Investigating the relationship between entrepreneurship and social networks

This study was conducted to investigate the relationship between entrepreneurship and social networks among entrepreneurs in Abha province using descriptive correlational method. The statistical population of this study included 313 entrepreneurs in Abha province who were selected by random sampling. Information was obtained using a questionnaire. The content and face validity of the questionnaires were confirmed by the views of the supervisor, several experts and a number of individuals of statistical population. Analysis was performed using SPSS 23 software at two levels of descriptive and inferential statistics; the statistical characteristics such as frequency, percentage, mean and standard deviation and in inferential section, Kolmogorov-Smirnov test were used to investigate normality and Pearson's correlation coefficient. The findings indicated that according to the first hypothesis of the research: There is a relationship between social sources of entrepreneurs' information in entrepreneurial opportunity recognition with correlation coefficient (0.867) and significance level (0.000). There is also a relationship between the structure of entrepreneurs' social relationships in entrepreneurial opportunity recognition with the correlation coefficient (0.777) and significance level (0.000). Findings of hypothesis of social relationship and entrepreneurial opportunity recognition show that there is a significant and direct relationship in amount of (r= +0.808). Also the results of the last hypothesis test show that the correlation coefficient between social network and entrepreneurial opportunity recognition is significant and there is a significant relationship between these two variables. i.e, there is a significant and direct relationship between social network and entrepreneurial opportunity recognition in amount of (r= 0.903).</jats:p

Implementing a new Torus network with intra-chip encryption

In recent years, the ad hoc network has emerged as a solution to the challenges of designing high-performance complex systems at the nanoscale. Maintaining information security in complex systems is an important issue. Therefore, this paper presents a new topology based on the Torres network with encryption capability to build internal network interfaces on the chip with tens or even hundreds of processor units considering the need for information security. The main platform for inter-chip communication is the PRDT network (2.1) and the basic encryption algorithm, RC6. Designing a new node and modifying the source and switched source network PRDT (2,1) encrypts the algorithm based on the RC6 accelerated algorithm. Evaluations performed by the synthesis of this method on the FPGA shows that this provides a 21% overhead of approximately 6% increase in hardware resources that cannot be compromised.</jats:p

Providing a Model for Promoting E-Businesses Using Website Optimization

The growth of computer-related technologies and the development of Internet in the world caused moving traditional businesses towards electronic, in addition to it, website-based startup businesses have been established. These businesses are faced with much larger target market than before, but reaching to a larger target market requires to cross from filter of search engines and to stay in the heart of customers and Internet users by their performance. This paper presents a model for promoting e-businesses using website optimization by studying previous studies in relation with electronic customer relationship management and website optimization techniques for search engines. The model consists of three main criteria of optimization of website features, on-page optimization and off-page optimization; each criterion contains sub-criteria that includes a total of 21 sub-criteria, after examining the criteria and sub-criteria using the hierarchical analysis process, a questionnaire was prepared to rank the criteria and sub-criteria and it was provided online to the web developers using the Press Line website. A large number of experts of Web design and development were invited by sending an invitation to social networks to participate. Ultimately the model was approved by analyzing the results and off-page optimization criteria had the highest importance and on-page optimization criteria had the least importance.</jats:p

Positioning and determining pupils being open and closed and its role in accident reduction

Despite wide spread advances in the car manufacturing industries around the world, the death toll from car accidents is worrying. This concern is heightened when reports of a road accident website reports that the main reason in 25% of road accidents in particular and 60% to road accidents resulting in death or injury. Therefore, there has been a great deal of researches and functions for the automatic and machine detection of drowsiness that of them have reached the production stage. The present study is a method for positioning the pupil of the eye, determining the opening and closing of the pupil in real time and in unlimited environments, using the color feature in the first step, in Ybcbr color space and with the help of Gaussian function and Euclidean distance detection and then the area of the eye is positioned using the Viola jones algorithm. Finally, in order to locate the pupil and detect its openness, we have used two parallel Kalman filters. If the pupils are closed, they follow the Harris Eye Detection algorithm to identify the drowsiness of the driver and use this system to minimize the deaths caused by fatigue (tiredness) while driving. Detection is performed more precisely in the case of face rotation, different lighting conditions, and the eyes being closed or missing one of the eyes, the presence of glasses, beards, makeup, hijab, or obstruction of the eyes. The low computational complexity and maximum stability, real-time and unrestricted environment are other advantages of this method, all because the filters operate in parallel and do not even require high-resolution images.</jats:p

An efficient feature selection algorithm for health care data analysis

Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.</jats:p

An efficient feature selection algorithm for health care data analysis

Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes

Investigating the Impact of Strategic Planning on Targeting Production-oriented Organizations Using BSC

Targeting in organizations is one of the main pillars of determining organizational fate. The extent of transparency of objectives has a great impact on the design and formulation of main key words of strategic planning. Regarding the attractiveness of the BSC method and its application in production-oriented organizations, it can increasingly achieve the macro goals, if it is focused on BSC components which are the four perspectives of finance, customer, internal processes, and development and learning, it becomes more dominant in the design of key organizational factors. Many of the techniques used in organizations have achieved acceptable results that have been very effective. Including these approaches and techniques, it can refer to the comprehensive management, customer-oriented and customer attraction based on BSC components to achieve excellent goals on thought and planning of manager of a product-oriented organization. A balanced scorecard can be considered a common goal among managers that are based on common goals based on BSC principles and corporate financial approaches and the process of attracting customer and attracting capital.</jats:p

Predicting Effective Factors in Schizophrenia Using Data Mining

Data mining is a technique for discovering new knowledge from databases and the use of data mining in medicine is considered one of the most widely used fields of data mining. Schizophrenia is one of the most common illnesses that cause many financial and social damages to society due to the loss of individual performance. In this study, we will examine the most effective fields of predictor in schizophrenia and then predict the age of occurring schizophrenia. In this study, some common classification methods such as support vector machine, decision tree and neural network have been used. The results show that the support vector machine model has more efficiency than other models.</jats:p