A Review of Spam Detection using Machine Learning
Abstract: The computerization of communications has increased the velocity of trade and greatly enriched the content. Emails, franchisees in emails (emails contraction) are increasingly used by individuals and more by businesses (70 billion emails per day). But as for traditional mail, users very quickly deal with unwanted email, or spam, and mostly quite undesirable. To counter this flood of trash (more than 50% of all email) and not lose emails that we are actually intended, only one viable solution: automate the detection and destruction of this type of digital pollution with the risk that a document or misfiled. This paper aims to present the progress of spam detection techniques.

Authors: Varsha Malik, Sanjay Kumar

File Name: Varsha_50100-16-152.pdf
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Energy Efficient Relay Selection & Optimized Power Allocation Scheme in Cooperative Communication Network
Abstract: High data rates, large coverage area and reduce the effect of multipath fading are key points of researcher for wireless communication systems. These facilities are provided by MIMO systems. However, to implement and use multiple antennas are very difficult due to size, power, cost, and weight constraints. So, Virtual MIMO concept was introduced. In cooperative wireless networks, a virtual antenna array is created using several single antenna devices. Hence it is often the case that number of sources and number of relays cooperate to send their data to destination. For the cooperative systems, selection of appropriate relay node is of very important. This research works is focused towards the Fuzzy Logic based relay selection and power allocation (PA) using Gray Wolf Optimizer (GWO) algorithm in cooperative communication system

Authors: Harshit Vishwakarma, Asst. Prof. Vandana Tripathi

File Name: Harshit_50200-16-123.pdf
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A Review on Transmission Line Faults Detection
Abstract: Power transmission and distribution lines plays vital roles in Power System it has to achieve the essential continuity of service of electrical power to the end users. Transmission lines connect the generating stations and load centers. As the generating stations are far away from the load centers they run over hundreds of kilometers. Hence, the probability of fault occurrences in transmission lines is very high. Since faults can destabilize the power system it must be isolated immediately for restoration of power supply. Fault analysis is very important issue in power system engineering in order that to clear faults quickly and restore power supply as soon as possible with minimum interruption. This paper presents a literature review of transmission line faults detection.

Authors: Manish Khandelwal, Amit Solanki

File Name: Manish_50100-16-155.pdf
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Newton-Raphson Power Flow Models of SVC Optimized by PSO
Abstract: Transmission lines consume a considerable amount of power. The necessity of power and its dependency has grown exponentially over the years. The void between limited production and tremendous demand has increased the focus on minimizing power losses. The losses like transmission loss range from the conjecture factors like physical or environmental losses to severe technical losses. The primary factors like reactive power and voltage deviation are significant in stretched conditions and long range transmission lines of powers. The short and medium range of transmission lines accounts for micro-static values of power loss but the transmission losses of vulnerable size are witnessed in long transmission range of more than 100 kilometres. In this paper, we have incorporated Static VAR compensator (SVC) as the FACTS (Flexible AC Transmission System) device to control the power loss in transmission lines. The optimal location SVC is studied on the basis of Particle Swarm Optimization (PSO) technique to minimize network losses. Validation through the implementation on the IEEE-14 and IEEE-30 bus systems shows that the PSO is found feasible to achieve the task.

Authors: Subhash Shankar Zope, Prof. Dr. R. P. Singh

File Name: Subhash_50200-16-121.pdf
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Hybrid Feature Extraction for Character Recognition using Genetically Optimized Neural Network
Abstract: The selection of features is an important step in any pattern recognition system. This selection of features is considered a combinatorial optimization problem and made the object of research in many disciplines. The main objective of the selection of features is to reduce the number of them by eliminating redundant and irrelevant features recognition system. The second objective of this feature selection is also to maintain and / or improve the performance of the classifier used by the recognition system. In this paper, Genetically Optimized Neural Network (GA-NN) algorithm is used to solve this type of feature selection problem in the recognition of character. The results in the selection of features have reduced the complexity of using GA-NN approach.

Authors: Vishal Mourya, Mrs. Aradhana Singh

File Name: Vishal_50200-16-124.pdf
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