A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Rehman, Mujeeb Ur; Shafique, Arslan; Ghadi, Yazeed Yasin; Boulila, ‪Wadii; Jan, Sana Ullah; Gadekallu, Thippa Reddy; Driss, Maha; Ahmad, Jawad

doi:10.1109/tnse.2022.3199235

Cited by 57 publications

(24 citation statements)

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“…This will help define the best configuration and video streaming parameters to optimize the system accuracy, reliability, and robustness. The second is securing the identity and the privacy of license plates over multiple parts of the system [ 55 , 56 , 57 ]. The third is protecting the ANPR system from forgery.…”

Section: Discussionmentioning

confidence: 99%

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference

Ammar

Koubâa

Boulila

et al. 2023

Sensors

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Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates’ Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models’ accuracy by taking advantage of the temporally redundant information of the video stream’s frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.

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Section: Discussionmentioning

confidence: 99%

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference

Ammar

Koubâa

Boulila

et al. 2023

Sensors

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“…The challenges with the reliability of healthcare data and the constraints of predictive modelling are causing data mining efforts to fail. As Big Data has gained interest in recent years, involvement in the implementation of data mining techniques and methods to analyze healthcare data has emerged [28][29][30][31].…”

Section: Challenges Of Data Mining In Healthcarementioning

confidence: 99%

Data mining in predicting liver patients using classification model

2022

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PurposeThis study proposes to identify potential liver patients based on the results of a liver function test performed during a health screening to search for signs of liver disease. It is critical to detect a liver patient at an early stage in order to treat them effectively. A liver function test's level of specific enzymes and proteins in the blood is evaluated to determine if a patient has liver disease. Methods According to a review of the literature, general practitioners (GPs) rarely investigate any anomalies in liver function tests to the level indicated by national standards. The authors have used data pre-processing in this work. The collection has 30691 records with 11 attributes. The classification model is utilized to construct an effective prediction system to aid general practitioners in identifying a liver patient using data mining. ResultsThe collected results indicate that both the Naïve Bayes and C4.5 Decision Tree models give accurate predictions. However, given the C4.5 model offers more accurate predictions than the Naïve Bayes model, it can be assumed that the C4.5 model is superior for this research. Consequently, the liver patient prediction system will be developed using the rules given by the C4.5 Decision Tree model in order to predict the patient class. The training set, suggested data mining with a classification model achieved 99.36% accuracy and on the testing set, 98.40% accuracy. On the training set, the enhanced accuracy relative to the current system was 29.5, while on the test set, it was 28.73. In compared to state-of-the-art models, the proposed approach yields satisfactory outcomes. ConclusionThe proposed technique offers a variety of data visualization and user interface options, and this type of platform can be used as an early diagnosis tool for liver-related disorders in the healthcare sector. This study suggests a machine learning-based technique for predicting liver disease. The framework includes a user interface via which healthcare providers can enter patient information.

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“…Their primary preferences include development of digitally enabled, ondemand and seamless patient-clinician interaction ensuring delivery of patient-centric services across geographical boundaries and socio-economic clusters [10]. Each individual patients' health journey stands unique and it is imperative to acknowledge this fact, thereby customize the specific services and elevate each interaction to the level of personalized healthcare experience [11]. The need to deploy advanced digital tools and services has become a necessity in order to provide optimized consumer satisfaction, enabling tracking, monitoring of health status, and improved adherence to medications.…”

Section: Introductionmentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

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A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Abstract: Research at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form.

Cited by 57 publications

References 95 publications

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference

Data mining in predicting liver patients using classification model

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

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