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Hybrid Algorithm with Variants for Feedforward

Neural Network

Thinakaran Kandasamy1 and Rajasekar Rajendran2

1Sri Venkateswara College of Engineering and Technology, India

2Excel Engineering College, India

Abstract: Levenberg-Marquardt back-propagation algorithm, as a Feed forward Neural Network (FNN) training method, has some limitations associated with over fitting and local optimum problems. Also Levenberg-Marquardt back-propagation algorithm is opted only for small network. This research uses hybrid evolutionary algorithm based on PSO in FNN training.  This algorithm includes a number of components that gives advantage in the experimental study. Variants such as size of the swarm, acceleration coefficients, coefficient constriction factor and velocity of the swarm are proposed to improve convergence speed as well as to improve accuracy. The integration of components in different ways in hybrid algorithm produces effective optimization of back propagation algorithm.  Also, this hybrid evolutionary algorithm based on PSO can be used for complex neural network structure.

Keywords: Back propagation, hybrid algorithm, Levenberg-Marquardt, Particle swarm optimization, variants of PSO algorithm

Received August 31, 2014; accepted April 12, 2015


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A Fuzzy Based Matrix Methodology for Evaluation and Ranking of Data Warehouse Conceptual Models Metrics

1Naveen Dahiya, 2Vishal Bhatnagar, and 3Manjeet Singh

1Maharaja Surajmal Institute of Technology, C-4, Janakpuri, India

2Ambedkar Institute of Advanced Communication Technology and Research, India

3YMCA University of Science and Technology, Sector-6, India

Abstract: The authors present a methodology for ranking data warehouse conceptual models metrics based on opinion of experts using fuzzy inference technique. The fuzzy based approach gives a precise ranking methodology due to its ability to handle imprecise data involved ranking of metrics and ambiguity involved in expert decision making process. The proposed work aims towards ranking of quality metrics already proposed and validated by Manuel Serrano along certain identified parameters based on expert opinion and evaluation of criteria matrix using permanent function. The results obtained are also compared with the actual experts ranking. The achieved results are better as the imprecise human thinking is taken into consideration during calculation of results to give realistic results. 

Keywords: Fuzzy, Data warehouse, Conceptual models, Quality metrics, Criteria matrix.

Received October 23, 2014; accepted July 7, 2015


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An Optimized Model For Visual Speech Recognition Using HMM

Sujatha Paramasivam1 and Radhakrishnan Murugesanadar2                                             

1Department of Computer Science and Engineering, Sudharsan Engineering College, India

2Department of Civil Engineering, Sethu Institute of Technology, India

Abstract: Visual Speech Recognition (VSR) is to identify spoken words from visual data only without the corresponding acoustic signals. It is useful in situations in which conventional audio processing is ineffective like very noisy environments or impossible like unavailability of audio signals. In this paper, an optimized model for VSR is introduced which proposes simple geometric projection method for mouth localization that reduces the computation time.16-point distance method and chain code method are used to extract the visual features and its recognition performance is compared using the classifier Hidden Markov Model (HMM). To optimize the model, more prominent features are selected from a large set of extracted visual attributes using Discrete Cosine Transform (DCT). The experiments were conducted on an in-house database of 10 digits [1 to 10] taken from 10 subjects and tested with 10-fold cross validation technique. Also, the model is evaluated based on the metrics specificity, sensitivity and accuracy. Unlike other models in the literature, the proposed method is more robust to subject variations with high sensitivity and specificity for the digits 1 to 10. The result shows that the combination of 16-point distance method and DCT gives better results than only 16-point distance method and chain code method.

Keywords: Visual speech recognition, feature extraction, discrete cosine transform, chain code, hidden markov model.

Received March 20, 2015; accepted August 31, 2015

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Progressive Visual Cryptography with Friendly and Size Invariant Shares

Young-Chang Hou1, Zen-Yu Quan2, Chih-Fong Tsai2

1Department of Information Management, Tamkang University, Taiwan

2Department of Information Management, National Central University, Taiwan

Abstract: Visual cryptography is an important data encoding method, where a secret image is encoded into n pieces of noise-like shares. As long as there are over k shares stacked out of n shares, the secret image can be directly decoded by the human naked eye; this cannot be done if less than k shares are available. This is called the (k, n)-threshold visual secret sharing scheme (VSS). Progressive visual Cryptography (PVC) differs from the traditional VSS, in that the hidden image is gradually decoded by superimposing two or more shares. As more and more shares are stacked, the outline of the hidden image becomes clearer. In this study, we develop an image sharing method based on the theory of PVC, which utilizes meaningful non-expanded shares. Using four elementary matrices (C0 - C3) as the building blocks, our dispatching matrices (M0 - M3) are designed to be expandable so that the contrast in both the shares and the restored image can be adjusted based on user needs. In addition, the recovered pixels in the black region of the secret image are guaranteed to be black, which improves the display quality of the restored image. The image content can thus be displayed more clearly than that by previous methods.

Keywords: Visual Cryptography, Progressive Visual Cryptography, Secret Sharing, Unexpanded Share, Meaningful (Friendly) Share.

Received April 8, 2015; accepted October 7, 2015


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Real Time Facial Expression Recognition for Nonverbal Communication

Md. Sazzad Hossain1 and Mohammad Abu Yousuf2

1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh

2Institute of Information Technology, Jahangirnagar University, Bangladesh

Abstract: This paper represents a system which can understand and react appropriately to human facial expression for nonverbal communications. The considerable events of this system are detection of human emotions, eye blinking, head nodding and shaking.  The key step in the system is to appropriately recognize a human face with acceptable labels. This system uses currently developed OpenCV Haar Feature-based Cascade Classifier for face detection because it can detect faces to any angle. Our system can recognize emotion which is divided into several phases: segmentation of facial regions, extraction of facial features and classification of features into emotions. The first phase of processing is to identify facial regions from real time video. The second phase of processing identifies features which can be used as classifiers to recognize facial expressions. Finally, an artificial neural network is used in order to classify the identified features into five basic emotions. It can also detect eye blinking accurately. It works for the active scene where the eye moves freely and the head and the camera moves independently in all directions of the face. Finally, this system can identify the natural head nodding and shaking that can be recognized in real-time using optical flow motion tracking and find the direction of head during the head movement for nonverbal communication.

Keywords: Haar-Cascade Classifier, Facial Expression, Artificial Neural Network, Template matching, Lucas-Kanade Optical flow.

Received April 4, 2015; accepted November 29, 2015


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