درخت حوزه‌های تخصصی

Purpose- Nowadays, the importance of rural business development and its key role in advancement of countries is undeniable. Development of rural business is considered as the main strategies for economic, socio-cultural development of deprived areas and it is used as a solution to poverty alleviation, poverty reduction, social and economic justice. Thus, the present study is aimed to provide a model for rural business development in Ilam province.Design/Methodology/Approach- To this end, a mixed-method approach (both quantitative and qualitative) was applied. The population of this research in the qualitative part includes experts and in quantitative section are the owners of rural businesses in Ilam province. To select the sample in the qualitative part of the research, snowball method was carried out and 12 people were selected. Also, in quantitative phase 120 owners of rural businesses in Ilam province were selected. Data collection tool in qualitative section was semi-structured interviews and in quantitative part was standardized questionnaire. In order to analyze data in the qualitative section thematic analysis method and in quantitative section structural equation modeling (SEM) was used.Findings- The results showed that 57 indicators in the form of 15 sub-themes and 6 main themes including: marketing capability, competitive intelligence, government policy, entrepreneurial empowerment, strategic communication and social acceptance were identified as factors affecting the development of rural businesses in Ilam province.Originality/value- In present research, marketing capability, competitive intelligence, government policy, entrepreneurial empowerment, strategic communication and social acceptance have been identified as the factors affecting the development of rural businesses. The results of this research can help the authorities to have a deeper insight into the factors affecting the development of rural businesses as well as create strategic planning in order to successfully perform the development of rural businesses.

An administrator is employed to identify network security breaches in their organizations by using a Network Intrusion Detection and Prevention System (NIDPS), which is presented in this paper that can detect and preventing a wide range of well-known network attacks. It is now more important than ever to recognize different cyber-attacks and network abnormalities that build an effective intrusion detection system plays a crucial role in today's security. NSL-KDD benchmark data set is extensively used in literature, although it was created over a decade ago and will not reflect current network traffic and low-footprint attacks. Canadian Institute of Cyber security introduced a new data set, the CICIDS2017 network data set, which solved the NSL-KDD problem. With our approach, we can apply a variety of machine learning techniques like linear regression, Random Forest and ID3. The efficient IDPS is indeed implemented and tested in a network environment utilizing several machine learning methods. A model that simulates an IDS-IPS system by predicting whether a stream of network data is malicious or benign is our objective. An Enhanced ID3 is proposed in this study to identify abnormalities in network activity and classify them. For benchmark purposes, we also develop an auto encoder network, PCA, and K-Means Clustering. On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. To compare, we looked at things like memory, Recall, Accuracy, and Precision.

Managing a single portfolio is a basic assumption in the most of research. However, in reality, an advisor manages many accounts at the same time; therefore, there is a significant dependency among portfolios and correlation between decisions on one portfolio with the performance of others, so the results of multi portfolio is different with classic models (single portfolio management, that portfolios are optimized independently) due to market impact and the trade dependency of one account to the other accounts. We propose a structural model to optimize accounts simultaneously, considering interdependences, decision’s correlation and mutual behavioral effects of managed portfolios. Moreover, to compare and analyze both single portfolio and multi portfolio approaches, real data from Tehran Stock Exchange in 1398 are used and model is solved with GAMS. Results indicate that multi portfolio optimization excel other approach and consequence notable improvement on the perspective of customer and advisor. Also, for the validation of the proposed model, the selected stocks are considered in pairs to solve the model and the results show the proper performance of the model with different stocks, thus indicating the validity of the model.