علی رجب زاده قطری

This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended to address the two main categories of challenges–operation management and data management–through three intelligent modules across the pandemic stages. In the pre-hospital stage, an intelligent infection prediction system is proposed that utilizes in-house data to address the lack of a simple, efficient, agile, and low-cost screening method for identifying potentially infected individuals promptly and preventing the overload of patients entering hospitals. In the in-hospital stage, an intelligent prediction system is proposed to predict infection severity and hospital Length of Stay (LoS) to identify high-risk patients, prioritize them for receiving care services, and facilitate better resource allocation. In the post-hospital stage, an intelligent prediction system is proposed to predict the reinfection and readmission rates, to help reduce the burden on the healthcare system and provide personalized care and follow-up for higher-risk patients. In addition, the distribution of limited Personal protective equipment (PPE) is made fair using private blockchain (BC) and smart contracts. These modules were developed using Python and utilized to evaluate the performance of state-of-the-art machine learning (ML) techniques through 10-fold cross-validation at each stage. The most critical features were plotted and analyzed using SHapely Adaptive exPlanations (SHAP). Finally, we explored the implications of our system for both research and practice and provided recommendations for future enhancements.

This paper aims to present the key research trends in the healthcare supply chain,critically analyze the research characteristics of these areas, define the main elements of thehealthcare supply chain and the effects of the COVID-19 pandemic on the healthcare supply chain,and present responses to overcome these impacts based on literature review.

Design/methodology/approach:

 A systematic literature review is conducted on the healthcaresupply chain research papers covering ten years of research (2010-2020) in peer-reviewed journalspublished in English. To achieve this goal, we performed a critical analysis of key research trendsin the healthcare supply chain, assessing the significant characteristics of the articles, such as issue,solution, research method, outcomes, and concerns,

We found 13 key research trends in the healthcare supply chain; healthcare supply chaincollaboration, performance measurement, and model/framework were the most significant areasaddressed in the papers reviewed in this study. The reviewed studies did not investigate therelationship between different entities in the healthcare supply chain in detail, and most hadlimitations in generalizing the findings to diverse companies/countries. The primary outcomes ofthe assessed studies were cost reductions and performance enhancements.

Originality/value: 

The analysis of the healthcare supply chain studies in diverse research trendsresulted in insights that help understand concerns in different trends and outcomes of the proposedsolutions. In addition, this study addressed the impact of the COVID-19 pandemic on thehealthcare supply chain and the responses to overcome them in particular.

This study aimed to develop IoT-based machine learning algorithms care and improvement while detecting and predicting real-time epidemics.The target disease is COVID-19 due to its importance and epidemic.The research method is based on design science. The research approach is forward-looking, so the mechanism of disease transmission and its effective characteristics enable us to make predictions about the disease and thus design disease control strategies and health care.The research was carried out in a seven-step process. IoT features were extracted in the present study with experts' opinions. The features obtained in the experiment of two different algorithms, "k nearest neighbor" and "decision tree," were created on the data to determine the best model.After selecting the best depth validation of the model were performed by confusion matrix analysis.The results of running k-nearest neighborhood and Decision Tree algorithms for the prediction of COVID-19 indicated an accuracy of > 98%. Higher sensitivity (99%) was obtained in the Decision Tree algorithm, which is very important diagnosing COVID-19 and indicates the minimum number of false negatives in the test results.

The research was conducted with the aim of analyzing innovative factors affecting customer loyalty in multi-level marketing based on business intelligence. This research in terms of purpose; It is applied and in the field of quantitative-qualitative studies which was done with survey and data-mining methods. The statistical population of the research consists of 15 senior managers and experts in the field of research, i.e. Panh-Riz cosmetics and hygiene company, were selected by snowball method to identify the components and factors, and in the second part of the data warehouse of Panh-Riz company in the period of 2013 Data mining was done until 2018, which includes 5,200,000 records. The method of data analysis is data mining and customer clustering, which was done using R software. The results of data analysis showed that 6 indicators could be considered for the model, which are: the length of customer cooperation, purchase delay, repeat purchase, high purchase amount, profitability, and discounts to customers. In general, it can be stated that the loyalty of the customers of multi-level marketing companies is in accordance with the intelligence of the business, considering five clusters of customers, it is influenced by these six indicators, and in order to improve the level of customer loyalty and achieve a competitive advantage, the company must Active in this field should be considered. Also, for the development of multi-level marketing, it is suggested that the investigated companies pay more attention to the role of discounting and credit sales to customers, and on the other hand, keep their main customers more satisfied by providing better services and more support. It should be mentioned that so far no research has been done at the country level regarding the design and development of customer loyalty management model in MLM multi-level marketing and business companies based on business intelligence (BI) and this research as a project A new study with an innovative approach to the cosmetic and health products market can be a guide for network marketing managers in achieving greater customer loyalty.

The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases. Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed. The disease studied in this study, due to its importance and prevalence, is Covid 19. In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost. The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity. The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results. Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.

Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival.

This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms.

A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics.

The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model.

Nowadays, customers in any industry are not just looking for a product and expect to receive a personalized service based on their needs and create a different experience from the organization. From another point of view, the design of services according to the customer's needs will require a careful examination of the data related to the customer in different dimensions. Therefore, knowing the customer requires a systematic approach in order to identify the goals, influencing factors, algorithms and methods suitable for this field.

The upcoming research has analyzed the dimensions of customer recognition in the banking industry and its considerations with a data-oriented approach and the application of machine learning. The research method is applied according to the purpose and is meta-Synthesis according to the collection of information. To select the articles, 43 documents published in the period of 2016-2022 were identified as relevant and valid documents by searching in the reliable databases of Web of Science and Scopus, and further, with a meta- Synthesis approach, they were studied and coded.

The results of meta-synthesis led to the identification of three main categories: 1) Customer identification objectives understanding customer insight, identifying customer risk, organizational goals, determining customer lifetime value and product management 2) Customer identification factors: demographic, financial and behavioral and 3) machine learning algorithms: Probabilistic, Neural Networks, Ensemble, Regularization, Regression, Bayesian, Decision Tree, Dimensionality Reduction, Instanced Based and Clustering.

Based on the findings of the current research, according to the purpose of customer identification, available data and selected factors, basic and combined algorithms can be a way forward, but the important point is accurate data pre-processing. Another point is that no distinction has been made between real and legal customers, and most studies have focused on real customers, which can be attributed to the complexity of the chain of financial interactions of legal customers. Also, considering the banks' lack of emphasis on branches regarding the need to complete the information contained in the account opening forms or the lack of designing a suitable service to complete the information on electronic platforms, a more accurate understanding of the customer requires a review of these processes. It is worth mentioning that in none of the studies, customer recognition was done using only demographic factors, and depending on the purpose of the study, the factors were used in combination.

The rail freight industry in Iran has been faced several challenges which affected its performance. Notwithstanding that Internet of Things leverage is rapidly increasing in railway industries-as an experienced solution in other countries-, Iran’s rail freight industry has not been involved in, yet. Related research and experiment has not been identified in Iran, as well. The aim of this survey is to identify the effects of the implementation of Internet of Things in the Rail Freight Industry in Iran. To gather the data, the Delphi method was selected, and the Snowball technique was used for organizing a Panel including twenty experts. To evaluate the outcomes, IQR, Binomial tests, and Mean were calculated. Several statements were identified and there was broad consensus on most of them, which approved that their implementation affects the Iranian rail freight industry, but in different ranks. Finally, the results formed in the Balanced Scorecard’s format. The internal business process has been affected more than the other aspects by the approved statements. Eleven recognized elements are affected in different ranks, including Internal Business Process, Financial, Learning, and Growth, Customers. The Financial perspective remarked as least consensus and the Internal Business Process has received the extreme consensus. The research outcomes can be used to improve the strategic planning of the Iranian rail freight industry by applying the achievements of information technology in practice.

The growing trend electromagnetic waves such as mobile phones, which causes more people to be exposed to electromagnetic fields; It doubles the necessity of research regarding the possible effects of these waves on the health of citizens. Therefore, the present study was conducted using the soft systems methodology (SSM) and the problem-solving structure with the aim of measuring the actual radiation level of the base station transmitter (BTS). Also, effective solutions were presented to reduce human risks in the face of waves. Methodology In this study, 10 high-risk spots were selected from the metropolis of Ahvaz in cooperation with the regulator, the Regulatory and Radio Communications Organization of the Southwest Region. Then, with the related equipment and the NARDA device, the radiation level of the transmitter base station was measured. For each point, measurements were made at 3 different heights (100, 150 and 170 cm from the ground) with different distances (20, 30 and 100 meters from the antenna (BTS). Also, the duration of the measurement in each. The height was determined by 6 minutes according to the relevant standards. Finally, the maximum amount of radiation level obtained as a result was recorded for each point. Despite choosing the highest value obtained for each point, after comparing the extracted results with the threshold limit set by the Atomic Energy Organization of Iran, it was determined that all the points in question met the threshold limit and there was a huge difference with the minimum They had a threshold. Therefore, it can be concluded that the level of radiation in these places is not worrisome and this result can be generalized to larger scales. At the same time, considering that the concerns regarding the long-term effects of exposing people to radio frequency radiation are ongoing; Effective solutions were presented to reduce the presence of people exposed to electromagnetic fields.

Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods. Agricultural insurance is also not exempted from this threat due to its nature and wide extent and every year a lot of money is spent on paying fake damages. This research was presented with the aim of providing a model to discover unrealistic damage claims in agricultural insurance by using data mining and machine learning techniques. It was used to build a deep learning model. The data used was obtained from the Agricultural Insurance Fund and related to wet and rainfed wheat insurance policies of Khuzestan province, for which compensation was paid in the 2018-2019 crop year. After preparing and preprocessing the data, using deep learning to discover unusual cases, the action and results were evaluated by the experts of the Agricultural Insurance Fund. After analyzing the results, it was found that 1% of the damages paid were related to unrealistic requests and more care should be taken in paying the damages. The accuracy of the model in detecting unusual cases for wet and dry wheat was 53.53 and 63.37 percent, respectively. In the review of the results, it was found that 5 categories of unusual behavior have led to the payment of unrealistic damages, and the behavior of not providing damage documentation was more frequent than the others.

Melanoma is one of the most commonly diagnosed cancers and the second cause of cancer-related death among people. This disease is the rarest and most malignant type of skin cancer. In advanced conditions, it has the ability to spread to internal organs and can lead to death. In Iran, for several years, significant data about melanoma have been collected either manually or electronically, due to its prevalence and the high costs it leaves on the country's healthcare system, but despite these valuable data, the health system is still unaware of the high potential of data mining in predicting the survival of patients with melanoma. Therefore, the aim of this study was to design an intelligent system to predict the survival of these patients.

This study was practical in terms of nature and descriptive-analytical and retrospective in terms of method. The research population consisted of patients with melanoma cancer from the database of the National Cancer Research Center affiliated to Shahid Beheshti University, located in Tajrish Martyrs Hospital, Tehran, Iran (between 2007 and 2012), who were followed up for 5 years (n = 4118). SPSS and Weka software were used to design the support system for melanoma cancer survival prediction. The final model for predicting melanoma cancer survival was selected based on the evaluation indices of data mining algorithms.

Neural network algorithms, simple Bayes, Bayesian network (BN) and combination of decision tree with simple Bayes, logistic regression, J48, and ID3 were selected as the used models of the country's database. Based on the findings, the neural network performed better with a value of 0.97 in terms of accuracy and 91.03 in terms of features.

The performance of the neural network in all evaluation indices was statistically higher than other selected algorithms. Therefore, this algorithm was chosen as a support system for predicting melanoma cancer survival.

The supply chains of medical equipment and necessities in healthcare centers are highly complex, diverse, and dynamic, making optimal selection and purchase a specialized and challenging task. This research aimed to design an intelligent decision support system that could aid the expertise process of purchasing medical supplies.

In this developmental-applied and descriptive-survey study, we used artificial intelligence, fuzzy sets, and neural networks as well as MATLAB software to design a model that could simulate the decision-making process of experts in the purchase of medical supplies by predicting the score of medical supplies after obtaining information. The required data was extracted from the website of the General Directorate of Medical Equipment and Knowledge of Specialists in 2022.

The results showed that a three-layer perceptron neural network, with a mean square error of 0.0011 and an overall correlation of 0.97, could be used as a suitable decision aid in the evaluation and selection of medical supplies.

The use of an intelligent decision support system can greatly aid the expertise process of purchasing medical supplies, thus helping the healthcare system to preserve resources and improve the quality of healthcare services. The high speed and accuracy of processing data by using neural networks, one of the most effective methods for learning and generalizing, can assist experts in purchasing medical supplies and lead to optimal decision-making.

The disruption caused by natural, economic and political crises, piracy, and disturbance in information systems and human agents (low probability factors with high impact intensity) travel like a wave from the upstream of the supply chain, i.e. from the suppliers to its downstream. The ripple effect is the phenomenon of disruption in the supply chain with serious impacts on the performance of the supply chain. It can influence sales, on-time delivery, and profits. It has more serious consequences in reducing short-term performance, which leads to market share reduction. This disruption affects the entire supply chain. It destroys the capacity or inventory of the chain, disrupts its facilities, and causes material loss and production reduction in the next stages of the supply chain. Since disruptions are inevitable in supply chains, it is necessary to address this issue and evaluate the impact of disruption risk and its distribution on the supply chain. The purpose of the present study is to provide a simulation model for the behavior of disruption propagation in a three-stage supply chain. It tries to take into account both the vulnerability and recovery capabilities of the disrupted supplier and to determine a criterion for quantitatively predicting the ripple effect of the supplier disruption on the distributor. As the disruption in suppliers prolongs deliveries to customers, and any increase in lead time will decrease sales, the current study seeks to present the quantitative estimation of the ripple effect of supplier disruption on the distributor in terms of lead time and lost sales.
First, using a discrete-time Markov chain, a recovery and vulnerability model was proposed for long-term disrupted suppliers with three states of operational, semi-operational, and fully disrupted. The model was integrated with a Bayesian network to show the way the supplier disruption spreads to the manufacturer and distributor by dynamic Bayesian modeling. Then, in order to quantitatively show the ripple effect of the supplier disruption on the distributor, a criterion was established in terms of the lead time for the delivery of goods and lost sales based on the decision tree and the data of the dynamic Bayesian simulation model.
The model was presented through figures, definitions, and mathematical relations. Its capability was shown by an example based on the status of suppliers, manufacturers, and distributors in the production of a voltage-increasing electronic component. The obtained results showed that when a disturbance occurs in upstream of a supply chain, it spreads as a ripple to its downstream and affects its performance.
The proposed model by this study can quantitatively show the spread and impact of disruption along the supply chain. It can reveal the hidden risk paths and the role of each entity in the chain at the time of disruption. By quantitatively estimating the vulnerability caused by any disruption in the supply chain, the proposed model can help managers identify deviations or the risk of deviations in the chain in time. It can also help them with analyzing and performing control measures to restore the operation and process of the chain and prioritize possible policies and recovery. It is also useful for the selection of suppliers and inventory plans and making the right decisions to reduce the vulnerability of the supply chain sectors quickly with minimal cost.

One of the main challenges of the health system is improving the quality of healthcare services by adjusting the costs. The right choice of tools will bring economic savings besides excellent service quality. Since medical supplies are among the fundamental and costly elements in providing services, the expertise of purchasing has become an important and complex issue. This study aimed to prioritize criteria for purchasing medical supplies.

This research is an applied study conducted with a descriptive survey method. In this study, the criteria collected from related studies and completed with the opinion of experts were prioritized and weighted for four categories of medical supplies by the linear best-worst multi-criteria decision-making method using Lingo and Excel software.

By weighting sixteen sub-criteria of purchasing medical supplies in the form of five categories, the importance of each criterion was determined. According to the results, the two sub-criteria of "quality" and "price" were recognized as the most important and "seller's record" as the least important indicator in the purchase of medical supplies.

Using the linear best-worst multi-criteria decision-making method with definite consistency of results and high reliability led to the prioritization and accurate weighting of criteria for purchasing supplies. Paying attention to the indicators in the purchase of medical supplies, in addition to guaranteeing the quality and continuity of services, will lead to a significant reduction in supply costs.

Reducing CO2 emissions is one of the most critical research frontiers. Additional oil recovery through CO2 injection can play an important role in stimulating carbon sequestration and storage and help earn additional oil revenues. This article is paid, for by building a model of electricity production and carbon emissions from the country's thermal power plants, to identify and feasibility of carrying out the CO2-EOR project through the transfer of carbon from power plant activities through a dedicated pipeline and injecting it into oil fields. This model, which has been applied using system modeling tools to better understand the complexities of the studied problem, with the design of the main sub-systems of thermal and renewable electricity generation and the examination of the relationship between these sub-systems preliminary economic analysis, provides a plan for the feasibility of the project. The research results, while confirming the acceptable success in achieving environmental and economic goals, emphasize the need to conduct wider and deeper research and pay special attention to this approach as an outstanding opportunity to achieve the country's sustainable development goals.

Customer Retention and maintaining customer relationships and preventing customers from Churn is one of the most important tasks of organizations in today's highly competitive markets. In this study, the issue of customer churn and customer retention strategies have been investigated. These issues have been studied through systematic literature review and from different angles such as the field of organization, degree of individualization of customer relationship management, customer segmentation, and selection of key customers, employee engagement and evaluation. In addition, a model based on deep learning networks has been used to predict customer churn. As a result, a conceptual framework and model is created based on the existing literature in this field and then combined with the customer churn prediction model using deep learning networks. The results show that the use of deep learning in predicting customer churn is a very effective and efficient way to solve the problem of customer retention and customer churn. This approach is not only able to accurately predict which of the organization's customers are turning away from the organization and disconnecting from the organization, but also can accurately identify the factors and parameters affecting customer churn and bring very valuable insight for the organization.

Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agility, management and control of current network devices as well as upcoming communication technologies reduces expenses and creates innovative businesses. Although, service providers are very interested in deploying SDN to transform their static infrastructures to a dynamic and programmable platform, they do not consider it as one of their priorities and still depend on traditional methods to manage their network. Therefore, this study highlights the factors affecting the acceptance of SDN architecture and its application by the national telecom operators, and proposes a comprehensive and new paradigm model using a systems approach and Grounded theory (Strauss and Corbin model). This innovative model is provided by systematically reviewing the theoretical foundations and conducting in-depth interviews with managers and experts in telecom industry. During the modeling process, more than a thousand initial codes were determined. Finally, based on the opinion of experts on these codes, a total of 73 open codes, 12 axial codes and 6 main categories have been extracted.

Abstract
States are willing to provide services that increase citizens' satisfaction. To do so, they need to increase the public services quality provided by the government organizational staffs. Today only the organizations that are able to attract and motivate the highly effective forces will succeed. Given this, the quality of human resource management is more important these days. This research aimed at studying the human resource management’s role in the organizational commitment, organizational identification and intent to leave job inside public organizations in Iran. In order to create a measure used mix method .This measure has been created based on interviewed with 30 public organizations employees in Iran  and a good governance framework in the Iran’s governmental organizations and the major administrative system of the Islamic Republic of Iran, and then it has been validated by the factor analysis method and as the next step the relationship between human resource management quality questionnaire organizational commitments - organizational identification and intent to leave job was evaluated on 384 public organizations employees. The results show the relationship between human resources management quality and organizational commitment and organizational identification has significant positive and significant negative relationship with intent to leave  job.
Human resource management quality has an extensive impact on employees attitudes, behaviors and their organizational performance.
Case study
Iran’s public sector  
This research is based on exploratory mixed methods.By analyzing literatures and the qualitative data collected from interviews,  and also using the roadmap of  Iran administration system correction and good governance in the public sector model, we are able to prepare a conceptual and operational framework which is validated by factor analysis and correlation of three criteria.
Discussion and The result of this research is a tool for measuring human resource management quality in Iran’s public sector.
The result of this research, is a validate tool for measuring human resource management quality in Iran’s public sector.

Customer retention is one of the most important issues of any organization and finding a way to retain and maintain the customer is one of the critical needs of any organization. The main purpose of the present study in the field of machine learning is to focus on the problem of correctly identifying customer needs with a method based on extracting opinion and sentiment analysis and quantifying customers' sentiment orientation. In the other word, the main issue is to design a Recommender System to provide appropriate services in accordance with customer satisfaction, sentiment, and experiences. The proposed method is that customers' opinions and experiences are obtained by evaluating tweets containing hashtags with the titles and headings of banking services as statistical population, and after revision, it results in providing correlation scores in terms of people's sentiment score due to the tweets, cosine similarity and reliability, consideration of relevant characteristic groups as well as recorded ideas in the training and testing process, in the form of submitting personalized offer to receive banking services. In order to represent a recommending solution, suitable classification methods are used along with opinion mining methods and proper validation approach as well, and the terminal designed system with a little error will take steps to provide personalized services as well as help banking system. Since there is no thorough provision of banking services tailored to the customers’ situation, so in this regard, the mentioned system will be extremely beneficial.

Small enterprises play a significant role in business development and improving the economic situation. However, studies show that these criteria do not have enough competitiveness and the increasing complexity of the competitive environment has created major challenges for these industries. Identifying a competitive position depends on identifying the sources of competitiveness. From a resource-based view, service delivery depends largely on the organization's resources and capabilities to create a sustainable competitive advantage in the long run. Based on a comprehensive resource-based view, through an extensive study of the research background, 28 criteria were extracted from the literature to identify and determine the capabilities that have the greatest impact on the competitive advantage of Iran's small enterprises. Then, using fuzzy Delphi method, questionnaires of 13 selected indicators, based on interpretive structural modeling and importance-performance analysis methods were prepared and completed in the panel of small enterprises experts and analyzed after obtaining expert opinions and aggregating opinions. The results showed that the criteria of "market orientation", "differentiation of activities and level of centralization", "planning, control and integration of activities", “interactive quality "and" Corporate quality "are the basic criteria, as well as "Leadership", "Operational Marketing" and "Market Orientation" can keep small enterprises in a competitive market and create a competitive advantage.

This study aimed to introduce the factors confirming the desirability of the data attributes in order to knowledge discovery from health big data systems by using factor analysis.

This research was a Survey-Analytical study. The statistical population consisted of 655 specialists in the fields of data science, bioinformatics, and health information analysis inside and outside Iran, who were identified by snow flack method, and the research tool, researcher-made questionnaire was sent for them, and 247 questionnaires were completed and turned back. Data analysis methods were exploratory and confirmatory factor analysis using SPSS 22.0 and Lisrel 8.8 software.

The results of Exploratory Factor Analysis explained six factors for identifying data characteristics that are: data quality, data stability, data compatibility, data value and accessibility, data efficiency and accuracy and completeness, which altogether had 68% of the total variance of the data. Confirmation analysis fit indices showed that the six-factor model has an acceptable fit with the data.

Findings indicate that based on the proposed model, data properties affect the process of knowledge discovery of health big data systems and six identified factors and 20 items of these factors, can be a good tool to identify the characteristics of data suitable for knowledge discovery of health big data systems for the effective use of these systems, emphasizing the independence of data format, and can help in the early diagnosis and treatment of diseases.

Customer retention is an important issue for any organization, so finding a way to retain the customer is one of the critical needs of any organization. In this regard, the goal in the field of machine learning is focusing on the problem of accurate customer needs with a method based on extracting opinion and sentiment analysis and quantifying customers' emotional orientation.In the other words, the issue is designing a recommender system to provide appropriate services to customers, using their opinions and experiences. The proposed solution, by receiving and reviewing customers' opinions and experiences in the form of extracting variables such as user sentiment score for tweets, relation score, cosine similarity, and confidence factor, and considering groups of relevant features and registration ideas in the process of training and testing, the result is presented in the form of a banking service suitable offer. In order to provide a recommending solution, appropriate classification methods are used along with opinion mining methods and an appropriate validation approach, and the final designed system with a small error, in order to provide personalized services, will step in to help bank managers.Since currently there is no complete provision of banking services tailored to the situation of customers, so in this regard, this mentioned system will be very helpful.

The purpose of this study is modeling of Electronic Word of Mouth Marketing with Emphasis on Customer Behavior and Business Improvement, In this regard, to identify and investigate the impact of various factors on the results of e-mouth marketing and Also examining the effects of successful implementation of this type of marketing on customer behaveor and Improving Electronic business. The research method is descriptive-survey, is of correlational type. In the conceptual model of the research, the independent variables of customer interaction, Electronic Word of Mouth marketing channel, Message quality and brand image With their components based on literature review Identification and research hypotheses have been developed. in order to Investigating the effectiveness of the identified components On the success of word of mouth marketing and the validity of the conceptual model of the research, Questionnaire tools and comments of users of internet sales sites in Iran have been used.The statistical sample to the unlimited statistical population it was estimated according to Cochran's formula 384, In order to review and present the final model from the structural equations approach with SmartPLS software was used. Statistical analysis of the collected data and research results show it customer interaction, Electronic Word of Mouth marketing channel, Message quality and brand image will have a positive and significant impact on the results of this type of marketing that will change of customer behavior and business improvement.