see

Table_1_Authors_singleyr_2023_pubs_since_1788_wopp_extracted_202408

https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/7

In recent years, a number of Stanford University researchers have been creating datasets using Scopus database data and calculating an index called composite citation index. The latest update of this dataset (version 3) was published on October 19, 2021 (27 October 2023), which extends the data coverage from 1960 to 2020. The purpose of this dataset - according to Stanford University researchers - is to provide a set of standardized citation metrics to evaluate the citation impact of scientists in different disciplines and scientific fields. The composite citation index (abbreviated as C) is a set of six separate citation indices that Stanford University researchers introduced and calculated in their 2016 article. Dr. Mohammadzadeh in 2021-2024 was among the top 2% researchers in the field of Artificial intelligence.

One of the key trends in information technology is the rise of artificial intelligence (AI) and machine learning. AI has the potential to revolutionize industries by automating processes, improving decision-making, and enhancing customer experiences. Machine learning algorithms are being used to analyze vast amounts of data and extract valuable insights, enabling organizations to make more informed decisions and drive innovation. On the other hand, intelligent automation and control systems are a trend that has primarily hit the manufacturing and production units and is estimated to only grow more in the coming years. Intelligent automation has also enabled processes to work faster and would allow companies to reach their goals much more efficiently. In this talk, first, the AI systems and majors are defined, the main application and challenges of AI in control systems are summarized, and a new approach of intelligent fuzzy systems is presented as a solution to deal with high dimensional problems. The concept of Fourier-based type-2 fuzzy neural networks is presented and by some examples such as face recognition problem, English handwriting digit recognition, and modeling problem with real-world data its effectiveness is illustrated.

Type-2 Fuzzy Predictive Control System and its Application for Glucose Level Regulation

Diabetes is one the important diseases that need to be considered. Based on the International Diabetes Federation, diabetes is a growing medical problem and it is predicted that more than 500 million people will be suffered by this illness. In recent years, various control methods have been applied to control the glucose-insulin system. The proposed approaches in literature can be classified into 4 categories. These categories are: Open-loop control , Linear control , Nonlinear control and Intelligent control methods. In this report a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The designed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman’s model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared
with the other T2 fuzzy and well-known model predictive controllers. The results of the
paper clearly show the superiority of the proposed T2 fuzzy logic control system.

Type-3 Fuzzy Logic systems

In the past decades, the application of the fuzzy systems has been extended to many branches of science, such as control systems, fault detection problems, decision making problems, energy management systems, forecasting problems, disease diagnosis systems, pattern recognition problems  and many others. The main capability of the fuzzy systems is their approximation ability. In most of the aforementioned applications, the fuzzy systems are used to estimate a nonlinear function or to derive a dynamic model from a data set. 

In this webinar, an interval type-3 fuzzy system (IT3FS) is investigated. The uncertainty modeling capability of the proposed IT3FS has been improved in contrast to type-1 and type-2 fuzzy systems (T1FS and T2FS). Because, in IT3FS, the membership is defined as an interval type-2 fuzzy set, while, in T1FS and T2FS the membership is crisp value and type-1 fuzzy set, respectively.

The headlines are summarized as follows:

1- A short introduction about fuzzy logic systems is presented

2- The generalized fuzzy logic systems and their features are investigated.

3-The main structure and equations of type-3 fuzzy logic systems are investigated.

https://research.ubonab.ac.ir/News/649/

https://pr.ubonab.ac.ir/News/1141/

In recent years, a number of Stanford University researchers have been creating datasets using Scopus database data and calculating an index called composite citation index. The latest update of this dataset (version 3) was published on October 19, 2021 (27 October 2023), which extends the data coverage from 1960 to 2020. The purpose of this dataset - according to Stanford University researchers - is to provide a set of standardized citation metrics to evaluate the citation impact of scientists in different disciplines and scientific fields. The composite citation index (abbreviated as C) is a set of six separate citation indices that Stanford University researchers introduced and calculated in their 2016 article. Dr. Mohammadzadeh in 2021-2023 was among the top 2% researchers.