scholarly journals Development of an Autonomous Wheelchair for The Disabled and Performance Analysis Using ANFIS Model

2020 ◽  
Author(s):  
Modestus O. Okwu ◽  
Lagouge K. Tartibu ◽  
Michael Ayomoh ◽  
Daniel Ighalo
Keyword(s):  
Fuzzy Inference ◽  
Research Work ◽  
Obstacle Detection ◽  
Educational Goals ◽  
Inference System ◽  
Research Fields ◽  
The Disabled ◽  
Wheel Chair ◽  
And Performance ◽  
Set Up

Abstract Navigation for persons with physical disability very often poses a major challenge to both the victims and their dedicated navigation assistance-provider. This age-long problem has been a major concern in diverse research fields in the literature ranging from assistive medicine to applied intelligence amongst others. This research work is a build-up to the literature, hence, has presented an automated wheelchair system designed, fabricated and enhanced with joystick capability for obstacle detection and autonomous stoppage. A microcontroller unit known as Arduino uno was built into the system architecture to synchronise the entire set-up by driving the DC motor for directional and linear motion of the wheel chair. The developed system would greatly improve the community of people who have lost some means of independent mobility thereby leading to an improvement in their self-esteem enabling them pursue their vocational and educational goals. Conclusively, the developed system was tested using Adaptive Neuro-Fuzzy Inference System (ANFIS), the sensitivity rule viewer at the first trial gave a total intelligence of 63.8%, further improvement was made and a second trial gave a rating of 75% and the final gave a value of approximately 80%. This shows that the system is efficient, effective and of excellent performance.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

Speech Noise Cancellation Based on a Neuro-Fuzzy System: Further Improvements

2001 ◽  
Vol 5 (1) ◽  
pp. 15-21
Author(s):  
Anna Esposito ◽  
◽  
Eugene C. Ezin ◽  
Carlos A. Reyes-Garcia ◽  
◽  
...  
Keyword(s):  
Nonlinear Distortion ◽  
Fuzzy Inference ◽  
Experimental System ◽  
Computational Time ◽  
Further Training ◽  
Inference System ◽  
Car Traffic ◽  
Neuro Fuzzy ◽  
Average Improvement ◽  
Set Up

This work reports on an experimental system based upon the Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture, which is employed for identifying a nonlinear model of the unknown dynamic characteristics of the noise transmission paths. The output of this model is used to subtract the noisy components from the received signal. The novelty of the system described in the present paper, with respect to our previous work, consists in a different set up, which requires more fuzzy rules, generated by seven trapezoidal membership functions, and uses a second order it sinc function to generate the nonlinear distortion of the noise. Once trained for few epochs (only three) with a long sentence corrupted with babble noise, the FIS obtained, has the ability to clean speech sentences corrupted by babble and also by car, traffic, and white noise, in a computational time almost close to realtime. The average improvement, in terms of SNR, was 37 dB without further training.

scholarly journals Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

2018 ◽  
pp. 1-9
Author(s):  
Ibrahim Goni ◽  
Christopher U. Ngene ◽  
Manga I. ◽  
Auwal Nata’ala ◽  
Sunday J. Calvin
Keyword(s):  
Fuzzy Inference ◽  
Intelligent System ◽  
Research Work ◽  
Back Propagation ◽  
Night Sweat ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
History Of ◽  
Loss Of Appetite

Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2.   

scholarly journals Noise and Echo Aware Accurate Dysarthria Speech Recognition Model

2019 ◽  
Vol 8 (4) ◽  
pp. 7531-7536
Keyword(s):  
Speech Recognition ◽  
Fuzzy Inference ◽  
Research Work ◽  
Accurate Prediction ◽  
Speech Signals ◽  
Least Mean Square ◽  
Matlab Simulation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Human Muscles

Dysarthria speech is the speech disorder which would be caused due to weakness of human muscles. The human with dysarthria disorder cannot speak normally whose speech will be very slow and congestive words which might be more difficult to understand. Thus it is required to create the environment where the speech of dysarthria disorder people can be recognized. This is done in our previous research work by introducing the method namely Hidden Markov Model based Speech Recognition (HMMSR). However this research work didn’t focus on accurate prediction and the echo noises presence in the speech signals. This would lead to inaccuracy in speech recognition. This is resolved by introducing the efficient dysarthria speech recognition framework namely Noise and Echo aware Dysarthria Speech Recognition Method (NE-DSRM). In this research work, Hybrid Least Mean Square-Adaptive Neuro Fuzzy Inference System (LMS-ANFIS) has been used for preprocessing. This method will remove both echo and noises present in the speech signals to ensure the accurate prediction outcome. And then speech recognition is performed by comparing the dysarthria speech with the phonological speech based on which relevancy would be identified. The accuracy of speech recognition can be improved by introducing the SVM based learning methodology which can classify the dysarthria speech based on which more relevant matching can be done. The objective of the system is, after being trained, to identify and classify limited-vocabulary sets of speaker-dependent. The overall assessment of the research work is done in the matlab simulation environment from which it is proved that the proposed method NE-DSRM tends to have better performance than the existing research works.

Air Pollutants and Meteorological Parameters Influence on PM2.5 Forecasting and Performance Assessment of the Developed Artificial Intelligence-Based Forecasting Model

2017 ◽  
Vol 68 (4) ◽  
pp. 864-868
Author(s):  
Marian Popescu ◽  
Sanda Florentina Mihalache ◽  
Mihaela Oprea
Keyword(s):  
Artificial Intelligence ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Fuzzy Inference ◽  
Computational Effort ◽  
Forecasting Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Performance ◽  
The Comparative Study

Particulate matter with an aerodynamic diameter lower than 2.5 �m (PM2.5) is one of the most important air pollutants. Current regulations impose measuring and limiting its concentrations. Thus, it is necessary to develop forecasting models programs that can inform the population about possible pollution episodes. This paper emphasizes the correlations between PM2.5 and other pollutants, and meteorological parameters. From these, nitrogen dioxide and temperature showed have the best correlations with PM2.5 and have been selected as inputs for the proposed forecasting model besides four PM2.5 concentrations (the values from current hour to three hours ago), the output of the model being the prediction of the next hour PM2.5 concentration. Two methods from artificial intelligence were used to build the forecasting model, namely adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN). The comparative study between these methods showed that the model which uses ANN have better results in terms of statistical indicators and computational effort.

Control of BLDC motor using MRPID controller with modified landsman converter

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Murali Dasari ◽  
A. Srinivasula Reddy ◽  
M. Vijaya Kumar
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Inference ◽  
Dynamic Properties ◽  
Bldc Motor ◽  
Inference System ◽  
Content Type ◽  
Brushless Dc ◽  
Simulink Model ◽  
And Performance ◽  
Bus Voltage

PurposeThe principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.Design/methodology/approachIn order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.FindingsBrushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.Originality/valueThe presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

Forecasting EGR Rate of Diesel Engine Based on Neural Syncretic Theory

2011 ◽  
Vol 301-303 ◽  
pp. 1789-1794
Author(s):  
Shi Bin Yang ◽  
Ming Jiang Hu
Keyword(s):  
Diesel Engine ◽  
Fuzzy Inference ◽  
Resolving Power ◽  
Emission Model ◽  
Inference System ◽  
Rbf Network ◽  
On Line ◽  
Set Up ◽  
Engine Emission ◽  
Diesel Engine Emission

To counter the influencing emission of the diesel engine by the EGR rate, the emission model of the diesel engine was set up by combining Radial Basis Function neural network with Adaptive Neural Fuzzy Inference System. The model first draws on the nonlinear approaching capacity of the RBF network to forecast the diesel engine emission which takes no account of the factor of the EGR rate, and then, based on influencing the diesel engine emission by the EGR rate, the ANFIS system was used to modify the results of the diesel engine emission obtained by using the RBF network so as to acquire the EGR rate curve. The result showed that the emission model of the diesel engine was reasonable; the forecasting strategy had the good resolving power and could be much fitted for the on-line aging forecast of the EGR rate.

scholarly journals CRITICAL THINKING SKILLS AMONG MOROCCAN PHD STUDENTS OF HEALTH SCIENCES

2020 ◽  
pp. 279-288
Author(s):  
Imane Ghazlane ◽  
◽  
Bouzekri Touri ◽  
Mohamed Bergadi ◽  
Khalid Marnoufi ◽  
...  
Keyword(s):  
Critical Thinking ◽  
Research Methodology ◽  
Research Work ◽  
Scientific Research ◽  
Thinking Skills ◽  
Health Sciences ◽  
Critical Thinking Skills ◽  
Close Relationship ◽  
And Performance ◽  
Set Up

The significant weakness in problem solving and innovation continues to affect scientific production in Morocco. That’s why, many reforms are set up to address the various problems raised. The national strategy for the development of scientific research by 2025 indicates the proper conduct and methodological integrity of research work. Literature states that critical thinking is the intellectual basis of the scientific research method. Furthermore, it has been empirically demonstrated that students with strong critical thinking skills(CTS)perform well in research methodology subjects. Therefore, the close relationship between critical thinking skills and performance in the research methodology application highlights the potential of young researchers in this area. The present work is the subject of an exploratory study that intends to reveal CTS, considered as an essential foundation for any research methodology, among 25 participants registered as researchers belonging to health sciences majors. The findings of this study scored moderate overall results of CTS. A significant correlation has been found between the overall score skills of the HSRT and the scores of the marks of their final projects. The correlation indicates that the success of their dissertation work was related to the deduction, evaluation, and inference subscales of the HSRT.

scholarly journals Prediction of strength and durability properties of HPC composites using Adaptive Neuro-fuzzy Inference System

2020 ◽  
Vol 184 ◽  
pp. 01102
Author(s):  
P Magudeaswaran. ◽  
C. Vivek Kumar ◽  
Rathod Ravinder
Keyword(s):  
Fuzzy Inference System ◽  
High Performance ◽  
Fuzzy Inference ◽  
High Performance Concrete ◽  
Intelligent System ◽  
Computing Methods ◽  
Inference System ◽  
Performance Requirements ◽  
Neuro Fuzzy ◽  
And Performance

High-Performance Concrete (HPC) is a high-quality concrete that requires special conformity and performance requirements. The objective of this study was to investigate the possibilities of adapting neural expert systems like Adaptive Neuro-Fuzzy Inference System (ANFIS) in the development of a simulator and intelligent system and to predict durability and strength of HPC composites. These soft computing methods emulate the decision-making ability of human expert benefits both the construction industry and the research community. These new methods, if properly utilized, have the potential to increase speed, service life, efficiency, consistency, minimizes errors, saves time and cost which would otherwise be squandered using the conventional approaches.

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