Informatics Publishing Limited

G. Manikandan ¹, N. Sairam ², S. Sharmili ², S. Venkatakrishnan ²

Affiliations
1 School of Computing, SASTRA University, Thanjavur, India-613401
2 School of Computing, SASTRA University, Thanjavur-613401, IN

Abstract

To extract the previously unknown patterns from a large data set is the ultimate goal of any data mining algorithm. Some private or confidential information may be revealed as part of data mining process. In this paper we use min-max normalization approach for preserving privacy during the mining process. We sanitize the original data using min-max normalization approach before publishing. For experimental purpose we have used k-means algorithm and from our results it is evident that our approach preserves both privacy and accuracy.

Keywords

Accuracy, Clustering, K-Means, Min-Max Normalization, Privacy

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R. Mukunthan ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur, TamilNadu, IN

Abstract

A new method for detecting Delaunay edge by modifying the links in the star of a vertex is proposed. This is based on selecting vertex points of the input triangulation in such a way that the star formed from the selected point should belong to the given input set S. That star should not have any convex hull point and the edges connecting the selected vertex. The edges formed in the proposed method based on star formation are Delaunay edges since it satisfies the empty circle property. This is experimentally verified using two dimensional input data. Finally, Delaunay triangulation is obtained by joining the remaining edges which are validated and verified using the circumcircle property of Delaunay triangulation.

Keywords

Convex Hull, Delaunay Triangulation, Star Formation

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V. Anand ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur - 613401, Tamil Nadu, IN

Abstract

The objective of this review article is to offer an insight into the performance issues pertinent to routing in Mobile Ad hoc Networks. This work has studied the approaches for solving the performance issues such as changes in topology, energy consumption of mobile nodes, delay, routing overhead, message delivery time, throughput, packet delivery ratio, security of networks and mobility management. To provide a perception of solutions for these issues, this study concentrates on various methodologies such as Effective Hierarchical Routing Algorithm, Link Stability with Energy Aware Multipath Routing protocol, Path Encounter Rate metric and Trust-based Source Routing protocol. Effective Hierarchical Routing Algorithm computes the route and has decreased the load of routing protocols. Link Stability with Energy Aware Multipath Routing protocol finds routes with small delay. Delivery ratio of packet in LSEA is improved on par with Ad hoc On-demand Distance Vector Routing (AODV). Delivery ratio of packets in LSEA was enhanced by 40%. To get an efficient metric for routing in high mobility situations, Path Encounter Rate metric was proposed. Throughput achieved with this metric was 30% higher than those obtained by the hop-count metric. To choose a least-cost route with security constraints, TSR was proposed. TSR has improved the packet delivery ratio, ratio of identifying the malicious nodes as well as network throughput. This review of different methods can be used by researchers to find a better solution to these issues.

Keywords

Delay, Multi Point Relays, Node Mobility, Reliability, Routing, Topology

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S. Karthika ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur, IN

Abstract

Manual classification of the individuals into different categories based on their educational qualification is a tedious task and it may vary respective to the considered scenario. This paper proposes a classification methodology utilizing the benchmark Naïve Bayesian classification algorithm for the classification of persons into different classes based on several attributes representing their educational qualification. The experimental results are appreciable indicating that the proposed classification method can be a promising one and can be applied elsewhere. The proposed method has been experimentally verified to be 90% accurate with a high kappa value thus proving its efficiency. This classification methodology can reduce the mundane manual labor and can easily assist in categorization.

Keywords

Classification, Data Mining, Educational Qualification, Kappa, Naïve Bayesian

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R. Nivedha ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur – 613 401, Tamil Nadu, IN

Abstract

A social media is a mediator for communication among people. It allows user to exchange information in a useful way. Twitter is one of the most popular social networking services, where the user can post and read the tweet messages. The tweet messages are helpful for biomedical, research and health care fields. The data are extracted from the Twitter. The Twitter data cannot classify directly since it has noisy information. This noisy information is removed by preprocessing. The plain text is classified into health and non-health data using CART algorithm. The performance of classification is analyzed using precision, error rate and accuracy. The result is compared with the Naïve Bayesian and the proposed method yields high performance result than the Naïve Bayesian. It performs well with the large data set and it is simple and effective. It yields high classification accuracy and the resulting data could be used for further mining.

Keywords

CART, Classification, Decision Tree, Machine Learning, Twitter

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K. Anuradha ¹, N. Sairam ²

Affiliations
1 School of Electrical and Electronics Engineering, SASTRA University, Tirumalaisamudram, Thanjavur - 613401, Tamil Nadu, IN
2 School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur – 613401, Tamil Nadu, IN

Abstract

The objective of this review article is to study the spatio-temporal approaches for addressing the key issues such as multi-view, cluttering, jitter and occlusion in recognition of human action. Based on high-level action units, a new sparse model was developed for recognition of human action in static background. Relevant to multi-camera view, a negative space approach for identifying actions taken from different viewing angles was proposed. An approach was based on space-time quantities was proposed to acquire the changes of the action instead of camera motion. This space-time based approach has handled both cluttering and camera jitter. In static background, a sparse model presented for recognition of human action acquires the fact that actions from the same class share same units. The presented method was assessed on numerous public data sets. This method has achieved a recognition rate of 95.49% in KTH dataset and 89% in UCF datasets. Based on negative space, a region based method was offered. This approach has managed the issue of long shadows in human action recognition. The approach was assessed by most common datasets and has attained higher precision than contemporary techniques. An approach based on space-time quantities was proposed to manage cluttering. This approach achieves a recognition rate of 93.18% in KTH dataset and 81.5% in UCF dataset. To handle occlusion, a model was presented with spatial and temporal consistency. The algorithm was appraised on an outdoor dataset with background clutter and a standard indoor dataset (HumanEva-I). Results were matched with advanced pose estimation algorithms.

Keywords

Action Recognition, Camera Jitter, Clutter, Multi-view, Occlusion, Segmentation

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S. Divya ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur – 613401, Tamilnadu, IN

Abstract

Objective: Data Stream Classification is a big problem. Ensemble based learning methods are used to tackle the data Stream classification problem. Method: Methods such as Random Forest and Very Fast Decision Tree (VFDT) for classification are used in the system. Findings: This hybrid approach is an Effective method for calculating hidden data and maintains accuracy in the large data set. It also calculates the neighbors between each pair of cases that can be used in clusters. It is used for finding unknown or (by scaling) gives informational views over the data. This hybrid approach achieves 85 % accuracy and result proves that the hybrid approach performs well when compared to other algorithms in terms of accuracy. This is the application where we can download data streams of any application at faster rate. Many methods are available to process data streams. But the proposed algorithm performs well when compared to other algorithms. Applications: Many real time data streams are downloaded and uploaded to test and train various Applications. Data streams are used in many applications such as medical applications and educational applications.

Keywords

Classification, Data Stream, Random Forest, Very Fast Decision Tree (VFDT).

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M. J. Soumiya ¹, N. Sairam ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Background/Objectives: Stock Trading is the action of buying or selling the products. In this complex habitat, generating the future trend is such a difficult process. Methods: Methods such as Sentiment survey, Technical survey and Trend based segmentation method are used in this system. The segmentation process, a hybrid model using Support Vector Regression and Naïve Bayes classification is used to extract the intensity levels of the gathered information. Findings: This is the application where the trading decisions can be automatically predicted to gain profit. Based on the reviews and ratings of a product from users, the system concludes whether to invest in that product or not. By using hybrid model, the predictive signals may be accurate. Decision for investors for investing in a better profit earning product is given by the system itself. The previous method uses the sentiments from the news articles and social networks, which is less reliable than getting the feedbacks from the users or the investors of that product. Applications: This framework may be used in real time applications such as trading activities, stock exchange in the stock market, etc.

Keywords

Classification, Sentiment Survey, Stock Trading, Trend Based, Technical Survey.

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