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Research Paper

Automatic Feature Selection from EHR & DNN Modeling

Recently there are a lot of advancements in healthcare technology. Amongst, Electronic Health Record (EHR) is an upcoming trend which stores patients’ demographics, lab tests & results, medical history, habits etc. collaborated in electronic form. EHR is huge data, which is difficult to maintain and retrieve. So the idea of health risk prediction is formulated in this work. To get the relevant data from EHR, feature selection technique is used. Feature selection is responsible to collect only important and needed data from the dataset. For feature selection regression method is used in which loss function is proposed due to which accuracy and performance of the model are increased. Further risk prediction is done using neural network model. Deep Neural Network (DNN) is best suited for pattern learning and prediction purpose. It consists of various layers which have their specific function. DNN uses transfer learning to avoid repeated training for the whole system. Dataset considered here is of hypertension. EHR data is also synthetically created for analysis.

Published by: Shreyal Gajare, Shilpa Sonawani

Author: Shreyal Gajare

Paper ID: V4I1-1159

Paper Status: published

Published: January 5, 2018

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Research Paper

Calibration of MQ-7 and Detection of Hazardous Carbon Mono-oxide Concentration in Test Canister

The most active research in recent years is estimating the noxious waste which has a very high influence on the human health. There are many gases which have adverse effects on human health. Here carbon monoxide(CO) is considered as one of toxic gas which is considered to cause various health issues based on the concentration the casualty is exposed. In this paper, we ensure the presence of hazardous gases and also provide the procedure to estimate the concentration of the same with the help of the MQ-7 sensor and test setup. The calibration of the sensor is carried out with a canister of known volume and the estimation of the CO in the test environment is also determined.

Published by: K. Senthil Babu, Dr. C. Nagaraja

Author: K. Senthil Babu

Paper ID: V4I1-1145

Paper Status: published

Published: January 4, 2018

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Research Paper

Formulation of Natural Mosquito Repellent

Medicinal plants contain numerous biologically active compounds which are helpful in improving the life and treatment of diseases and these are the primary source of synthetic and traditional herbal medicine. The presence of various life-sustaining constituents in plants made scientists investigate these plants for their uses. The objective of the work was to formulate a mosquito repellent product containing Curcumin and Embelin obtained from Curcuma longa and Embelia ribes respectively. Isolated Curcumin and Embelin were confirmed by TLC technique. Bands were compared with standards by calculating Rf 0.74 and 0.60 respectively at visual detection and 254 nm. The formulation of cream was evaluated for various parameters like smooth texture and spreadability with a pH 7 which is non-irritant and suitable for the skin. There is no phase separation during thermal stability. From the present work, it was concluded that 1% Curcumin and 1.5% Embelin containing cream is safe, effective, usable for the skin and stable too.

Published by: Dr. Sneha Agrawal, Nidhi Haldankar, Aniket Jadhav

Author: Dr. Sneha Agrawal

Paper ID: V4I1-1143

Paper Status: published

Published: January 3, 2018

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Review Paper

A Review Article of Fundamental Video Pre-processing Image Quality Enhancement Technique by Using Segmentation Approach

All Color image segmentation may be a terribly rising analysis topic within the space of color video frame extracting image analysis and video Quality sweetening. Several progressive algorithms are developed for this purpose. But, usually, the segmentation results of those algorithms appear to be laid low with miss classifications and over-segmentation. The explanations behind this are the degradation of video frame extracting image Quality throughout the acquisition, transmission and color area conversion. So, there arises the necessity of Associate in nursing economical image sweetening technique which might take away the redundant pixels or noises from the color image before continuing for final segmentation. During this paper, an endeavor has been created to check and analyze completely different image sweetening techniques and thereby checking out the higher one for color image segmentation. Also, this comparative study is finished on 2 well - known color areas HSV and color saturation on an individual basis to seek out that color area supports segmentation task additional expeditiously with relation to those sweetening techniques.

Published by: Baviskar Vaibhavkumar, S. R Goyal

Author: Baviskar Vaibhavkumar

Paper ID: V4I1-1136

Paper Status: published

Published: January 1, 2018

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Research Article

Stabilization of Black Cotton Soil Using Sodium Chloride

Stabilization is the process of physical and chemical alteration of soils to enhance their engineering properties and thus improving the load-bearing capacity of a sub-grade or a sub-base to support pavements and foundations. Sodium chloride has been used for many years as a stabilizing admixture in selected base course materials. Sodium chloride added to raw soil were found to have negligible effects on soil plasticity while increasing compacted density and decreasing optimum moisture content. In this project, the stability of soil is found out by adding Sodium chloride (Nacl) and thus compared with the stability of soil without adding Sodium chloride (Nacl) followed by different laboratory tests.

Published by: Ramkumar Guttikonda, Nadakuditi Abhilash

Author: Ramkumar Guttikonda

Paper ID: V4I1-1142

Paper Status: published

Published: January 1, 2018

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Review Paper

Rainfall Runoff Modelling Using Artificial Neural Network

The use of an artificial neural network (ANN) is becoming very common nowadays due to its ability to analyze complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature and therefore, representing their physical characteristics is challenging. In this paper, the influences of back propagation algorithm and their efficiencies which affect the input dimensions of the rainfall-runoff model have been demonstrated. The capability of the Artificial Neural Network with different input dimensions has been attempted and demonstrated with a case study on Sarada River Basin. The ANN models developed were able to map the relationship between input and output data sets used. The model developed on rainfall and runoff pattern have been calibrated and validated. The significant input variables for the training of ANN models were selected based on statistical parameters like cross-correlation, autocorrelation, and partial autocorrelation function. It was found that those models considering rainfall lag rainfall and discharge as inputs were performing better than those considering rainfall alone. It was found that the neural network model developed was performing well. It can be inferred from the developed model that the Neural Network model was able to predict runoff from rainfall data fairly well for a small semi-arid catchment area considered in the present study.

Published by: Danish Hussain, Ashraf Usmani, Deeak Kumar Verma, Farooq Jamal, Maaz Allah Khan

Author: Danish Hussain

Paper ID: V3I6-1457

Paper Status: published

Published: January 1, 2018

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