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Saranya, N.
- Reduction of Test Data Volume Based On Viterbi Compression Algorithm
Abstract Views :140 |
PDF Views:3
Authors
Affiliations
1 P. A. College of Engineering and Technology, Pollachi-2, IN
1 P. A. College of Engineering and Technology, Pollachi-2, IN
Source
Software Engineering, Vol 7, No 3 (2015), Pagination: 74-79Abstract
Test vector compression has been an active area of research, yielding a wide variety of techniques. A test pattern compression scheme is proposed in order to reduce test data volume and application time. The proposed scheme finds a set of compressed test vectors using the Viterbi algorithm instead of solving linear equations. By assigning a cost function to the branch metric of the Viterbi algorithm, an optimal compressed vector is selected among the possible solution set. This feature enables high flexibility to combine various test requirements such as low-power compression and/or improving capability to repeat test patterns. The proposed on chip decompressor follows the structure of Viterbi encoders which require only one input channel. Experimental results compared with the dictionary algorithm compression. Dictionary based compression techniques are also popular in embedded systems domain. Since they provide a dual advantage of good compression efficiency as well as fast decompression mechanism. Viterbi algorithm provides better compression ratio compared to dictionary algorithm.Keywords
Test Data Compression, Viterbi, Dictionary, Compression Efficiency.
- Black Money Check: Integration of Big Data & Cloud Computing To Detect Black Money Rotation with Range-Aggregate Queries
Abstract Views :168 |
PDF Views:0
Authors
Source
Software Engineering, Vol 8, No 6 (2016), Pagination: 151-152Abstract
The big data is difficult to be analyzed due to the presence and characteristics of huge amount of data. Hadoop technology plays a key role in analyzing the large scale data. The aggregate queries are executed on more columns concurrently and it is difficult for huge amount of data. This paper is proposing the method in which the fast RAQ is dividing the big data in to autonomous partitions by means of a balanced partition algorithm and later for each partition a local assessment sketch is generated. By the arrival of the range-aggregate query demand the fast RAQ gets the result in a direct manner by shortening local estimate from all partition and then the cooperative results are provided. Thus in fast RAQ technique three tier Architecture is insisted and they are of
1. Extracting the helpful information’s from Unstructured Data, 2.Implementation of the big data in Multi system Approach, 3.Application Deployment – Insurance/ Banking.
- Solvation Number and Optical Absorbance Studies in Polyaniline Derivatives
Abstract Views :176 |
PDF Views:6
Authors
Affiliations
1 Department of Physics, Seethalakshmi Ramaswami College, Tiruchirappalli-620002, IN
1 Department of Physics, Seethalakshmi Ramaswami College, Tiruchirappalli-620002, IN
Source
Journal of Pure and Applied Ultrasonics, Vol 37, No 4 (2015), Pagination: 63-67Abstract
Over the past two decades the polyaniline has received the greatest attention due to its stability, easy synthesis and the potential as a material for numerous applications. This makes PANI an attractive material for researchers. The solvation number in the polyaniline solution has been found to be of great interest during the recent years. Solvation number has effects of both physical and chemical. In the present work the solvation number on some polyaniline derivatives is analysed. The solutions of some polyaniline derivative are prepared for different molalities and measured at various temperatures. Solvation number of solutions depends on the number of moles of the solute and the solvent molecules present in the solution. The optical absorption spectra suggests that the presence of molecular interaction between the PANI and the solvent through enolic form. This analysis also confirms the dual nature of the solvent.Keywords
Polyaniline, Solvation Number, Optical Absorption.Full Text
- Drowsy Driver Alert System Using Image Processing
Abstract Views :173 |
PDF Views:3
Authors
Affiliations
1 Bannari Amman Institute of Technology, Sathyamangalam, TN, IN
2 Bannari Amman Institute of Technology, Sathyamangalam, TN, IN
1 Bannari Amman Institute of Technology, Sathyamangalam, TN, IN
2 Bannari Amman Institute of Technology, Sathyamangalam, TN, IN
Source
Digital Image Processing, Vol 9, No 7 (2017), Pagination: 153-156Abstract
A non-intrusive machine vision based concept is adopted in the development of Drowsy Driver Alert System. The system uses a small monochrome security camera that points directly towards the driver's face and monitors the driver's eyes in order to detect fatigue. The aim of this work is to develop a prototype drowsiness detection system. This paper describes about how to monitor the eye and detect for the eye closure. The system deals with information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. By considering the fact that the eye regions in the face reflect greater intensity changes than other parts, the eyes are located by finding the significant intensity changes in the face. The intensity changes in the eyes area determine whether eyes are open or closed. If the eyes are found closed for five consecutive frames, the driver is falling asleep and issues a warning signal.Keywords
Drowsiness Detection, Feature Extraction, Frame, Image Segmentation, Non-Invasive System.References
- Road safety information, rospa, -“driver fatigue and road accidents”, www.rospa.com, 2011.
- RuianLiu,et.al, "Design of face detection and tracking system," Image and Signal Processing (CISP),2010 3rd International Congress on ,vol.4, no., 16-18 Oct. 2010, pp.1840-1844.
- P.C. Philipp, E. Udo and U. Peter,“Experimental evaluation of eye-blink parameters as a drowsiness measure” European Journal of App lied Physiology, Volume 89, Issue 3 -4, 2003,319-325.
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- R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based featureextraction algorithm,” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 121– 131, Jan. 2011.
- S. Vitabile, A. De Paola, F. Sorbello, J Ambient Intell Human Comput, “A real- time non-intrusive FPGA-based Drowsiness system” Springer, University of Palermo, Italy 2011, pp.251-262.
- Ralph Oyini Mbouna, Seong G.Kong, SeniorMember, IEEE, Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring, (IEEE), vol.14 ,2013,pp.1462-1469
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- Shabnam Abtahi, Behnoosh, “Driver Drowsiness Monitoring Based on Yawning Detection”, Distributed Collaborative Virtual Environment Research Laboratory,University of Ottawa,Canada
- Swati P. Kale, Deepak Dandekar, “Face Tracker for Head Position Detection”, National Conference on Innovative Paradigms in Engineering & Technology NCIPET, 2012
- R. I. Hammoud, G. Witt, R. Dufour, A. Wilhelm, and T. Newman, “On driver eye closure recognition for commercial vehicles,” SAE Int. J. Com- mercial Veh., vol. 1, no. 1, pp. 454–463, Apr. 2009.
- Sentimental Analysis using Asymmetric Least Squares Twin Support Vector Machine (LSTSVM)
Abstract Views :195 |
PDF Views:1
Authors
N. Saranya
1,
R. Gunavathi
2
Affiliations
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, IN
2 Department of Computer Application, Sree Saraswathi Thyagaraja College, Pollachi, IN
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, IN
2 Department of Computer Application, Sree Saraswathi Thyagaraja College, Pollachi, IN
Source
Fuzzy Systems, Vol 10, No 9 (2018), Pagination: 217-223Abstract
Sentiment analysis known as opinion mining is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention. It is also known as opinion mining used in the voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. This paper works on finding approaches that generate output with good accuracy through (aLSTSVM) Asymmetric Least Squares Twin Support Vector Machine. aLSTSVM is a new version of support vector machine (SVM) based on asymmetric least square and non-parallel twin hyperplanes. It is an efficient fast algorithm for binary classification and its parameters depend on the nature of the problem. A result on several benchmark datasets is applied to train a sentiment classifier in order to demonstrate the accuracy of the proposed algorithm. N-grams and different weighting scheme were used to take out the most classical features. It also analyzes Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection in aLSTSVM may provide significant improvement on classification accuracy.
Keywords
Chi-Square Weight, Asymmetric Least Squares Support Vector Machine (aLSTSVM), Support Vector Machine (SVM).References
- Feldman, R., 2013. Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), pp.82-89.
- Mostafa, M.M., 2013. More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), pp.4241-4251.
- Pang, B. and Lee, L., 2004, July. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). Association for Computational Linguistics.
- Taboada, M., Anthony, C. and Volt, K., 2006, May. Methods for creating semantic orientation dictionaries. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06) (pp. 427-432).
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- Uysal, A.K. and Gunal, S., 2012. A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, pp.226-235.
- MartinezCamera, E., Valdivia, M., Teresa, M., Peres Ortega, J.M. and Urea Lopez, L.A., 2011. Technical de classification de opinions palisades a un corpus en Espanola.
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- Pang, B., Lee, L. and Vaithyanathan, S., 2002, July. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.
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