Nanosensors for smart cities: an introduction

Nazari, Atefeh

doi:10.1016/b978-0-12-819870-4.00001-3

Cited by 10 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach relies on accurately identifying features such as lane markings, spots, and other distinct features of the road to accurately identify and navigate around them. Nazari et al [37] discussed that Deep learning has become an essential technology for the future of smart cities. It can be used to develop algorithms to solve complex problems such as traffic optimization, energy management, water quality management, and public safety.…”

Section: Related Workmentioning

confidence: 99%

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Ismaeel,

Janardhanan,

Sankar

et al. 2023

Sustainability

View full text Add to dashboard Cite

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Ismaeel,

Janardhanan,

Sankar

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Fundamentally, the definition of nanotechnology is the skilful arrangement of functionally effective systems at the level of molecules. The actual meaning of the nanotechnology refers to the capability to build functional objects using the bottom-up build approach, making use of methods which are being developed today thus enabling complete highly efficient, high power and performing endproducts [2].…”

Section: Introductionmentioning

confidence: 99%

New Insights on Molecular Communication in Nano Communication Networks and their Applications

Koshy¹,

Jeerapan²,

Manjunatha³

2022

ECS Trans.

View full text Add to dashboard Cite

The advancement in nanotechnology has carved out a way to establish a powerful base to fall on for implementation of nanoscale machines having the capability to carry out significant yet trivial tasks. This impetus has led the researchers to continually bring up various solutions for implementing and executing nanoscale communications taking into consideration of biological as well as electromagnetic communication. The primary aims are expanding the abilities of nanoscale devices to integrated multiple composite tasks by reciprocated coordination, which can be carried out through this communication type. This paper describes the current research developments in nano communication networks, focusing on molecular communication (MC). MC makes use of molecules as an information transporter between nanoscale machines. Molecules help in enciphering messages to send between a sender and a receiver. MC has, thereby, become an inseparable part of the IoNT (Internet of Nano-Things) as a communication model among nanoscale machines. This paper also gives a description of the design of MC systems and various channelling models used to address the various challenges and opportunities in this new upcoming and developing field. In addition, this review article is also inclusive of an investigative survey on the recent developments in this field.

show abstract

Nanoproducts and Legal Aspects of Consumer Protections: An Evaluation

Karim¹

2021

Handbook of Consumer Nanoproducts

View full text Add to dashboard Cite

Nanosensors for smart cities: an introduction

Cited by 10 publications

References 36 publications

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

New Insights on Molecular Communication in Nano Communication Networks and their Applications

Nanoproducts and Legal Aspects of Consumer Protections: An Evaluation

Contact Info

Product

Resources

About