A novel energy efficient model of smart dustbin for waste segregation

Sadana, Jass; Aggarwal, Kunal; Jindal, Prachi; Verma, Devansh; Antony, Saji M.; Parikh, Jolly

doi:10.1007/s41870-024-01783-4

Int. j. inf. tecnol.

2024

DOI: 10.1007/s41870-024-01783-4

|View full text |Cite

A novel energy efficient model of smart dustbin for waste segregation

Jass Sadana,

Kunal Aggarwal,

Prachi Jindal

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

William,

Patil,

Panda

et al. 2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

William,

Patil,

Panda

et al. 2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

A secured deep learning based smart home automation system

Sanjay,

Jahnavi,

Karanth

2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A novel energy efficient model of smart dustbin for waste segregation

Cited by 2 publications

References 18 publications

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

A secured deep learning based smart home automation system

Contact Info

Product

Resources

About