Types Of Fuzzy Membership Functions Research Articles

An Adaptive Fuzzy Modeling of Visual Attention in Real-world Interaction with Health Information System

Bottom-up and top-down processes are the two mechanisms of visual attention allocation, which allow people to efficiently spot task-relevant stimuli from cluttered and noisy environments, while staying alert to abnormalities within the visual field of view. This paper presents a preliminary study of the physicians’ real-life interaction with Information Communication Technology (ICT) in their own offices, along with extensively analyzing one case of an hour-long interaction of a physician, in which one performs a daily routine of reviewing patient electronic health records (EHRs) and writing diagnostic notes to the system interface. The physician interactions were captured in a time series data by recording display screen, keystrokes and mouse movements, also by simultaneously tracking eye movements. Then, a fuzzy-based model that can distinguish bottom-up and top-down processes were defined by using statistical random variables in terms of eye-movement patterns. The shift between those two attentional processes was detected by tracking the parametric changes of gaze behaviors as input: significant shift of fixation, sustained gazing, and fixation trajectory over time. Based on those gaze metrics, a random variable was assigned to the discrete probability of low (0), medium (0.5), or high (1.0), for a quantified fuzzy output, which was further machine-learned into an Adaptive Neuro-Fuzzy Inference System (ANFIS) model in order to judge how a physician is likely to be dominated by a bottom-up or top-down processes in performing a task at that instance in time. On training the ANFIS model with three different types of fuzzy membership functions (Gaussian, triangular and trapezoidal), the model performed best with the Gaussian function (after 100 iterations, the predicted root mean-square error (RMSE) converged at 0.07%, yielding a smooth linear curve). For a proof-of concept, the model was implemented by using one physician’s gaze behaviors, of which the average, machine-learned fuzzy output probability indicated that the physician was veering toward bottom-up visual attention. This individualized, task-specific pattern of visual attention has implications for the designs of intelligent interface in ICT. Our ANFIS model can scale up to different physicians and tasks to predict the likelihood of bottom-up or top-down information processing based on real-world gaze behaviors.

Analysis of Different Fuzzy C-Means Membership Functions for the Design of CAD Tool of Early Breast Cancer Detection

In this paper, analysis of the different membership function for fuzzy C-means based segmentation technique are done to overcome the issue of random initialization of membership matrix, 116 hybrid features set and analysis of various classifier with modified K nearest neighbor (KNN) are proposed for primary breast cancer detection. The steps involved to design CAD tool for breast cancer detection includes preprocessing, segmentation, feature extraction, selection, and classification. In pre-processing, manual cropping of original mammograms for removal of background details, and contrast enhancements are used. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancement of mammographic images. Further, nonlinear complex diffusion based unsharp-masking and crispening method is utilized to highlight the abnormalities of mammogram such as micro calcifications, tumors, etc. Further, 2D DWT is applied for taking approximation coefficient followed by fuzzy C-means segmentation with different types of fuzzy membership functions, which is utilized to segment and extract the region of interest. For feature extraction, 116 hybrid features are extracted from each image such as Geometric based features, Texture based features, Gabor based features and Histogram based features. Next step is Minimum Redundancy and Maximum Relevancy (MRMR) is used as features selection technique for discarding repetitive and irrelevant features. Final step is classification, the modify KNN classifier based on mutual information in place of Euclidean distance is proposed while finding the nearest neighbors. The comparative analysis of the fuzzy C-means segmentation techniques with different types of fuzzy membership methods are presented in terms of RI, GCE, VOI and the various classifier performance are taken in terms of accuracy, precision, sensitivity, specificity.

Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions

Due to the numerous applications that utilize different types of fuzzy membership functions (MFs), it may sometimes be difficult to choose an appropriate MF for a particular application. In this paper, we establish preliminary guidelines to direct this selection by proposing a three-stage method. In the “forward” stage, different MFs, such as crisp MFs, type-1 (T1) fuzzy MFs, and type-2 (T2) fuzzy MFs, are generated from multidimensional data sets. Next, in the “reverse” stage, data is generated back from these MFs by considering different bin sizes. In doing so, various data sets may be generated for different applications which require fuzzy data. Finally, for the “similarity analysis” stage, we propose an iterative algorithm that makes use of the results of Wilcoxon signed rank (WSR) and Wilcoxon rank sum (WRS) tests to compare the original data and the generated data. From the results of these tests, recommendations concerning the suitability of MFs for a specific application may be suggested by observing the accuracy of representation and the requirements of the application. With this analysis, the objective is to gain insight on when T2 fuzzy sets may be considered to outperform T1 fuzzy sets, and vice versa. Several examples are provided using synthetic and real data to validate the iterative algorithm for data sets in various dimensions.

Color differences based fuzzy filter for extremely corrupted color images

In this paper, a color difference based fuzzy filter is presented for fix and random-valued impulse noise. Noise detection scheme of two stages was applied to detect noise efficiently whereas for noise removal an improved Histogram based Fuzzy Color Filter (HFC) is presented. Pixels detected as noisy by the noise detection scheme are deliberated as candidate for the removal of noise. Candidate noisy pixels are then processed using a modified Histogram based Fuzzy Color Filter to estimate their non-noisy values. The idea of using multiple fuzzy membership functions is presented, so that best suitable membership function for local image statistics can be used automatically. In the proposed technique we have used three different types of fuzzy membership functions (bell-shaped, trapezoidal-shaped, and triangular-shaped) and their fuzzy number construction algorithms are proposed. Experimentation is also performed with three, five, and seven membership functions. Type and number of suitable fuzzy membership functions are then identified to remove noise. Comparison with the existing filtering techniques is established on the basis of objective quantitative measures including structural similarity index measure (SSIM) and peak-signal-to-noise-ratio (PSNR). Simulations show that this filter is superior to that of the existing state-of-the-art filtering techniques in removing fix and random-valued impulse noise whereas retaining the details of the image contents.

Performance Comparison of Collaborative Filtering withk-Anonymized Data by Fuzzyk-Member Clustering

In order to perform collaborative filtering with published databases in a privacy preserving manner, databases must be anonymized beforehand. This paper studies the applicability of fuzzyk-member clustering in privacy preserving collaborative filtering withk-anonymized data, in which users’ historical data ofkor more users are suppressed considering soft data partitions. By allowing boundary samples to be shared by multiple clusters, data anonymization is performed without significant loss of information. Its performances are compared with several different types of fuzzy membership functions.

Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security

Analysis of acquired nuclear detector gamma-ray signals for recognition of present radioisotopic signatures is crucial to national security and security applications. Identification algorithms must be accurate and rapid. Artificial intelligence is a scientific field with a variety of tools suitable to implement automated processing of nuclear signals. The use of low resolution portable detectors to measure gamma-ray signals has found a wide use in security and safeguards applications. In this paper, the fuzzy logic based analysis methodology that has been previously developed is applied and assessed on a variety of nuclear signals obtained with a low resolution scintillation detector, and more particularly a sodium iodide (NaI) detector. Various types of fuzzy membership functions are employed and their performance is assessed with regard to the number of positive detections, misses, and false alarms. Furthermore, recorded results from the set of low resolution gamma ray signals are used to estimate the detection sensitivity for each membership function. Results demonstrate the overall effectiveness of the fuzzy logic based identifier, and consist of the main course for the assessment of each membership function. Furthermore, comparison of results designates the triangular membership function as the best membership shape for this type of detector signals.

Automated leukocyte recognition using fuzzy divergence

This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others. In addition, image thresholding is modified for better recognition. Results are studied and discussed.

Parameter conditions for monotonic Takagi–Sugeno–Kang fuzzy system

This paper presents sufficient conditions on the parameters of the Takagi–Sugeno–Kang (TSK) fuzzy system under which the output of the TSK fuzzy system becomes monotonic with respect to its input. The output of the TSK fuzzy system is first differentiated with respect to its input and the parameter conditions are derived so as to make the derivative nonnegative. The parameter conditions are developed for both single-input and multi-input TSK fuzzy systems where the involved fuzzy membership functions are differentiable everywhere or bar some finite points. The derived monotonicity conditions consist of two parts: the conditions on the consequent parts and the conditions on the fuzzy membership functions. They are further characterized for the most important types of fuzzy membership functions.

FUZZY PREDICTIVE CONTROL OF UNCERTAIN CHAOTIC SYSTEMS USING TIME SERIES

In this paper, a simple fuzzy logic based intelligent mechanism is developed for predicting and controlling a chaotic system to a desired target, using only input–output data obtained from the unknown (or uncertain) underlying chaotic system. In the chaos prediction phase, a fuzzy system approach incorporating with Gaussian type of fuzzy membership functions is used. Only system input–output data are needed for prediction, and a recursive least-squares computational algorithm is employed for the calculation. In the controller design phase, the Lyapunov stability criterion is used, which forms the basis of the main design principle. Some simulation results on the chaotic Sin map and Hénon map are given, for both prediction and control, to illustrate the effectiveness and control performance of the proposed method.