Nowadays, environmental protection involves many issues and problems, among which the waste generated by various human activities makes up a significant share, which is becoming newer day by day. Moreover, the production of normal, industrial, special, hospital, and agricultural waste and improper management of these materials has created many health, safety, and environmental problems. Based on this approach, this research study aims to determine the model of waste management and energy efficiency in smart homes using the Internet of Things (IoT). The research method used by this study is estimative-computational. For this purpose, the required data were collected using a computational approach. For this purpose, the required views and data were collected through experts in this field and calculated in MATLAB and STATA software. The data analysis tool was represented by fuzzy calculations and for this purpose MATLAB software was used. The study revealed that energy costs in smart homes using the IoT technology are impressive. The number of home residents in smart homes using the IoT is impressive. Home area in smart homes using the innovative technology of IoT is also impressive.
Background and Aim: In general, examining and prioritizing the factors influencing the increase of the new COVID-19 virus is essential for the survival of the world. The purpose of this study is to rank the effective factors (12 identified factors) on the increase of Coronavirus from the perspective of the people of Alborz province, in the second half of 2020, in this province.
Materials and Methods:This study was conducted using the Friedman test. In this applied research, A questionnaire was used, which was randomly distributed among 402 people (sample size using was derived using the Cochran's formula).
Results:In the forthcoming research, three fundamental questions were answered: 1) what are the factors influencing the increase in the number of patients with COVID-19 disease in Alborz province? This was done studying the literature and announcements of the Central Headquarters for Corona and library studies and "12" factors were identified, (other factors may also be involved), 2) considering the above factors, which factor is the first priority? 3) what are the basic solutions to reduce the number of patients.
Conclusion:Based on the results, the increase in the number of people infected with the Corona virus is affected by 12 factors from the perspective of the people of Alborz province. Regarding the importance of each of these "12" factors, the most effective factor, was identified as insufficiency of personal hygiene. The results of this research can help with monitoring, reducing the number of people who are infected, and creating and maintaining better conditions in this province and other parts of the country, and even other countries.
Today, the importance of customer relationship is not hidden from anyone, and predicting the value of customer life can
help organizations to create an optimal relationship with their customers. The concept of industrial society represents a
symbiosis between social and industrial activities using mass-production technologies. A sustainable CRM approach
can generate significant benefits for the development of the textile industry. This paper compares ARIMA and neural
network models in predicting customer lifetime value. The time-domain of the research is related to the year 2021 in the
Lojoor company. To identify the variables needed to predict the value of customer longevity, experts in this field and
university professors were used through descriptive survey method and using databases to collect other data. After
collecting the data, the required variables were first identified by the Delphi method and then the databases were
analysed using the artificial neural network method and the ARIMA model, for which MATLAB software was used. The
results showed that both ARIMA and artificial neural network models can be used to predict customer lifetime value. In
the case of the artificial neural network, it was observed that in addition to better prediction of the relationship between
variables, which assumes them to be nonlinear, the artificial neural network model also performed better in terms of
prediction results. In total, the values of MAPE error are 10.3% and MSE error is 11.6% for the neural network model.
The neural network model is acceptable.
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