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Review Paper

The classification scheme for the heart disease prediction

With the enormous enhancement of diseases in medical and the other communities of healthcare, it is extremely important to have an analysis of the heart diseases at the early stages. Since, nowadays it is very important to detect the diseases and lessen the death of patients at early stages. Every person has different values of cholesterol, blood pressure and many more that are linked with heart disease prediction. But it has scientifically proven that the normal person blood pressure is counted to be 120/90 along with this the pulse rate and the cholesterol value is 72. In this paper, the various “machine learning algorithms” are explained that include Support Vector Machine, Decision tree, neural network and many more are explained so that complete description can be provided. Along with this, the entire description of the heart disease has been provided that depicts about the need for the topic to be selected. There are some of the issues present in the Data Mining algorithm that are also described in the paper. The ultimate aim is to improve efficiency in different parameters by describing the classification approach for detecting heart disease. The parameters on which the prediction can be done are the age, serum cholesterol, gender, blood pressure, pulse rate. The accuracy and the efficiency in the prediction can be increased only if the number of attributes is more. For the classification of heart disease, the most efficient algorithm is the Support Vector Machine algorithm since it will not only reduce the prediction time but will also improve the efficiency of the algorithm.

Published by: Saloni Kapoor, Ashwinder Tanwar

Author: Saloni Kapoor

Paper ID: V4I6-1260

Paper Status: published

Published: November 23, 2018

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Review Paper

Diabetes analysis using machine learning methods

In this paper, various kinds of algorithms are explained that include Support Vector Machine. The aim is to improve efficiency in different parameters by describing the classification approach for detecting diabetes. In this, it will predict diabetes with SVM.SVM will classify the data into positive and negative data points. In this, we predict the diabetes of Type 1and Type 2.Type 1is a type of diabetes that has no cure. Type 2 diabetes is common diabetes. It develops from child. Diabetes is the fastest growing problem with more health and economic results. The increasing rate is predicted to increase to 430 million. Different types of data mining techniques are used. With SVM it will predict better accuracy. When we will predict the result with SVM, it will give accuracy. With prediction of different parameters, we can predict the target value. With diabetes, there can be eye blindness, stress and many more can happen. With the help of data mining, we can aware about diabetes. In this paper, mention all the data mining techniques, types of classifiers. At the end, In this paper describes the diabetes types and what we have done and accuracy of the data. Type 2 diabetes is not easy to predict all the effects.

Published by: Harwinder Kaur, Gurleen Kaur

Author: Harwinder Kaur

Paper ID: V4I6-1261

Paper Status: published

Published: November 23, 2018

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Review Paper

Ovarian cancer sign, symptoms and detection techniques

The current medical field is improved in many ways by accessing the applications of the technology. Digital image processing is one of them that being fascinating for researchers as well as for doctors such as ultrasound images and others. Due to various reasons, the diseases are growing rapidly, Cancer is one of them. Basically, cancer is a disease in which the blood cells grow uncontrollably and abnormal that causes diseases. Ovarian cancer is the most occurred form of the disease in females and every year the majority of females are survived from it. The cancer is produced in the ovaries and spread in the other parts of the body. The detection and diagnosis are crucial in the early stages because of the diagnosis become harder at the last stages. In the research, a deep representation of ovarian cancer is described as its generating process, signs and symptoms and the major causes of ovarian cancer. There are also descriptions of various diagnosis techniques that helped to discover the cancer cells and treatment of the patient.

Published by: Uroosa Shafi, Sugandha Sharma

Author: Uroosa Shafi

Paper ID: V4I6-1254

Paper Status: published

Published: November 23, 2018

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Others

Development of safe flying protocol aided by Artificial Intelligence

The paper is about how machine learning can impact the air traffic control systems and future proof it.

Published by: Karan Ganesh, Jessysonia S. P., Ajith Raj R., Rohith I. J.

Author: Karan Ganesh

Paper ID: V4I6-1189

Paper Status: published

Published: November 23, 2018

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Others

Antimicrobial activity of substituted fluoro derivatives of benzyl amine with benzofuran

In the present project, the synthesis of substituted benzopyran derivatives have been reported as one-pot reaction by reaction of 2,2 Dimethyl-2,7b dihydro-1 aH- oxireno[2,3-c] chromen-5-yl acetate with ethanol and 4- methyl benzylamine. The synthesized compounds in this work were screened in vitro for their antimicrobial activity against some strains of bacteria. The antibacterial activities of synthesized compounds were compared with the antibacterial activity of the standard antibiotics Ciprofloxacin. The tested compounds revealed antibacterial properties. This review is summarized to know about the different pharmacological activities of Benzopyran nucleus with the extended knowledge about its antimicrobial activity.

Published by: Mejo Joseph, Dr. S. Alaxander

Author: Mejo Joseph

Paper ID: V4I6-1245

Paper Status: published

Published: November 23, 2018

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Research Paper

Study on development and quality evaluation of fasting purpose biscuits by using buckwheat flour sago flour and peanuts flour

The composite flour was based on fasting ingredients Buckwheat flour (shingadaata), Sago flour (sabudanaata), Peanut flour (mongfaliatta). Fasting biscuits were prepared by using Buckwheat flour is a good and inexpensive source of carbohydrate are considered as a foodstuff of high nutritional value. Sago flour contains more amount of calorie (350), carbohydrate (85.5g), fat (0.2g) and protein (0.2g). It also provides a large amount of starch low amount of minerals, vitamins. Peanuts flour improves satiety and helps to maintain weight loss. Milk powder, saltless butter, sugar, and cardamom thoroughly were mixed, coconut powder used for garnishing and sodium bicarbonate used as a preservative increased the safety and quality of biscuits. By making the combination of these flour became nutritionally advantageous. In this experimensT1(100:0:0),T2(90:10:0),T3(90:0:10),T4(80:10:10,)T5(80:0:20),T6(60:20:20),T7(80:10:10).Buckwheat, Sago, and peanut flour respectively in various proportion were used to prepared three blended flour samples from which fasting biscuits were prepared. The proximate composition of the various flour blends used for the preparation of fasting biscuits was determined using standard methods. The physicochemical analysis and sensory evaluation were done to know the acceptability of fasting biscuits. Sensory evaluation by taste flavor texture overall acceptability of fasting biscuits was also done. The biscuit analyzed for analytical and chemical analysis includes moisture content, crude fat, crude protein, total sugar, and carbohydrate content. Physical evaluation showed that there was no change in diameter and spread ratio of biscuits as compared to control. However, hardness and strength of biscuits increased with increase in the quantity of sago flour. Combination of these flour biscuits having high calorie and low-fat content compared to wheat flour. From proximate analysis showed that the moisture content of fasting biscuit samples ranged between % fat content of sample T7 is very low in fat content 32.04% that is best for diabetes patients. Data obtain from the sensory scores clearly indicated that significantly higher scores were observed for appearance, taste, color, texture, flavor and overall acceptability in fasting biscuits containing flour Buckwheat, Sago and peanut flour respectively ratio of (60:20:20). Result obtained could be valuable for the bakery industries to utilize the nutritional advantage of fasting biscuits is high compared to buckwheat flour biscuits

Published by: Sneha Shukla, Mayank Tripathi, K. L. Bala, Avanish Kumar

Author: Sneha Shukla

Paper ID: V4I6-1206

Paper Status: published

Published: November 23, 2018

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