scholarly journals Data Mining in Employee Healthcare Detection Using Intelligence Techniques for Industry Development

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Abolfazl Mehbodniya ◽  
Ihtiram Raza Khan ◽  
Sudeshna Chakraborty ◽  
M. Karthik ◽  
Kamakshi Mehta ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Acquisition ◽  
Well Being ◽  
Ensemble Classifier ◽  
Healthcare Sector ◽  
Mining Machine ◽  
Computational Statistics ◽  
Whole Process ◽  
Performance Results ◽  
Five Phases

Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

scholarly journals Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment

2019 ◽  
Vol 8 (2) ◽  
pp. 76 ◽  
Author(s):  
Ahmad Saleem Nouman ◽  
Ata Chokhachian ◽  
Daniele Santucci ◽  
Thomas Auer
Keyword(s):  
Data Mining ◽  
Data Acquisition ◽  
Well Being ◽  
Environmental Data ◽  
Urban Spaces ◽  
Reasonable Accuracy ◽  
Climate Monitoring ◽  
Outdoor Comfort ◽  
Environmental Sensor ◽  
The Relationship

Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is a fundamental tool to evaluate transient outdoor comfort. This study explains the prototyping and validation of a mobile environmental sensor kit. The results show the prototype has reasonable accuracy despite its affordability with respect to industrial sensors.

Policy Review: Dubai Health Authority’s COVID-19 Rapid Response

2020 ◽  
Vol 5 (Special) ◽  
Keyword(s):  
United Arab Emirates ◽  
Well Being ◽  
Healthcare Services ◽  
Rapid Response ◽  
Policy Implications ◽  
World Health ◽  
Healthcare Sector ◽  
Services Delivery ◽  
First Case ◽  
The World

Dubai Health Authority (DHA) is the entity regulating the healthcare sector in the Emirate of Dubai, ensuring high quality and safe healthcare services delivery to the population. The World Health Organization (WHO) declared COVID-19 a pandemic on the 11th of March 2020, indicating to the world that further infection spread is very likely, and alerting countries that they should be ready for possible widespread community transmission. The first case of COVID-19 in the United Arab Emirates was confirmed on 29th of January 2020; since then, the number of cases has continued to grow exponentially. As of 8th of July 2020 (end of the day), 53,045 cases of coronavirus have been confirmed with a death toll of 327 cases. The UAE has conducted over 3,720,000 COVID-19 tests among UAE citizens and residents over the past four months, in line with the government’s plans to strengthen virus screening to contain the spread of COVID-19. There were vital UAE policies, laws, regulations, and decrees that have been announced for immediate implementation to limit the spread of COVID- 19, to prevent panic and to ensure the overall food, nutrition, and well-being are provided. The UAE is amongst the World’s Top 10 for COVID-19 Treatment Efficiency and in the World’s Top 20 for the implementation of COVID-19 Safety measures. The UAE’s mission is to work towards resuming life after COVID-19 and enter into the recovery phases. This policy research paper will discuss the Dubai Health Authority’s rapid response initiatives towards combating the control and spread of COVID-19 and future policy implications and recommendations. The underlying factors and policy options will be discussed in terms of governance, finance, and delivery.

scholarly journals The Healthcare Sector Employer’s Duty of Care: Implications for Worker Well-Being

2021 ◽  
Vol 18 (11) ◽  
pp. 6015
Author(s):  
Melissa McDiarmid ◽  
Marian Condon ◽  
Joanna Gaitens
Keyword(s):  
Middle East ◽  
Healthcare Workers ◽  
Care Program ◽  
Well Being ◽  
Middle East Respiratory Syndrome ◽  
Worker Safety ◽  
Healthcare Sector ◽  
Duty Of Care ◽  
Protective Equipment ◽  
Extreme Risk

Pandemic diseases of this century have differentially targeted healthcare workers globally. These infections include Severe Acute Respiratory Syndrome SARS, the Middle East respiratory syndrome coronavirus Middle East respiratory syndrome coronavirus (MERS-CoV) and Ebola. The COVID-19 pandemic has continued this pattern, putting healthcare workers at extreme risk. Just as healthcare workers have historically been committed to the service of their patients, providing needed care, termed their “duty of care”, so too do healthcare employers have a similar ethical duty to provide care toward their employees arising from historical common law requirements. This paper reports on results of a narrative review performed to assess COVID-19 exposure and disease development in healthcare workers as a function of employer duty of care program elements adopted in the workplace. Significant duty of care deficiencies reported early in the pandemic most commonly involved lack of personal protective equipment (PPE) availability. Beyond worker safety, we also provide evidence that an additional benefit of employer duty of care actions is a greater sense of employee well-being, thus aiding in the prevention of healthcare worker burnout.

Quality Monitoring System for Seismic While Drilling

2013 ◽  
Vol 318 ◽  
pp. 572-575
Author(s):  
Li Li Yu ◽  
Yu Hong Li ◽  
Ai Feng Wang
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Signal Analysis ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Quality Of Data ◽  
Whole Process ◽  
Engineering Parameters ◽  
Network Status

In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals Afrontamiento de problemas de salud en personas muy mayores

2015 ◽  
Vol 31 (3) ◽  
pp. 1008 ◽  
Author(s):  
Ana B. Navarro ◽  
Belén Bueno
Keyword(s):  
Old Age ◽  
Self Efficacy ◽  
Satisfaction With Life ◽  
Well Being ◽  
Health Problems ◽  
Old People ◽  
Very Old ◽  
Degree Of Satisfaction ◽  
The One ◽  
Performance Results

<p>This paper assesses the strategies for coping with health problems in advanced old age and their contribution in terms of several performance results. 159 people aged 75 or over and living at home identified their most recent health problem, the strategies used to deal with it, their perception of self-efficacy in handling the problem and their degree of satisfaction with life. The results confirm the use of a range of strategies, with the active-behavioural approach to solving the problem being the one most widely used. In addition, together with active coping strategies of both a cognitive and behavioural nature, correlational analyses indicate that very old people resort to passive and avoidance coping methods. Furthermore, multiple regression analyses highlight the fact that the use of direct and rational actions for solving health problems predicts self-efficacy in dealing with the problem and protects satisfaction with life at this stage. These results confirm that very old people retain the ability to deal effectively with their health problems and, at the same time, uphold their well-being, providing evidence of the adaptive role of coping in very old age.</p>

Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

2015 ◽  
Vol 639 ◽  
pp. 21-30 ◽  
Author(s):  
Stephan Purr ◽  
Josef Meinhardt ◽  
Arnulf Lipp ◽  
Axel Werner ◽  
Martin Ostermair ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Acquisition ◽  
Data Driven ◽  
Quality Analysis ◽  
Process Conditions ◽  
Data Sets ◽  
Body Parts ◽  
Car Body ◽  
Sample Data ◽  
Mining Methods

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

scholarly journals Compiling and analyzing a tagged learner corpus: a corpus-based study of adjective uses

2020 ◽  
Vol 81 ◽  
pp. 115-136
Author(s):  
Cristina Castillo Rodríguez ◽  
José María Díaz Lage ◽  
Beatriz Rubio Martínez
Keyword(s):  
Rich Source ◽  
Postgraduate Students ◽  
Learner Corpus ◽  
Whole Process ◽  
Five Phases ◽  
Source Of Information

A learner corpus (LC) is widely known as a rich source of information regarding the use of expressions and the errors made by students in their productions. In fact, we, as teachers, can profit from the compilation of their tasks so as to analyze in detail their way of writing. However, the mere compilation of texts does not guarantee a successful exploitation, as more steps than saving texts must be involved in the whole process. Therefore, it seems essential to follow a protocolized methodology of compilation. In this paper we propose five phases for compiling a LC containing texts from the spontaneous written productions from undergraduate and postgraduate students. The outcomes thrown with the LC exploitation will reveal the errors in students’ productions regarding the use of plural, comparative and superlative in adjectives and also other fails detected in the tagging phase, most of which are due to students’ misuses.

scholarly journals Rituals in Traditional Chinese Medicine: Taking a Case of Infertility as an Example

2022 ◽  
pp. 9-12
Author(s):  
Jian Liang ◽  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Practice ◽  
Chinese Herb ◽  
Well Being ◽  
Close Connection ◽  
Research Topics ◽  
Whole Process ◽  
Traditional Chinese Herb ◽  
The World

Ritual is one of the most classic research topics in the field of Anthropology, and rituals have close connection with medial practice. However, the research on this topic from the experience of Traditional Chinese Medicine is limited. This paper presents the whole story that a patient suffering from infertility got cured got cured by a doctor of Traditional Chinese Medicine(TCM) and finally became a mother. With the detailed description of each medical practice, including pulse-taking, traditional Chinese herb therapy, and postpartum confinement, this paper analyzes the ritualized elements in the whole process, interprets how ritual play a role in the practice of TCM, and points out ritual’s essential significance in contributing to human’s well being and adjusting the relationships between individual and the world.

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