scholarly journals An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

Author(s):  
Ranjit Kaur ◽  
Kamaldeep Kaur ◽  
Aditya Khamparia ◽  
Divya Anand

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.

Author(s):  
Kai Keng Ang ◽  
Chai Quek

Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000). NFS is capable of abstracting a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises of if-then fuzzy rules and linguistic terms described by membership functions. Hence, the main strength of NFS in modeling data is universal approximation (Tikk, Kóczy, & Gedeon, 2003) with the ability to solicit interpretable if-then fuzzy rules (Guillaume, 2001). However, modeling data using NFS involves the contradictory requirements of interpretability versus accuracy. Prevailingly, NFS that focused on accuracy employed optimization which resulted in membership functions that derailed from human-interpretable linguistic terms, or employed large number of if-then fuzzy rules on high-dimensional data that exceeded human level interpretation. This article presents a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS). RNFS synergizes the sound concept of knowledge reduction from rough set theory with NFS. RNFS reinforces the strength of NFS by employing rough set-based techniques to perform attribute and rule reductions, thereby improving the interpretability without compromising the accuracy of the abstracted fuzzy model.


Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 484 ◽  
Author(s):  
Stéfano Frizzo Stefenon ◽  
Roberto Zanetti Freire ◽  
Leandro dos Santos Coelho ◽  
Luiz Henrique Meyer ◽  
Rafael Bartnik Grebogi ◽  
...  

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.


2022 ◽  
Author(s):  
M.Uma Maheswar Rao ◽  
Kanhu Charan Patra ◽  
Suvendu Kumar Sasmal

Abstract Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.


Author(s):  
Shahrani Shahbudin ◽  
Murizah Kassim ◽  
Roslina Mohamad ◽  
Saiful Izwan Suliman ◽  
Yuslinda Wati Mohamad Yusof

This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classification tasks using extracted features based on S-transforms methods. The ANFIS model with a five-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal.  Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.


Author(s):  
Jimmy Singla

In this chapter, the neuro-fuzzy technique has been used for the diagnosis of different types of diabetes. It has been reported in the literature that triangular membership functions have been deployed for Mamdani and Sugeno fuzzy expert systems that have been used for diagnosis of different types of diabetes. The Gaussian membership functions are expected to give better results. In this context, Gaussian membership functions have been attempted in the neuro-fuzzy system for the diagnosis of different types of diabetes in the research work, and improved results have been obtained in terms of different parameters like sensitivity, specificity, accuracy, precision. Further, for the comparative study, the dataset used for neuro-fuzzy expert system developed in this research work has been considered on Mamdani fuzzy expert system as well as Sugeno fuzzy expert system, and it has been confirmed that the result parameters show better values in the proposed model.


2018 ◽  
pp. 69-72
Author(s):  
O.V. Gurzhiy ◽  
S.V. Kolomiiets ◽  
V.L. Melnik ◽  
O.E. Berezhna

Improvement of teaching methods, search for new forms of active cognitive practice education for internship-doctors are important tasks of improving the quality of specialists training in the system of post-graduate education. Since 1991, internship-doctors specializing in "Therapeutic dentistry", "Dentistry of childhood", "Orthopedic stomatology", "Orthodontics" and "Surgical dentistry" study at the Department of Postgraduate Education of Doctors-Dentists of the Ukrainian Medical Stomatological Academy. The period of study on the full-time basis of the discipline "Surgical Dentistry" is 1 month. During seminars, practical classes, and lecture courses, internship-doctors study the basic issues of surgical stomatology: emergency care, anesthetics of the tissues of the maxillofacial area, typical and atypical teeth removal, inflammatory diseases, traumatic lesions of soft tissues and bones of the face, neoplasms of the maxillofacial area and more. Moreover, much attention is paid to differential diagnosis and modern methods of treatment. Undoubtedly, the basic and necessary form of training is lectures. In our opinion, the main methods that increase the effectiveness of lectures are discussions, the usage of interactive learning tools and, at the same time, the ability of the lecturer to get in contact with the audience easily paying no regard to the level of their training, who is a professional able to utilize high multimedia technologies along with pedagogical techniques and foreign languages. In addition, internship-doctors are present at the advisory activities of the professors and associate professors of the department, are involved in clinical treatments of not ordinary patients, have an independent appointment. The teachers of the department carry out individual work with each doctor, especially those who wish to link their future medical activities with surgical dentistry, and it is aimed at professional training of a future specialist. The success of the professional work of the dental surgeons depends, first of all, on their experience and knowledge of theirs specialty, the grounds of modern medical science in general, but their innate characteristics, features of character, correspondence to medical activity are not of less importance. For preparation to practical and seminar classes at the department are used self-developed study guides, monographs on surgical dentistry. It contains not only relevant educational material, but also models of test control, situational tasks, the solution of which helps in mastering the topic and indicates the ways of the practical application of the acquired knowledge. Interns receive computer-controlled training twice a month, and also use thematic programs that provide the possibility to study any sections of the course in surgical dentistry. The organization of research work of interns at the Department of Postgraduate Education in dentistry is carried out in three stages of different directions. Modern society considers a specialist not only as a person who possesses knowledge and skills in the professional field, but also as a person who is able to act effectively in complicated, non-standard situations, to make decisions independently, to self-develop and to perfect themselves, to be able to communicate people, and for this purpose All conditions are present at the department. These and others professionally important properties and personal qualities determine the professional competence of a specialist, especially a dental surgeon.


Author(s):  
Renato Morales-Nava ◽  
Víctor Manuel Zamudio-Rodriguez ◽  
Francisco Javier Navarro-Barrón ◽  
David Asael Gutierrez-Hernandez ◽  
María del Rosario Baltazar-Flores ◽  
...  

Fuzzy logic systems provide a set of proven tools and methods to imitate or emulate human basic reasoning, that is, transform it into instructions that the computer can understand or transform into binary instructions. Based on the structure with multiple layers, subsystems and varied topologies that in previous research have shown that fuzzy hierarchical systems have been used to improve the interpretability, in this research work the objective is to design a fuzzy hierarchical system using fuzzy composite concepts artificial intelligence compounds to measure the efficiency of simulated scenarios. As a fundamental part of the present investigation, an analysis is made of the sensitivity of the results of the fuzzy system with respect to its inputs and with a set of membership functions, in a virtual scenario; which allows demonstrating the advantages obtained by applying a fuzzy hierarchical system to systems oriented to the area of health.


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