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Venkatesan, D.
- Building a Custom Sentiment Analysis Tool based on an Ontology for Twitter Posts
Abstract Views :205 |
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Authors
Affiliations
1 School of Computing, SASTRA University, Tamil Nadu - 613 40, IN
1 School of Computing, SASTRA University, Tamil Nadu - 613 40, IN
Source
Indian Journal of Science and Technology, Vol 8, No 13 (2015), Pagination:Abstract
Twitter is a popular micro-blogging platform which allows the users to share their opinion on any domain. The thoughts of the people vary according to the domain and also the opinion may contain both positive and negative words which are called as opinion words and are given in the form of dictionary called lexicon dictionary. The sentiment analysis done without feature extraction fails to give the deep result about the users opinion but in our proposed approach , features of the domain are extracted by building ontology which helps in getting the refined sentiment analysis. Feature based sentiment analysis gives the best result. While analyzing the sentiment, scores are assigned to the tweets so that the sentiment score of our tweets are compared with the third party like American Customer Satisfaction Index score. This comparison shows that our score assignment gives the detailed analysis of the features than the third party.Keywords
Opinion Words, Ontology, Protégé, Sentiment, Sentiment score, Tweets, Twitter- Improving Coverage Deployment for Dynamic Nodes using Genetic Algorithm in Wireless Sensor Networks
Abstract Views :213 |
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Authors
Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 16 (2015), Pagination:Abstract
Wireless Sensor Network is a group of sensor nodes aimed to monitor various environmental conditions at the field locations. Eventually the sensed data is sent to a processing center. Each sensor node consists of a different number of sensors for sensing different parameters in the field area. The sensor nodes are usually equipped with a pair of limited alkaline battery. Achieving maximum coverage deployment is a major problem in wireless sensor networks. In the Existing system, by using the Random Deployment some nodes may get overlapping this will cause unbalanced structure. By using maximum coverage sensor deployment problem, the Coverage achieved only for static nodes. In the proposed system, a Genetic Algorithm is used to deploy Sensor Nodes for the Maximum coverage within the area, where the sensors are of different types. In this work, first analyze the total coverage area the WSN, identify the types of Sensor nodes and Coverage sensing distance, and calculate the coverage sensing distance for the combination of all sensor types based on radius of each node. Improving the deployment of Dynamic nodes for achieving maximum coverage deployment by using Genetic Algorithm. As a result, we were implemented this work in Java. This will show the best performance in coverage and network lifetime.Keywords
Coverage Deployment, Genetic Algorithm (GA), Wireless Sensor Networks (WSN)- DI-ANN Clustering Algorithm for Pruning in MLP Neural Network
Abstract Views :186 |
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Authors
P. Monika
1,
D. Venkatesan
1
Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 16 (2015), Pagination:Abstract
Data mining is an emerging technology for applications such as text based mining, web based mining and it performs a major role in various domains for numerical data analysis, data statistics and machine learning. In this paper, data mining is used in machine learning of ANN (Artificial Neural Network). The Pruning technique in an MLP (The Multilayer Perceptron) neural network is to remove the unwanted neurons based upon their corresponding weights; as a result it improves the accuracy and speed of the network. In the existing system, based on their synaptic weights the pruning is performed and it removes the lowest weight neuron form the network. The result obtained from the existing method does not produce an optimized removal of the neuron. In the proposed system pruning is performed by using divisive clustering in MLP neural network. The main purpose of the Divisive algorithm in ANN is to split each neuron weight into sub neuron up to the fixed level and then remove the least weighted hidden neuron. The proposed method is implemented using the Java language. The Performance result obtained from the proposed method shows that it reduces the error rate and improves efficiency and accuracy of the MLP network. The present results confirm that DI-ANN (Divisive Artificial Neural Network) can provide a fast, accurate, and consistent methodology applicable to the neural network.Keywords
DI-ANN (Divisive Artificial Neural Network) Algorithm, MLP (Multi Layer Perceptron), Neural Network (NN), Pruning Method- Particle Swarm Optimization and Discrete Wavelet Transform based Robust Image Watermarking
Abstract Views :140 |
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Authors
S. Gayathri
1,
D. Venkatesan
1
Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:Abstract
Objectives: Watermarking technique on digital images with improved robustness and with optimum results can be achieved by using the intelligent technique Particle Swarm Optimization (PSO). Discrete Wavelet Transform (DWT) is used to uncover perceptually significant coefficients from the digital image. Methods: PSO is one amongst the efficient techniques in optimizing a problem having many numbers of candidates. This robust technique consists of two stages namely embedding and extraction stages. Insertion of watermark into the image is done at the embedding stage. Before embedding watermark into the image perform DWT and select any one of the bands LL, LH, HL and HH. Initially select the coefficients randomly and the optimal DWT coefficients for embedding the watermark at various positions are identified by PSO. Findings: In watermark extraction stage, the embedded watermark is identified by doing the reverse process. Robustness of the digital image watermarks implies the strength of the watermark against various image processing attacks and this can be checked by evaluating Cross Correlation value that is Normalized (NCC) and for fidelity Peak Signal to Noise Ratio (PSNR). It is evident that the robustness of the image is well maintained by this technique. As a further improvement any of the other intelligent technique namely Ant Colony Optimization can be used for optimization evaluation. Applications: Digital image watermarking plays a vital role in copyright infringement issues and it helps in predicting the authorized user by the digital signature that is watermarked in the image.Keywords
Discrete Wavelet Transform, Fidelity, NCC, Particle Swarm Optimization, PSNR, Robustness, Watermarking.- Selection of Effective Platform for Reviews by Fuzzy TOPSIS Method
Abstract Views :144 |
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Authors
Affiliations
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur − 613401, Tamil Nadu, IN
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur − 613401, Tamil Nadu, IN