درخت حوزه‌های تخصصی

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

Today, internal controls have grown so dramatically that we see its progress towards internal consulting in the company's risk management operations. The current research was carried out with the aim of designing a causal model to eliminate the weak points of implementing internal controls in educational institutions according to the Fuzzy Dimtel method. This research is of mixed type (qualitative-quantitative) in terms of nature and content. Information of this research is related to 15 experts in educational institutions (Azad University, higher education, non-profit institutions), which constitute the statistical sample. This research is descriptive in terms of data collection and analysis, practical in terms of purpose, and documentary in terms of method. In order to collect data, the tool of paired comparison questionnaire is used. The results of this research showed that the four main factors of risk components, operational risk, independence of the board of directors, and institutional ownership have an effect on the weak implementation of internal controls. Also, Dimtel's method showed that the factor of independence of the board of directors with an average influence of 18.740, the factor of institutional ownership with an average impact of 9.497 and the factor of risk components with an average impact of 8.742, are among the influential factors. On the other hand, operational risk factor with an influenced average of 14.975 is an influenced factor.

In recent years, circular business models have become an inevitable requirement to foster improvements in social, environmental and economic performance. The present study was conducted by combining previous research with the aim of providing a comprehensive picture of the components of the role of digital technologies in circular entrepreneurship with a focus on business models. The research methodology is qualitative with a meta- synthesis approach. Accordingly, after searching the databases including Elsevier, MDPI, Taylor & Francis, ScienceDirect, Emerald, Wiley, Springer, Noormags and SID in the time range of 2010 to 2024, 132 related studies were evaluated and finally, after multiple screenings, 36 articles were selected. Using seven-step model of Sandelowski and Barroso, the findings of 36 previous studies related to the objectives of the study were reviewed, aggregated, combined, and interpreted, and with the help of coding method, 26 codes, 11 concepts and 3 components were identified and validated through Kappa Cohen coefficient. The identified components were introduced under three components, including improving the element of the value proposition of the circular business model, improving the element of value creation of the circular business model, and improving the element of value capture of the circular business model. Finally, the model of the role of digital technologies in circular entrepreneurship with a focus on business models was presented and drawn.

Rural tourism, with careful planning, can enhance the quality of life and create economic, social, and cultural benefits. The aim of this research was to present a sustainable rural tourism model with a focus on social capital. The research was applied in terms of its goal and qualitative in terms of methodology, based on the foundation data theory. The research was framed using a cognitive approach, utilizing various data collection methods such as library research, examination of specialized sources and texts, as well as semi-structured interviews. Fourteen elites, experts, and managers in tourism and rural area management were interviewed in 2023. The conducted interviews were coded using Atlas .t i software.

The current research was conducted with the aim of providing a sustainable marketing model in the insurance industry with a focus on artificial intelligence. According to the type of data, the research method is mixed (qualitative-quantitative) of exploratory nature. In the statistical population, 12 people were selected from among the experts until the theoretical saturation was reached. The experts included professors of the faculty of business management and senior managers of Alborz insurance company, who were selected by non-probability, targeted and chain sampling. Quantitative part of 400 managers were insurance industry experts, using Cochran's formula, 196 people were selected by stratified random method. To collect data, semi-structured interviews and researcher-made questionnaires were used. The analysis method has been carried out in several consecutive steps. In the first stage, to identify the components of the model, the qualitative method and semi-structured interviews using the grounded theory have been used. In the next section, using interpretative structural modeling, the identified indicators were leveled. Finally, in order to fit the designed model in the qualitative part, a quantitative method with structural equation modeling approach was used with PLS4 software. The findings showed that 6 components were identified and there is a significant relationship between all components and sustainable marketing and the designed model has an acceptable fit. It can be concluded that AI can be incorporated as an essential component of a sustainable marketing strategy, enabling marketing professionals and businesses to make positive changes while increasing their brand reputation and profitability.

With the aim of comprehensively investigating the most important consequences of website quality in e-commerce, this research has collected and analyzed data about the relationship between website quality and customer trust, satisfaction, and loyalty. In this regard, various studies were collected and coded. After that, the eligible studies to enter the meta-analysis based on the determined criteria were identified to be 49, 68, and 29 studies for the relationship between website quality and trust, satisfaction, and loyalty, respectively. The findings of data analysis using CMA2 software showed that website quality has the strongest relationship with satisfaction with an effect size of 0.591 and the weakest relationship with loyalty with an effect size of 0.516. The effect size of trust was also positive and equal to 0.555. In addition, the findings from the analysis of moderator variables also showed that an increase in the year of publication led to a decrease in the effect size of website quality-trust studies but an increase in the effect size of website quality-satisfaction and website quality-loyalty studies. Additionally, with an increase in the sample size, the effect sizes of website quality-trust studies increased, but the effect sizes of website quality-satisfaction and website quality-loyalty decreased.

In this paper, we present a comprehensive approach for evaluating efficiency in complex networks by integrating network data envelopment analysis (NDEA) with the Malmquist productivity index. The proposed method addresses the inherent challenge of accommodating negative data within the network efficiency evaluation framework, which is a common occurrence in real-world network operations. Through the introduction of a two-stage structure, the model not only effectively manages the presence of negative values, but also provides a robust and insightful assessment of network efficiency. A case study from banking sector is employed to demonstrate the efficacy of the proposed approach, showcasing its capacity to offer valuable and actionable insights for decision-making in complex network environments. The results highlight the practical applicability and importance of the extended approach in addressing the complexities associated with evaluating efficiency in diverse network settings.