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.

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.

Background and Objective: 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 ...

The best customer refers to the potential interaction of customers with the company during certain time periods. When companies understand the best customer and realize that the best customer can provide customized services for different customers, then they will achieve effective customer relationship management. This research is focused on the banking industry and systematically integrates data mining techniques and management topics to analyze the best customers. This study first uses the fuzzy hierarchical analysis method to weight the existing variables and then examines the DFMT model as an input to the k-means technique for clustering customers based on the desired criteria in the DFMT model. By using the proposed scoring model, it starts forming a customer value pyramid and categorizes customers into 4 value spectrums. Finally, in order to analyze the classes obtained from the customer value pyramid and implement the learning process from the available data, it uses the tenor classification techniques of decision tree, support vector machines and random forest along with the six characteristics and among They introduce the most appropriate model-characteristic based on available criteria.