Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis

Network security is a crucial concern when it comes to computation, concerns like threats can have high consequences, and critical information will be shared with unauthorized persons. This paper presents a detailed survey on Fifth Generation (5G) and security aspect. This is more predictable since the core technology; the synonymous approach is possible with Fifth Generation (5G) and Beyond Technologies though with limited access. Many incidents have shown that the possibility of a hacked wireless network, not just impacts privacy and security worries, but also hinders the diverse dynamics of the ecosystem. Security attacks have grown in frequency and severity throughout the near past, making detection mechanisms harder.

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“…The attackers were finding it difficult to understand the complex mobile protocols. This barrier in 4G has been eased by the IP centre [52].…”

Section: G Security and Risk Environmentmentioning

confidence: 99%

Overview of 5G & Beyond Security

Shokoor

Shafik

Matinkhah

2022

EAI Endorsed Trans IoT

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“…Now the G-IoT needs some technological solutions to manage waste and use it for energy production. In using the G-IoT concept, many countries, e.g., China, are producing energy, and the most developed countries are using their waste for many other purposes as well, in addition to producing several resources as alternatives to natural resources [26][27][28][29][30][31][32][33][34][35]. is strategy has been realized through the adoption of G-IoT concepts and innovative technologies such as sensors, smart waste collection, and waste burning systems.…”

Section: Reduction Of Hazardous Gas Emissionsmentioning

confidence: 99%

Green Internet of Things and Big Data Application in Smart Cities Development

et al. 2021

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This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G-IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

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“…These systems leverage advanced machine learning (ML) and statistical techniques to analyze vast amounts of biomedical data, including patient records, medical images, genetic information, and clinical measurements (Ahmad Mir et al, 2020). By integrating and analyzing this diverse data, biomedical prediction systems can identify patterns, detect early signs of diseases, predict patient outcomes, and support evidence-based decision-making by healthcare professionals (Shafik et al, 2020). These systems have the potential to improve patient care, optimize treatment strategies, reduce healthcare costs, and ultimately save lives .…”

Section: Introductionmentioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

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Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis

Cited by 16 publications

References 29 publications

Overview of 5G & Beyond Security

Overview of 5G & Beyond Security

Green Internet of Things and Big Data Application in Smart Cities Development

Trends in using deep learning algorithms in biomedical prediction systems

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