توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A New Artificial Intelligence Method for Prediction of Diabetes Type2
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی مهندسی کامپیوتر و فناوری اطلاعات، سال
تعداد صفحات: ۱۲
Diabetes is a chronic illness without a conclusive cure, and is the most common cause of amputations, blindness, and chronic kidney failure, and an important risk factor in heart problems. The only hope for these patients is through proper care. The main difficulty, regarding this dangerous and destructive illness, is not detecting it in time, and generally, a weakness in detection. Hence, implementation of a method that can help in the detection of this illness is an important step toward the prevention and control of this illness, especially in the early stages. In this article, using adaptive neural fuzzy inference system (ANFIS), we have attempted to predict this illness. The speed and the validity of the suggested algorithm is more than the other smart methods used. The method proposed in this article, with a 10% validity increase during training and a 5% validity increase during experimentation has a better performance than previous smart methods<\div>

۲Using Feed–back Neural Network Method for Solving Linear Fredholm Integral Equations of the Second Kind
نویسنده(ها): ،
اطلاعات انتشار: Journal of Hyperstructures، دوم،شماره۱، ۲۰۱۳، سال
تعداد صفحات: ۱۹
Integral equations play major roles in di®erent ¯elds of science and engineering, therefore a new method for ¯nding a solution of the Fred– holm integral equation is presented. So we have applied a structure of hybrid neural networks (NNs). The proposed neural net can get a real input vector and calculates its corresponding output vector. Next a learning algorithm based on the gradient descent method has been de– ¯ned for adjusting the connection weights. Eventually, we have showed this method in comparison with existing numerical methods such as trapezoidal quadrature rule provides solutions with good generaliza– tion and high accuracy. The proposed method is illustrated by several examples with computer simulations.

۳A framework for prioritizing and allocating the six sigma projects using fuzzy TOPSIS and fuzzy expert system
اطلاعات انتشار: Scientia Iranica، بيست و يكم،شماره۶، ۲۰۱۴، سال
تعداد صفحات: ۱۴
Project selection process can be known as the most important action in the success of Six Sigma projects. In this way, ranking and assigning projects to implementation teams are considered as the most important steps in this process. There are copious of researches have worked on Six Sigma Projects Selection (SSPS). None of them, although, have not focused on selecting and allocating the projects as coherent process simultaneously. In this regard, this article presents a framework for decision making, selecting and assigning the six sigma projects to implementation teams. Owing to this, first of all, the most important criteria in SSPS process are selected. Subsequently, after identifying six sigma potential projects in the organization, fuzzy TOPSIS methodology is utilized to prioritize them. Afterwards, the Impact and Effort indexes for each project are calculated. Then, the Takagi–Sugeno–Kang(TSK) Fuzzy Expert System is used to allocate the projects to six sigma specialists. Finally, a case study in automobile industry is presented and then the framework is discussed to illustrate the application of the frameworkdeveloped.
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