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Intrusion Detection System by Machine Learning-A Review

Efficient intrusion detection is needed as a defense of the network system to detect the attacks over the network. A feature selection and classification based Intrusion Detection model is presented, by implementing feature selection, the dimensions of NSL-KDD data set is reduced then by applying machine learning approach, we are able to build Intrusion detection model to find attacks on system and improve the intrusion detection using the captured data. With the increasing number of new unseen attacks the purpose of this model is to develop a system for intrusion detection, and the model will be capable of detecting new and previously unseen attacks using the basic signatures and the features of known attack.

Published by: Aanchal Kumar, Er. Jaspreet Kaur, Er. Inderpreet Kaur

Author: Aanchal Kumar

Paper ID: V2I3-1178

Paper Status: published

Published: June 15, 2016

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Research Paper

Software Defect Prediction using Ensemble Learning Survey

Machine learning is a science that explores the building and study of algorithms that can learn from the data. Machine learning process is the union of statistics and artificial intelligence and is closely related to computational statistics. Machine learning takes decisions based on the qualities of the studied data using statistics and adding more advanced artificial intelligence heuristics and algorithms.

Published by: Ramandeep Kaur, Er. Harpreet Kaur, Er. Jaspreet Kaur,

Author: Ramandeep Kaur

Paper ID: V2I3-1177

Paper Status: published

Published: June 15, 2016

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Review Paper

Review of Diabetes Detection by Machine Learning and Data Mining

The most common action in data mining is classification. It recognizes patterns that describe the group to which an item belongs. It does this by examining existing items that already have been classified and inferring a set of rules. Similar to classification is clustering. The major difference being that no groups have been predefined. Prediction is the construction and use of a model to assess the class of an unlabeled object or to assess the value or value ranges of a given object is likely to have. The next application is forecasting.

Published by: Preeti Verma, Inderpreet Kaur, Jaspreet Kaur, ,

Author: Preeti Verma

Paper ID: V2I3-1176

Paper Status: published

Published: June 15, 2016

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Study Of Various Vehicle Detection Techniques –A Review

ABSTRACT- The Vehicle detection is method to detect the vehicles in the image or video data. The vehicle detection is the branch of the object detection, where the vehicle is the primary object. The vehicle detection can be performed on various kinds of the vehicle data obtained from the horizontal, aerial, parking or road surveillance cameras. In this paper, the vehicle detection and classification method has been proposed by using the hybrid deep neural network over the image data and video obtained from the aerial and satellite images to determine the vehicle density. The non-negative matrix factorization (NMF) will be utilized for the feature extraction and compression for the purpose of vehicle detection and classification. The 2nd level feature compression will be performed to create the quick response vehicle detection and classification system. The model will be programmed to detect the maximum vehicles visible as full or partial object in the image. The vehicle density reporting, vehicle movement reporting and upside & downside reporting for highways will be performed to achieve the goal of the vehicle detection and classification. The aim of this review is to produce the robust algorithm to detect and analyze the vehicle features like whether the vehicle is heavy or light in the images and videos with higher accuracy and precision.

Published by: Geetika Garg, Amardeep Kaur

Author: Geetika Garg

Paper ID: V2I3-1174

Paper Status: published

Published: June 15, 2016

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A Novel Hybrid Classification Technique for Blur Detection

Image, audio and video are the popular entertainment and communication services of internet. Sometime they suffer from many problems, Blur is one of them. Blur is a factor that breakdown the status of image. In this paper, we are going to perform comparison of four different Blur Detection classifiers. This paper introduces our proposed technique (Hybrid Classifier). To verify the accuracy of hybrid Classifier we collect 1000 images from internet and hence results are predicted. From result and discussion, it is clear that Proposed Classifier give 96% accuracy which is 10% more than existing Classifier (SVM).

Published by: Indu Kharb, Abhishek Sharma

Author: Indu Kharb

Paper ID: V2I3-1173

Paper Status: published

Published: June 14, 2016

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Document Image Binarization Technique for Degraded Document Images by using Morphological Operators

Segmentation of badly degraded document images is done for discriminating a text from background images but it is a very challenging task. So, to make a robust document images, till now many binarization techniques are used. But in existing binarization techniques thresholding and filtering is an unsolved problem. In the existing method, edge based segmentation can be done and Canny edge detector used. In our proposed technique, Image Binarzation for degraded document images has being use Region based segmentation. Firstly, an RGB image covert into gray image then image filtering can be done on the basis of Wiener Filtering and Gaussian filter. Secondly, morphological operators use to discriminate foreground from background. Then Otsu and Sauvola’s thresholding did for better results. Finally, proposed method results compare with the method used in DIBCO 2011 dataset. The evaluation based on few parameters like F-measure, PSNR, DRD and MPM. Keywords: Filtering, Morphological operators, and thresholding.

Published by: Divya Jyoti, Bodh Raj, Kapil Kapoor, Arun Sharma

Author: Divya Jyoti

Paper ID: V2I3-1172

Paper Status: published

Published: June 14, 2016

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