Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Himeur, Yassine; Al‐Maadeed, Somaya; Almadeed, Noor; Abualsaud, Khalid; Mohamed, Amr; Khattab, Tamer; Elharrouss, Omar

doi:10.1016/j.scs.2022.104064

Cited by 18 publications

(8 citation statements)

References 145 publications

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“…Computing technologies, such as AI, wireless communication, the blockchain, and computer vision, enable social distancing, 5,6 and there has been considerable interest in using AI algorithms to calculate distances between people from optical images. Himeur et al 7 classified the tools in existing social distancing monitoring frameworks into "visual" and "nonvisual." Visual-based social distancing includes analysis of media streams using hand-crafted feature-based, CNN-based, transfer learning-based, and 3D-based algorithms.…”

Section: Related Workmentioning

confidence: 99%

Fighting Pandemics With Augmented Reality and Smart Sensing-Based Social Distancing

Cao

Mehmood

Liu

et al. 2023

IEEE Comput. Grap. Appl.

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Section: Related Workmentioning

confidence: 99%

Fighting Pandemics With Augmented Reality and Smart Sensing-Based Social Distancing

Cao

Mehmood

Liu

et al. 2023

IEEE Comput. Grap. Appl.

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“…Its main goals are to increase public safety, stop possible threats, and facilitate speedy action in emergency situations. The CSS make use of a variety of surveillance technologies, including video cameras, sensors, and data analytics, to give real-time situational awareness, allow proactive security measures, and assist crowd management decisionmaking processes [3].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Bhat,

Paul,

Sahu

et al. 2024

Eng. Res. Express

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Recent global efforts to create sustainable smart cities have significantly transformed society and improved the lives of people. Nowadays, crowd surveillance (CS) has become essential in sustainable smart cities and society to protect public safety and security. In this regard, the face-based human detection system has received considerable attention because it is recognized as an emerging method in crowd surveillance applications. Thus, in this work, a new method for real-time identification of people for a crowd surveillance system (CSS) that uses facial and speech recognition technology has been introduced. In traditional CS systems, human operators are frequently used by crowd surveillance systems to watch and evaluate video feeds. Human error and operator weariness may result in lost opportunities or slow replies, which reduce the system's efficacy. Certain procedures, including the initial identification and monitoring of people in video feeds, can be automated using a voice-activated system. To address the issues with the present CSS, a new framework Voice-Activated Face Recognition (VAFR) is proposed in this work. The proposed framework combines the speech and face recognition models for crowd surveillance. Experimental and simulation studies have been performed to analyze the performance of the proposed VAFR framework. The proposed framework uses the Viola-Jones algorithm for face identification and the Conformer architecture for speech analysis, reaching a noteworthy 99.7% accuracy rate in live video feeds. In addition, the ethical and safety aspect of the proposed VAFR system is presented.

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“…Studying security, risk, and trust on healthcare, AI adoption shows that trust is crucial. Oncology's leading killer affects several organs [3,4]. Thyroid carcinoma is a prevalent endocrine malignancy worldwide.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

Raj

2024

IJACSA

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Thyroid cancer, a prevalent endocrine malignancy, necessitates advanced diagnostic techniques for accurate and early detection. This study introduces an innovative approach that integrates hybrid Machine Learning (ML) algorithms with metabolomics, offering a novel pathway in thyroid cancer diagnostics. Our methodology employs a range of hybrid ML models, combining the strengths of various algorithms to analyze complex metabolomic data effectively. These models include ensemble methods, neural network-based hybrids, and integrations of unsupervised and supervised learning techniques, tailored to decipher the intricate patterns within metabolic profiles associated with thyroid cancer. The study demonstrates how these hybrid ML algorithms can efficiently process and interpret metabolomic data, leading to enhanced diagnostic accuracy. By leveraging the distinct characteristics of each ML model, our approach not only improves the detection of thyroid cancer but also contributes to a deeper understanding of its metabolic underpinnings. The findings of this study pave the way for more personalized and precise medical interventions in thyroid cancer management, showcasing the potential of hybrid ML models in revolutionizing cancer diagnostics. Our system analyzes thyroid cancer metabolomic data using ensemble methods, neural network-based hybrids, and unsupervised and supervised learning integrations. The research shows hybrid ML models may revolutionize cancer diagnoses by improving accuracy. LSTM+CNN, LSTM+GRU, and CNN+GRU have high accuracy rates, helping us comprehend thyroid cancer's biochemical roots. Hybrid ML models enhance thyroid cancer diagnosis and management, enabling more tailored and accurate medical treatments. The hybrid machine learning models like LSTM+CNN, LSTM+GRU, and CNN+GRU beat CNN, VGG-19, Inception-ResNet-v2, decision support, and random forests (99.45%).

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Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Cited by 18 publications

References 145 publications

Fighting Pandemics With Augmented Reality and Smart Sensing-Based Social Distancing

Fighting Pandemics With Augmented Reality and Smart Sensing-Based Social Distancing

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

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