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Smart virtual assistant bot using voice control and object detection

Manual efforts being put up by human beings are greatly reduced by smart robots. The robot is voice-controlled and uses object detection using a virtual assistant. The human voice commands are given to the robotic assistant remotely by using a smart mobile phone or a raspberry pi microcontroller or a computer via the virtual assistant. The robot can perform different movements such as turn, start/stop operations, displaying the temperature and humidity of the environment. This robot also locates an object when moving from one place to another. The voice commands are processed in real-time, using an online cloud server. The speech signal commands converted to text form are communicated to the robot over a Wi-Fi network. This robot contains one Raspberry Pi and NodeMCU microcontroller as the central unit. The camera is connected to Raspberry Pi for detection of objects that is then streamed to the user’s mobile or computer. All the motors and sensors are connected to the NodeMCU microcontroller. This idea is used for surveillance purpose, healthcare assistant and human-machine interaction purposes.

Published by: Abhishek B. R., Gokul Krishnan V., Balamurugan S., Anitha R.

Author: Abhishek B. R.

Paper ID: V5I1-1436

Paper Status: published

Published: March 4, 2019

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Research Paper

Learning style preferences of students 2017-2019 batch of diploma in pharmacy of National Institute of Health Sciences (NIHS)– Sri Lanka

The term ‘learning style’ is defined as different and unique ways used by individuals as they prepare to learn and recall information. The VARK questionnaire is used to study learning styles. VARK model was introduced by Neil Fleming, which classifies the learning preferences based on the sensory pathways into four models. V- Visual, A- Aural, R- Read/Write, K-Kinaesthetic. Data were collected by using a questionnaire. VARK questionnaire is the standard one for analysing learning styles. A total of 92(74.19%) were multi-modal in their learning preference and only 32 students (25.81%) were uni-modal. The uni-modal preferences were K-12.90%, A- 4.84%, R-4.84%, V- 3.23%. The overall distribution of scores of all modalities of learning styles. The commonest learning preference was the multimodel category, of which the highest percentage was seen in the VARK - 43.55%. Other multimodal preferences were VAR-6.45%, VKA-3.22% ARK -7.25% VRK-1.61% RK – 1.61% VK – 2.42% AK-4.03%AR- 4.03% .The total individual scores in each category. This study has revealed a number of important facts. It's a more multi-modal percentage than in previous studies. When teaching pharmacy students, multiple methods should be use.

Published by: G. R. S. Rajapaksha, M. Karunathilaka

Author: G. R. S. Rajapaksha

Paper ID: V5I1-1437

Paper Status: published

Published: March 4, 2019

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Research Paper

A study on floating families in Dhaka City: Problems and prospects

Dhaka is the capital city of Bangladesh. There are many types of problems in this city. One of these is the population problem. A large number of floating families live here. They face many problems here. They also create many problems by getting involved in antisocial activities. This study tries to present the socio-economic conditions of floating families in Dhaka city. It reveals the problems and prospects of floating families in the city. For this,200 respondents were selected and a semi-structured questionnaire was used to collect data from respondents. Data were analyzed using MS Excel. This study tries to find out some factors of floating families such as education, number of family members, monthly income, and expenditure of family member, living conditions, ownership of property, allowance from GO and NGO. Causes of migration etc. Hence in this research, there are some recommendations that will be helpful for policymakers to make effective policy.

Published by: Muhammad Rashedur Rahman

Author: Muhammad Rashedur Rahman

Paper ID: V5I1-1405

Paper Status: published

Published: March 1, 2019

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Research Paper

Machine images and application marketplace- Cloud PaaS and SaaS for enterprise

Although cloud computing is in all mouth today it seems that there exists only a little evidence in the literature that it is more economically effective than classical data center approaches. Cloud computing is revolutionizing however information technology resources and services area unit used and managed whereas the revolution comes with new security issues. Among these is that the drawback of firmly managing the virtual-machine images that encapsulate every application of the cloud. This paper explains how the Machine Images and Application Marketplace are the new revolution in providing Cloud PaaS and SaaS for an Enterprise. In this paper, we will be discussing different cloud services, Machine Images and Application marketplaces which uses multiple and different API’s to provide services to the Enterprise. We will talk about multiple Marketplace service providers like Kong, AWS, Microsoft Azure, etc. This paper is done by taking and analyzing a survey held through Google forms which targeted working professionals from the IT industry who may or may not be using Cloud services for their organization. A total of 56 responses were analyzed and formulated for this research paper.

Published by: Harish Kumar, Neel Desai

Author: Harish Kumar

Paper ID: V5I1-1438

Paper Status: published

Published: February 28, 2019

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Research Paper

No end in sight – Television is here to stay

When video began to stream over mobile and computers, speculation gusted about the end of television. Just as there were speculations about TV replacing radio or TV replacing cinema. But given the recent proliferation of FM channels and number of movies releasing every week and hitting the box office, it is clear that one media did not kill the other. The same is with the television and the new media. Technological convergence and explosion of new media did not and cannot end television. This study clearly establishes that television is alive, stronger and bigger today.

Published by: Darshana Choudhury

Author: Darshana Choudhury

Paper ID: V5I1-1439

Paper Status: published

Published: February 28, 2019

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Research Paper

Stock prediction marketing using machine learning

The share market has traditionally been the proving grounds for machine learning applications. There is a lack of an algorithm which can find the heuristic reasoning of humans based on current events/trends. The proposed system is an attempt to reconcile computed sentiments alongside traditional/more common data mining. Datasets consisting of historical data as well as recent headlines will be mined to ascertain stock price movement. In the proposed system it is to be hoped, more accurately predict stock price movement by emulating instinctual reasoning by implementing sentiment analysis.

Published by: Rupali Borole, Sonali Govilkar, Dipali Duble, Manisha Sonawane

Author: Rupali Borole

Paper ID: V5I1-1433

Paper Status: published

Published: February 28, 2019

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