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• Open Access   Article

  Sentimental Analysis of Twitter Using Long Short-Term Memory and Gate Recurrent Unit

  S. Suriya, K. Sindhu Meena

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.1-6, Feb-2020

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  Sentimental analysis is the process of discovering and categorizing the opinions in the texts/reviews. It is to determine the attitude of the speaker and also to identify the topic, product whether it is positive, negative or neutral. It is used in the process on analysing the texts, computing linguistics and biometrics for identifying systematically extracting, quantifying the information. This paper aims to classify the reviews and apply the word embedding techniques and to find which is more suitable word embedding for LSTM and GRU.LSTM and GRU are the solution for the vanishing gradient problem. LSTM more suits for classifying, processing and predicting the time series data. It is composed of 3 states input, output and forget state. The main thing about GRU is it can train to keep a information from long ago without wasting it through time or remove irrelevant information to be predicted. GRU is composed of update and reset gates. The performance are evaluated by using the accuracy, precision, recall and f1score.The anaconda navigator is used as a tool and python is the language used.

  Key-Words / Index Term
  LSTM, GRU, sentimental analysis, word embedding techniques

  References
  [1] Chikersal, P., Poria, S., & Cambria, E., “SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning”, In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 647-651, 2015.
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  [9] Wen, S., Wei, H., Yang, Y., Guo, Z., Zeng, Z., Huang, T., & Chen, Y., “Memristive LSTM Network for Sentiment Analysis”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.
  [10] Gaikar, D., Sapare, G., Vishwakarma, A., & Parkar, A., “Twitter Sentimental Analysis for Predicting Election Result using LSTM Neural Network”, International Research Journal of Engineering and Technology, Vol.6, Issue.4, 2019.
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  [12] Minaee, S., Azimi, E., & Abdolrashidi, A., “Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models”, Computation and Language, Cornell University, ref: arXiv preprint arXiv:1904.04206, 2019.
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  [14] Li, C., Li, C., & Liu, P., “Sentiment Analysis Based on LSTM Architecture with Emoticon Attention”, In Pacific-Asia Springer Conference on Knowledge Discovery and Data Mining, pp. 232-242, 2019.
  [15] Rosenthal, S., Farra, N., & Nakov, P., “Sentiment analysis in Twitter”, Computation and Language, Cornell University, ref: arXiv preprint arXiv:1912.00741, 2019
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  Citation
  S. Suriya, K. Sindhu Meena, "Sentimental Analysis of Twitter Using Long Short-Term Memory and Gate Recurrent Unit," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.1-6, 2020

• Open Access   Article

  Sentiment Analysis on Large Scale Amazon Product Reviews

  Sayyed Johar, Samara Mubeen

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.7-15, Feb-2020

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  The world nowadays is becoming more digitalized. In this digitalized world e-commerce is taking the ascendancy by making products available within the reach of customers where the customer doesn’t have to go out of their house. As now a day’s people are relying on online products so the importance of a review is going higher. For selectng a product, a customer needs to go through thousands of reviews to understand a product. But in this prospering day of machine learning, going through thousands of reviews would be much easier if a model is used to polarize those reviews and learn from it. So by implementing supervised learning method on a large scale Amazon dataset to polarize it and get satisfactory accuracy.

  Key-Words / Index Term
  e-commerce, review of product, machine learning, supervised learning, Amazon dataset

  References
  [1] Amandeep Kaur, DeepeshKhaneja ," Sentiment analysis on twitter using apache spark." International Conference on Computing, Communication and Automation (ICCCA), 2016., vol. 1, pp. 1–5 ,2016.
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  [7] X. Fang and J. Zhan, “Sentiment analysis using product review data,” Journal of Big Data, vol. 2, no. 1, 2015. [Online]. [11] Z. Zhou and L. Xu, “Amazon Food Review Classification using Deep Learning and Recommender System,” Stanford University, 2009
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  [9] KuatYessenov,“Sentiment Analysis on YouTube Movie Trailer comments to determine the impact on Box-Office Earning”,2016.
  [10] Su SuHtay, KhinThidar Lynn"Sentiment Analysis and Sentiment Classification of Amazon Product Reviews." Conference paper Swarthmore College, 2016.
  [11] “Amazon Reviews for Sentiment Analysis.” [Online]. https://www.kaggle.com/bittlingmayer/amazonreviews#train.ft.txt.bz2
  

  Citation
  Sayyed Johar, Samara Mubeen, "Sentiment Analysis on Large Scale Amazon Product Reviews," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.7-15, 2020

• Open Access   Article

  Biometric Security System for Identification and Verification

  Kaushal Kumar, Harshit Kumar, Piyush Singh, Amit Kumar, K.M. Shikha

  Survey Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.16-19, Feb-2020

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  This research work has applied for the Biometrics identification System. It is used for security purposes. Biometrics system used for individual identification to scan finger, hand geometry, face recognition, iris retinal scanning, vascular pattern, ear, DNA, Biometrics identification is used for biometric attendance and it used to identify the criminals and decrease the crime rates and terrorism.

  Key-Words / Index Term
  Biometric, Verification, Identification, Security

  References
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  [7] Pantic, M. & Rothkrantz, L.J.M., 2004. Facial Action Recognition for Facial Expression Analysis from Static Face Images. Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 34(3), pp.1449 -1461.
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  Citation
  Kaushal Kumar, Harshit Kumar, Piyush Singh, Amit Kumar, K.M. Shikha, "Biometric Security System for Identification and Verification," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.16-19, 2020

• Open Access   Article

  An Intelligent Approach for Prediction of Candidate Heart Disease Patients

  A. Abdo, Ramadan Bakir

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.20-36, Feb-2020

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  Abstract
  Coronary is serious disease and one of the most common heart diseases that needs great care in doctoral analysis and diagnosis. Doctoral experience is considered one of the most common factors that negatively affect diagnosis of Coronary Heart Disease (CHD); sometimes due to lack of experience. Under this context, many attempts have been done to build a CHD prediction system using different data mining (DM) methods to assist in diagnosis, and also to minimize risk in case of mistaken diagnosis. We have noted two weaknesses in these attempts, First: many of the previous researches have used the symptoms of the CHD disease as factors leading to the disease, which already exists; Second: there was highly imbalanced class distribution in the dataset, which in turn led to imbalanced class distribution in the results. We have worked on the first problem in a previous research to predict heart disease using risk factors using AHDBP (An Adaptive Heart Disease Behavior-Based Prediction model). In this research we have developed ACHDBP as an extension to AHDBP. A balancing technique called Synthetic Minority Over-sampling Technique (SMOTE) is used in the current research to handle this problem. Different classification algorithms will be deployed to get the most accurate results. 18 attributes were used to build the prediction system. Different SMOTE ratios between 100 and 500 were applied to the data. Results were recorded and compared with results obtained without using SMOTE algorithm. For all SMOTE sampling ratios the most classifier that achieved the best classifier was NB classifier. The average accuracy values for DT, SVM, NN, and NB was 73.85%, 73.76%, 73.04%, and 72.58% respectively. The obtained class distributions were balanced but the accuracy of the system was lowered, and we believe that happens because SMOTE algorithm is more effective for large databases.

  Key-Words / Index Term
  Coronary, Classification, SMOTE Oversampling, symptoms, Risk Factors

  References
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  Citation
  A. Abdo, Ramadan Bakir, "An Intelligent Approach for Prediction of Candidate Heart Disease Patients," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.20-36, 2020

• Open Access   Article

  Analysis of Huffman Coding and Lempel–Ziv–Welch (LZW) Coding as Data Compression Techniques

  Gajendra Sharma

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.37-44, Feb-2020

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  Abstract
  Huffman Coding is a statistical data compression providing to reduce code length that is used to represent the symbols of an alphabet. This is a standard technique for the creation of Minimum-Redundancy Codes. LZW is a dictionary based compression tool which is widely popular. This implies that instead of tabulating character counts and building trees LZW encodes data by referencing a dictionary. Compared to any adaptive and dynamic compression method, the concept is to initiate with an initial model, read data and update the framework and data encoding. In this paper, we investigated the following question: Which coding, LZW or Huffman, is more suitable compared to each other? The implemented results show that LZW requires no prior information about the input data stream, and also LZW can compress the input stream in one single pass allowing fast execution. LZW coding is more feasible when the high compression ratio and less compression-decompression time are needed.

  Key-Words / Index Term
  Digital data, input symbols, compression, minimum-redundancy, LZ Wcoding, Huffman coding

  References
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  Citation
  Gajendra Sharma, "Analysis of Huffman Coding and Lempel–Ziv–Welch (LZW) Coding as Data Compression Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.37-44, 2020

• Open Access   Article

  A Comparative Study of Symmetric Cryptography Mechanism on DES, AES and EB64 for Information Security

  K.B. Logunleko, O.D. Adeniji, A.M. Logunleko

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.45-51, Feb-2020

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  Abstract
  The usages of Information technology were numerous in every aspect of life. As it were, an adequate security has often been a great challenge which has to be more focused on. Different methods have been employed to secure and protect the shared and sensitive data. This research work basically focused on cryptography in ensuring zero tolerance for insecurity of data in transit. However, in order to improve security and efficiency of valuable data, the Private or Secret Key Encryption Infrastructure needs to be adopted as the model to implement security. This research work implemented three encryption techniques such as DES, AES and EB64 algorithms and compared their performances on both encryption and decryption techniques based on the analysis of encryption and decryption time allotted at each different experimental stage. The experimental results obtained were used to analyse the effectiveness of each algorithm on a mobile phone. The result that was gathered during the various experiments for SMS plaintext shows that EB64 encryption and decryption time was shorter as compared with AES and DES.

  Key-Words / Index Term
  Cryptography, Encryption, Decryption, Private key encryption, Security, AES, DES, EB64

  References
  [1] D. S AbdulElminaam, H. M AbdulKader, M.M Hadhoud, “Performance Evaluation of Symmetric Encryption Algorithms” Volume 8, ISSN: 1943-7765, pp 58-64, 2009.
  [2] M. Agoyi, D.Seral, “SMS security: an asymmetric encryption Approach” Proc.6th International Conference on Wireles and Mobile Communication, 2012.
  [3] Amalraj, Raybin, “A Survey Paper on Cryptography Techniques”. International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 5, Issue 8, pg.55 – 59, ISSN 2320–088X, 2016.
  [4] Bodur, Kara. “Secure SMS Encryption Using RSA Encryption Algorithm on Android Message Application” H. BODUR et al./ ISITES2015 Valencia - algorithms with secret and public key”. International Journal of Engineering Research and Development, e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 8, Issue 2 (August 2013), pp. 45, Spain, Pp 160-170, 2015.
  [5] R. Chehal, K.Singh, “Efficiency and Security of Data with Symmetric Encryption Algorithms”. International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X , Volume 2, Issue 8, pp 1. 2012
  [6] S. Gurpreet, S. Supriya “A Study of Encryption Algorithms (RSA, DES, 3DES and AES) for Information Security” International Journal of Computer Applications (0975 – 8887), Vol 67– No.19, pp 33-38.2013
  [7] S. Isnar, P Andysah, S. Utama, Arpan “Base64 Character Encoding and Decoding Modeling” International Journal of Recent Trends in Engineering & Research, Volume 02, Issue 12; [ISSN: 2455-1457], pp. 63-68, 2016.
  [8] W.K Muhammad, “SMS Security in Mobile Devices: A Survey”,Int. J. Advanced Networking and Applications Volume: 05,. Pages:1873-1882, 2013.
  [9] A. Padmapriya , P. Subhasri, “Cloud Computing: Security Challenges & Encryption Practices”. International Journal of Advanced Research in Computer Science and Software Engineering, ISSN:22 77128X, Volume 3, Issue 3, pp. 257, 2013.
  [10] P. Paritosh, “Encryption for secure SMS transmission” An international journal of advanced computer technology, 4 (6), (Volume-IV, Issue-VI), pp 1876-1881, 2015.
  [11] Prerna, Abhishek “A Study of Encryption Algorithms AES, DES and RSA for Security”. Global Journal of Computer Science and Technology Network, Web & Security Vol.13, Issue 15 Version 1.0. Online ISSN: 0975-4172 & Print ISSN: 0975-4350, 2013.
  [12] R. Bhanot, R. Hans, “A Review and Comparative Analysis of Various Encryption Algorithms,” International Journal of Security and Its Applications, vol. 9, no. 4, pp. 289-306, 2015.
  [13] H. Monther, Al-Bsool, “Real Implementation for SMS Encryption–Based on Android Message Application”. Network and Complex Systems. ISSN 2224-610X (Paper) ISSN 2225-0603 (Online), Vol.6, No.3, 2016.
  [14] S. Pavithra, E. Ramadevi, “Performance evaluation of symmetric algorithms” Journal of Global Research in Computer Science, Volume 3, No. 8, pp 43-45, 2012.
  [15] K.B. Logunleko, O.D. Adeniji, A.M. Logunleko, M.O. Odufowora, “Data Encryption Scheme Using an Enhanced Base64 Algorithm”, University of Ibadan Journal of Science and Logics in ICT Research (UIJSLICTR), Vol. 3, No 1 pp 16-24, 2019.
  [16] M. Shashi, M. Rajan, “Comparative Analysis of Encryption Algorithms for Data Communication”, IJCST Vol.2, Issue 2, 2011.
  

  Citation
  K.B. Logunleko, O.D. Adeniji, A.M. Logunleko, "A Comparative Study of Symmetric Cryptography Mechanism on DES, AES and EB64 for Information Security," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.45-51, 2020

• Open Access   Article

  Inter-organizational Learning and Knowledge Sharing Management in Global Software Development

  Muhammad Yaseen, Atta Ur Rahman, Inayat Ur Rahman, Zia Ullah, Mohim Bacha

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.52-57, Feb-2020

  Abstract

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  Abstract
  Inter-organizational learning and knowledge sharing management play an important role during requirements collection and implementation for any software system. In Global Software Development (GSD), its significance increase more as stakeholders are far away across the globe. In GSD where critical challenges such as language differences, geographical distance, culture differences and time zone differences exist, thus need of inter-organizational learning and knowledge sharing become more increase. This study aims to identify possible practices from literature for inter-organizational learning and knowledge sharing management during development in GSD. To achieve goals, systematic literature review (SLR) is conducted to identify best practices for collaboration and coordination. Through SLR, 13 practices are identified.

  Key-Words / Index Term
  Practices; coordination and collaboration; Global Software Development; Systematic Literature Review

  References
  [1] M. Yaseen, A. Mustapha, and N. Ibrahim, ‘MINIMIZING INTER-DEPENDENCY ISSUES OF REQUIREMENTS IN PARALLEL DEVELOPING SOFTWARE PROJECTS WITH AHP’, vol. 8, no. Viii, 2019.
  [2] M. Yaseen, A. Mustapha, and N. Ibrahim, ‘Prioritization of Software Functional Requirements : Spanning Tree based Approach’, vol. 10, no. 7, pp. 489–497, 2019.
  [3] M. Yaseen, A. Mustapha, and N. Ibrahim, ‘An Approach for Managing Large-Sized Software Requirements During Prioritization’, 2018 IEEE Conf. Open Syst., pp. 98–103, 2019.
  [4] M. Yaseen, N. Ibrahim, and A. Mustapha, ‘Requirements Prioritization and using Iteration Model for Successful Implementation of Requirements’, Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 1, pp. 121–127, 2019.
  [5] M. Yaseen, S. Baseer, and S. Sherin, ‘Critical Challenges for Requirement Implementation in Context of Global Software Development : A Systematic Literature Review’, pp. 120–125, 2015.
  [6] M. Yaseen and Z. Ali, ‘Success Factors during Requirements Implementation in Global Software Development : A Systematic Literature Review’, vol. 8, no. 3, pp. 56–68, 2019.
  [7] M. Yaseen and Z. Ali, ‘Practices for Effective Communication during Requirements Elicitation in Global Software Development’, vol. 8, no. 06, pp. 240–245, 2019.
  [8] A. U. Rahman, M. Yaseen, and Z. Ali, ‘Identification of Practices for Proper Implementation of Requirements in Global Software Development : A Systematic Literature Review Protocol’, vol. 177, no. 13, pp. 53–58, 2019.
  [9] A. Mahajan and P. Sharma, ‘Object Oriented Requirement management Tools for maintaining of status of requirements’, vol. 6, no. 6, pp. 27–30, 2018.
  [10] T. Illes-Seifert, A. Herrmann, M. Geisser, and T. Hildenbrand, ‘The Challenges of Distributed Software Engineering and Requirements Engineering: Results of an Online Survey’, 1st Int. Glob. Requir. Eng. Work. GREW 2007, pp. 55–65, 2007.
  [11] D. Damian, ‘An empirical study of requirements engineering in distributed software projects : is distance negotiation more effective ?’, vol. 4, no. 04, pp. 149–152, 2001.
  [12] I. Kwan, D. Damian, and S. Marczak, ‘The effects of distance, experience, and communication structure on requirements awareness in two distributed industrial software projects’, Glob. Requir. Eng. Work. conj. Intl Conf Glob. Softw. Eng. 2007., no. May, 2007.
  [13] W. Hussain, J. Buchan, and T. Clear, ‘Managing Requirements in Globally Distributed COTS Customization’, Proc. - Int. Comput. Softw. Appl. Conf., vol. 18–21–Augu, pp. 33–38, 2014.
  [14] D. E. Damian and D. Zowghi, ‘RE challenges in multi-site software development organisations’, no. 2003, pp. 149–160, 2007.
  [15] R. Lai and N. Ali, ‘A Requirements Management Method for Global Software Development’, Humanpub.Org, vol. 1, no. March, pp. 38–58, 2013.
  [16] A. M. Søderberg, S. Krishna, and P. Bjørn, ‘Global software development: Commitment, trust and cultural sensitivity in strategic partnerships’, J. Int. Manag., vol. 19, no. 4, pp. 347–361, 2013.
  [17] M. Yaseen, S. Baseer, S. Ali, S. U. Khan, and Abdullah, ‘Requirement implementation model (RIM) in the context of global software development’, 2015 Int. Conf. Inf. Commun. Technol. ICICT 2015, 2016.
  [18] M. Yaseen and U. Farooq, ‘Requirement Elicitation Model (REM) in the Context of Global Software Development’, Glob. J. Comput. Sci. Technol., vol. 1, no. 2, pp. 1–6, 2018.
  [19] M. Yaseen, Z. Ali, and M. Humayoun, ‘Requirements Management Model (RMM): A Proposed Model for Successful Delivery of Software Projects’, Int. J. Comput. Appl., vol. 178, no. 17, pp. 32–36, 2019.
  [20] Z. Ali and M. Yaseen, ‘Critical Challenges for Requirement Implementation in Global Software Development : A Systematic Literature Review Protocol with Preliminary Results’, vol. 182, no. 48, pp. 17–23, 2019.
  [21] M. Yaseen, S. Ali, . A., and N. Ullah, ‘An Improved Framework for Requirement Implementation in the context of Global Software Development: A Systematic Literature Review Protocol’, Int. J. Database Theory Appl., vol. 9, no. 6, pp. 161–170, 2016.
  [22] D. Requirements, ‘Enabling Collaboration in Distributed Requirements Management - ABI INFORM Collection - ProQuest’, 2006.
  [23] C. L. Campbell, B. V. A. N. D. E. Walle, and A. G. S. Development, ‘Asynchronous Requirements Engineering : Enhancing Distributed Software Development’, pp. 133–136, 2003.
  [24] B. Berenbach, ‘Impact of Organizational Structure on Distributed Requirements Engineering Processes: Lessons Learned’, Int. Work. Glob. Softw. Dev. Pract. GSD 06, pp. 15–19, 2006.
  [25] G. N. Aranda, A. Vizcaíno, A. Cechich, and M. Piattini, ‘Strategies to Minimize Problems in Global Requirements Elicitation RE-GSD Methodology’, CLEI Electron. J., vol. 11, no. 1, 2008.
  [26] Z. Haag, R. Foley, and J. Newman, ‘Software process improvement in geographically distributed software engineering: an initial evaluation’, EUROMICRO 97. Proc. 23rd EUROMICRO Conf. New Front. Inf. Technol. (Cat. No.97TB100167), pp. 134–141, 1997.
  [27] D. Damlan, ‘Stakeholders in global requirements engineering: Lessons learned from practice’, IEEE Softw., vol. 24, no. 2, pp. 21–27, 2007.
  [28] A. Hoffmann and C. Lescher, ‘Collaboration and Intercultural Issues on Requirements: Communication, Understanding and Softskills (CIRCUS)’, 2009 Collab. Intercult. Issues Requir. Commun. Underst. Softskills, CIRCUS 2009, no. Circus 2009, pp. 1–4, 2009.
  [29] J. M. C. de Gea, J. Nicolás, J. L. F. Alemán, A. Toval, A. Vizcaíno, and C. Ebert, ‘Reusing Requirements in Global Software Engineering’, pp. 171–197, 2013.
  [30] C. Ebert, B. K. Murthy, and N. N. Jha, ‘Managing Risks in Global Software Engineering: Principles and Practices’, 2008 IEEE Int. Conf. Glob. Softw. Eng., pp. 131–140, 2008.
  [31] Darja Šmite, ‘Requirements Management in Distributed Projects’, Knowl. Creat. Diffus. Util., vol. 1, no. 2, pp. 69–76, 2006.

  Citation
  Muhammad Yaseen, Atta Ur Rahman, Inayat Ur Rahman, Zia Ullah, Mohim Bacha, "Inter-organizational Learning and Knowledge Sharing Management in Global Software Development," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.52-57, 2020

• Open Access   Article

  Data Anonymization Techniques for Preserving Privacy in Public Release Data Model: A Technical Review

  Arun Amaithi Rajan, Anitha Amaithi Rajan

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.58-62, Feb-2020

  Abstract

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  Abstract
  The protection of sensitive records is very necessary for a modern scenario. Lately, the informational index is accessible for open use for statistical analysis. In this situation increasingly sensitive information like medical records, nation resident`s data, worker`s compensation data and so on are affecting to a higher extent since we are giving our data to people in general. Thus, Data anonymization assumes significance in the present day to protect the open discharge of sensitive information. In this paper, we reviewed some anonymization techniques and proposed a simple anonymization technique which is the combination of synthetic data generation and pseudonymization approach which reduces attacks on sensitive facts.

  Key-Words / Index Term
  Anonymization techniques, privacy-preserving algorithm, synthetic data generation and pseudonymization technique

  References
  [1] H. Kargupta, S. Datta, Q.Wang, and K. Sivakumar, “On the Privacy Preserving Properties of Random Data Perturbation Techniques,” In Proceedings of the International Conference on Data Mining (ICDM), pp. 99-106, 2003.
  [2] Kavita Rodiya and Parmeet Gill, “A Review on Anonymization Techniques for privacy preserving data publishing,” IJRET: International Journal of Research in Engineering and Technology, November 2015.
  [3] Disha Dubli and D.K Yadav, “Secure Techniques of Data Anonymization for Privacy Preservation,” International Journal of Advanced Research in Computer Science, Vol. 8, Issue. 05, pp. 1694-1698, 2017.
  [4] Surendra .H, Dr. Mohan .H .S, “A Review of Synthetic Data Generation Methods for Privacy Preserving Data Publishing,” International Journal of Scientific & Technology Research, Vol. 6, Issue. 03, pp. 95-101, March 2017.
  [5] White Paper on Pseudonymization Drafted by the Data Protection Focus Group for the Safety, Protection, and Trust Platform for Society and Businesses in Connection with the 2017 Digital Summit.
  [6] Ajayi, Olusola Olajide, Adebiyi, Temidayo Olarewaju, “Application of Data Masking in Achieving Information Privacy,” IOSR Journal of Engineering (IOSRJEN), Vol. 04, Issue. 02, pp. 13-21, 2014.
  [7] Thakkar A., Bhatti A.A., Vasa J., “Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems,” In Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, Vol. 320, pp. 581-592, 2015.
  [8] Yang Xu, Tinghuai Ma, Meili Tang and Wei Tian, “A Survey of Privacy Preserving Data Publishing using Generalization and Suppression,” Applied Mathematics & Information Sciences. Vol. 8, Issue. 03, pp. 1103-1116, 2014.
  [9] Anisha Tiwari1, Minu Choudhary, “A Review on K-Anonymization Techniques,” Scholars Journal of Engineering and Technology (SJET), Vol. 5, Issue. 06, pp. 238-245, 2017.
  [10] Tanashri Karle and Prof Deepali Vora, “Privacy Preservation in Big Data Using Anonymization Techniques,” In International Conference on Data Management Analytics and Innovation (ICDMAI), pp. 340-343, 2017.
  [11] Ramesh Bandaru , Rao S Basavala "Information Leakage through Social Networking Websites leads to Lack of Privacy and Identity Theft Security Issues." International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol 1, Issue. 03, pp. 1-7, 2013.

  Citation
  Arun Amaithi Rajan, Anitha Amaithi Rajan, "Data Anonymization Techniques for Preserving Privacy in Public Release Data Model: A Technical Review," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.58-62, 2020

• Open Access   Article

  Application of Traditional & fuzzy FMEA to analyse & assess failure of in-house intranet server

  Anupam Negi, Vedna Sharma

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.63-69, Feb-2020

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  Abstract
  In present era, organizations are increasingly implementing ‘channel shift’: moving their services towards digital to become more responsive, better & easily accessible. Organization Intranet service is one of the major digital service used by many government or private sector organizations. This paper proposes a model of fuzzy based FMEA as a systematic knowledge extraction tool to assess the risk associated with failure causes of an in-house server of intranet. Present study attributed to the two methods for assessing the criticality of in-house server. The first one is based on the traditional Failure Mode Effects Analysis and second method is fuzzy based Failure Mode Effects Analysis (FMEA). Former method composed of evaluating RPN by multiplying severity; occurrence & delectability parameters but they pose problem of dependency in between, while the latter one having more flexible structure for combining severity, occurrence & delectability parameters. After that a comparison has been made between both the approaches to highlight the critical failure mode require troubleshooting.

  Key-Words / Index Term
  Server, Fuzzy Expert Estimates, FIS, Linguistic Phrase, Fuzzy Rules, RPN, Risk Management

  References
  [1] Sohail Jehangir Malik, Hina Nazli and Kashif Abrar, "Solving Organizational Problems with Intranet Technology",JSTOR, vol. 38, issue 4, 1021-1036, 1999
  [2] TV Afanasiev, AO Kozlov, Regression algorithm-fuzzy modeling IT resources: Radio engineering. Number 11. 2014. pp 99-103.
  [3] Behzad Mosallanezhad1, Abbas Ahmadi1*, Using fuzzy FMEA to increase patient safety in fundamental processes of operating room, Journal of Industrial and Systems Engineering Vol. 11, No. 3, pp. 146-166 Summer (July) 2018
  [4] Sharma, Rajiv & Kumar, Pradeep, Systematic Failure Mode Effect Analysis (FMEA) using fuzzy linguistic modeling. International Journal of Quality & Reliability Management. 22. 986-1004. 10.1108/02656710510625248, (2005).
  [5] Akbari, Mohsen & Khazaee, Pezhman & Sabetghadam, I. & Karimifard, Failure modes and effects analysis (FMEA) for power transformers, Conference: 28th International Power System Conference, At Tehran, Iran P (2013)
  [6] John B. Bowles & C. Enrique Peldez, Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis ,Reliability Engineering & System Safety Volume 50, Issue 2, 1995, Pages 203-213
  [7] RossT.J, "Fuzzy logic with Engineering Applications", McGraw-Hill Publication, New York, NY(1995)
  [8] Debasis Samanta, Defuzzification Techniques, IIT Kharagpur dsamanta@iitkgp.ac.in 09.02.2018
  [9] H. Shirouyehzad, M. Badakhshian, R. Dabestani, H. Panjeh fouladgaran, Fuzzy FMEA Analysis for Identification and Control of Failure Preferences in ERP Implementation, TJMCS Vol .1 No.4 (2010) 366-376
  [10] Elyasi-Komari, Iraj & Gorbenko, Anatoliy & Kharchenko, Vyacheslav & Mamalis, Analysis of Computer Network Reliability and Criticality: Technique and Features. IJCNS. 720-726. 10.4236/ijcns.2011.411088, (2011).
  [11] Tejaskumar S. Parsana and Mihir T. Patel,A Case Study: A Process FMEA Tool to Enhance Quality and Efficiency of Manufacturing Industry Bonfring, International Journal of Industrial Engineering and Management Science, Vol. 4, No. 3, August 2014
  [12] Alok Pandey, Meghraj Singh, A. U. Sonawane, Prashant S. Rawat , FMEA Based Risk Assessment of Component Failure Modes in Industrial Radiography, International Journal of Engineering Trends and Technology (IJETT) – Volume 39 Number 4- September 2016

  Citation
  Anupam Negi, Vedna Sharma, "Application of Traditional & fuzzy FMEA to analyse & assess failure of in-house intranet server," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.63-69, 2020

• Open Access   Article

  Fuzzy-Based Model for Predicting Football Match Results

  Omomule T.G., Ibinuolapo A. J., Ajayi O.O.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.1 , pp.70-80, Feb-2020

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  Abstract
  Predicting the outcome of football matches has been an interesting challenge for which it is realistically impossible to successfully do so for several bettors and football fans across the world. Despite this, there are significant loss in financial resources expended on the correct prediction of football matches. This paper proposes a prediction model for football matches using Fuzzy Logic. In the proposed system, results of matches between two football teams are generated based on the providence of several parameters associated with every football team. Fuzzy Logic Gaussian Membership function (Gmf) technique is adopted for the computation of membership grade for each input parameter. Datasets were obtained from the twenty (20) football teams in the English Premier League, 2017/2018 Season and were used to test the system predictability. The proposed model was implemented using MATLAB version R2017. Evaluation results showed the practicability of the system to the prediction of football outcomes based on the consideration of many input parameters for higher prediction accuracy when compared to reported literatures.

  Key-Words / Index Term
  Football, Betting, Prediction, Financial, Matches, Fuzzy Logic

  References
  [1] O.O. Ajayi, “Implementing a Score-Time based Model for Handling Deadlocks in Football Group Matches”, International Journal of Computer Applications (0975 – 8887), Vol. 122, Issue. 21, pp. 29-33, 2015.
  [2] G. Baio, and M. Blangiardo, “Bayesian Hierarchical Model for Prediction of Football Results”, Journal of Applied Statistics, vol.37, Issue 2, pp.253-264, 2010
  [3] D. Buursma, “Predicting Sports Event from Past Result: Towards Effective Betting on Football Matches”. Preceding 14thTwente Student Conference on IT. University of Twente, Faculty Electrical Engineering, Mathematics and Computer Science, Netherlands, pp. 1-7, 2011
  [4] D. Enke, M. Grauer. And N. Mehdiyev, “Stock Market Prediction with Multiple Regression, fuzzy type-2 Clustering and Neural Networks”, Procedia Computer Science, vol. 6 (2011) 201–206, 2011.
  [5] E. Esme, and M.S. Kiran, “Prediction of Football Match Outcomes Based on Bookmakers Odds by Using K-Nearest Neighbour Algorithm”, International Journal of Machine Learning and Computing, Vol. 8, Issue 1, pp. 26-32, 2018
  [6] O. Farzin, E. Parinaz, and S.M. Faezeh, “Football result prediction with Bayesian network in Spanish league-Barcelona Team”. International Journal of Computer Theory and Engineering, vol. 5, Issue. 5, pp. 812-815, 2013
  [7] A. Gangal, A. Talnikar, A. Dalvi, V. Zope, and A. Kulkarni, “Analysis and Prediction of Football Statistics using Data Mining Techniques”. International Journal of Computer Applications, Vol.132, Issue.5, pp. 8-11, 2015
  [8] FIFA, “Laws of the Game”. FIFA-Strasse 20, 8044 Zurich, Switzerland, 1992
  [9] K.Y. Huang, and W.L. Chang, “A Neural Network Method for Prediction of 2006 World Cup Football Game”. 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1 –8, 2010
  [10] C.P. Igiri, and E.O. Nwachukwu, “An Improved Prediction System for Football Match Result”. IOSR Journal of Engineering. Vol. 4, Issue 12, pp. 12-20, 2014
  [11] A.P. Rotshtein, M. Posner, and A.B. Rakityanskaya, “Football Predictions based on a Fuzzy model with Genetic and Neural Tuning”, Cybernetics and Systems Analysis, Vol. 41, Issue. 4, pp. 619–630, 2005
  [12] V. Vaidehi, S. Monica, S. M. Sheik Safeer, M. Deepika, S. Sangeetha, “A Prediction System Based on Fuzzy Logic”, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, pp. 1-6, 2008
  [13] A. Yezus, “Predicting Outcome of Football Matches using Machine Learning”, A Term Paper, Mathematics and Mechanics Faculty, Saint-Petersburg State University, pp. 1-12, 2014
  [14] L.M. Hvattum, and H. Arntsen, “Using Elo Ratings for Match Result Prediction in Association Football”, International Journal of Forecasting, Vol. 26, Issue. 3, pp. 460–470, 2010.
  [15] V. Sillanpaa, and O. Heino, “Forecasting Football Match Results- A Study on Modelling Principles and Efficiency of Fixed-Odds Betting Markets in Football”. Thesis submitted to the Department of Information and Service Economic, School of Business, Aalto University, pp. 1-124, 2013
  [16] F.I. Amadin, and J.C. Obi, “English Premier League (EPL) Soccer Matches Prediction using an Adaptive Neuro-Fuzzy Inference System (ANFIS)”, Transactions on Machine Learning and Artificial Intelligence, Vol. 3, Issue 2, pp. 1-7, 2015
  [17] J. Bih, “Paradigm Shift - An Introduction to Fuzzy Logic”, IEEE Potentials, Vol. 25, Issue 1, pp. 6–21, 2006
  [18] L. B. Bhajantri, “Fuzzy Logic Based Fault Detection in Distributed Sensor Networks”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.27-32, 2018

  Citation
  Omomule T.G., Ibinuolapo A. J., Ajayi O.O., "Fuzzy-Based Model for Predicting Football Match Results," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.1, pp.70-80, 2020