iosr-JCE   Volume-17 ~ Issue-4 ~ July-August 2015

Abstract: Road image analysis is an important step towards building automated driver guidance system with the aid of computer vision. Several road accidents and mishaps are reported every year due to driver negligence and non ideal road conditions like narrow bridge, potholes, and bumps and so on. Little research is carried out towards the direction of Indian road image analysis. In this work we propose a unique system for real time processing of Indian Road images and video stream. We proposed techniques for a) Road boundary and lane detection b) Pothole detection c) Object detection on the road from video and Road sign classification. In this we test lane detection and object detection on video streams to justify that the techniques can be used in real time. Pothole detection is performed on the static images and road sign classification is performed on isolated, already separated road sign images.

[1]. E. Atkociunas, R. Blake, A. Juozapavicius, M. Kazimianec, "Image Processing in Road Traffic Analysis, Nonlinear Analysis", Modelling and Control, 2005
[2]. Jack Greenhalgh and Majid Mirmehdi, Senior Member, IEEE," Real-Time Detection and Recognition of Road Traffic Signs" Manuscript received January 13, 2012.
[3]. Ajit Danti, jyoti Y, Kulkarni and P.S. Hiremath "An Image Processing Approach to Detect Lanes, Pot Holes and Recognized Road Signs in Indian Roads" International Journal of Modeling and Optimizaton, Vol.2 No.6, December 2012
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[6]. Hasan Fleyeh, "color detection and segmentation for road and traffic signs", proceedings of the 2004 ieee Conference on cybernetics and intelligent systems singapore, december, 2004.

Abstract: Energy Efficiency is one of an indispensible concern in Wireless Sensor Networks (WSNs) which minimizes their efficiency and also shortens the lifetime of network. Many efficient routing as well as algorithmic schemes are proposed in last decade which help in prolonging the lifetime of a network by reduces the consumption of battery by the sensor nodes. Among all the proposed routing schemes, the concept of hierarchical routing plays in important role to overcome the energy efficiency problem of a network. In this paper, an improved version of highly efficient hierarchical routing protocol LEACH is proposed which reduces the consumption of power from the battery by the nodes which help in increasing the lifetime of a network along with Ant Colony Optimization (ACO) algorithm.

[1] J. Yick, B. Mukherjee and D. Ghosal, Wireless sensor network survey, Computer Networks, 52(12), 2008, 2292-2330.
[2] I. F. Akyildiz, W. Su., Y. Sankarabramaniam, and E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 40, 2002, 102-114.
[3] B.P. Singh, Anish Arora and Ashish Kumar, Security in Wireless Sensor Networks, Journal of Advances in Physics, 1( 3), 2013, 1-4.
[4] B. P. Singh, Rajni and G. Singh, Investigation of Efficient Energy Coverage Problem in Wireless Sensor Networks with ACO and ACB-SA, in Proceeding of international conference on Emerging research in computing, information, communication and application, Elsevier, ERCICA, 2014, 559 563.
[5] J. N. Al-Karaki and A.E. Kamal, Routing Techniques in Wireless Sensor Networks: A Surevey, Wireless Communications, IEEE, 11(6), 2004, 6—28.

Abstract: The face is our key of attention in social communication, playing a main role in transmission identity and sentiment. Hence, Face recognition has become an important issue in many applications such as security systems, credit card verification and criminal identification. In human face profiles, the shape and size of eyes, nose, mouth and their relationship have been commonly used as a feature. With correctly extracted features, we can easily recognize a humane face. However, only little work has been done in literature on low-resolution face recognition and partial face recognition. Numerous alignment methods and techniques have been developed for face recognition.

[1]. Sushil Gangwar and Krishna Kumar, "3D Face Recognition Based On Extracting PCA Methods", International Journal of Engineering Research and Applications, Vol. 2, No. 2, pp.693-696, Mar-Apr 2012.
[2]. N.M. Khan, R. Ksantini, I.S. Ahmad and B. Boufama, "A novel SVM+NDA model for classification with an application to face recognition", Pattern Recognition, Vol. 45, No. 1, pp. 66–79, 2012.
[3]. Gaurav Aggarwal, Amit K. Roy Chowdhury and Rama Chellappa, "A system identification approach for video-based face recognition", In proceedings of the 17th International Conference on Pattern Recognition, Vol. 4, pp. 175-178, 2004.
[4]. Timothy F. Cootes, Gareth J. Edwards, and Christopher J. Taylor, "Active Appearance Models", IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 23, No. 6, pp. 681-685, June 2001.
[5]. Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi and Aicha Majda, "Learning a Back propagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition", International Journal of Image, Graphics and Signal Processing, Vol.4, No.8, pp. 8-14, August 2012.

Abstract: DNA containing data obtained from more conventional binary storage media. Plaintext message data encoded in DNA strands by use of a (publicly known) Key of alphabet of short oligonucleotide sequences.

Keywords: Steganography, Decryption, cipher text, biological data

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[4]. Slatalla, Michelle; Joshua Quittner (1995). Masters of Deception: The Gang That Ruled Cyberspace. HarperCollins. ISBN 0-06-017030-1.
[5]. Dreyfus, Suelette (1997). Underground: Tales of Hacking, Madness and Obsession on the Electronic Frontier. Mandarin. ISBN 1-86330-595-5.

Abstract: Twitter is a popular social media platform with millions of users. The tweets shared by these users have recently attracted the attention of researchers from diverse fields. In this paper, we focus primarily on predicting user's personality from the analysis of tweets shared by the user. However, different techniques have been used to predict a user's personality from tweets but there are certain shortcomings which still need to be addressed. The aim of this paper is to consider the current state of this research and explore the need of predicting personality from tweets by crucially reviewing the literature done till date and provide an overview of the different measures taken to alleviate the issues faced by researchers in this field.

Keywords: Classification, Machine Learning Algorithms, Personality Prediction, Supervised Learning, Twitter

[1]. Twitter Stats 2015, Twitter Inc.
[2]. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2.1-2 (2008):1-135
[3]. Asur, Sitaram, and Bernardo A. Huberman. "Predicting the future with social media." Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on Vol 1 IEEE, 2001
[4]. Cambria, Erik, et al. "New avenues in opinion mining and sentiment analysis. "IEEE Intelligent Systems 28.2(2013): 15-21
[5]. Golbeck, Jennifer, Cristina Robles, and Karen Turner. "Predicting personality with social media." CHI'11 extended abstracts on human factors in computing systems. ACM, 2011.
[6]. Barrick, Murray R., and Michael K. Mount. "The Big Five personality dimensions and job performance: A meta-analysis." (1991).

Abstract: Twitter is a popular social media platform with millions of users. The tweets shared by these users have recently attracted the attention of researchers from diverse fields. In this research, we focus primarily on predicting user's personality from the analysis of tweets shared by the user. An associative study of different research works done in personality prediction shows that different techniques have been used to predict a user's personality from tweets but there are certain shortcomings which still need to be addressed.

[1]. Twitter Stats 2015, Twitter Inc.
[2]. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2.1-2 (2008):1-135
[3]. Asur, Sitaram, and Bernardo A. Huberman. "Predicting the future with social media." Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on Vol 1 IEEE, 2001
[4]. Cambria, Erik, et al. "New avenues in opinion mining and sentiment analysis. "IEEE Intelligent Systems 28.2(2013): 15-21
[5]. Golbeck, Jennifer, Cristina Robles, and Karen Turner. "Predicting personality with social media." CHI'11 extended abstracts on human factors in computing systems. ACM, 2011.

Abstract: For long, the human brain has intrigued Researchers, Psychologist, Doctors and everyone alike. It has left many unanswered questions and the more it is studied the more questions arise. This paper presents a comparison with the Human brain with that of the Memory System of the Computer. Undoubtedly Technology has progressed to such an extent that one is able to store and retrieve, but how far have we progressed? Are the machines better in storing than our grey matter inside our hard skull? The Human Brain memory is far more complex than the whole of the computer world and fascinating to an extent we cannot even understand the complexity completely.

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[3]. Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob Filho W, Lent R, Herculano-Houzel S. J Comp Neurol. 2009 Apr 10;513(5):532-41. doi: 10.1002/cne.21974 from Reference: http://www.nature.com/scitable/blog/brain-metrics/are_there_really_as_many

[4]. Coltheart, Max "Iconic memory and visible persistence". Perception &Psychophysics(1980). 27(3): 183–228. doi:10.3758/BF03204258.

[5]. Atkinson, R. C. &Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes