Tourist hot spots prediction model based on optimized neural network algorithm

Huang, Xiaofei; Jagota, Vishal; Espinoza-Muñoz, Einer; Flores-Albornoz, Judith

doi:10.1007/s13198-021-01226-4

Cited by 31 publications

(17 citation statements)

References 37 publications

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“…In other words [ 10 ], by utilizing blockchain technology's inherent security, once transaction information has been recorded and updated, they can no longer be changed or deleted. The distributed data ledger can support exceptional immutability and data security even though it is shared across all nodes in the cloud [ 17 – 19 ]. Because of the employment of cryptographic algorithms in the blockchain blocks, the privacy of the data is more likely to be protected.…”

Section: Introductionmentioning

confidence: 99%

Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Jawarneh¹,

Alshar’e²,

Bhargava

et al. 2022

Computational and Mathematical Methods in Medicine

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Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud’s security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers’ interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people’s knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.

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Section: Introductionmentioning

confidence: 99%

Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Jawarneh¹,

Alshar’e²,

Bhargava

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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“…Therefore, the noncarcinogens are Cd, Cr, Pb, Zn, Ni, Cu, and Fe. Heavy metal pollutants in the surface water of the mining area enter into the human body mainly through direct contact, drinking water, and food intake, which are very important ways [ 24 ]. As there is no water environment health risk assessment model in China, the water environment health risk assessment model recommended by the United States Environmental Protection Agency (USEPA) is selected to calculate the risk value of heavy metals in the water body of the study area, and the risk value of heavy metals in the water body of the study area is assessed according to the technical guidelines for environmental impact assessment issued by the Ministry of environmental protection of China.…”

Section: Methodsmentioning

confidence: 99%

Environmental Health Risk Evaluation Model for Coastal Chemical Industry

Zhao

Zhang

Niu

et al. 2021

Journal of Healthcare Engineering

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There are numerous uncertainties associated with environmental health risk assessment (EHRA), and it is unavoidable to apply the best models and information available to save human lives. The EHRA is a method for determining the type and likelihood of adverse health effects on people who are exposed to chemicals in the workplace. To address the environmental health problems caused by harmful gas leakage and water pollution generated by the coastal regional chemical industry, a novel EHRA model for the coastal chemical industry has been developed. The premise of the Gauss plume diffusion model is used to define the model’s parameters and the evaluation criterion for harmful gas concentration health risk. The EHRA model is evaluated against the leakage of harmful gases and consists of three steps. The first step is to identify the threat posed by the chemical industry in the coastal region; the second step is to quantify the risk; the third step is to develop a model for assessing water-related environmental health risk. The water-related environmental health assessor analyzes the pollutant variables and parameters of the assessment model to estimate the health risk caused by dangerous compounds in the water, using the assessment model of chemical carcinogen health risk and noncarcinogen health risk Type B. The experiments’ findings suggest that the model can effectively assess the dangers to human health from hazardous gases and heavy metals in the water bodies of chemical factories in coastal communities.

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“…Well with the growing number of machine learning techniques in health care, we can see a future in which data, analysis, and invention collaborate to help countless individuals without their knowledge. Soon, ML-based applications packed with real-time patient data from many healthcare systems across several nations will be ubiquitous, increasing the efficacy of hitherto inaccessible treatment options [ 10 , 11 ]. Machine algorithm learning is discovered by John McCarthy, and the division of machine learning is discovered by Arthur Samuel.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Healthcare System for Quality of Service

Bordoloi

Singh

Sanober

et al. 2022

Journal of Healthcare Engineering

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Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

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Tourist hot spots prediction model based on optimized neural network algorithm

Cited by 31 publications

References 37 publications

Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Environmental Health Risk Evaluation Model for Coastal Chemical Industry

Deep Learning in Healthcare System for Quality of Service

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