Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization technique has been vastly concentrated for improving fuzzy expert systems. In this paper, the GA capabilities have been applied for optimization of the membership function parameters in a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children. The fuzzy expert system utilizes the high interpretability of the Mamdani reasoning model to explain system results to experts in a high level and combines it with the GA optimization capability to improve its performance. The hybrid proposed Fuzzy-GA approach was implemented in Matlab software and evaluated on the real patients’ dataset. High accuracy of this system was achieved after GA tuning process with an accuracy about 98%. The results reveal the hybrid fuzzy-GA approach capability to assist computer-based diagnosis of medical experts, and consequently early diagnosis of the disease which is promising for providing suitable treatment for patients and saving more children’s lives. تفاصيل المقالة

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hybrid Fuzzy-Evolutionary algorithms to predict the dust phenomenon. For this, first a fuzzy expert system was designed and then it was optimized using evolutionary algorithms like Genetic and Differential Evolutionary algorithms. Evolutionary nature of these algorithms have been taken into account to optimize the fuzzy system in the complex area of the dust phenomenon. To evaluate the proposed hybrid models a real dataset including 55 years of the dust phenomenon in Zanjan province in Iran was considered. Performance of these methods was investigated through an ROC curve analysis in combination with a 10-fold cross validation technique. The accuracy of the fuzzy expert system was 92.13% and after optimization through the Fuzzy-Genetic model and hybrid differential evolutionary model was reached to 93.5% and 97.30%, respectively. The results are promising for early forecasting of the dust phenomena and preventing its consequences. تفاصيل المقالة

Fuzzy logic has a high potential for managing the uncertainty sources associated with the medical expert systems. Application of fuzzy inference model has been widely concentrated for managing uncertainties in computer based practices of medicine. This paper has proposed two fuzzy expert systems for prognosis of the heart disease based on: 1) Mamdani inference model, and 2) Sugeno inference model. These methods initially received clinical parameters as input sand define their corresponding fuzzy sets. The performance of the FESs (Fuzzy Expert System) based on the Mamdani and Sugeno model, have been evaluated using real patients dataset through conducting two different studies. The dataset includes 380 real cases collected from the Parsian Hospital in Karaj. The accuracy of the proposed Mamdani FES is equal to79.47% and its accuracy using Sugeno model is equal to 88.43%. This FES is promising for prognosis of the heart disease and consequently early diagnosis of the disease and improving survival rates. تفاصيل المقالة

Fuzzy expert systems are one of the most practical intelligent models with the high potential for managing uncertainty associated to the medical diagnosis. In this paper, a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children has been introduced. The fuzzy expert system applies Mamdani reasoning model that has high interpretability to explain system results to experts in a high level. The system has been designed based on the specialist physician’s knowledge. The proposed systems, has been implemented in Matlab and evaluated on real patients’ dataset. High accuracy of this system (with an accuracy about 96%) revealed its capability for helping experts to early diagnosis of the disease. that the results are promising for more earlier diagnosis and then providing good treatment of patients and consequently saving more children’s lives. تفاصيل المقالة

Concurrency control is one of the important problems in operation systems. Various studies have been reported to present different algorithms to address this problem, although a few attempts have been made to represent an overall view of the characteristics of these algorithms and comparison of their capabilities to each other. This paper presents a survey of the current methods for controlling concurrency in operating systems. Classification of current algorithms in operating systems has been proposed. Current concurrency control algorithms are classified into four groups: 1) software-based algorithms, 2) hardware-based algorithms, 3) based operating system, and 4) based on message passing. Furthermore, it presents an analysis of the capabilities and characteristics of current algorithms' in their own category (intra-group comparison analysis) and between different categories (inter-group comparison analysis) to put a light on the way of selecting a proper algorithm for various circumstances in operating systems. تفاصيل المقالة