Deep learning model for home automation and energy reduction in a smart home environment platform

2018 ◽  
Vol 31 (5) ◽  
pp. 1317-1337 ◽  
Author(s):  
Dan Popa ◽  
Florin Pop ◽  
Cristina Serbanescu ◽  
Aniello Castiglione
Keyword(s):  
Deep Learning ◽  
Home Environment ◽  
Smart Home ◽  
Learning Model ◽  
Energy Reduction ◽  
Home Automation ◽  
Smart Home Environment ◽  
Deep Learning Model

scholarly journals Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

2022 ◽  
Vol 2022 ◽  
pp. 1-22
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu ◽  
Olaide N. Oyelade ◽  
Mubarak S. Almutairi
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Learning Model ◽  
The Internet ◽  
Automation System ◽  
Home Automation ◽  
Home Security ◽  
Automation Systems ◽  
Home Automation System ◽  
Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

scholarly journals Smart Home Environment Future Challenges and Issues - A Survey

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Sathesh ◽  
Yasir Babiker Hamdan
Keyword(s):  
Energy Management ◽  
Home Environment ◽  
Smart Home ◽  
Automation System ◽  
Home Automation ◽  
Negative Effects ◽  
Research Article ◽  
Home Energy Management ◽  
Smart Home Environment ◽  
Home Automation System

The smart home automation is that the exploitation internet enabled devices remotely and mechanically management appliances such as lighting, heating system and security measures in and around your home. This papers talks about relative emission effects in Home Energy Management. Also the result outcome is that consumption of the electricity will be reduced towards green environment. Moreover, the research paper is considering the analysis of calculate the negative effects in environment due to full home automation system. While calculating these negative effects, the Life Cycle Assessment (LCA) should be in sum total. This study uses to analysis the electricity consumption for environment impact of Home Energy Management system (HEMs). The research article discusses home automation system consumes the energy for different devices connected for smart home. The maximum energy consumption in smart home network is smart plugs due to an uninterrupted supply. Therefore this research article comprises about home automation energy management that shows the balance energy consumption between the devices in a regular interval. Also this research article provides a future challenge tasks in security issues in smart home environment. Also the perception for smart home environment focuses the Interoperability, Reliability, Integration of smart homes and term privacy in context, term security and privacy vulnerabilities to smart home.

IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

2019 ◽  
Vol 6 (5) ◽  
pp. 8553-8562 ◽  
Author(s):  
Valentina Bianchi ◽  
Marco Bassoli ◽  
Gianfranco Lombardo ◽  
Paolo Fornacciari ◽  
Monica Mordonini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Home Environment ◽  
Smart Home ◽  
Integrated Approach ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Smart Home Environment

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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