CURRENT ISSUE (PROCESSING)VOLUME 5 ISSUE 04 NOVEMBER 2016

Algorithm for Grading a Computer Program using the Inverse Function
Abstract: Computer Programs are written to perform a particular task. We design various computer programs to simplify our job and make our work flawless.So we must have a way to evaluate the correctness of the program written by us. In this research we have evolved an algorithm to grade computer programs for the efficiency. This paper is about students who want to learn programming languages from basics or advanced and have to evaluate their programming skills for further development.

Authors: Harendra Kumar, Shivraj Sharma, Suneel Kumar

File Name: Harendra_50300-16-131.pdf
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A Novel Approach for Phishing URLs Detection using Nave Bayes, Neural Network and Random Forest Classifiers
Abstract: Seeking sensitive user data in the form of online banking user - id and passwords or credit card information, which may then be used by phishers for their own personal gain is the primary objective of the phishing e - mails. With the increase in the online trading activities, there has been a phenomenal increase in the phishing scams which have now started achieving monstrous proportions . This paper gives strategies for distinguishing phishing sites by d issecting different components of phishing URLs by Machine learning systems. It talks about the systems utilized for identification of phishing sites in view of lexical features, host properties and page significance properties. We consider different machi ne learning algorithms for assessment of the features to show signs of improvement comprehension of the structure of URLs that spread phishing. We use Nave Bayes, Neural Network and Random Forest Classifiers .

Authors: Gangeshwari Sharma, Abhishek Tiwari

File Name: Gangeshwari_50300-16-129.pdf
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Review on Global Planning of Fourth Generation (4G) Mobile Networks
Abstract: In the current environment where the information is the key to success context, no matter the field where one stands, the telecommunication networks are increasingly solicited. Enormous amounts of information circulating on the networks at every second. It is essential to ensure the availability of these networks to ensure the transmission of such data in all circumstances. This paper survey a global model including survivability for the planning of 4G (WiMAX) networks.

Authors: Surbhi Vyas, Pankaj Rathi

File Name: Surbhi_50300-16-132.pdf
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Spectrum Sensing Framework in Cognitive Radio using GA and Grey Wolf Optimization
Abstract: Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. The performance of spectrum detection is regularly traded off with multipath fading, shadowing and receiver uncertainty problems. To relieve the effect of these issues, spectrum sensing has been appeared to be a powerful technique to enhance the performance of detection by using spatial diversity. This paper proposes a comparative analysis of Singular Value based Detection (SVD) using Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) for spectrum sensing.

Authors: Shruti Sharma, Rahul Gedam

File Name: Shruti_50400-16-121.pdf
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Comparative Analysis of Energy Detection, Eigenvalue Detection and PSO Optimized Singular Value Detection Techniques for Cognitive Radio Networks
Abstract: With the rapid development in wireless communications, the demand for the high data transmission require increases in spectrum resources because of fixed spectrum assignment policy is characterized in wireless network these lead to low spectrum utilization in many frequency bands but the availability of the spectrum resources is limited. Cognitive radio is key enabling technology for improving the utilization of electromagnetic spectrum. It senses the spectral environment over wide range of frequency band and exploits the unoccupied band. One of the most challenging issues in cognitive radio system is to sense the spectrum environment accurately and determine whether the primary user is active, or not over a specific band reliably. So, there is need of good sensing algorithm have the property have low sensing time, ability to detect primary signal at low SNR. Energy detection method is an efficient spectrum sensing technique for high SNR environment but it gives poor performance under low SNR. So by applying some technique result can be improved. Therefore, in this paper a comparative research is performed among energy detection, Eigenvalue based detection and PSO optimized singular value based detection methods in order to obtain improved result. Performance analysis and comparison of techniques are carried out and developed on MATLAB 2014 R.

Authors: Vivek Baghel, Dr. M. R. Khan

File Name: IJDACR_50400-16-123.pdf
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Transmission Line Fault Detection and Classification using Artificial Neural Network
Abstract: Electrical power systems suffer from unexpected failures due to various random causes. The functions of the protective systems are to detect, then classify and finally determine the location of the faulty line of voltage and/or current line magnitudes. Then at last, for isolation of the faulty line the protective relay have to send a signal to the circuit breaker. The ability to learn, generalize and parallel processing, pattern classifiers is applications of Neural Network used as an intelligent tool for detection. The features of Neural Networks, such as their ability to learn, generalize and parallel processing, among others, have made their applications for many systems ideal. The use of neural networks as pattern classifiers is among their most common and powerful applications. The use of back-propagation neural network architecture as an alternative method for fault detection, classification and isolation in a transmission line system. The main goal is the implementation of complete scheme for distance protection of a transmission line system. In order to perform this, the distance protection task is subdivided into different neural networks for fault detection, fault identification (classification) as well as fault location in different zones. Three common faults were discussed; single phase to ground faults, double phase faults and double phase to ground faults. The result provides a reliable and an attractive alternative approach for the development of a protection relaying system for the power transmission systems.

Authors: Manish Khandelwal, Amit Solanki

File Name: Manish_50200-16-125.pdf
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Adaptive L-LEACH Protocol for Heterogeneous Wireless Sensor Network
Abstract: Heterogeneous Wireless Sensor Network (WSN) comprises of sensor nodes with distinctive capability, for example, diverse computing power and sensing range. Contrasted with homogeneous WSN, arrangement, and topology control are more perplexing in heterogeneous WSN. Distinctive energy efficient clustering protocols for wireless sensor networks systems and thinks about these protocols on a few focuses, in the same way as clustering method, location awareness, heterogeneity level and clustering attributes. Though, each protocol is not appropriate for heterogeneous WSNs. This paper proposes an improvement of Low Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol under a few distinctive situations holding high-level heterogeneity to low-level heterogeneity.

Authors: Namrata Bhagat, Praneeja Kasture

File Name: Namrata_50400-16-122.pdf
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