Ethanol: A Clean Fuel
Curiosity in producing ethanol from biomass is an incentive attempt for sustainable transportation. Ethanol is a colorless, slightly odoured and a nontoxic liquid produced from plants, and is formed by the fermentation of carbohydrates in the presence of yeast. It is also prepared from sorghum, corns, potato wastes, rice straw, corn fiber and wheat. A biofuel forms low green house gases, when burned compared to other conventional fuels. It is a substitute to fossil fuel which allows for fuel safety and security for many countries where there is less oil reserves. It is made from plants and other agricultural products through biological process rather than the geological process, which is involved in the formation of coal and petroleum. Biofuel is widely used as transportation fuels. Ethanol is considered a biofuel, and is widely used in some countries like U.S and Brazil. In this study, we studied the rising temperature of ethanol, diesel, and kerosene at a fixed point of time and found that ethanol as highest rising temperature compared to kerosene and diesel. It was also observed that the ethanol doesn’t produce any smoke while burning compared to diesel and kerosene which makes it an excellent alternative and clean fuel.
Published by: Samarth Bhardwaj
Author: Samarth Bhardwaj
Paper ID: V2I4-1172
Paper Status: published
Published: July 30, 2016
Review of Different Approaches in Mammography
Breast cancer screening remains a subject of intense and, at times, passionate debate. Mammography has long been the mainstay of breast cancer detection and is the only screening test proven to reduce mortality. Although it remains the gold standard of breast cancer screening, there is increasing awareness of subpopulations of women for whom mammography has reduced sensitivity. Mammography has also undergone increased scrutiny for false positives and excessive biopsies, which increase radiation dose, cost and patient anxiety. In response to these challenges, new technologies for breast cancer screening have been developed, including; low dose mammography.
Published by: Prabhjot Kaur, Amardeep Kaur
Author: Prabhjot Kaur
Paper ID: V2I4-1171
Paper Status: published
Published: July 29, 2016
Robustness against Sharp and Blur Attack in Proposed Visual Cryptography Scheme
The fundamental reason of watermarking invention was to protect originality of image message in the first place from outside attack. The quality of image depends on its ability to survive against various kinds of attacks that try to remove or destroy the originality. However, attempting to remove or destroy the message meaning should produce a noticeable debility in image quality. The robustness is a factor that plays an important role to test and verify the algorithm whether it will withstands against these attacks or not. In this paper the robustness of the proposed algorithm [15] for secret image share in Visual Cryptography Scheme is identified. The robustness of the image against various attacks, specifically image blur attack and image sharp attack are tested. The study of calculated PSNR value signifies the proposed algorithm withstands successfully on these attacks.
Published by: Dhirendra Bagri, R. K. Kapoor
Author: Dhirendra Bagri
Paper ID: V2I4-1170
Paper Status: published
Published: July 28, 2016
Analytical Review of the News Data Classification Methods with Multivariate Classification Attributes
The new classification has been emerged as the important sub-branch of the data mining. A lot of work has been already done on the news classification with variety of classifiers and feature descriptors. A number of news classification projects are working on the real-time systems in existence today. The news classification is the important part of the online news portals. The online news portals are rising every year, and adding more users to the news portals. The news classification is the branch of text classification or text mining. The researchers have already done a lot of work on the text classification models with different approaches. The news works has to be classified in the form of various categories such as sports, political, technology, business, science, health, regional and many other similar categories. The researchers have already worked with many supervised and unsupervised methods for the purpose of news classification. The supervised models have been found more efficient for the purpose of news classification. The major goal of the news classification research is to improve the accuracy while decreasing the elapsed time. Our news classification models purposes the use of k-means and lexicon analysis of the news data with nearest neighbor algorithm for the news classification. The k-means algorithm is the clustering algorithm and used primarily to produce the text data clusters with the important information. Then the lexicon analysis would be performed over the given text data and then final classification of the news is done using k-nearest neighbor. The results would be obtained in the form of the parameters of accuracy, elapsed time, etc.
Published by: Mandeep Kaur
Author: Mandeep Kaur
Paper ID: V2I4-1169
Paper Status: published
Published: July 26, 2016
Review of Copy Move Forgery with Key Point Features
It involves the following steps: first, establish a Gaussian scale space; second, extract the orientated FAST key points and the ORB features in each scale space; thirdly, revert the coordinates of the orientated FAST key points to the original image and match the ORB features between every two different key points using the hamming distance; finally, remove the false matched key points using the RANSAC algorithm and then detect the resulting copy-move regions. The experimental results indicate that the new algorithm is effective for geometric transformation, such as scaling and rotation,and exhibits high robustness even when an image is distorted by Gaussian blur, Gaussian white noise and JPEG recompression.
Published by: Mrs. Nisha, Mr. Mohit Kumar
Author: Mrs. Nisha
Paper ID: V2I4-1168
Paper Status: published
Published: July 25, 2016
Classification through Artificial Neural Network and Support Vector Machine of Breast Masses Mammograms
Breast Cancer is one of the most common types of cancer among women. Breast cancer occurs inside the breast cells due to excessive amount increase in production of cells. Most often this can cause death if not cure at a right time. There are many techniques to detect breast cancer and various abnormalities which are described in this report. But, in this research mammography technique is used to deal with the abnormality type: breast masses. These mammograms (X-ray images) of breast masses are stored in the standard mini-MIAS/DDSM databases. To finding the region of interest there are two methods are applied on it these are: segmentation and noise removal by using neural segmentation and thresholding respectively. After the extraction of abnormal part or region of interest, feature extraction is done through using three features: GLCM, GLDM and geometrical feature on which feature selection is applied to get higher accuracy. After calculating the value of each and every feature the classification is done through using method ANN (Artificial neural network) in which 40 mammograms are used to evaluate the terms named as True Positive, True Negative, False Positive, and False Negative with the help of confusion matrix. By using these confusion matrices, the system can understand the stage of each case. Performance evaluation explains that how much effective and beneficial the new research is. Hence, ANN are used to evaluate the performance through defining Accurateness (precision), Sensitivity and Specificity and also compare the results with existing SVM classification technique.
Published by: Kamaldeep Kaur, Er. Pooja
Author: Kamaldeep Kaur
Paper ID: V2I4-1167
Paper Status: published
Published: July 25, 2016
