrasekaran.RM ,”Applying Machine Learning for Fault Prediction Using Software Metrics”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, June 2012, ISSN: 2277 128X.
[6] Meenakshi P.C, Meenu S, Mithra M, Leela Rani P,” Fault Prediction using Quad Tree and Expectation Maximization Algorithm”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868, Volume 2– No.4, May 2012
[7] Yuanxun SHAO, Bin LIU, Guoqi LI, Shihai WANG,” Software Defect Prediction Based on Class Association Rules”, The Second International Conference on Reliability Systems Engineering (ICRSE 2017)
[8] N.P. Gopalan, B. Sivaselvan, “Data Mining Techniques and Trends”, PHP Learning Private Limited, New Delhi-110001, India, ISBN-978-81-203-3812-B, 2009
[9] G.K. Gupta, “Introduction To Data Mining With Case Studies”, Second Edition, PHP Learning Private Limited, New Delhi-110001, India, ISBN-978-81-203-4326-9, 2011
[10] D. Hand, H. Mannila and P. Smyth, “Principles of data mining”, MIT, 2001.
[11] Fong, P.K. and Weber-Jhanke, J.H , “Privacy Preserving Decision Tree Learning using Unrealized Data Sets”, IEEE Transactions on knowledge and Data Engineering, Vol.24,No.2, February 2012, pp. 353-364.
[12] Langley P, Iba W, Thompson K., “An analysis of Bayesian classifiers”, in Proceedings of the 10th National Conference on Artificial Intelligence, 1992, pp. 223-228.

Ashwni kumar, D.L.Gupta, "Software Fault Prediction using Data Mining Techniques: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.671-674, 2019.

Contextual Hierarchical Model (CHM) was a semantic image segmentation model which learned contextual information in a hierarchical framework. A Logistic Disjunctive Normal Networks (LDNN) classifier was used in each hierarchy level of CHM for semantic image segmentation. The class average accuracy of CHM may be affected due to the absence of global constraint. So, different Conditional Random Field (CRF) models were introduced to define global constraints through energy functions on a discrete random field. The efficiency of CHM based semantic image segmentation was greatly depended on the performance of LDNN. The performance of LDNN was enhanced by using a proximal gradient which minimizes the quadratic error of LDNN with fast convergence rate. Moreover, a Grey Wolf Optimization (GWO) algorithm was introduced to optimize the user specified weight and bias terms of LDNN which reduce the time complexity of LDNN. In this paper, CHM based semantic image segmentation is further improved by using ontology-based contextual knowledge representation in CHM. The ontology-based contextual knowledge representation constructs a relation based on taxonomic relations. In order to tackle the complex types of relations in images, a fuzzification is introduced in the ontology which is used to define the semantic relation between the concepts more effectively. Based on the fuzzified taxonomic relation, a relation is constructed which is given as additional input to the CHM for semantic image segmentation. The ontological taxonomic knowledge representation adjusts the segmentation results of CHM based on taxonomic relations. The experimental results show that the proposed Ontology-based contextual knowledge representation with CHM- Higher order Hierarchical CRF-Improved Optimized LDNN (OCHM-HHCRF-IOLDNN) has better performance in terms of class accuracy, pixel accuracy, F-measure and G-mean than the other method.

Semantic image segmentation, Contextual Hierarchical Model, Logistic Disjunctive Normal Networks, ontology-based contextual knowledge representation, fuzzification

[1] H. F. Ates, S. Sunteci, “Multi-hypothesis contextual modeling for semantic segmentation”, Pattern Recognition, Vol.117, pp.104-110, 2019.
[2] X. Liu, Z. Deng, Y. Yang, “Recent progress in semantic image segmentation”, Artificial Intelligence Review, pp.1-18, 2018.
[3] P. Umorya, R. Singh, “A comparative based review on image segmentation of medical image and its technique”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, 2017.
[4] N. Ghuse, Y. Deore, A. Potgantwar, “Efficient image processing based liver cancer detection method”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.33-38, 2017.
[5] Z. Tu, X. Bai, “Auto-context and its application to high-level vision tasks and 3d brain image segmentation”, IEEE transactions on pattern analysis and machine intelligence, Vol.32, Issue.10, pp.1744-1757, 2010.
[6] C. Li, A. Kowdle, A. Saxena, T. Chen, “Toward holistic scene understanding: Feedback enabled cascaded classification models”, IEEE transactions on pattern analysis and machine intelligence, Vol.34, Issue.7, pp.1394-1408, 2012.
[7] M. Seyedhosseini, T. Tasdizen, “Semantic image segmentation with contextual hierarchical models”, IEEE transactions on pattern analysis and machine intelligence, Vol.38, Issue.5, pp.951-964, 2016.
[8] T. Sreedhar, S. Sathappan, “Different Conditional Random Field based Contextual Hierarchical Model for Semantic Image Segmentation”, Journal of Advanced Research in Dynamical and Control Systems, Vol.11, Issue.1, pp.48-57, 2019.
[9] M. Seyedhosseini, M. Sajjadi, T. Tasdizen, “Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks”, In the Proceedings of the IEEE International Conference on Computer Vision, pp.2168-2175, 2013.
[10] T. Sreedar, S. Sathappan, “An Improved Optimized Logistic Distinctive Classifier for Efficient Semantic Image Segmentation”, International Conference on IoT, Social, Mobile, Analytics and Cloud in Computational Vision and Bio-Engineering Lecture Notes in Computational Vision and Biomechanics”, Communicated, 2019.
[11] N. Singh, S.B. Singh, “A modified mean Gray Wolf optimization approach for benchmark and biomedical problems”, Evolutionary Bioinformatics, Vol.13, pp.1-27, 2017.
[12] D. Zomahoun, “Collaborative Semantic Annotation of Images: Ontology-Based Model”, Signal & Image Processing: An International Journal (SIPIJ), Vol.4, Issue.6, pp.71-81, 2013.
[13] Z. Ren, G. Shakhnarovich, “Image segmentation by cascaded region agglomeration”, In 2013 IEEE conference on Computer vision and pattern recognition (CVPR), pp.2011-2018, 2013.
[14] M. Seyedhosseini, T. Tasdizen, “Multi-class multi-scale series contextual model for image segmentation”, IEEE Transactions on Image Processing, Vol.22, Issue.11, pp.4486-4496, 2013.
[15] D. Pei, Z. Li, R. Ji, F. Sun, “Efficient semantic image segmentation with multi-class ranking prior”, Computer Vision and Image Understanding, Vol.120, pp.81-90, 2014.
[16] L. L. Wang, N.H. Yung, “Hybrid graphical model for semantic image segmentation”, Journal of Visual Communication and Image Representation, Vol.28, pp.83-96, 2015.
[17] M. Zand, S. Doraisamy, A.A. Halin, M.R. Mustaffa, “Ontology-based semantic image segmentation using mixture models and multiple CRFs”, IEEE Transactions on Image Processing, Vol.25, Issue.7, pp.3233-3248, 2016.
[18] K. Chen, K. Fu, M. Yan, X. Gao, X. Sun, X. Wei, “Semantic Segmentation of Aerial Images with Shuffling Convolutional Neural Networks”, IEEE Geoscience and Remote Sensing Letters, Vol.15, Issue.2, pp.173-177, 2018.
[19] J. Li, W. Speier, K. C. Ho, K. V. Sarma, A. Gertych, B.S. Knudsen, C.W. Arnold, “An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies”, Computerized Medical Imaging and Graphics, Vol.69, pp.125-133, 2018.
[20] Y. Sun, X. Zhang, Q. Xin, J. Huang, “Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.143, pp.3-14, 2018.
[21] A.B. Benitez, D. Zhong, S.F. Chang, J.R. Smith, “MPEG-7 MDS content description tools and applications”, In International Conference on Computer Analysis of Images and Patterns, Springer, Berlin, Heidelberg, pp.41-52, 2001.

T. Sreedhar, S. Sathappan, "An Ontology-Based Contextual Knowledge Representation for Semantic Image Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.675-682, 2019.

Nutrient deficiency may cause degradation in productivity of crop, the commercial plants like tomato usually gets affected by Nutrient deficiency. There is requirement of device which will predict Nutrient deficiency on the basis of visual symptoms. We have analysed tomato leaf using parameters like Uniformness detection (Deviation matrix method and Histogram analysis method), Lightness in colour detection, Chlorosis and Necrosis detection and by using some structural parameters like Status of Major vein, Length to Width ratio etc. On the basis of above parameters and PH of soil, we can accurately predict the Nutrient deficiency through which plant is suffering from. It is more relevant and non-destructive method of Nutrient deficiency detection. This method can detect deficiency at any stage of growth. Also similar techniques can be used for Nutrient deficiency detection of other plants like pomegranate, chilly, grape etc.

Nutrient deficiency, Tomato leaf processing, Image processing in Agriculture, Machine Vision in Agriculture, Deficiency Symptoms

[1] G. Xu, F. Zhang, S. G. Shah, Y. Ye, H. Mao, “Use of leaf color images to identify nitrogen and potassium deficient tomatoes”, Pattern Recognition Letters 32, 1584–1590, ELSEVIER, 2011.
[2] D. Story, M. Kacira, C. Kubota, A. Akoglu, L. An, “Lettuce calcium deficiency detection with machine vision computed plant features in controlled environment” Computer and Electronics in Agriculture 74, 238-243, ELSEVIER, 2010.
[3] Y. Sun, S. Zhu, X. Yang, M. V. Weston, Ke Wang, Z. Shen, H. Xu, L. Chen, “Nitrogen diagnosis based on dynamic characteristics of rice leaf image” PLoS ONE 13(4):e0196298, 2018.
[4] M. Wiwart, G. Fordonski, K. Zuk-Golaszewska, E. Suchowisska, “Early diagnostics of macronutrient deficiencies in tree legume species by color image analysis”. Computer and Electronics in Agriculture 65, 125-132. ELSEVIER, 2009.
[5] A. Mercado-Luna1, E. Rico-Garcia, A. Lara-Herrera, G. Soto-Zarazua, R. Ocampo-Velazquez, R. Guevara-Gonzalez1, G. Herrera-Ruiz and I. Torres-Pacheco1 “Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by color image analysis (RGB)”, African Journal of Biotechnology Vol. 9(33), pp. 5326-5332, 2010.
[6] M. V. Latte, S. Shidnal, “Multiple Nutrient Deficiency Detection in Paddy Leaf Images using Color and Pattern Analysis”, International Conference on Communication and Signal Processing, IEEE, 2016.
[7] M. A Hairuddin , L. M. Romualdo, “Overview of Image Processing Approach for Nutrient Deficiencies Detection in Elaeis Guineensis” , IEEE International Conference on System Engineering and Technology (ICSET), 2011.
[8] R. H. M. Condori, L. M. Romualdo, “Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops”, Workshop of Computer Vision, IEEE, 2017.
[9] M. Merchant, V. Paradkar, M Khanna, S. Gokhale, “Mango Leaf Deficiency Detection Using Digital Image Processing and Machine Learning”, Third International Conference On Convergence In Technology (I2CT), IEEE, 2018.
[10] S.Kawashima, M,Nakatani , “An algorithm for estimating chlorophyll content in leaves using a video camera”, Ann. Bot. 81: 4954, 1998.
[11] R. Radha, S. Jeyalakshmi, “An Effective Algorithm for Edges and Veins Detection in Leaf Images”, World Congress on Computing and Communication Technologies, IEEE, 2014.
[12] A. McCauley, C. Jones, J. Jacobsen, “Plant Nutrient Functions and Deficiency and Toxicity Symptoms”, Montana state university extension 4449-9, june 2011.

R.V. Ahire, S.L. Nalbalwar, N.S. Jadhav, Sachin Singh, "Tomato Nutrient Deficiency Detection on The Basis of Visible Symptoms Using Digital Image Processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.683-689, 2019.

In Today’s world it is easy to recognize the emotion of a person by just looking at his/her facial expressions. For a sales person it is important to know whether his customers is convinced to buy a product or not, the factors through which he can identify this is by observing the behaviour and emotions. For e-commerce such as Amazon and Flipkart it becomes difficult to identify the emotional state of a person. The interaction and communication between human beings and computers will be more natural if computers are able to understand and respond to the emotions of an individual [1]. This paper provides us a way through which the e-commerce business can plan strategies, recommend relevant products and keep a track of customer’s habit using facial emotions.

FER – Facial emotion recognition, SCQ Framework, Factor Mapping, AI, Video file ingestion, Restful service.

[1] Carlos Busso, Zhigang Deng , Serdar Yildirim, Murtaza Bulut, Chul Min Lee, Abe Kazemzadeh, Sungbok Lee, Ulrich Neumann,
Shrikanth Narayanan, “Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information.”, 2004
[2] Hitesh Patil & Shivkumar Goel, “Machine learning based credit card fraud analysis, modelling, detection and deployment”,
IJAR, June 2017.
[3] https://www.12manage.com/methods_minto_pyramid_principle.html
[4] Paweł Tarnowski, Marcin Kołodziej, Andrzej Majkowski, Remigiusz J. Rak, “Emotion recognition using facial expressions.”
ICCS 2017,, 12-14 June 2017.
[5] https://sebastianraschka.com/faq/docs/difference_classifier_model.html
[6] https://www.shutterstock.com/image-photo/portrait-sad-girl-close-262567214?src=NFgYsCmJmPq3IxzznFJ9Og-1-2
[7] Byoung Chul Ko, “A Brief Review of Facial Emotion Recognition Based on Visual Information”. 30th January 2018
[8] Lundqvist D., Flykt A., Öhman A., The Karolinska Directed Emotional Faces - KDEF, CDROM from Department of Clinical
Neuroscience, Psychology section, Karolinska Institutet, no.1998.

Sagar Navle, Uttara Athawale, "Emotion Analysis of E-Customers Using Face Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.690-694, 2019.

To achieve long distance coverage and better resolution, high gain antennas is convenient for airborne radars. So a great deal of attention has been devoted to exploiting new approaches towards the problem. Radar application demands a low profile, minimal weight and high gain antennas. Over the last decade microstrip antennas are used as an alternative element for the bulky and heavy weight reflector antennas. Presently researchers are implementing metasurface antennas and their imitative for the radar applications. A high gain transmitting microstrip lens antenna is presented by putting a layered phase gradient coded metasurface with 0 & 1 elements for 0 and π phase responses. Four types of unit cell with two bits coding elements 00,01,10,11 is implemented for four phase differences. The lens antenna results a gain enhancement of 11.7 dbi and return loss of -34 dB, which is approximately 7 dbi and -17dB in case of a normal microstrip antenna, so the gain is enhanced by 4.7dB and a return loss is reduced by 17 dB at 10.3 MHz frequency.

Codded phase gradient, Lens, Metasurface, Microstrip, X-Band Radar

[1] H. Li, G. Wang, X. He-Xiu, T. Cai, “X-band phase-gradient metasurface for high-gain lens antenna application”, IEEE Transactions on Antennas and Propagation, Vol. 63, Issue.11, pp. 5144-5149,2015.
[2] Y. Zhou, C. Xiang-yu, G. Jun G, "RCS reduction for grazing incidence based on coding metasurface", Electronics Letters, Vol. 53, Issue. 20, pp. 1381-1383, 2017.
[3] X. Li, S. Xiao, B. Cai, H. Qiong, “Flat metasurfaces to focus electromagnetic waves in reflection geometry”, Optics letters, Vol. 37, Issue. 23, pp. 4940-4942, 2012.
[4] B. Rahmati, H. R. Hassani, “Low-profile slot transmit array antenna”, IEEE Transactions on Antennas and Propagation, Vol. 63, Issue.1, pp.174-181, 2015.
[5] Z. Yue-Jun, G. Jun, Z. Yu-Long, "Metamaterial-based patch antenna with wideband RCS reduction and gain enhancement using improved loading method", IET Microwaves, Antennas & Propagation, Vol. 11, Issue. 9, pp. 1183-1189, 2017.
[6] S. Liu, C. Tie, X. Quan, “Anisotropic coding metamaterials and their powerful manipulation of differently polarized terahertz waves”, Light: Science & Applications, Vol. 5, pp. 16076, 2016.
[7] N. Yu, G. Patrice, A. Kats, “Light propagation with phase discontinuities: generalized laws of reflection and refraction”, Science, 6054, pp. 333-337, 2011.
[8] K. S. Beenamole, “Microstrip Antenna Designs for Radar Applications”, DRDO Science Spectrum, pp. 84-86, 2009.
[9] J. Shi, F. Xu, P. Eric, “Coherent control of Snell’s law at metasurfaces”, Optics express, Vol. 22, Issue. 17, pp. 21051-21060, 2014.
[10] W. E. Liu, Z. N. Chen, X. Qing, J. Shi, F.H. Lin, “Miniaturized Wideband Metasurface Antennas,” IEEE Transactions on Antennas and Propagation, Vol. 65, Issue .12, pp.7345-7349, 2017.
[11] X. Liu, J. Gao, L. Xu, X. Cao, Y. Zhao, S. Li, S., “A coding diffuse metasurface for RCS reduction,” IEEE Antennas and Wireless Propagation Letters, Vol.16, pp. 724-727, 2017.
[12] C. L. Holloway, E. F. Kuester, J. A. Gordon, J. O` Hara, J. Booth, D. R. Smith, “An overview of the theory and applications of metasurfaces: The two- dimensional equivalents of metamaterials,” IEEE Antennas and Propagation Magazine, Vol. 54, Issue. 2, pp. 10-35, 2012.
[13] F. Y. Kuo, R. B. Hwang, “High-isolation X-band marine radar antenna design,” IEEE Transactions on Antennas and Propagation, Vol. 62, Issue. 5, pp. 2331-2337, 2014.
[14] J. Li, D. Jiang, “Low-complexity propagator based two dimensional angle estimation for coprime MIMO radar,” IEEE Access, Vol. 4, Issue. 2,2018.

Monalisa Nayak, Devika Jena, Kodanda Dhar Sa, Dillip Dash, "Coded Phase Gradient Metasurface Antenna Design for X- Band Radar," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.695-703, 2019.

In Today’s world clustering techniques are used in different fields like Image Classification, AI, E-commerce, etc. The advantage of clustering is that it provides a summarized output for the user, where the user can obtain the exact results and predict the outcome. Once implemented, clustering offers a clarified outcome to the user which strikes out the necessity for further research and development. Clustering plays a major role in areas where there is a probability and necessity of figuring out similar as well as dissimilar objects. One of the major aspects of data mining is to differentiate between objects based on their properties or attributes. This paper focuses on three such fields namely Medical Science, Agriculture and E-Commerce.

Clustering, Medical Science, Agriculture, E-commerce

[1] Rachid Ait daoud, Abdellah Amine, Belaid Bouikhalene, Rachid Lbibb,” Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: International Journal of Computer and Information Engineering Vol:9, No:8, 2015 2000International.
[2] HuaYu, XiZhang, “Research on the application of IoT in E-commerce”, 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).
[3] Patel, Hetal, Patel, Dharmendra (2014). A Brief survey of Data Mining Techniques Applied to Agricultural Data. International Journal of Computer Applications (0975 – 8887) Volume 95– No. 9, June 2014.
[4] Yadav, Dileep Kumar (2015). A Comparative Analysis Of Clustering Algorithms For Agricultural Data. International Journal of Current Research Vol. 7, Issue, 07, pp.18361-18364, July, 2015
[5] Mucherino, A., Papajorgji, P., & Pardalos, P. (2009). Data mining in agriculture (Vol. 34). Springer.
[6] Ruß, Georg, Kruse, Rudolf, Schneider, Martin, Wagner, Peter (2008). Visualization of Agriculture Data Using Self-Organizing Maps. International Conference on Innovative Techniques and Applications of Artificial Intelligence SGAI 2008: Applications and Innovations in Intelligent Systems XVI pp 47-60. Springer.
[7] K.J. and G.W. Moore Cios, "Uniqueness of medical data mining ," Artificial Intelligence in Medicine, pp. 1-24, 2002.
[8] Anamika Gupta, Naveen Kumar, and Vasudha Bhatnagar," Analysis of Medical Data using Data Mining and Formal Concept Analysis, World Academy of Science, Engineering and Technology International Journal of Medical and Health Sciences Vol:1, No:11, 2007 .
[9] H. R. Warner and O. Bouhaddou, “Innovation review: Iliad—A medical diagnostic support program” Top Health Inf. Manage., vol. 14, no. 4, pp. 51–58, 1994.
[10] Dr. Bushra M. Hussan,” Data Mining based Prediction of Medical data Using K-means algorithm”, Computer Science Department - Collegeof Science - Basrah University, Basrah Journal of Science (A) Vol.30(1),46-56 2012.

Syed Zishan Ali, Surbhi Chundawat, Shrinjanee Shukla, Akshat Choudhary, "Implementation of Clustering Techniques in Various Fields: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.704-707, 2019.

The emerging technologies have highly incorporated with crypto-currencies and one of them is bitcoin which has a great effect on the digital marketing and day by day the amount of data associated with it is facing a steep growth and lots of security challenges are coming into play. In order to prevent the system from fraudulent transactions several methodologies have been used. In this study we have described how a digital currency can be maintained by the use of distributed decentralized ledger.

Bitcoin, Blockchain, Mining, Proof-of-Work, minting, forging, proof-of-stake, Ledger Maintenance

[1] Mohamed Rahouti, Kaiqi Xiong and Nasir Ghani,Bitcoin Concepts, Threats, andMachine-Learning Security Solutions, 10.1109/ACCESS.2018.2874539, IEEE Access.
[2] Hayungmin Cho, ASIC-Resistance of Multi-Hash Proof-of-Work Mechanisms forBlockchain Consensus Protocols, 10.1109/ACCESS.2018.2878895, IEEE Access
[3] www.bitfury.com/content/downloads/pos-vs-pow-1.0.2.pdf
[4] www.bitcoin.org/bitcoin.pdf
[5] Rishav Chatterjee and Rajdeep Chatterjee, An Overview of the Emerging Tchnology: Blockchain, 2017 International Conference on Computational Intelligence and Networks
[6] www.jwar.org.uk/comsec/resources/cipher/sha256-384-512.pdf
[7] www.gdre-scpo-aix.sciencesconf.org/195470/document
[8] Aradhana1, Dr. S. M. Ghosh International Journal of Technical Innovation in Modern Engineering & Science (IJTIMES) , e-ISSN: 2455-2584 Volume 3, Issue 05, May-2017.

[9] Karl J. O’Dwyer and David Malone, Bitcoin Mining and its Energy Footprint, ISSC 2014 / CIICT 2014, Limerick, June 26–27
[10] Deepak K. Tosh, Sachin Shetty, Peter Foytik, Charles A. Kamhoua and Laurent Njilla, CloudPoS: A Proof-of-Stake Consensus Design forBlockchain Integrated Cloud, 2018 IEEE 11th International Conference on Cloud Computing.
[11] Sunny King and Scott Nadal, PPCoin: Peer-to-Peer Crypto-Currency with Proof-of-Stake, August 19th, 2012

Syed Zishan Ali, Dolly Sahu, Jatin Sahu, "Bitcoin in Blockchain: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.708-712, 2019.

Big data has become more popular as people and organizations realize the importance and the value that the data has in formulating important information. As the data continue to increase, some challenges arise on the methods or techniques that are needed to be used in extracting meaningful information from the big data. Increase in data has led the researchers to make expansions on the existing data mining techniques to help with adapting to the evolving nature of big data thus leading to the development of new analytical techniques. Research has led to the development of various data mining techniques used on big data. It is, therefore, necessary to evaluate and compare different data mining techniques for big data.

Data Mining, Big Data, WEKA Software Tool

[1] The Baheti, A., & Toshniwal, D. (2014). Trend Analysis of Time Series Data Using Data Mining Techniques. 2014 IEEE International Congress on Big Data. doi:10.1109/bigdata.congress.2014.69
[2] Garg, S., & K. Sharma, A. (2013). Comparative Analysis of Various Data Mining Techniques on Educational Datasets. International Journal of Computer Applications, 74(5), 1-5. doi:10.5120/12878-9673
[3] Gole, S., & Tidke, B. (2015). A survey of big data in social media using data mining techniques. 2015 International Conference on Advanced Computing and Communication Systems. doi:10.1109/icaccs.2015.7324059
[4] Jamil, J. M., & Shaharanee, I. N. (2014). Comparative analysis of data mining techniques for business data. doi:10.1063/1.4903641
[5] Shobanadevi, A., & Maragatham, G. (2017). Data mining techniques for IoT and big data — A survey. 2017 International Conference on Intelligent Sustainable Systems (ICISS). doi:10.1109/iss1.2017.8389260

Srinivasa Rao Putta, "Comparative Analysis of Data Mining With Big Data Using WEKA Software Tool," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.713-715, 2019.

Users can enjoy High Definition (HD) video content instead of being satisfied with Standard Definition (SD) video content. Such HD-grade User Created Content (UCC) streaming requires significant bandwidth and download time due to their large sizes. However, the established Internet service environment does not ensure sufficient network bandwidth for directly streaming such high quality content between members of a family or group in different Universal Plug and Play (UPnP) enabled home networks. Hence we design a system that enables a source media server to stream high quality content to multiple renderers in physically separated homes through peer-to-peer overlay paths established between home servers with content distribution and QoS device capability.

High Definition, User Created Content, Universal Plug and Play, Standard Definition, content distributor and content collector

[1] M. Abrams, C. Standridge, G. Abdulla, S. Williams and E. Fox, “Caching Proxies: Limitations and Potentials”, Electron. Proc. 4th World Wide Web Conf’95: The Web Revolution, Boston MA, Dec. 11-14, 1995.
[2] T. Anderson, Y. Breitbart, H.F. Korth and A. Wool, “Replication, Consistency and Practicality: Are These Mutually Exclusive?”, ACM SIGMOD’98, Seattle, June 1998.
[3] M.F. Arlitt and C.L. Williamson, “Internet Web Servers: Workload Characterization and Performance implications”, IEEE/ACM Trans. on Networking, Vol. 5, No. 5, pp. 631-645, Oct. 1997.
[4] M. Baentsch, L. Baum, G. Molter, S. Rothkugel and P. Sturm, "Enhancing the web infrastructure - from caching to replication", IEEE Internet Computing, pp. 18-27, Mar-Apr 1997.
[5] L.W. Dowdy and D.V. Foster, “Comparative Models of the File Assignment problem”, ACM Computing Surveys, Vol.14(2), June 1982.
[6] M. Rabinovich, I. Rabinovich, R. Rajaraman and A. Aggarwal, "A dynamic object replication and migration protocol for an Internet hosting service." IEEE Int. Conf. on Distributed Computing Systems , May 1999.
[7] “Apache Hadoop,” Jun. 2011. [Online]. Available: http://hadoop. apache.org
[8] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, “Dryad: Distributed data-parallel programs from sequential building blocks,” in Proc. Eur. Conf. Comput. Syst. (EuroSys), pp. 59–72 , 2007
[9] F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber, “Bigtable: A distributed storage system for structured data,” ACM Trans. Comput. Syst., vol. 26, no. 2, 2008.
[10] G. Ananthanarayanan, S. Agarwal, S. Kandula, A. Greenberg, I. Stoica, D. Harlan, and E. Harris, “Scarlett: Coping with skewed popularity content in MapReduce clusters,” in Proc. Eur. Conf. Comput. Syst. (EuroSys), 2011.
[11] J. Dixon, “Pentaho, Hadoop, and data lakes,” blog, Oct. 2010. [Online]. Available: http://jamesdixon.wordpress.com/2010/ 10/14/pentaho-hadoop-and-data-lakes
[12] J. Zuanich, “Twitter analytics lead, Kevin Weil, interviewed,” Cloudera Blog, Sep. 2010. [Online]. Available: http://www.cloudera. com/blog/2010/09
[13] “Powered-by – Hadoop Wiki,” Jul. 2011. [Online]. Available: http://wiki.apache.org/hadoop/PoweredBy
[14] M. Zaharia, D. Borthakur, J. Sen Sarma, K. Elmeleegy, S. Shenker, and I. Stoica, “Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling,” in Proc. Eur. Conf. Comput. Syst. (EuroSys), 2010.
[15] UPnP Forum, "UPnP AV Architecture V.83," UPnP Forum, June 2002.
[16] T. Hwang, H. Park, and 1. Chung, "Personal Mobile AIV Control Point for Home-to-Home Media Streaming," IEEE Transaction on Consumer Electronics, January 2008.
[17] UPnP Forum, "UPnP QoS Architecture:2," UPnP Forum, October 2006.
[18] 1. Lee and G. Veciana, "On Application-level Load Balancing in FastReplica," Computer Communications, November 2007.

P.T. Vasanth Raj, A. Vijayaraj, "Home-To-Home Media Streaming System Based on Adaptive Fast Replica," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.716-719, 2019.

A learning base is an effectively available information stockpiling center that contains data about a specific item, administration, theme, or idea. Associations make learning bases to house the majority of the information inside their association about a specific subject, to give one area to get to this data. Information bases can target inside representatives (on account of an organization learning base) or the general population - clients or potential clients - who need to study a specific item, subject, or idea. The objective of a learning base is to legitimately give data to these clients, and, on account of an interior framework, to expand the general comprehension of the whole association. Designing and development of a efficient knowledge base system is a challenging task. In this paper author describe the challenges for KBS and failure of KBS and its causes. Author also provide a deep insight the remedial solution to improve the efficiency of a KBS.

Knowledge Base System, Expert System, Inference Engine, Knowledge Acquisition, Concept Development

[1]. Hemant Kumar Soni, S. Sharma, and M. Jain, “Frequent Pattern Generation Algorithms for Association Rule Mining : Strength and Challenges”, In: Proc. of IEEE International Conference on Electrical, Electronics and Optimization Techniques, pp. 3744-3747, 2016.
[2]. Hemant Kumar Soni, S. Sharma and M Jain, “Plausible Characteristics of Association Rule Mining Algorithms for E-Commerce”, In: Proc. of the 3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, pp. 36-39, 2017.
[3]. ITL ESL, Introduction to Information Technology, Chapter 22.
[4]. N. Mishra, H. K. Soni, S. Sharma and A K Upadhyay, “A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction”, Journal of ICT Research and Applications, Vol.11, No.2, p 2017p. 167-183,.
[5]. Hemant Kumar Soni, S. Sharma, AK Upadhyay, “An empirical algorithm for high and low correlative association rule mining”, International Journal of Intelligent Engineering Systems, Vol. 11, Issue 3, , pp 223-232, 2018.
[6]. Russell .S, and P.Norvig, Artificial Intelligence: A modern approach, Second Edition, Prentice-Hall, New Delhi. 2002.
[7]. Dennis Ritchie, Artificial Intelligence, Tata McGraw-Hill, New Delhi. 1996
[8]. K. P. Tripathi, “A review on knowledge-based Expert System: Concept and architecture.” IJCA Special issues on Artificial intelligence techniques, pp19-23, 2011.
[9]. Avram Gabriela, “Empirical Study on Knowledge Based Systems, The Electronic Journal of Information Systems Evaluation, Vol 8.Iss 1, pp. 11-20, 2005.
[10]. Eliciting, collecting and developing requirements, available at https://www.mitre.org/publications/systems-engineering-guide/se-lifecycle-building-blocks/requirements-engineering/eliciting-collecting-and-developing-requirements
[11]. Software and Artificial Intelligence, Executive summary, NESSI, issue 1, pp 1-6., 2019

Hemant Kumar Soni, "Remedial Solutions to Improve the Efficiency of Knowledge Based Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.720-724, 2019.