Volume-7 , Issue-5 , May - 2019 Go Back

Vehicle maintenance is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Generally, Service Engineer receives customers claim for repair or replacement or compensation for nonperformance in the warranty period. When the customer claims his warranty, the claim contains customer voice, dealership investigation and claim manager’s action pertaining to particular “Defect Code”. Analyzing the correct defect code based on the description provided by customer requires a lot of efforts. So, our goal is to design an automated system using Natural Language Processing and Machine Learning which will decode the description and will find the most appropriate Defect Code. For this, techniques used are TF-IDF (Term Frequency-Inverse Document Frequency) and Naive Bayes Algorithm. Also, the system will help us in providing quick results, avoid wastage on time on manually validating the warranty claim by the customer. Thus, we can increase the efficiency in the process of vehicle maintenance. Completion of this project will make sure that incorrect faults are not addressed during warranty claim analysis.

Defect Codes, TF-IDF, Natural Language Processing

[1] Alan S. Abrahim, Jian Jiao, G. Alan Wang, Weiguo Fan., “Vehicle Defect Discovery from Social Media.” Decision Support System 54 (2012) 87-97.
[2] Alan S. Abrahams, Weiguo Fan .“An Integrated Text Analytic Framework for Product Defect Discovery”, Vol. 0, No. 0, xxxx–xxxx 2014, pp. 1–16 DOI 10.1111/poms.12303 ISSN 1059-1478|EISSN 1937-5956|14|00|0001
[3] Eriks Sneiders, “Automated FAQ Answering with Question-Specific Knowledge Representation for Web Self-Service”, Catania, Italy, May 21-23, 2009

Shailaja Jadhav, Sanika Bhide, Sakshi Borse, Parimal Ghodke, Utkarsha Sane, "Validation of Warranty Defect Codes To Ensure Vehicle Quality Within Warranty," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.873-876, 2019.

The health care environment is found to be rich in information, but poor in extracting knowledge from the information. This is because of the lack of effective analysis tool to discover hidden relationships and trends in them. By applying the data mining techniques, valuable knowledge can be extracted from the health care system. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in the world over past ten years. Researches have been made with many hybrid techniques for diagnosing heart disease. This paper deals with an overall review of the application of data mining in heart disease prediction.

Cardio Vascular Disease, Data Mining, Feature Selection, Classification, Association Rule Mining, Clustering

[1] Nahar, Jasmine, et al, “Association rule mining to detect factors which contribute to heart disease in males and females”, Expert Systems with Applications, Vol. 40 Issue. 4, pp. 1086-1093, 2013.
[2] Vijiyarani, S., and S. Sudha, “An efficient classification tree technique for heart disease prediction”, International Conference on Research Trends in Computer Technologies (ICRTCT-2013) Proceedings published in International Journal of Computer Applications (IJCA)(0975–8887). Vol. 201, 2013.
[3] Gayathri, P., and N. Jaisankar, “Comprehensive study of heart disease diagnosis using data mining and soft computing techniques”, 2013.
[4] Shouman, Mai, Tim Turner, and Rob Stocker, “Integrating clustering with different data mining techniques in the diagnosis of heart disease”, J. Comput. Sci. Eng, Vol. 20 Issue.1, 2013.
[5] Amato, Filippo, et al, “Artificial neural networks in medical diagnosis”, pp. 47-58, 2013.
[6] Persi Pamela, I., and P. Gayathri, “A fuzzy optimization technique for the prediction of coronary heart disease using decision tree”, 2013.
[7] Chaurasia, Vikas, and Saurabh Pal, “Data mining approach to detect heart diseases”, 2014.
[8] Thenmozhi, K., and P. Deepika, “Heart disease prediction using classification with different decision tree techniques”, International Journal of Engineering Research and General Science, Vol. 2, Issue. 6, pp. 6-11, 2014.
[9] Kim, Jae-Kwon, et al, “Adaptive mining prediction model for content recommendation to coronary heart disease patients”, Cluster computing, Vol. 17, Issue. 3, pp. 881-891, 2014.
[10] Seera, Manjeevan, and Chee Peng Lim, “A hybrid intelligent system for medical data classification”, Expert Systems with Applications, Vol. 41, Issue. 5, pp. 2239-2249, 2014.
[11] Bashir, Saba, Usman Qamar, and M. Younus Javed, “An ensemble based decision support framework for intelligent heart disease diagnosis”, Information Society (i-Society), 2014 International Conference on. IEEE, 2014.
[12] Shabana, ASMI P., and S. Justin Samuel, “An analysis and accuracy prediction of heart disease with association rule and other data mining techniques”, Journal of Theoretical and Applied Information Technology, Vol. 79, Issue. 2, pp. 254-60, 2015.
[13] Aljaaf, A. J., et al, “Predicting the likelihood of heart failure with a multi level risk assessment using decision tree”, Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015 Third International Conference on. IEEE, 2015.
[14] Bashir, Saba, Usman Qamar, and Farhan Hassan Khan, “BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting”, Australasian physical & engineering sciences in medicine, Vol. 38, Issue. 2, pp. 305-323, 2015.
[15] Kim, Jaekwon, Jongsik Lee, and Youngho Lee, “Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree”, Healthcare informatics research, Volume. 21, Issue. 3, pp. 167-174, 2015.

G. Srinaganya, A. Kiruba, "A Survey on Heart Disease Prediction Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.877-880, 2019.

Sign language is a natural language that is used to communicate with deaf and mute people. It is a significant way of communication between normal and deaf and dumb people, which does not require an interpreter. The main objective of this project is to develop a system that helps hearing and speech impaired people to convey their messages to ordinary people. There are different sign languages in the world. But the main focus of system is on Indian Sign Language (ISL) which is on the way of standardization. This system will concentrate on hand gestures only. Hand gesture is very important part of the body for exchanging ideas, messages, thoughts among deaf and dumb people. The proposed system will recognize the Indian hand sign language of words and sentences and translate the signs into Marathi text with images which have been extracted from the input videos. The process is divided into three parts i.e. preprocessing, feature extraction, classification. It will initially identify the gestures from Indian Sign language. Finally, the system processes the gesture to recognize character with the help of classification.

Image processing, Feature extraction, Gesture recognition, SVM, thinning algorithm

[1] Umme Santa, Farzana Tazreen and Shayhan Ameen Chowdhury "Bangladeshi Hand Sign Language Recognition from Video" ,2017 20th International Conference of Computer and Information Technology (ICCIT)22-24 December, 2017.
[2] Miss. Juhi Ekbote and Mrs. Mahasweta Joshi “Indian Sign Language Recognition Using ANN And SVM Classifier”, International Conference on Innovations in information Embedded and Communication Systems (ICIIECS), 2017.
[3] P. Subha Rajam and Dr. G. Balakrishnan “Real Time Indian Sign Language Recognition System to aid Deaf-dumb People”.
[4] Dr. Dharaskar Rajiv, Dr. Mr.Futane Pravin, “Hand Gesture Recognition System for numbers uses Thresholding”, 2011.
[5] Priyal SP, Bora PK “A study on static hand gesture recognition using moments”. In: Proceedings of international conference on signal processing and communications (SPCOM), 2010.
[6] Akanksha Singh, Saloni Arora.“ Indian Sign Language Gesture Classification as Single or Double Handed Gesture” In: Third International Conference on Image Intonation Processing, 2015.
[7] Barkoky A, Charkari NM “Static hand gesture recognition of Persian sign numbers using thinning method”. In: Proceedings of international conference on multimedia technology (ICMT), pp 6548–6551, 2011.

Prajakta Rokade, Neha Sali, Dipti Shinde, Shalini Yadav, "Indian Sign Language Recognition System in Marathi Language Text," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.881-885, 2019.

People search for quality in every artifact they come across. The concept of quality has also entered in the area of software development where it becomes crucial to thoroughly check the software system at different levels of testing. Now-a-days the competition is increased highly and the frequency of changes in platforms and business requirements are also very high, so for a software to be stable and in use for long run, requires to support and update based on the current requirements. Software testing is one of the umbrella activities performed at any organization to provide value and quality, to ensure the longitivity of software product in the market. This paper covers the concept of testing, its role in assuring quality, test cases, levels of testing, methods of testing and test planning, executing and monitoring. The papers emphasize on the use and impact of test driven environment with concept of story board based implementation.

Software testing, Software quality, Test Driven Environment

[1] P. Ron. Software testing. Vol. 2. Indianapolis: Sam’s, 2001.
[2] S. Amland, "Risk-based testing:" Journal of Systems and Software, vol. 53, no. 3, pp. 287–295, Sep. 2000.
[3] Redmill and Felix, “Theory and Practice of Risk-based Testing”, Software Testing, Verification and Reliability, Vol. 15, No. 1, March 2005.
[4] B. Agarwal et al., “Software engineering and testing”. Jones & Bartlett Learning, 2010.
[5] Mailewa, Akalanka, Jayantha Herath, and Susantha Herath. "A Survey of Effective and Efficient Software Testing." The Midwest Instruction and Computing Symposium. Retrieved from http://www.micsymposium. org/mics2015/ProceedingsMICS_2015/Mailewa_ 2D1_41. pdf. 2015.
[6] Mohd. Ehmer Khan, “Different Approaches to White Box testing Technique for Finding Errors,” IJSEIA, Vol. 5, No. 3, pp 1-13, July 2011.
[7] Mohd. Ehmer Khan, “Different Approaches to Black Box Testing Technique for Finding Errors,” IJSEA, Vol. 2, No. 4, pp 31-40, October 2011.
[8] L. Osterweil. Strategic directions in software quality. ACM Computing Surveys, 4:738–750, Dec. 1996.
[9] A. Bertolino. Software testing research: Achievements, challenges, dreams. In 2007 Future of Software Engineering, pages 85–103, 2007.
[10] M. J. Harrold. Testing: A roadmap. In Proceedings of the Conference on the Future of Software Engineering, pages 61–72, 2000.
[11] M.Kumar, S.K.Singh,. R.K.Drivedi, “A Comparative Study of Black Box Testing and White Box Testing Techniques”, International Journal of Advance Research in Computer Science and Management Studies, Volume-3, Issue 10,pp. 32-44,October 2015, ISSN: 2321-7782
[12] M.E. Khan, “Different Approaches to Black Box Testing Technique for Finding Errors”, IJSEA, Volume- 2, Issue- 4, pp 31-40, October 2011.
[13] Oluigbo I.V., Asiegbu B.C., Ezeh G.N., Nwokonkwo O.C., "Group Membership Prediction of an Outsourced Software Project: A Discriminant Function Analysis Approach", International Journal of Scientific Research in Multidisciplinary Studies , Vol.3, Issue.4, pp.12-18, 2017.
[14] Suresh Jat and Pradeep Sharma, “Analysis of Different Software Testing Techniques “,International Journal of Scientific Research in Computer Science and Engineering Vol.5, Issue.2, pp.77-80, April 2017.
[15] Chandraprakash Patidar ,“Test Case Generation Using Discrete Particle Swarm Optimization Algorithm”, International Journal of Scientific Research in Computer Science and Engineering ISSN 2320-7639 Volume-1, Issue-1, Jan- Feb-2013.

Nirali Honest, "Role of Testing in Software Development Life Cycle," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.886-889, 2019.

The stimulation of thyroid hormones has a greater impact in maintaining the metabolism our body. If there is any misbehavior in the hormones it will affect the functioning of other organs too. It is such an important gland and proper clinical advices should be taken if there is a misbehavior. The machine learning algorithms plays a major role in the early detection of thyroid disorder. This work focuses on applying random forest algorithm in prediction of thyroid disorder. The random forest algorithm classifies the class attribute and predicts the occurrence of hypo or hyper or normal scenario of thyroid. The algorithm predicts the result with maximum accuracy. The work is implemented in R. R is a statistical tool and it very much handles large volumes of data compared to other traditional mining tools. The algorithm predicts more accurately and the various performance metrics has been analysed.The data set has been taken from UCI Machine repository.

Thyroid, random forest, big data, R studio, Confusion Matrix

[1].A.M. Ahmed and N.H. Ahmed”History of disorders of thyroid dysfunction”Eastern Mediterranean Health Journal, Vol. 11, No. 3, 2005.
[2]. K. Ramya, A.Sumathi, "Big Data Applications in Aadhar Card Fraud Detection", International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.865-867, 2019.
[3].Han Liu, Mihaela Cocea “Semi-random partitioning of data into training and test sets in granular computing context” December2017, Volume 2, Issue 4, pp. 357–386, Springer International Publishing.
[4]. Liu H, Gegov A, Cocea M (2016c) “Rule based systems for big data: a machine learning approach.” Springer, Switzerland.
[5]. L. Breiman, Random forests, Mach. Learning, 45 (1). (2001) 5-32. http : // dx.doi.org / 10.1023 /A:1010933404324.
[6]. Shobana.V, Dr.K.Nandhini,” Application of Classification Algorithms for Disease Diagnosis Using Big Data Analytics”, IJERCSE Vol.4, Issue 12, 2017.
[7]. Ammulu.K, Venugopal.T“Thyroid Data Prediction using Data Classification Algorithm”, IJIRST Vol. 4 Issue 2, July 2017.
[8]. Waheed Ahmad, Ayaz Ahmad, Chuncheng Lu, Barkat Ali Khoso, Lican Huang “A novel hybrid decision support system for thyroid disease forecasting” Springer January 2018.
[9]. Sakshi Gujral, "Predicting and Detecting Hectoring on Social Media Using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.173-176, 2017.

V. Shobana, K. Nandhini, "Identifying Oversampling and under sampling of Data-A Practical Approach Using R," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.890-896, 2019.

WiMAX stands for Worldwide Interoperability for Microwave Access and can be utilized for longer distance wireless communication escorted easily delivering high data rates to large geographical areas. WiMAX aims to give a metropolitan Access Network, which will give large coverage and bandwidth. This paper provides basic information about WiMAX in terms of what it is its features, and Architecture of the network, Portable WiMAX, QoS (Quality of Service) of WiMAX, Parameters as well as Security.

WiMAX,PortableWiMAX,QoS,WirelessMAN,Security

[1]. Anuragsingh, Namarata Gadani, Aakash Patel, Poonam Pansinia“ A Review Paper on WiMAX Technology” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 5, Issue 6, June 2016.
[2]. IEEE 802.16e: IEEE 802.16e Task Group (Mobile Wireless MAN) http://www.ieee802.org/16/tge/.
[3]. Jigeesha Joshi, Kanakkumar Yadav “ A Study on WiMAX Network Technology” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 8, August 2014.
[4]. 5. Mojtaba Seyedzadegan and Mohamed Othman “IEEE 802.16: WiMAX Overview, WiMAX Architecture” International Journal of Computer Theory and Engineering, Vol. 5, No. 5, October 2013.
[5]. Gyan Prakash, Sadhana Pal “WiMAX Technology and its Applications” International Journal of Engineering Research and Applications (IJERA) Vol. 1, Issue 2, pp. 327-336.
[6]. Divya Garg, Prof. Hari Om Tyagi “Analysis of QoS for WiMAX” International Journal of Computer Science and Mobile Computing (IJCSMC) Vol. 6, Issue. 10, October 2017, pg. 18-23.
[7]. Vikram Mehta, Dr. Nenna Gupta “Performance Analysis of QoS Parameters for WiMAX Networks” International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012.

Mandeep Singh, Dalveer Kaur, Rajwinder Singh, "A Survey Paper on WiMAX Technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.897-900, 2019.

Latent Heat Flux is the flux of heat from the Earth’s surface to the atmosphere that is associated with evaporation of water at the surface and subsequent condensation of water vapour in the troposphere. It is key component of global water and energy cycles. It also responsible for to maintain the salinity budget of the ocean surface. Prediction of Latent Heat Flux is essential for understanding ocean-atmospheric interaction process which helps to explore the climate change condition, global warming conditions and also monitor the atmospheric conditions. Ocean and atmosphere data is a non-linear type of data to extract the more information from non-linear data, Artificial Neural Network gives a better solution. Among the Indian Ocean only Bay of Bengal having a numerous significant features. Changes in a Bay of Bengal responsible for climate change condition, precipitation and cyclone formation etc., In present analysis used an ocean parameters such as Wind Speed at 10m above sea surface and Sea Surface Temperature for the prediction of Latent Heat Flux over the latitude 80°E: 100°E and longitude 0-25°N for Bay of Bengal from 2011 to 2015. To achieve the objective use a Feed-forward Neural Network model with Levenberg-Marquardt training algorithm. Performance of model is calculate using performance analysis parameters such as Correlation Coefficient and Room Mean Square Error. The result of Correlation Coefficient and Root Mean Square Error indicates that proposed neural network model gives a better prediction of Latent Heat Flux.

Latent Heat Flux, Bay of Bengal, Sea Surface Temperature, Wind Speed, Artificial Neural Network

[1] S. Karmakar, S. Coubey, P. Mishra, “Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review”, International Journal of Applied Information Systems, Vol.10, Issue.10, pp.33-54, 2016.
[2] A. S. Gandhi, S. D’Souza, N. B. Arjun, “Prediction of daily sea surface temperature using Artificial Neural Networks”, International Journal of Remote Sensing and Remote Sensing Letters, Vol.39, Issue.12, pp.1-25, 2018.
[3] C. Amrender Kumar, C. Chattopadhyay, A. K. Mishra, A. K. Jain, “Neural network based prediction models for evaporation” Mausam, Vol.67, Issue.2, pp.389-396, 2016.
[4] M. Narvekar, P. Fargose, “Daily Weather Forecasting using Artificial Neural Network” International Journal of Computer Applications, Vol.121, Issue.22, pp.9-13, 2015.
[5] A. Erdil, E. Arcaklioglu, “The prediction of meteorological variables using artificial neural network”, Neural Comput & Applic, Vol.22. pp. 1677-1683, 2013.
[6] A. J. Litta, S. M. Idicula, U. C. Mohanty, “Artificial Neural Network Model in Prediction of Metrological Parameters during Premonsoon Thunderstorms”, International Journal of Atmospheric Sciences, Vol.2013, pp.1-15, 2013.
[7] L. Bodri, V. Cermak, “Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia”, Advances in Engineering Software, Vol.31, Issue.5, pp.311-321, 2000.
[8] K. C. Luk, J. E. Ball, A. Sharma, “A study of optical model lag and spatial inputs to artificial neural network for rainfall forecasting”, Journal of Hydrology, Vol.227, pp.56-65, 2000.
[9] I. Maqsood, M. R. Khan, A. Abraham, “An ensemble of neural networks for weather forecasting”, Neural Computing and Applications, Vol.13, Issue.2, pp.112-122, 2004.
[10] S. Chaudhuri, S. Chattopadhyay, “Neuro-computing based short range prediction of some meteorological parameters during pre-monsoon season”, Soft Computing, Vol.9, Issue.5, pp.349-354, 2005.
[11] C. Cheng, K. Chau, Y. Sun, J. Lin, 2, “Long-term prediction of discharges in manwan reserviour using artificial neural network models”, Advances in Neural Networks, Vol.3498, pp.1040-1045, 2005.
[12] K. W. Chau, C. L. Wu, Y. S. Li, “Comparison of several flood forecasting models in Yangtze River”, Journal of Hydrologic Engineering, Vol.10, Issue.6, pp.485-491, 2005.
[13] N. Muttil, K. Chau, “Neural network and genetic programming for modelling coastal algal blooms” International Journal of Environment and pollution, Vol.28, Issue.3-4, pp.223-238, 2006.
[14] C. L. Wu, K. W. Chau, Y. S. Li, “Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques”, Water Resource Research Vol.45, Issue.8, pp.1-23, 2009.
[15] B. Praveen Kumar, J. Vialard, M. Lengaigne, V. S. N. Murty, M. J. McPhaden, “TropFlux: air-sea fluxes for the global tropical oceans-description and evaluation”, Climate Dynamics, Vol.38, pp.1521-1543, 2012.
[16] B. Praveen Kumar, J. Vialard, M. Lengaigne, V. S. N. Murty, M. J. McPhaden, M. F. Cronin, F. Pinsard, K. G. Reddy, “TropFlux wind stresses over the tropical oceans: evaluation and comparison with other products”, Climate Dynamics, Vol.40, pp. 2049-2071, 2013.

Kanchan. V. Shende, Vishal S. Shirsat, M. R. Ramesh Kumar, K.V. Kale, "Artificial Neural Network Model for Prediction of Latent Heat Flux over Bay of Bengal," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.901-905, 2019.

Social networking sites play an important role in our day to day life. Data generated from these sites is large in amount. Here sentiment analysis is used to analyze such large amount of data and classify the text into different polarity. Sentiment analysis helps business and organization because it’s easy for them to know how people feel about their product or services so that they can make a better decision or improve their services. Data is collected from twitter. Existing sentiment analysis was established on the multinomial naïve bays where TF-IDF is used as feature extraction. In this paper, multinomial naïve Bayes is used as classifier and TF and Count Vectorizer hybrid approach is used at the time of feature extraction and used random forest classifier as feature selection. It also focuses on parameters like precision, recall, and f1-measure.

sentiment analysis, removing re-tweet, hybrid approach (TF, Count Vectorizer), Random forest as feature selection.

[1] Jain, A. P., & Dandannavar, P. (2016, July). Application of machine learning techniques to sentiment analysis. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on (pp. 628-632). IEEE.
[2] Singh, T., & Kumari, M. (2016). Role of text pre-processing in twitter sentiment analysis. Procedia Computer Science, 89, 549-554.
[3] Jianqiang, Z., & Xiaolin, G. (2017). Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis. IEEE Access, 5, 2870-2879.
[4] Tripathi, A., & Trivedi, S. K. (2016, October). Sentiment analysis of Indian movie review with various feature selection techniques. In Advances in Computer Applications (ICACA), IEEE International Conference on (pp. 181-185). IEEE.
[5] Kumar, K. S., Desai, J., & Majumdar, J. (2016, December). Opinion mining and sentiment analysis on online customer review. In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on (pp. 1-4). IEEE.
[6] Fiarni, C., Maharani, H., & Pratama, R. (2016, May). Sentiment analysis system for Indonesia online retail shop review using hierarchy Naive Bayes technique. In Information and Communication Technology (ICoICT), 2016 4th International Conference on (pp. 1-6). IEEE.
[7] Shivaprasad, T. K., & Shetty, J. (2017, March). Sentiment analysis of product reviews: A review. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 298-301). IEEE.
[8] Youness, M., Mohammed, E., & Jamaa, B. (2017, October). A parallel semantic sentiment analysis. In Cloud Computing Technologies and Applications (CloudTech), 2017 3rd International Conference of (pp. 1-6). IEEE.
[9] Maghilnan, S., & Kumar, M. R. (2017, June). Sentiment analysis of speaker specific speech data. In Intelligent Computing and Control (I2C2), 2017 International Conference on (pp. 1-5). IEEE.
[10] Chachra, A., Mehndiratta, P., & Gupta, M. (2017, August). Sentiment analysis of text using deep convolution neural networks. In Contemporary Computing (IC3), 2017 Tenth International Conference on (pp. 1-6). IEEE.
[11] Algur, S. P., & Patil, R. H. (2017, December). Sentiment analysis by identifying the speaker`s polarity in Twitter data. In Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017 International Conference on (pp. 1-5). IEEE.
[12] Lubis, F. F., Rosmansyah, Y., & Supangkat, S. H. (2017, September). Improving course review helpfulness prediction through sentiment analysis. In ICT For Smart Society (ICISS), 2017 International Conference on (pp. 1-5). IEEE.
[13] Tayal, D. K., & Yadav, S. K. (2017, August). Analysis of sentiments & polarity computation of opinions. In Telecommunication and Networks (TELNET), 2017 2nd International Conference on (pp. 1-6). IEEE.
[14] Song, J., Kim, K. T., Lee, B., Kim, S., & Youn, H. Y. (2017). A novel classification approach based on Naïve Bayes for Twitter sentiment analysis. KSII Transactions on Internet and Information Systems (TIIS), 11(6), 2996-3011.
[15] Thapa, Bal. (2016, December).Classifying Sentiments in Nepali Subjective Texts. 2016 &7th International Conference on information, intelligence, System & Application (IISA).

Avinash Kumar, Savita Sharma, Dinesh Singh, "Sentiment Analysis on Twitter Data using a Hybrid Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.906-911, 2019.

Several persons in a company use the local LAN for most of their communication & data transfer. The local LAN has several unsecured protocols & services i.e. FTP, Telnet etc. Persons who exchange highly confidential & sensitive information need a secure system on local LAN for secure communications. This paper describes the Internet Protocol Security (IPSec) & how IPSec framework can be used for secure & private communications over Internet Protocol, in local LAN environment. This paper also describes the various Protocols used in IPSec, Security Architecture of IPSec & various modes of operations in IPSec.

Internet Protocol Security (IPSec), Internet Key Exchange (IKE), Virtual Private Network (VPN).

[1] W. Stalling, “Cryptography and Network Security Principles & Practice”, Pearson Publication, USA, pp. 615-650, 2011.
[2] B. A Forouzan, D. Mukhopadhyay, “Cryptography and Network Security”, McGraw Hill Education Publication, India, pp. 487-520, 2015.
[3] A. T. Zamani, J. Ahmad, “Adoption Ipv6: Security and Future”, International Journal of Scientific Research in Computer Sciences and Engineering, VOL.2, Issue.1, pp. 17-21, 2014.
[4] R. Ganguli, S. Roy, “Designing a Graph Anonymization Framework for Secure Packet Transmission in the IP over Ethernet LAN”, International Journal of Computer Sciences and Engineering, VOL.6, Issue.9, pp. 650-654, 2018.
[5] J. Wu, “Implementation of Virtual Private Network based on IPSec Protocol”, IEEE 2009 ETP International Conference on Future Computer & Communication, Wuhan, China, pp. 138-141, 2009.
[6] H. Dhall, D. Dhall, S. Batra, P. Rani, “Implementation of IPSec Protocol”, IEEE 2012 International Conference on Advanced Computing & Communication Technologies, Rohtak, India, pp. 176-181, 2012.
[7] C. A. Shue, M. Gupta, S. A. Myers, “IPSec: Performance Analysis and Enhancements”, IEEE 2007 International Conference on Communications, Glasgow, Scotland, pp. 1527-1532, 2007.
[8] R. Perlman, C. Kaufman, “Analysis of the IPSec Key Exchange Standard”, IEEE Computer Society 10th IEEE International Workshop on enabling Technologies-Infrastructure for collaborative enterprises, Cambridge, MA, USA, pp. 150-156, 2001.
[9] K. Kedarnath, “IPSEC: Internet Protocol Security”, International Journal of Scientific Research in Network Security and Communication, VOL.1, Issue.3, pp. 1-8, 2013.
[10] R. A. A. Al-faluji, “Internet Protocol Security for Secure Communication: Fundamentals, Services and Application”, International Journal of Computer Engineering & Information Technology, VOL.9, Issue.9, pp. 186-191, 2017.

Manoj Kumar, Amit Kishor, "Network Traffic Encryption by IPSec," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.912-915, 2019.

As social networking sites are popular, so they become major part of person’s social interaction. These social networking sites are rich in emotions where people share their feelings, opinions, emotions. Extracting emotions from these social networking sites play an important role in various fields. Many techniques are proposed by various authors to extract emotions from these social networking sites. This paper presents various studies carried out in the field of EmoMining. The basic objective is to extract the emotional content of texts in online social networks. For this purpose, text mining techniques are performed on comments retrieved from a social network. This paper includes data collection, database schemas, data pre-processing and data mining steps. The informal language of online social networks is a main point to take into account before performing any emotion mining techniques. Here EmoMining related to tweets from social networking site is presented. Also emotion mining based on fuzzy rule base is also discussed along with brief description of fuzzy set theory.

Emotion mining, social networking, tweets, twitter, fuzzy rule, fuzzy set theory

[1] P. Ekman, “An argument for basic emotions”, Cognition and Emotion, 1992, pp. 169-200.
[2] SanjeevDhawan, Kulvinder Singh, DeepikaSehrawat, “Emotion Mining Techniques in Social Networking Sites”, International Journal of Information & Computation Technology, Vol. 4, No. 12, 2014, pp. 1145-1153.
[3] Hany Mohamed, AymanEzzat, Mostafa Sami, “The Road to Emotion Mining in Social Network”, International Journal of Computer Applications, Vol. 123, No. 18, 2015, pp. 41-47.
[4] Mohamed Yassine, Hazem Hajj, “A Framework for Emotion Mining from Text in Online Social Networks”, IEEE, International Conference on Data Mining Workshops, 2010, pp. 1136-1142.
[5] RashmiKumari, MayuraSasane, “Emotion analysis using text mining on social networks”, International Journal of Innovative Research in Technology, 2015, pp. 20-27.
[6] SanjeevDhawan, Kulvinder Singh, VandanaKhanchi, “A Framework for Polarity Classification and Emotion Mining from Text”, International Journal Of Engineering And Computer Science, 2014, pp. 7431-7436.
[7] Anu Sharma, Dr. M.K Sharma, Dr. R.K Dwivedi, “Literature Review and Challenges of Data Mining Techniques for Social Network Analysis”, Advances in Computational Sciences and Technology, Vol. 10, No. 5, 2017, pp. 1337-1354.
[8] SaqibQamar, Parvez Ahmad, “Emotion Detection from Text using Fuzzy Logic”, International Journal of Computer Applications, Vol. 121, No.3, July 2015, pp. 29-32.
[9] Er. VibhaDutta, Dr. R. C. Gangwar, Er. MohitMarwaha, “An Effectual Emotion Recognition System Based on Fuzzy Logic”, International Journal of Latest Trends in Engineering and Technology, Vol. 8, Issue. 1, pp.122-131.
[10] Mohan Arava, TalasilaVamsidhar, “Driver Emotional Status Recognition Using Fuzzy Logic in ADAS: An Indian Perspective”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue. 10, October 2014, pp. 328-334.
[11] NikoColneric, JanezDemsar, “Emotion Recognition on Twitter: Comparative Study and Training a Unison Model”, IEEE Transactions on Affective Computing, 2018, pp. 1-14.
[12] Stojanovski, Dario, et al. "Emotion identification in FIFA world cup tweets using convolutional neural network."Innovations in Information Technology (IIT), 2015 11th International Conference on.IEEE, 2015.
[13] Mishne, Gilad. "Experiments with mood classification in blog posts."Proceedings of ACM SIGIR 2005 workshop on stylistic analysis of text for information access.Vol.19. 2005.
[14] Roberts, Kirk, Michael A. Roach, Joseph Johnson, Josh Guthrie, and Sanda M. Harabagiu. "EmpaTweet: Annotating and Detecting Emotions on Twitter."In LREC, vol. 12, pp. 3806-3813.2012.
[15] Wang, Wenbo, Lu Chen, KrishnaprasadThirunarayan, and Amit P. Sheth. "Harnessing twitter" big data" for automatic emotion identification." In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), pp. 587-592. IEEE, 2012.

Jaskaranjit Kaur, Navneet Kaur, "Descriptive Study on EmoMining from SoNet Sites," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.916-921, 2019.