فهرست مطالب ashkan sami

- The research environment plays a major role in students’ satisfaction and productivity. So far, many studies have tried to identify factors that affect student satisfaction and productivity, but they have not addressed the most important environmental factors. In this paper, we explored different environmental factors of computer students at one of the Shiraz University labs. For this purpose, we used mixed methods of multiple stage research design. At first 14 students were interviewed in various computer majors. As a result, the most important environmental factors were the ability to communicate with supervisors and other students, noises, social norms and signals, cleanliness, view, and light. A survey was designed based on interviews and previous work factors with alpha Cronbach 85%. Then, the survey was sent to 175 students and received 73 responses. Survey’s data analyzed at two levels of descriptive statistics and statistical models. Statistical models were built for satisfaction with the work environment and perceived productivity. In the satisfaction model, a specific place in the lab and the ability to communicate with the supervisor were important factors among others. In productivity models light, communicating with supervisor, overall satisfaction, and social norms were important factors.

This paper proposes data mining-based models to diagnose outage data in distribution power systems. In this work, outage data from a local distribution company is gathered and aligned with weather data. Then, a subset of features is selected to reduce the processing time and simplifying purposes. To increase the fairness of final models and to account for differences in misclassification cost, using a customized cost matrix is proposed. Two decision tree-based modeling algorithms are trained and tested. Results show the ability of the established models to diagnose the root cause of an outage fairly well. In addition, an ensemble of the decision tree-based models is built, which outperforms the other two models in almost all cases. Finally, applications of such models in decreasing outage duration and improving the reliability of the power distribution network are discussed.

 The central venous catheter (CVC) has been shown to increase mortality in hemodialysis (HD) patients compared with the arteriovenous fistula (AVF). However, no study has examined the mortality of HD patients based on the time of conversion from the CVC to AVF. In this study, we investigated the association between patients’ survival and length of time of using each access.

The C5.0 algorithm was used to find rules about the relationship between duration of the different access usage and survival. The cox model was applied to assess the association of the obtained duration categories and mortality.

From 2367 adult patients who received maintenance HD from 2012 to 2014, 705 patients were eligible for the study. Using an AVF for more than 8 months and a CVC for less than 4.2 months had the highest one-year survival rate (91.8% and 87.4%). The hazard ratio (HR) for mortality of less than 2.8 months of AVF usage compared to the longest usage was 6.90 (95% CI: 4.60 - 10.30) before adjustment and 5.03 (95% CI: 3.20 - 8.00) after adjustment for all confounders. For the CVC, the ratio was 8.8 (95% CI: 6.00 - 13.00) when comparing more than 9.2 months of usage with the lowest usage duration before an adjustment and 6.00 (95% CI: 3.80 - 9.41) after adjustment.

Our results presented that regardless of the type of initial vascular access, limiting the length of the time using CVC as well as switching to AVF could significantly improve the survival of HD patients

Since clinical data contain abnormalities, quality assessment and reporting of data errors are necessary. Data quality analysis consists of developing strategies, making recommendations to avoid future errors and improving the quality of data entry by identifying error types and their causes. Therefore, this approach can be extremely useful to improve the quality of the databases. The aim of this study was to analyze hemodialysis (HD) patients’ data in order to improve the quality of data entry and avoid future errors.
The study was done on Shiraz University of Medical Sciences HD database in 2015. The database consists of 2367 patients who had at least 12 months follow up (22.34±11.52 months) in 2012-2014. Duplicated data were removed; outliers were detected based on statistical methods, expert opinion and the relationship between variables; then, the missing values were handled in 72 variables by using IBM SPSS Statistics 22 in order to improve the quality of the database. According to the results, some recommendations were given to improve the data entry process.
The variables had outliers in the range of 0-9.28 percent. Seven variables had missing values over 20 percent and in the others they were between 0 and 19.73 percent. The majority of missing values belong to serum alkaline phosphatase, uric acid, high and low density lipoprotein, total iron binding capacity, hepatitis B surface antibody titer, and parathyroid hormone. The variables with displacement (the values of two or more variables were recorded in the wrong attribute) were weight, serum creatinine, blood urea nitrogen, systolic and diastolic blood pressure. These variables may lead to decreased data quality.
According to the results and expert opinion, applying some data entry principles, such as defining ranges of values, using the relationship between hemodialysis features, developing alert systems about empty or duplicated data and entering directly HD data or lab results into the database can improve the data quality drastically. Expert's opinion in detecting outliers as a complement to statistical methods can have an effective role in detection of real outliers. For the analysis of HD databases, the relationship between the variables because of their effect on the quality should be focused more to improve the quality of the database.

To control the exponential growth of malware files, security analysts pursue dynamic approaches thatautomatically identify and analyze malicious software samples. Obfuscation and polymorphism employedby malware make it difficult for signature-based systems to detect sophisticated malware files, the dynamicanalysis or run-time behavior provides a better technique for identifying the threat.In this paper, a dynamic approach for extracting features from binaries is proposed. Run-timebehavior of the binary files were found and recorded in a controlled environment tool developed in-house.The approach based on DyVSoR assumes that run-time behavior of each binary can be represented by thevalues of registers contents. A method to compute the similarity between two binaries based on their registersvalue sets is presented. To do, registers values are traced before and after invoked API calls in eachbinary and mapped to some vectors. To detect an unknown file, it is enough to compare it with datasetbinaries by computing the distance between registers content of this file and all binaries. This methodcould detect malicious samples with 96.1% accuracy and 4% false positive. List of execution traces anddataset can be found at: http://home.shirazu.ac.ir/~sami/malware.