Smart Cities: Emerging technologies and Potential solutions to the Cyber security threads

Nafrees, Abdul Cader Mohamed; Sujah, Ameer Mohamed Aslam; Mansoor, C. M. M.

doi:10.1109/iceeccot52851.2021.9707994

Cited by 10 publications

(3 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of metrics for evaluating the ML models was guided by the nature of the problem and the requirements of the system. Accuracy, loss, IOU (Intersection over Union), precision, recall, and F1 score were chosen as they provide a comprehensive view of the model's performance 6 . Accuracy measures the proportion of correct predictions, while loss quantifies the difference between the predicted and actual values.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

“…Email: {s.subramanianmurugesan, s.velu}@se22.qmul.ac.uk, {m.golec, s.s.gill}@qmul.ac.uk H. Wu is with the Center for Applied Mathematics, Tianjin University, Tianjin, China. Email: whming@tju.edu.cn (Corresponding author: Huaming Wu) innovations allow for real-time monitoring of patients' health conditions and the seamless transmission of medical data to healthcare providers [6], enabling more timely and accurate diagnoses and treatments [7]. TB is a contagious ailment primarily impacting the lungs, resulting from specific bacteria.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing

Murugesan,

Velu,

Golec

et al. 2024

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serverless based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

show abstract

Section: A Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing

Murugesan,

Velu,

Golec

et al. 2024

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Smart Trasportation system can be developed through various emerging technologies such as IoT, Big Data, and many more [1], [2]. The VANETs are a subset of MANETs that refer to a group of smart automobiles on the road.…”

Section: Introductionmentioning

confidence: 99%

Security issues of Vehicular Ad Hoc Networks (VANET): A Systematic Review

Asra

2022

TIERS

View full text Add to dashboard Cite

Vehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks (MANETs) that refer to a group of connected cars. The various networking layers in common Internet protocol stack topologies are linked to security vulnerabilities in VANETs. A security breach in a VANET is typically serious and harmful. Also Current VANET security standards handle the bulk of the security issues that vehicle networks confront. This paper presents VANET’s different types of security attacks in a systematic review approach. The information was gathered by a systematic examination of existing research articles. Furthermore, the study's shortcomings were the dataset's size and the absence of quality characteristics.

show abstract

Mathematical Treatment for Real-Time Vehicle Recognition Using Traditional Road Surveillance Cameras

Wang,

Atkison

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Smart Cities: Emerging technologies and Potential solutions to the Cyber security threads

Cited by 10 publications

References 92 publications

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing

Security issues of Vehicular Ad Hoc Networks (VANET): A Systematic Review

Mathematical Treatment for Real-Time Vehicle Recognition Using Traditional Road Surveillance Cameras

Contact Info

Product

Resources

About