S. Senthamilarasu ¹, M. Hemalatha ¹

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore-21, TN, IN

Abstract

This paper initiates perceptions and algorithms of feature selection, survey of existing feature selection algorithms and assesses diverse algorithms with a classifying frame based on search approaches, valuation criteria, and provides strategy in selecting feature selection algorithms. A unifying platform is projected to continue our efforts headed for building an incorporated system for intelligent feature selection. Feature selection intends to reduce the dimensionality of patterns for classification by choosing the most informative instead of irrelevant and/or redundant features. In this proposed work Intuitionistic fuzzy based feature clustering is proposed for grouping features based on the degree of membership and degree of indeterminacy among the attributes and clusters. In this proposed work a novel approach which uses an Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. Genetic algorithm reinstating which is the variation of traditional genetic algorithm is then applied to appraise whether the measured feature is independent of class labels; hence, it leads to eliminate unrelated clusters to classification process and progress the selection of features. The proposed method achieves improvement on classification accuracy and perhaps to select less number of features which show the way to simplification of learning task to a big extent. The Experiment results have been demonstrated by the good performance and also find good enough subset features of this method on using UCI benchmark datasets that are for data mining methods such as Breast Cancer, Sensor and Iris Records.

Keywords

Feature Selection, Cluster, Genetic, K-means, Fuzzy, Intuitionistic

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T. Jayasri ¹, M. Hemalatha ²

Affiliations
1 Embedded Systems, School of Computing, SASTRA University, Thanjavur, TamilNadu,, IN
2 School of Computing, SASTRA University, Thanjavur, TamilNadu, IN

Abstract

Sleep apnea is a dishevelment that causes interruption in breath or shoal of the respiration. The respiratory signal is classified into three states such as normal respiration, motion artifacts, and sleep apnea and it is obtained from a physionet. Firstly, using a second order auto regressive modeling, an algorithm is developed to attain the energy and frequency parameters of the signal and then the signal is classified with threshold based manual classification into any of the above taxonomy. In addition to this dataset, MLP is trained with a back propagation learning algorithm that results in reduced time, iterations and errors. Consequently, the training of SVM, a binary classifier used to solve multiple class problems is done with the same data set and classification is made to reduce overall errors. The overall efficiency of the above techniques is compared.

Keywords

Feature Extraction, Autoregressive Model, Burgs Method, Multilayer Perceptron, Back Propagation Learning Algorithm, Support Vector Machine

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S. Bhagavathy Nanthini ¹, M. Hemalatha ², D. Manivannan ¹, L. Devasena ³

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

Abstract

As the wireless communication greatly depends on spectrum utilization, the increase in demand for new wireless services and their application leads to the spectrum scarcity. In order to utilize the available spectrum efficiently, "cognitive radio"- The demanding technology is introduced. It is a dynamic technology that can sense the medium, utilizes the available white spaces, for transmission by detecting its neighboring devices. The cognitive radio targets to increase the efficiency of the spectrum changes without causing any intervention to the licensed users. Since cognitive radio works in the open network space, it increases the chance of the attacker to show intervene on the spectral medium. So, the security becomes the key factor. This leads to the realization of various security threats in the cognitive radio. There are various papers covering the security issues over the threats in cognitive radio, but this paper provides an advanced survey over attacks and common threats and the possibility of securing the available spectrum from the attackers. In addition to that future scope and challenges are also addressed. This survey will help the researchers to identify the space left out and the problems to be attached related to security issues on cognitive radio.

Keywords

Attacks, Digital Signatures, Cognitive-radio, Security, Spread Spectrum Modulation

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Sanu Mathew ¹, K. Shylaja ¹, T. Jayasri ¹, M. Hemalatha ¹

Affiliations
1 School Of Computing, SASTRA University, Thanjavur, Tamil Nadu, IN

Abstract

The portable wireless technology with wide accessibility facilitates the people to enter into a new dynamic environment for optimizing the overall productivity. Establishing reliable communication is a challenging aspect, because the signal propagation is heavily subjected to interference and fading effects resulting in severe path loss. In the field of telecommunication system, the effect of Path Loss in the signal is used to analyze and design the link budget system. Previously, many path loss prediction models like HATA, and Okumura are proposed where the path loss is determined with the help of experimental field values and verified with the help of the model graph. In this work, BPSK modulated signal is used to determine the path loss using HATA empirical formula derived with the help of the Okumura curve for various urban, suburban and rural areas. The identified values are given to triangular membership function and defuzzification is performed using faster and flexible center of sum method. The inferred results show maximum loss in case of urban and increases at an average rate of 10dB per decade with distance. The proposed technique optimizes signal transmission by determining the path loss accurately.

Keywords

Binary Phase Shift Keying, Fuzzy Logic, HATA Model, Okumura Model, Path Lossa

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J. John Livingston ¹, M. Hemalatha ¹

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

Abstract

The method of generating electrical voltage for charging an electronic gadget from the piezoelectric sensor that is fixed to the sole of the footwear is illustrated in this paper.

Keywords

Energy Harvesting, Piezoelectricity, Piezoelectric Sensor, Portable Electric Power

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A. Edwin Robert ¹, M. Hemalatha ¹

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore, IN

Abstract

In present world applications of software in other domains have their own privileges and their own control over other application also fulfilling their own testing methods acting as a tool in solving the given problem. Application integrity is highlighted in existing work but in our proposed approach MCBA (Malicious Code Behavior Analysis). In our proposed study the method of MCBA approach error correcting codes in the kernel is incorporated. Our objective is to incorporate a protection mechanism that saves the application even though the system’s memory gets corrupted. In exploring the trusted MCBA Server to identify and monitor the portion of the system where corruption occurs and the server segregates the reason for various malicious impacts. Therefore, two approaches have been simulated: one is authenticated check and the next is unauthenticated check. In an authenticated check, a matching schema (e.g., the MCBA) applies dataset pattern recognition techniques to check malicious pattern by comparing it to the incoming applications during execution, if malicious packet is found it protects the system, in an unauthenticated check, the malicious packet from the guest OS for example ischolar_main kits enters into our local system and it securely stores a cloned image of the guest OS memory at boot time, this method incorporates a VMM (Virtual Memory Monitor) and it allows only the instructions to read from the cloned copy of memory but never execute the instruction, though it is so, sometimes the instructions are malicious and it is unsecured. This paper emphasizes the MCBA architecture, incorporates monitoring, detecting and healing process which are so feasible to apply in real time environment ,it is very economically used for the technical programmers who are designing source code for various domains in Software market.

Keywords

Dataset, Error Correcting, Malicious, Matching, Privileges

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K. Juliana GnanaSelvi ¹, M. Hemalatha ¹

Affiliations
1 Bharathiyar University, Coimbatore – 641046, Tamil Nadu, IN

Abstract

Objectives: The vital goals of the face recognition research was for the use of optimization algorithms in an efficient biometric process. The algorithms are compared for biometrics on face recognition to implement a cost-effective and efficient continuous authentication system to substantially reduction the risk of fraud. Methods/Statistical Analysis: By trial comes to fruition, the proposed system upgraded the face recognition precision procured by the processed content was around 2.97% which is greater above Eigen face procedure when compared to standard PCA strategy. In addition, above trial comes about describing that the given strategy has increased and efficient optimization exactness. Findings: To enhance persistent authentication handle amid online procedure of different continuous application and retain both the client’s hard and soft biometric data and persistently verifying whether the individual using the terminal is as an indistinguishable substantial client from the one login toward the start. It is a financially savvy and productive persistent authentication framework to significantly lessen the danger of extortion. The resistance against ridiculing is inspected. Application/Improvements: Improvements are done on differentiating PSO and ACO and E (pso+aco)- based approaches for highlight assurance. The typical acknowledgment rate of E (pso+aco) was good when compared to PSObased component decision. On analyzing it was found that, number of parts needed in E (pso+aco) was not that much required for acknowledgment utilizing PSO

Keywords

ABC, Biometrics, Face recognition, Passive Continuous Authentication, PSO, Swarm Observation

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