فهرست مطالب majid khedmati

Diagnosis of diseases is a critical problem that can help for more accurate decision-making regarding the patients’ health and required treatments. Machine learning is a solution to detect and understand the symptoms related to heart disease. In this paper, a logistic regression model is proposed to predict heart disease based on a dataset with 299 people and 13 variables and to evaluate the impact of different predictors on the outcome. In this regard, at first, the effect of each predictor on the precise prediction of the outcome has been evaluated and analyzed by statistical measurements such as AIC scores and p-values. The logit models of different predictors have also been analyzed and compared to select the predictors with the highest impact on heart disease. Then, the combined model that best fits the dataset has been determined using two statistical approaches. Based on the results, the proposed model predicts heart disease with a sensitivity and specificity of 84.21% and 90.38%, respectively. Finally, using normal probability density curves, the likelihood ratios have been established based on classes 1 and 0. The results show that the likelihood ratio classifier performs as satisfactorily as the logistic regression model.

Since the beginning of the Covid-19 epidemic, many countries have been involved in fighting the disease. The outbreak of this disease has caused irrecoverable damages to countries in various aspects such as human resources, economic situation, and social welfare. The purpose of this study was to evaluate the performance of world countries in the face of covid-19 in respect of the role of vaccination and to propose applicable approaches for countries with lower performance.

In this research, a three-phase method was developed to evaluate the performance of 88 countries in the face of Covid-19. In the first phase, the countries were clustered, using the k-means algorithm. In the second phase, the data envelopment analysis model was implemented once on the clusters and then on all the countries without considering the clusters. In the third phase, after presentation of the concept of resilience in the Covid-19 era, an Analytic Hierarchy Process was implemented with three criteria, including the results of data envelopment analysis model without considering the clusters, the results of data envelopment analysis model considering the clusters and resilience, and then the ranking of the countries' performance in the face of Covid-19 was provided.

Based on the results, 15 performance scores were obtained for the countries in the face of Covid-19, among which China, Fiji, Seychelles, Kyrgyzstan and Tanzania had the highest rankings and Bangladesh, Nepal, Georgia and Pakistan had the lowest performance levels. Iran is in the sixth place and in the average performance position of the countries.

Based on the findings of this study, the countries with low performance level in the face of Covid-19 need to apply various strategies in different areas in order to improve their performance level. These strategies include planning and approval of joint programs to control and prevent the spread of disease, applying quarantine policy and restrictions on communities, planning and implementation of appropriate strategies regarding the export and import of drugs and medical equipment, allocating appropriate funds to research projects on Covid-19, banning the export of drugs effective in the treatment of Covid-19, and providing accurate and up-to-date information for raising people’s awareness.

Beta-thalassemia major is a type of inherited blood disease that results in variable outcomes such as severe anemia due to haemoglobin chains. Recurrent and lifelong blood transfusions as a treatment in beta-thalassemia major disease lead to iron deposition in various organs and cause the failure of multiple organs. Failure of affected organs leads to Body mass index (BMI) abnormality. This study aimed to evaluate the association between BMI and serum ferritin level as a marker for iron overload.

A cross-sectional study designed and conducted with total number of 740 paediatrics, with mean age about 14.2±8.7 years old and with beta-thalassemia major requiring recurrent blood transfusion. Patient information, including demographics, serum ferritin level and percentage of BMI, was recorded and analysed by SPSS 25.0 and the statistical significant level, considered as 0.05.   

A total number of 740 paediatrics with beta-thalassemia major disease (mean age about 14.2±8.7 years) were included to study to examine the association between serum ferritin level and their BMI.  The total mean serum level of ferritin calculated about 3326 ± 3859 Nanogram/mililitter (ng/ml). Totally, 447 (60.4%) case of them had BMI percentile less than 5%, 274 (37.02%), 16 (2.16%) and 3 (0.4%) had BMI percentile 5%-85%, 85%-95% and more than 95%. There was no relation between gender and serum ferritin levels. The relationship between age and BMI has been positive (P=0.002). Finally, it resulted that there was a negative relationship between the BMI percentile and mean serum ferritin levels in paediatrics with beta-thalassemia major (P=0.031).

Frequent Blood transfusion is associated with elevated serum ferritin level in paediatrics with beta-thalassemia major disease and experiencing lower percentiles of BMI in these patients.

Traffic congestion is one of the issues in transportation planning which imposes environmental consequences and costs. Therefore, decision-makers and policymakers should focus on appropriate transportation planning models. One of the approaches to relieve traffic congestion is imposing tolls on the users. In the present paper, attempts are made to present three transportation planning and traffic congestion management models. The first model assumes that the transportation network and the traffic flows within it are determined. Decision-maker seeks to adjust the transportation network flows so that traffic congestion can be prevented. In the second model, unlike the first one, attempts are made to design urban transportation networks via the development of routes. The third model is a mixture of the first and second models. All models proposed here are bi-objective which were addressed under uncertain conditions and disturbances. According to the results, a decision-making model was extended to rank routes. In the end, a numerical example is considered for analyzing and evaluating the proposed models. The results of the numerical example showed that the first model is the most inefficient and the third model is the most efficient. Since the proposed model can be implemented in road networks in addition to urban transportation networks, the application of the proposed models is demonstrated based on a real-world case study. The case study results showed that the efficiency of road network depends on the time interval.

In this paper, a data mining approach is proposed for duration prediction of the town trips (travel time) in New York City. In this regard, at first, two novel approaches, including a mathematical and a statistical approach, are proposed for grouping categorical variables with a huge number of levels. The proposed approaches work based on the cost matrix generated by repetitive post-hoc tests for different pairs. Then, a random forest model is constructed for the prediction of the type of trips, short or long. Finally, based on the trip type and each of the mathematical and statistical approaches, separate artificial neural networks (ANN) are developed to predict the duration time of the trips. According to the results, the mathematical approach performs better and provides more accurate results than the statistical approach. In addition, the proposed methods are compared with some other methods in the literature in which the results show that they perform better than all other methods. The RMSE of mathematical and statistical approaches is, respectively, 4.23 and 4.27 minutes for short trips, and the related value is 9.5 minutes for long trips. In addition, a modified version of the nearest neighborhood approach, entitled modified nearest neighborhood (MNN), is proposed for the prediction of the trip duration. This model resulted in accurate predictions where its RMSE is 4.45 minutes.

Petroleum (crude oil) is one of the most important resources of energy and its demand and consumption is growing while it is a non-renewable energy resource. Hence forecasting of its demand is necessary to plan appropriate strategies for managing future requirements. In this paper, three types of time series methods including univariate Seasonal ARIMA, Winters forecasting and Transfer Function-noise (TF) models are used to forecast the petroleum demand in OECD countries. To do this, we use the demand data from January 2001 to September 2010 and hold out data from October 2009 to September 2010 to test the sufficiency of the forecasts. For the TF model, OECD petroleum demand is modeled as a function of their GDP. We compare the root mean square error (RMSE) of the fitted models and check what percentage of the testing data is covered by the confidence intervals (C.I.). Accordingly we conclude that Transfer Function model demonstrates a better forecasting performance.

The organizations need to continuously improve their processes so that they can succeed, increase their customers’ satisfaction, and survive. The process maturity models of organizations are models developed to assess and improve organizational process. In order that an organization can continuously improve, it is essential to focus on its maturity in personnel, process and organizational levels. The integrity maturity model is a fundamental model used to assess an organization’s maturity level. This model, based on the principle that “the system or a product’s quality is influenced by the process used in its development and maintenance”, is developed through reviewing all the knowledge and experiences gained from processes management. Organizations can advance with this model toward improvement of their processes and fulfillment of goals. In this paper, the concept of integrity maturity model is first reviewed; then, following indentifying organizational processes in the selected NAJA police stations and an examination of their statues que, the integrity maturity model for organizational process of the police stations was developed. The presented model in this paper can be regarded as a criterion to assess the maturity levels of process in the selected police stations, based on which the weakness and strengths of the organization can be identified.

The use of bio-fertilizers may due to heavy metals immobilization in the soil. This research has been conducted based on a factorial experiment in a completely randomized design with four replications in Malayer University. Pots contained a mixture of soil and different amounts (0 and 60% by weight) of vermicompost as bio fertilizer. Plants exposed to cadmium nitrate concentrations (0, 4 and 8 mmol per kg). The results showed that by increasing the concentration of cadmium root length and plant height significantly (P

Assuming a first-order auto-regressive model for the auto-correlation structure between observations, in this paper, a transformation method is first employed to eliminate the effect of auto-correlation. Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of the profile. The performance of the proposed change-point estimator is next compared to the one of the built-in change-point estimator of EWMA control chart through some simulation experiments. The results show that the proposed MLE of the change point accurately estimates the true change point and outperforms the built-in estimator of EWMA chart for almost all shift values and auto-correlation coefficients, while the built-in estimator of EWMA chart, in general, underestimates the true change point.

In many situations, the quality of a process can be characterized better by a relationship, known as a profile, between a response variable and one or more predictors. Almost all research efforts assume that response variable is continuous and follows a Normal distribution while there are instances in which the response is a binary variable and methods such as logistic regression are commonly used. In this paper four control schemes namely Hotelling, MEWMA, likelihood ratio test (LRT) and LRT/EWMAare proposed to monitor binary response profiles in phase II. The performance of the proposed control charts are evaluated and compared by simulation experiments for different shift values in the parameters of the profile in terms of the average run length (ARL) criterion. The results show that all methods work well in the sense that they can effectively detect shifts in the process parameters. Based on the results, MEWMA and LRT/EWMA methods display a better performance for small to moderate and large shift values, respectively.

In this paper, a new control chart to monitor multi-binomial processes is first proposed based on a transformation method. Then, the maximum likelihood estimators of change points designed for both step changes and linear-trend disturbances are derived. At the end, the performances of the proposed change-point estimators are evaluated and are compared using some Monte Carlo simulation experiments, considering that the real change type presented in a process are of either a step change or a linear-trend disturbance. According to the results obtained, the change-point estimator designed for step changes outperforms the change-point estimator designed for linear-trend disturbances, when the real change type is a step change. In contrast, the change-point estimator designed for linear-trend disturbances outperforms the change-point estimator designed for step changes, when the real change type is a linear-trend disturbance.