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Research Paper

Development of healthy fibrous brownies

Due to modernization, a sedentary lifestyle and unhealthy food habits has led to problems in recent times. A lot of gastrointestinal disorders arise due to unhealthy food. Disorders like indigestion, loose stools, diverticulosis, colon cancer, obesity, and diabetes. Phoenix dactylifera (dates), Avena sativa (oats) & Ipomoea batatas (sweet potatoes) were used in the preparation of brownies. This brownie was analyzed with respect to their physical, biochemical, and sensory properties. The quality of the brownie was determined on the basis of physicochemical characteristics such as- moisture content (28.37%), ash content (1.5%), total carbohydrate content (40.12g), fat content (5.36%), protein content (18.34g), dietary fiber content (6.35 g), Energy(282.08Kcal) including organoleptic analysis including the attributes such as color, odor, taste, texture, mouthfeel and overall acceptability. Microbiological testing and shelf-life studies were also carried out and sustainable packaging for the product was also developed

Published by: Sahaja Gajam, Shraddha Dinesh Dubey, Dr. Gauri Vahalkar

Author: Sahaja Gajam

Paper ID: V6I4-1296

Paper Status: published

Published: August 8, 2020

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Review Paper

Acute Sheehan’s Syndrome – An Update

Sheehan’s syndrome (SS) remains a frequent obstetric complication in emergent and developed countries that to date still reports a relatively high prevalence of moderate to severe postpartum hemorrhage. Sheehan’s syndrome (SS), which is a common cause of parturition related hypopituitarism resulting from postpartum pituitary infarction. It is usually the result of severe hypotension or shock caused by massive hemorrhage during or after delivery. Patients with SS have varying degrees of anterior pituitary hormone deficiency. Several studies have shown that the latent period between symptoms and postpartum hemorrhage can be several years in Sheehan’s syndrome.

Published by: Nithya Natarajan, Dr. V. Selvanayaki

Author: Nithya Natarajan

Paper ID: V6I4-1289

Paper Status: published

Published: August 8, 2020

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Research Paper

Fleet optimization for time and cost factors in residential building

Construction sectors are one of the widest sectors in the world. In which there is tremendous growth in terms of profit and development. it increases in terms of new inventions, new designs, new equipment, and a new concept in a wide manner. In that road transportation or road construction has a huge scope of work and chances of development of roads to connect cities and villages to each other. Basically roads of the particular area define their development of the area in terms of success, quality area of living, workplaces respectively. road construction is mainly considered as important work for connecting states, cities, and villages for transportation of goods and other things. In the construction sectors, mainly in road constructions, fleet management is one of the most important factors that define total cycle time, total cost, cost index, and total time required for completing the activity and related parameters. Fleet management can be defined by equipment assignment and optimization. Equipment assignment and optimization is the main reason many construction companies choose to implement fleet management systems in the first place. By enabling the scheduling and assignment of all types of equipment from multiple manufacturers as well as shift change management from a central office location, fleet management helps minimize unproductive machine wait time and optimize equipment usage on site.

Published by: Pravin Sable, S. M. Waysal

Author: Pravin Sable

Paper ID: V6I4-1351

Paper Status: published

Published: August 8, 2020

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Research Paper

Software Defect Prediction by optimizing features weight with a CNN

Machine Learning approaches are helpful & have well-tried to be helpful in resolution issues & technical problems that lack data. In most cases, the package domain issues may be characterized as a method of learning that depends on the assorted circumstances and changes of the technical issue being addressed in keeping with the principles of machine learning, a prophetic model is made by exploitation machine learning approaches and classified into defective and non-defective modules. Machine learning techniques facilitate developers to retrieve helpful data when the classification of kinds of technical problems being addressed in an exceedingly specific field. This successively permits them to analyze knowledge from totally different views, which may be used because of the formation base of constructive concepts & varied techniques to handle the technical problems. Machine learning techniques are well-tried to be helpful within the detection of package bugs. during this analysis prediction by Convolution based mostly feature choice and Learning by Random forest. In the proposed approach, the accuracy and precision always improve and it also improves class wise. There is a significant enhancement in defective and non-defective class prediction as the random forest non-linearity features help to improve the selection of effective parameters by bagging approach. In the proposed approach, hybridization of three approaches such as deep learning, machine learning and sampling approach is done which significantly improve overlapping of features and imbalance of class like KC2 dataset.

Published by: Asheesh Raju, Anuj Gupta

Author: Asheesh Raju

Paper ID: V6I4-1353

Paper Status: published

Published: August 7, 2020

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Research Paper

Voice over IP mobile telephony using Wi-Fi

Today, we are on the verge of wireless networking where everyone wants to be online without a bunch of cables. Colleges, B-Schools, corporate offices, and cafeterias provide Wi-Fi internet connectivity. The aim of this project is to make use of available resources to provide a free voice calls facility, without using a service provider. Pre-established Wireless Fidelity (i.e. Wi-Fi) service more often uses WLAN, which is offered as a voice transmission medium by Wi-Fi. Smartphones allowed by Wi-Fi can be connected to the router and can communicate with one another. This system will be the perfect alternative to the present intercom device.

Published by: Bharath M. S., Dr. Padmashree T.

Author: Bharath M. S.

Paper ID: V6I4-1335

Paper Status: published

Published: August 6, 2020

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Research Paper

Vehicle detection based on deep learning

Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In recent years convolutional neural networks (CNN) have achieved great success. Presently the state-of-art R-CNN and YOLO architectures are explored, implemented, and adopted for performance comparison on real-time data. From the experimental results, the R-CNN has faster detecting speed and accuracy in complex scenes. However, the transfer learning-based CNN model can be further analyzed and validated over other datasets and can be carried out. In comparison to the state-of-art, the proposed transfer learning approach may accurately regress the vehicle shape and classify vehicle fine-grained categories. Presently, the performances are validated over the UA-DETRAC datasets. The obtained performance strongly suggests the RCNN and has better classification accuracy. The proposed transfer learning approach can be a better alternative competing to the state-of-art approaches.

Published by: Debasmita Mishra, Rashmita Routray

Author: Debasmita Mishra

Paper ID: V6I4-1329

Paper Status: published

Published: August 6, 2020

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