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
Pharmacological Studies on Hypnea Musiformis (Wulfen) Lamouroux
Hypnea musciformis belonging to family Rhodophyceae Genus name is Hypnea. To the best of our knowledge the algae Hypnea musciformis was evaluated for Phytochemical study Such as Physico-chemical analysis, elemental study, metal analysis. The different extracts undergo Preliminary Phytochemical analysis for the identification of various Phytoconstituents. It answers positively alkaloid, carbohydrate, glycosides, tannins, protein, amino acid and steroid ...Pharmacological activity was screened by which methanol extract showed the maximum inhibition of arthritis. Then Methanolic extract was subjected to column chromatography to isolate the compound and identified by TLC and confirmed as Flavonoid by spectral studies as Astaxanthin and Hesperidin. Which responsible for reduction of arthritic activity and Free radical like Nitric oxide and DPPH. In Histopathological studies Methanolic extract of Hypnea musciformis shows effective in curing the synovial damage as compared to arthritic control. Our result showed that the methanol extracts and isolated compound possess significant anti-rheumatoid activity. It may due to the presence of Phenolic and Carotenoids terpene constituents. From the above results it can be concluded that Hypnea musciformis can be used in the treatment of anti-rheumatoid arthritic disease as a novel drug on the basis of clinical trials. Chemistry of marine natural products is a newer area of potential resources for discovering new therapeutic tangents developing new leads.
Published by: B. Lavanya, N. Narayanan, A. Maheshwaran
Author: B. Lavanya
Paper ID: V2I4-1164
Paper Status: published
Published: July 19, 2016
Review Data De-Duplication by Encryption Method
Data deduplication is a technique to improve the storage utilization. De-duplication technologies can be designed to work on primary storage as well as on secondary storage. De-duplication with the use of chunking Data that is passed through the de-duplication engine is chunked into smaller units and assigned identities using crystallographic hash functions. Thereafter, two chunks of data are compared to ascertain whether they have the same identity. Chunking for de-duplication can be frequency based or content based. Frequency based chunking identifies high frequencies of occurrences of data chunks. The algorithm uses this frequency information to enhance data duplication gain.
Published by: Sonam Bhardwaj, Poonam Dabas
Author: Sonam Bhardwaj
Paper ID: V2I4-1163
Paper Status: published
Published: July 18, 2016
An Experimental Study on Performance of Jatropha Biodiesel using Exhaust Gas Recirculation
Today the world is in dilemma for the prevention of both of fuel depletion and environmental degradation crises. Due to excessive need, indiscriminate extraction and consumption of fossil fuels have led to a reduction in petroleum reserve. Developing countries such as India depend heavily on oil import. Diesel being the main transport fuel in India, finding a suitable alternative to diesel is an urgent need of the hour. Jatropha based bio-diesel (JBD) is a non-edible, renewable fuel suitable for diesel engines and has a potential of large-scale employment for wasteland land with relatively low environmental degradation. As Jatropha oil is free from sulphur and still exhibits excellent lubricity and is a much safer fuel than diesel because of its higher flash and fire point. Performance parameters including brake thermal efficiency (η), brake specific fuel consumption (BSFC) with varying loading conditions showed Jatropha biodiesel as an effective alternative on four stroke single cylinder compression ignition engine. Also the effect of exhaust gas recirculation (EGR) at 10% re circulation showed Jatropha as an effective fuel since the inherent oxygen present in the bio-diesel structure compensates for oxygen deficient operation under EGR.
Published by: Kiranjot Kaur
Author: Kiranjot Kaur
Paper ID: V2I4-1162
Paper Status: published
Published: July 18, 2016
Automated Supervision of PCB Circuits
Machine vision intelligence(MVI) is the capacity of a Computer to "see" and "take appropriate decision". A machine-vision framework utilizes one or more camcorders, simple to-computerized transformation ( ADC ), and advanced sign processing software ( DSP etc ). The subsequent information goes to a PC or robot controller. Two critical determinations in any vision framework are the affectability and the determination. Affectability is the capacity of a machine to see in faint light, or to distinguish feeble motivations at imperceptible wavelengths. Determination is the degree to which a machine can separate between items. When all is said in done, the better the determination, the more restricted the field of vision. Affectability and determination are associated. Every single other variable held steady, expanding the affectability diminishes the determination, and enhancing the determination lessens the affectability.
Published by: Manoj Kumar, Mrs. Shimi S.L
Author: Manoj Kumar
Paper ID: V2I4-1161
Paper Status: published
Published: July 18, 2016
Automated Checking of PCB Circuits using Labview Vision Toolkit
LabVIEW has developed very strong vision intelligence software. The investigator has taken very useful industrial problem and has given a solution. All the PCB fabricating/Electrical and Electronic assembling organization, after culmination of the procedure physically check the PCB if every one of the segments are available or not. In the event that any segment will be missing, then it will be send back again for correction. All of these PCB industry do this procedure physically. As the creation of complete PCB is huge (in the scope of thousand and lakhs pieces for each month), thusly enormous labour and time it takes to check all PCB. Generally it takes 5-20 minutes to check each PCB relying on its complexity. So to physically check 1 lakh PCB, approximately 5-20 lacs minutes are required. It is really a huge problem for electrical and electronics industries. It is one of the biggest challenge and hurdle in PCB manufacturing industry now a days.
Published by: Manoj Kumar, Mrs. Shimi S.L
Author: Manoj Kumar
Paper ID: V2I4-1160
Paper Status: published
Published: July 18, 2016
