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The social media has immense and popularity among all the services today. Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization and all other entities. In this research, design a framework for sentiment analysis with opinion mining for the case of airlines service feedback. Most available datasets of hotel reviews are not labelled which presents a lot of works for researchers as far as text data pre-processing task is concerned. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. The airline industry is a very competitive market which has grown rapidly in the past 2 decades. Airline companies resort to traditional customer feedback forms which in turn are very tedious and time consuming. In this work, worked on a dataset comprising of tweets for 6 major Indian Airlines and performed a multi-class sentiment analysis. This approach starts off with pre-processing techniques used to clean the tweets and then representing these tweets as vectors using a deep learning concept to do a phrase-level analysis. The analysis was carried out using 7 different classification strategies: Decision Tree, Random Forest, SVM, K-Nearest Neighbors, Logistic Regression, Gaussian Naïve Bayes and AdaBoost. The outcome of the test set is the tweet sentiment (positive/negative/neutral).

Sentiment Analysis, Machine Learning, Classification techniques, Deep Learning, Distributed Memory Model, Twitter Analysis

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[5] Michael Hagenau, Michael Liebmann, Dirk Neumann. Automated news reading: stock price prediction based on financial news using context-capturing features. Decis Supp Syst; 2013.
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[16] Zhao Yan-Yan, Qin Bing, Liu Ting. Integrating intra- and interdocument evidences for improving sentence sentiment classification. Acta Automatica Sinica 2010;36(October’10).
[17] Yi Hu, Li Wenjie. Document sentiment classification by exploring description model of topical terms. Comput Speech Lang 2011;25:386–403.
[18] Cao Qing, Duan Wenjing, Gan Qiwei. Exploring determinants of voting for the ‘‘helpfulness’’ of online user reviews: a text mining approach. Decis Support Syst 2011;50:511–21.
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[22] Qiong Wu, Tan Songbo. A two-stage framework for crossdomain sentiment classification. Expert Syst Appl
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[26] Xu Kaiquan, Liao Stephen Shaoyi, Li Jiexun, Song Yuxia. Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst 2011;50:743–54.
[27] Chin Chen Chien, Tseng You-De. Quality evaluation of product reviews using an information quality framework. Decis Support Syst 2011;50:755–68.
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T. Hemakala, S. Santhoshkumar, "Advanced Classification Method of Twitter Data using Sentiment Analysis for Airline Service," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.331-335, 2018.

Theoretical Based on deoxyribonucleic corrosive (DNA) coding and two dimensional turbulent frameworks, another shading picture cryptosystem is proposed in this paper. The displayed picture cryptosystem comprises of two procedures: In the principal arrange is DNA substitutionin which the first picture is changed over into the DNA succession by the DNA encoding rules. After this square based DNA encryption is connected for computerized pictures that we will scramble. After this subsequent figure picture is gotten. Security examination and trial result demonstrated brilliant execution of our proposed calculation in picture encryption.

Security, Image Encryption, DNA Encryption, chaos theory

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S. Verma, S. Indora, "Efficient DNA based Image Encryption Scheme," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.336-341, 2018.

In developing country use of concrete is very high and availability of raw material is comparatively less. Total replacement of concrete is not possible due to no material plays the role of concrete in terms of strength, durability, and workability. To obtain the results, tests are conducted with respect to The Indian Standards for Mix Design IS 10262 (2009) and Scheme of Testing and Inspection - Bureau of Indian Standards to determine the compressive strength of concrete after heating at 200°C temperature for steel fibre Recycled Aggregate Concrete. The natural aggregate is replaced 20% by recycled aggregate and steel fibres are added by volume (0.5%, 1%, and 1.5%). Results of this tests supports the hypothesis that this type of Concrete Mix may be suitable for partial substitution after proper Mix Designing. Aim of this study is to suggest alternate low-cost and environment suitable building materials from industrial wastes in an economic way with good compressive strength than normal concrete. Investigation suggests successful use of steel fibres in concrete for construction activities.

Concrete, Recycled aggregate, Steel waste, Fire resistance

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[3] Parekh D. N., Dr. Modhera C. D., “Assessment of recycled aggregate concrete”, Journal of Engineering Research and Studies, E-ISSN 0976-7916, Vol. 2, Issue.1, 2011.
[4] Mirjana Malešev, Vlastimir Radonjanin, Gordana Broćeta, “Properties of recycled aggregate concrete”, Contemporary Materials, Vol.2, Issue 3, 2014.
[5] S R Yadav, S R Pathak, “Use of recycled concrete aggregate in making concrete- an overview”, Singapore Concrete Institute journals, Article Online Id: 100034047, pp.245-252, 2009.
[6] Mr. Tushar R Sonawane, Prof. Dr. Sunil S. Pimplikar “Use of Recycled Aggregate Concrete”, IOSR Journal of Mechanical and Civil Engineering, Vol.5, Issue.13, ISSN: 2278-1684, pp. 52-59, 2013.
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[15] Sachin sharma, Jeevan Singh Bisht, “Performance Analysis of Data Encryption Algorithms”, International Journal of Scientific research in Network Security and Communication, ISSN: 2321-3256, Vol. 3, Issue.1, pp.01-05, February 2015.
[16] R. Sri Ravindrarajah, R. Lopez, H. Reslan, “Effect of Elevated Temperature on the Properties of High-Strength Concrete containing Cement Supplementary Materials”, 9th International Conference on Durability of Building Materials and Components, Rotterdam, Netherlands, , Paper 081, 8 pages. 17-20th March, 2002.
[17] Tomasz Drzymałaa, Wioletta Jackiewicz-Rekb, Mariusz Tomaszewskib, Artur Kuśb, “Effects of High Temperature on the Properties of High performance concrete (HPC)”, Modern Building Materials, Structures and Techniques, MBMST 2016, Procedia Engineering 172, pp. 256 – 263, 2017.
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D.J. Jani, V.N. Kumar, "A Study on Effect of Elevated Temperature on Compressive Strength of Steel Fiber Recycled Aggregate Concrete," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.342-348, 2018.

Software Transactional Memory (STM) is a promising new approach to programming shared-memory parallel processors which does not suffer from the drawbacks of locks. However STM also has some limitations. One of the limitations of STM is that while programming with STM users have to identify the critical sections explicitly and enclose them in transactions using appropriate STM calls to ensure synchronization. This approach is similar to using locks in parallel programs. This paper introduces a new flexible approach for programming using STM in which users do not need to identify critical sections explicitly. In this approach whenever users need to perform read or write operations they can do so using appropriate STM calls and STM will ensure synchronization by its internal constructs. Thus users can concentrate only on the algorithm of the parallel problem without thinking about synchronization. Thus this approach is very user-friendly. Time taken will also be less than lock programming as users do not have to identify critical sections explicitly.

Multiprocessing, Parallel Processing, Locks, Software Transactional Memory, Flexible Programming Approach

[1] Yang Ni, Vijay Menon, Richard L. Hudson, Ali-Reza Adl-Tabatabai, J. Eliot, B. Moss, Bratin Saha, Antony L. Hosking, Tatiana Shpeisman,“Open Nesting in Software Transactional Memory”, In the Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming, pp. 68-78, 2007
[2] Zhengyu He, Bo Hong, “Impact of Early Abort Mechanisms on Lock-Based Software Transactional Memory”, In the Proceedings of International Conference on High Performance Computing (HiPC), 2009
[3] Yossi Lev, Victor Luchangco, Virendra J. Marathe, Mark Moir, Dan Nussbaum, Marek Olszewski, “Anatomy of a Scalable Software Transactional Memory”, In the Proceedings of the 4th ACM SIGPLAN Workshop on Transactional Computing , 2009
[4] Justin E. Gottschlich, Manish Vachharajani, Jeremy G. Siek, “An Efficient Software Transactional Memory Using Commit-Time Invalidation”, In the Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization , pp. 101-110, 2010
[5] Sandhya S.Mannarswamy, Ramaswamy Govindarajan, “Variable Granularity Access Tracking Scheme for Improving the Performance of Software Transactional Memory”, In the Proceedings of International Conference on Parallel Architectures and Compilation Techniques, pp. 232-242, 2011
[6] Anupriya Chakraborty, Sourav Saha, Ryan Saptarshi Ray, Utpal Kumar Ray,“ Lock-Free Readers/Writers”, International Journal of Computer Science Issues (IJCSI), ISSN (PRINT): 1694 – 0814, ISSN (ONLINE): 1694 – 0784, Volume- 10, Issue-4, No-2, pp. 180-186, 2013
[7] Sandeep Agrawal, Shweta Das, Manjunatha Valmiki, Sanjay Wandhekar, Prof. Rajat Moona, “A case for PARAM Shavak: Ready-to-use and affordable supercomputing solution”, In the Proceedings of the International Conference on High Performance Computing & Simulation, pp. 396-401, 2017
[8] Ryan Saptarshi Ray, Parama Bhaumik, Utpal Kumar Ray,“ Hybrid Parallel Programming Using Locks and STM”, International Journal of Computer Sciences and Engineering (IJCSE) E-ISSN:2347-2693, Volume- 5, Issue-10, pp. 185-192, 2017
[9] Anjum Mohd Aslam, Mantripatjit kaur,“ A Review on Energy Efficient techniques in Green cloud: Open Research Challenges and Issues”, International Journal of Scientific Research in Computer Sciences and Engineering ISSN: 2320-7639, Volume- 6, Issue-3, pp. 44-50, 2018
[10] S. Vimala, P. Uma, S. Senbagam,“ Adaptive Vector Quantization for Improved Coding Efficiency”, International Journal of Scientific Research in Network Security and Communication ISSN: 2321-3256, Volume- 6, Issue-3, pp. 18-22, 2018

Ryan Saptarshi Ray, Parama Bhaumik, Utpal Kumar Ray, "Flexible Programming Approach using STM," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.349-353, 2018.

The purpose of the proposed work is to identify the factors contributing to fatal accidents. This is achieved by analysing road accidents using Convolutional Neural Networks by considering appropriate features and effectively clustering the records. Several combinations of attributes of large datasets are analysed to discover hidden patterns that are the root cause for accidents. The chances of accident occurrence could be identified by considering various criteria like speed limit and injury severity, time of accidents and drunk driver, month and weather during the accident, lightness and speed limit, human factors, surface and light conditions. The experimental results on road accident data set FARS (Fatality Analysis Reporting System) generated risk factors that cause fatal accidents which will be helpful in generating safer driving principles.

Association Rules, Classification, Convolutional Neural Networks, Traffic Data

[1] K.Jayasudha and C.Chandrasekar, “An overview of data mining in road traffic and accident analysis”, Journal of Computer Applications, Vol.2, Issue.4, pp:32–37, 2009.

[2] Eric M Ossiander and Peter Cummings, “Freeway speed limits and traffic fatalities in Washington state”, Accident Analysis & Prevention, Vol.34, Issue.1, pp:13–18, 2002.

[3] William M Evanco, “The potential impact of rural mayday systems on vehicular crash fatalities”, Accident Analysis & Prevention, Vol.31, Issue.5, pp:455–462, 1999.

[4] KMA Solaiman, Md Mustafizur Rahman and Nashid Shahriar, “AVRA Bangladesh collection, analysis & visualization of road accident data in Bangladesh”, In Proceedings of International Conference on Informatics, Electronics & Vision, pp.1–6, IEEE, 2013.

[5] Sachin Kumar and Durga Toshniwal, “Analysing road accident data using association rule mining”, In Proceedings of International Conference on Computing, Communication and Security, pp.1–6, 2015.

[6] S.Krishnaveni and M.Hemalatha, “A perspective analysis of traffic accident using data mining techniques”, International Journal of Computer Applications, Vol.23, Issue.7, pp:40–48, 2011.

[7] Amira A El Tayeb, Vikas Pareek, and Abdelaziz Araar, “Applying association rules mining algorithms for traffic accidents in Dubai”, International Journal of Soft Computing and Engineering, 2015.

[8] L.Li, S.Shrestha and G.Hu, “Analysis of road traffic fatal accidents using data mining techniques”, IEEE 15th International Conference on Software Engineering Research Management and Applications (SERA), pp.363-370, 2017.

[9] Sami Ayramo, Pasi Pirtala, Janne Kauttonen, Kashif Naveed and Tommi Karkkainen, “Mining road traffic accidents”, Reports of the Department of Mathematical Information Technology Series C. Software and Computational Engineering, University of Jyvaskyla, pp.1-53, 2009.

[10] S. Shanthi and R. Geetha Ramani, “Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms”, International Journal of Computer Applications (0975–8887), Vol.35, Issue.12, pp:30-37, 2011.

[11] S. Shanthi, R. Geetha Ramani, “Feature Relevance Analysis and Classification of Road Traffic Accident Data through Data Mining Techniques”, Proceedings of the World Congress on Engineering and Computer Science, Vol 1, 2012.

[12] Dharmendra Sharma and Suresh Jain, “Evaluation of Stemming and Stop Word Techniques on Text Classification Problem”, International Journal of Scientific Research in computer Science and Engineering, Vol.3, Issue.2, 2015.

[13] Mohnish Patel, Aasif Hasan, Sushil Kumar, “Preventing Discovering Association Rules For Large Data Base”, International Journal of Scientific Research in computer Science and Engineering Vol.1, Issue.3, 2013.

[14] Neeraj Chhabra, “Comparative Analysis of Different Wireless Technologies”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, 2013.

[15] V. Kapoor, “A New Cryptography Algorithm with an Integrated Scheme to Improve Data Security”, International Journal of Scientific Research in Network Security and Communication Vol.1, Issue.2, 2013.

L. Yasaswini, G. Mahesh, R. Siva Shankar, L.V. Srinivas, "Identifying Road Accidents Severity using Convolutional Neural Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.354-360, 2018.

Over the last few years, the generation of computerized information has increased exponentially. Most people use digital media to share news and their views on a topic. To analyze this outsized web information, new analytical techniques are required which automatically portrays the data open on the Web. Most of us are more comfortable in expressing our viewpoints and outlooks in Mother tongue. Sentiments of the social users on various topics expressed in their own mother tongue leads to the necessity of mining the sentiments in various dialects. In fact, some data do not have an effect on the classification result even removing them and some carries similar meanings, therefore a pre-processing phase has to accomplish and thus the dataset can be more precise. In this paper, the authors are focusing on pre-processing the words given by the user through their reviews in the social networking sites expressed in Malayalam language. The authors calculated the reduction in word count after performing the preprocessing processes and the experiments shows that more than 20% of word count reduction occurred.

Opinion Mining, POS Tagging, Stemming, Stopword Removal, Malayalam

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[3] Gagandeep Kaur, Kamaldeep Kaur, “Sentiment Detection from Punjabi Text using Support Vector Machine”, International Journal of Scientific Research in Computer Science and Engineering, 5(6), 39-46., 2017
[4] Islam, M., Uddin, M., & Khan, M., “A light weight stemmer for Bengali and its Use in spelling Checker”, 2007.
[5] Akram, Q. U. A., Naseer, A., & Hussain, S. “Assas-Band, an affix-exception-list based Urdu stemmer”, In Proceedings of the 7th workshop on Asian language resources (pp. 40-46). Association for Computational Linguistics, 2009
[6] Dutta, P. K., “An Online Semi Automated Part of Speech Tagging Technique Applied To Assamese” (Doctoral dissertation), 2013.
[7] Kasthuri, M., & Kumar, S. B. R., “An improved rule based iterative affix stripping stemmer for Tamil language using K-mean clustering”, International Journal of Computer Applications, 94(13), 2014
[8] Prajitha, U., Sreejith, C., & Raj, P. R., “LALITHA: A light weight Malayalam stemmer using suffix stripping method”, In Control Communication and Computing (ICCC), 2013 International Conference on (pp. 244-248). IEEE, 2013.
[9] Pragisha, K., & Reghuraj, P. C., “STHREE: Stemmer for Malayalam using three pass algorithm”, In Control Communication and Computing (ICCC), 2013 International Conference on (pp. 149-152). IEEE, 2013.
[10] Jayan, J. P., Rajeev, R. R., & Sherly, E.. “A hybrid statistical approach for named entity recognition for malayalam language”. In Proceedings of the 11th Workshop on Asian Language Resources (pp. 58-63), 2013
[11] Nair, D. S., Jayan, J. P., & Sherly, E., “SentiMa-sentiment extraction for Malayalam”, In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 1719-1723). IEEE, 2014.
[12] K, Manju & Peter S, David & Mary idicula, Sumam, “An Extractive Multi-document Summarization System for Malayalam News Documents”. 10.4108/eai.27-2-2017.152340.
[13] Renjith, S. R., & Sony, P, “An automatic text summarization for Malayalam using sentence extraction”. In Proceedings of 27th IRF International Conference, 14th June, 2015
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Deepa Mary Mathews, Sajimon Abraham, "Effects of Pre-processing Phases in Sentiment Analysis for Malayalam Language," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.361-366, 2018.

Human activity recognition has seen an enormous success in the last decade performing a dominant part in the field of ubiquitous computing. This rising demand can be attributed to several real life applications fundamentally dealing with human-centric applications like healthcare and eldercare systems. Many research experiments with data mining and machine learning procedures have been experiencing precisely to recognize human activities for healthcare systems. This work proposes a hybrid method to recognize the patient actions under care using a simple camera instead of multiple expensive sensors using machine learning with LBPTOP algorithm and body joint features with majority voting framework for real time monitoring applications with greater efficiency of recognition. This work uses different classifiers to achieve the experimental results approximately above 90% which clearly shows a remarkable recognition achievement compared to the other activity recognition techniques.

Machine Learning, Human activity, Body joint features, Real time, LBP-TOP, Classifiers

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Halakundi Chidananda, T Hanumantha Reddy, "A Robust Hybrid Human Activity Recognition System using Lbptop and Body Joint Features with Majority Voting for High Accuracy," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.367-374, 2018.

Social media emergence has gained significant impact on how people communicate and socialize. Twitter provides the social media platform from where opinions of the people can be heard. Sentimental analysis can be applied to obtain the useful information by analyzing these tweets carefully. To characterize the human opinion, this paper studies users perception regarding a controversial product, namely self-driven cars. To find people’s opinion regarding this new technology, self-driven car Twitter dataset is used. Based on the people’s reaction about the self driven car in the social media(Twitter), human opinions are characterized like whether the people gave positive statement, negative or neutral statement regarding the self-driven car tweets. To classify the tweets, different machine learning algorithms, such as Logistic regression, Support Vector Machine, Random forest classifier and AdaBoost classifier are used. By using these tweets, opinions are characterized as “positive”, “negative” and “neutral”. To evaluate the performance of four algorithms, comparisons is carried out over the metrics like accuracy, recall, precision and f1-score. From the experimental results Logistic regression outperforms Support Vector Machine, Random forest classifier and AdaBoost classifier algorithms.

Random forest, Support Vector Machine, Logistic Regression , AdaBoost classifier, Sentiment analysis

[1] Munir Ahmad, Shabib Aftab, Iftikhar Ali, “Sentiment Analysis of Tweets using SVM”, International Journal of Computer Applications (0975 – 8887) Volume 177 – No.5, November 2017.
[2] Rizwan Sadiq and Mohsin Khan, “Analyzing Self-Driving Cars on Twitter”
[3] Ajay Deshwal, Sudhir Kumar Sharma, “Twitter Sentiment Analysis using Various Classification Algorithms”, 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Sep. 7-9, 2016, AIIT, Amity University Uttar Pradesh, Noida, India.
[4] K Lavanya, C Deisy, “Twitter Sentiment Analysis Using Multi-Class SVM”, 2017 International Conference on Intelligent Computing and Control (I2C2`17).
[5] Shreya Ahuja, Gaurav Dubey2, “Clustering and Sentiment Analysis on Twitter Data”, 2017 2nd International Conference on Telecommunication and Networks (TEL-NET 2017) .
[6] Vishal A. Kharde, S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11, April 2016.
[7] Neethu M S, Rajasree R, “Sentiment Analysis in Twitter using Machine Learning Techniques”, IEEE – 31661.
[8] Della Fitrayani Budiono, Anto Satriyo Nugroho, “Twitter Sentiment Analysis of DKI Jakarta’s Gubernatorial Election 2017 with Predictive and Descriptive Approaches”, 2017 International Conference on Computer, Control, Informatics and its Applications.
[9] Sanket Sahu, Suraj kumar Rout, Debasmith Mohanty, “Twitter Sentiment Analysis”, 2015 IEEE International Symposium on Nanoelectronic and Information Systems.
[10] Ike Pertiwi Windasari, Fajar Nurul Uzzi, Kodrat Iman Satoto, “Sentiment Analysis on Twitter Posts: An analysis of Positive or Negative Opinion on GoJek”, Proc. of 2017 4th Int. Conf. on Information Tech., Computer, and Electrical Engineering (ICITACEE), Oct 18-19, 2017, Semarang, Indonesia.
[11] Adyan Marendra Ramadhani, Hong Soon Goo, “Twitter Sentiment Analysis using Deep Learning Methods”, 2017 7th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia.
[12] A53053719 Che-Lin, A53087422 Yu-Ching Hu, A53093903 Chien­Han Lin, “Twitter Sentiment Analysis”.
[13] C. Nanda1, M. Dua, “A Survey on Sentiment Analysis”, International Journal of Scientific Research in Computer Science and Engineering Vol.5, Issue.2, pp.67-70, April (2017).
[14] Gagandeep Kaur1, Kamaldeep Kaur, “Sentiment Detection from Punjabi Text using Support Vector Machine”, International Journal of Scientific Research in Computer Science and Engineering Vol.5, Issue.6, pp.39-46, December (2017).

Lavanya V S, Savita K Shetty, "Characterizing Human Opinion in Social Network Using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.375-381, 2018.

Data compression is a procedure of shortening the data by following some encoding rules to store or transmit the particular file from one location to another. Data compression is an important task as it cost more to store a large amount of data and to transfer the large amount of data over a network. So we can say that the data compression is a technique by which reduction of data is performed from its original representation so that it can be stored and transferred over the network easily and with lesser cost. In the proposed system, a dynamic nibble based data compression approach is used to compress the data. In this approach data is compressed in the combination of four bits. Performance of the proposed system is evaluated on the various input strings and is compared with the existing systems which is static but reduction algorithm. Performance results shows that the propose system is better than that of the existing system in terms of output bits, saving bits and compression ratio.

Data compression, Lossless data compression, Nibble based data compression, Static Bit Reduction

[1] U. N. Katugampola, “A New Technique for Text Data Compression”,International Symposium on Computer, Consumer and Control , 978-0-7695-4655-1 pp. 405-409, 2012.
[2] A.S. Sidhu, M. Garg, “Text Data Compression Algorithm using Hybrid Approach”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.12, pp. 01-10, 2014.
[3] S. Shanmugasundaram and R. Lourdusamy, “A Comparative Study of Text Compression Algorithms”,International Journal of Wisdom Based Computing, Vol. 1 (3), pp 68-76 , 2011.
[4] S. Kapoor and A. Chopra, "A Review of Lempel Ziv Compression Techniques" IJCST, Vol. 4, Issue 2, 2013.
[5] M.A.D. Suarjaya, "A New Algorithm for Data Compression Optimization", International Journal of Advanced Computer Science and Applications, Vol. 3, No. 8, pp.14-17,2012.
[6] S.R. Kodituwakku and U. S. Amarasinghe , "Comparison Of Lossless Data Compression Algorithms For Text Data" Indian Journal of Computer Science and Engineering, Vol. 1, No. 4, pp. 416-425 , 2010.
[7] R. Kaur and M. Goyal, “An Algorithm for Lossless Text Data Compression” International Journal of Engineering Research & Technology , Vol. 2 Issue 7, 2013
[8] H. Altarawneh and M. Altarawneh, "Data Compression Techniques on Text Files: A Comparison Study ”, International Journal of Computer Applications, Vol. 26 No.5, 2011.
[9] U. Khurana and A. Koul, “Text Compression And Superfast Searching” Thapar Institute Of Engineering and Technology, Patiala, Punjab, India-147004
[10] A. Singh and Y. Bhatnagar, “Enhancement of data compression using Incremental Encoding” International Journal of Scientific & Engineering Research, Vol. 3, Issue 5,2012.
[11] A.J Mann, “Analysis and Comparison of Algorithms for Lossless Data Compression", International Journal of Information and Computation Technology, Vol. 3, No.3, pp. 139-146, 2013.
[12] K. Rastogi, K. Sengar, “Analysis and Performance Comparison of Lossless Compression Techniques for Text Data” International Journal of Engineering Technology and Computer Research, Vol. 2 (1), pp. 16-19, 2014.
[13] M. Sharma, “Compression using Huffman Coding”, International Journal of Computer Science and Network Security, Vol.10 No.5, 2010.
[14] S. Shanmugasundaram and R. Lourdusamy, "IIDBE: A Lossless Text Transform for Better Compression " International Journal of Wisdom Based Computing, Vol. 1 (2), 2011
[15] P. Kumar and A.K Varshney, “ Double Huffman Coding ” International Journal of Advanced Research in Computer Science and Software Engineering , Vol. 2, Issue 8, pp. 517-520, 2012.

Sukhbir Kaur, Paramjeet Singh, Shaveta Rani, "Advanced Lossless Text Compression System based on Dynamic Nibble Reduction Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.382-386, 2018.

In urban environment the trusted traffic lights identification and classification is very important for automated driving vehicles. In urban driving presently there is no such systems that has to become aware of dependable traffic lights in real time and we can find the traffic lights in automated vehicles without map based information and enough distance require for regular surface in urban driving. Here we suggest a complete system made up of traffic light detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometer which become aware of traffic lights in real time The first is a precisely marked traffic signals data sets contains 5000 guiding pictures and 8335 motion pictures for development. Tiny traffic signals dataset is distributed by Bosch Company is used as a basic tool. The traffic signal identification which works at 10 frames per second on 1280*720 images is the second achievement. Third achievement is a traffic signal which identifies utilizes stereo vision and odometer of vehicles to calculate the moment computation of traffic signals.

Convolution Neural Network , Odometer, Stereo vision, , Traffic Light, GPS Detector, AdaBoost Algorithm, RGB Conversion, Pixels, Grey scale Images, Dimensional Matrix

[1] C. Wang, T. Jin, M. Yang, and B. Wang, “Robust and real-time traffic lights recognition in complex urban environments,” International Journal of Computational Intelligence Systems, vol. 4, no. 6, pp. 1383–1390, 2011.

[2] R. de Charette and F. Nashashibi, “Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates,” in Intelligent Vehicles Symposium, 2009 IEEE. IEEE, 2009, pp. 358–363.

[3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolution neural networks,” in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States., 2012, pp.1106–1114.

[4] V. Haltakov, J. Mayor, C. Unger, and S. Ilic, “Semantic segmentation based traffic light detection at day and at night,” in German Conferenceon Pattern Recognition. Springer, 2015, pp. 446–457.

[5] M. Diaz-Cabrera, P. Cerri, and P. Medici, “Robust real-time traffic light detection and distance estimation using a single camera,” ExpertSystems with Applications, vol. 42, no. 8, pp. 3911–3923, 2015.

[6] M. P. Philipsen, M. B. Jensen, A. Møgelmose, T. B. Moeslund, and M. M. Trivedi, “Traffic light detection: a learning algorithm and evaluations on challenging dataset,” in Intelligent TransportationSystems (ITSC), 2015 IEEE 18th International Conference on. IEEE, 2015, pp. 2341–2345.

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[9] M. Diaz-Cabrera, P.Cerri, and J. Sanchez-Medina, “Suspended Traffic lights detection and distance estimation using color features”, in 2012 15th international IEEE conference on Intelligent Transportation Systems. IEEE, 2012, pp. 1315-1320.

Priyanka S.N., Shashidhara H.S., "Deep Learning Technique for Real-time Traffic Light Detection by Automated Vehicles," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.387-392, 2018.