Home automation using IoT and a chatbot using natural language processing

2017 ◽  
Author(s):  
Cyril Joe Baby ◽  
Faizan Ayyub Khan ◽  
J. N. Swathi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Home Automation

scholarly journals Desktop Voice Assistant Using Natural Language Processing (NLP)

2020 ◽  
Vol 6 (12) ◽  
pp. 332-335
Author(s):  
Lalit Kumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cloud Services ◽  
Home Automation ◽  
The People ◽  
The Future ◽  
The Voice ◽  
The Way

Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. the voice assistant was first introduced on smartphones and after the popularity it got. It was widely accepted by all. Initially, the voice assistant was mostly being used in smartphones and laptops but now it is also coming as home automation and smart speakers. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.

scholarly journals Home Automation Using Chatbot and Voice Assistant

2020 ◽  
Vol 32 ◽  
pp. 01002
Author(s):  
Bhavyasri Kadali ◽  
Neha Prasad ◽  
Pranaya Kudav ◽  
Manoj Deshpande
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Raspberry Pi ◽  
Automation System ◽  
Home Automation ◽  
Voice Input ◽  
Text Input ◽  
Automation Systems ◽  
Home Automation System

In a world with ever increasing needs for comfort, human race is relying more and more on technological advancements to find solutions to their problems. Home Automation Systems have become a go-to arena in the recent years. In the following paper, we propose a Home Automation system that uses a wholesome blending of some technologies like Internet of Things, Natural Language Processing and Machine Learning. The prime feature of this system is that, it provides two modes of communication to the user : Text and Voice. The text input from the user will be given via a Chatbot Application and the voice input from the user will be given via a voice assistant. The input will undergo Natural Language Processing to find the action that the user wants the system to perform. The IoT component, Raspberry Pi would perform the actuations in the form of switching On or Off of Lights and Fans of a room in the house.

scholarly journals Voice Controlled Home Automation Using AI and NLP

2021 ◽  
pp. 195-197
Author(s):  
P. Navaraja ◽  
P. Kishore ◽  
S. Dineshkumar ◽  
R. Karthick ◽  
C. Kavinkumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Disabled Persons ◽  
Vital Role ◽  
Primary Objective ◽  
User Preferences ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Home Automation

The aim of home automation is to make our lives easier and to improve the quality of life. The concept of Smart Homes builds on the progressing maturity of areas such as Artificial Intelligence and Natural Language Processing. Here, natural language processing (NLP) plays a vital role since it acts as an interface between human interaction and machines. Through NLP users can either command or control devices at home even though disabled persons command or request varies from presets. An application area of AI is Natural Language Processing (NLP). Voice assistants incorporate AI using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporate them in households nowadays. Our project aims at providing a fully automated voice based solution that our users can rely on, to perform more than just switching on/off the appliances. The user sends a command through speech to the mobile device, which interprets the message and sends the appropriate command to the specific appliance. The primary objective is to construct a useful voice-based system that utilizes AI and NLP to control all domestic applications and services and also learn the user preferences over time using machine learning algorithms.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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