ANFIS Classifier Research Articles

A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.

Analysis and Attack Detection in GSM Mobile Network With an Intelligent Jammer Using ANFIS Classifier

Analysis and Attack Detection in GSM Mobile Network With an Intelligent Jammer Using ANFIS Classifier

Retraction Note to: Computer aided innovation method for detection and classification of cervical cancer using ANFIS classifier

Retraction Note to: Computer aided innovation method for detection and classification of cervical cancer using ANFIS classifier

Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them. The purpose of this classifier is to distinguish between normal and pathological cervical pictures. Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization. The proposed methodology outperforms standard CC identification and classification methodologies.

Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies.

Prediction of Surface Roughness in CNC Turning Process using Adaptive Neural Fuzzy Inference System

This paper presents the methodology of surface roughness inspection in the CNC Turning process. Adaptive Neural Fuzzy Inference System classifier can predict the high accuracy roughness value by insisting on surface roughness image. The vision system captures the image and determines the mean value by using the ANFIS algorithm. Training sets variables speed, depth of cut, feed rate, and mean value are feed as the input, and manual stylus probe surface roughness value is feed as the output. After the simulation process, the testing input was performed, and finally getting the vision measurement value. This higher accuracy (above 95%) and low error rate (below 4%) can be achieved by using the ANFIS classifier, which is predominantly helpful for the industry to measure surface roughness. Assign the quality of the product by evaluating the manual stylus probe and vision measurement value.

Classification of abnormalities in breast ultrasound images using ANN, FIS and ANFIS classifier: A comparison

When diagnosed early, cancer can be cured. The leading cause of death for women is breast cancer, the most common form of cancer in women. However, there are several ways to think about breast cancer, the US is widely used as it is not aggressive and painless. The appearance of dots on ultrasound images reduces image clarity, affecting image quality. Dots are drawn to accurately define distorted images. This is accompanied by the separation of the wound using a level setting method, after which the features of the lesions are removed. Only the most distinct traits are considered. Using the division of ANN, FIS and ANFIS, with a accuracy of 92.3 percent, 88 percent and 96 percent, the inconsistencies in the 41 images of Breast Ultrasound are identified and classified.

Detection of Brain Tumor in MRI Images Using Optimized ANFIS Classifier

Tumor is basically a most common disease of brain and the Brain Tumor (BT) treatment has crucial significance. A diagnostic procedure called MRI image that is employed for detecting BT. It is the utmost important and intricate tasks in numerous medical-image applications since it typically involves a huge quantity of data. A lot of methods were applied in BT detection ranging as of image processing to examine the BT; however, the prevailing BT technique is tedious and less effective. So, this paper proposed the detection of the BT in MRI images utilizing optimized ANFIS classifier. Originally, the input MR image is preprocessed utilizing Gaussian Filter (GF) that removes the noise from the inputted image, additionally, the non-brain tissues (NBT) are removed using the technique of skull stripping (SS). After that, segmentation is performed wherein the tumor part is segmented utilizing CBAC technique and edema part is segmented utilizing HLSS segmentation technique. Then, GLCM in addition to GLRLM features are extracted afterward that extorted features is chosen by BFO algorithm. Finally, the selected features inputted to the optimized ANFIS classifier that classifies the tumor class types as Meningioma, Glioma, along with Pituitary. In ANFIS, the optimization procedure is achieved utilizing the PSO. The proposed system’s performance is contrasted to the prevailing systems regarding precision, recall, specificity, sensitivity, accuracy, together with F-Measure.

WITHDRAWN: Retinal image classification by glaucoma based on ANFIS classifier

Glaucoma is a disorder of eye and tends to permanent blindness when left undetected or untreated till final stage. Colour fundus images are the primary way to identify this disease. With the help of image features it can be classified as disease affected image and healthy image. The image is pre-processed by using CLAHE, ISODTA and kurtosis method. An approach to automatically classify the images by extracting features from a fundus image is essential. Identifying optical disc and optical cup from the retinal image and then analysing it to identify disease patterns are the steps to be evaluated. This paper approaches a method to classify the retinal image based on its disease pattern. This proposed model use neural network algorithm for classification. For prediction and extraction it uses features of an image. There are techniques implemented previously for classification namely fuzzy and state-of-art method. But these methods was unable to provide an accurate results over fast time. This proposed model eliminates the negative impacts of previous techniques successfully. Thus the proposed neural network based image classification method can be utilized in opthomologistical applications.

Enhanced artificial bee colony approach for the enhancement and classification of underwater images

ABSTRACT In an underwater view, wavelength-reliant light absorption in addition to scattering worsens the visibility of images that lead to lower contrast and also distorted color casts. To handle these problems, a proficient approach is proposed for the enhancement and also for the classification of UI. Originally, the underwater input image RGB is modified using Enhanced ABC algorithm. The subsequent phase is to extract the features. The extracted attributes are inputted to the Modified PCA approach, in this phase, the dimensionality of the features is reduced. Then, the classification operation is performed by utilizing ANFIS classifier. At last, the classified enhanced deeper water images along with the enhanced shallow water images are analyzed during the testing phase. The performance analysis is made for the proposed classifiers and existing techniques such as NN, SVM, and KNN. In addition, the performance of the proposed EABC model is compared over ABC, GA, and PSO in terms of correlation, Spearman rank correlation, sharpness, EME, Mutual information and NMI. The proposed classified method obtain the accuracy (0.9473), sensitivity (0.9230), specificity (0.9677) however, the existing methods provide only 0.8771 accuracy. Similarly, the proposed Enhanced ABC methods provide the improved performance while considering the other optimization algorithm. Abbreviations: ABC: artificial bee colony; ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; CM: covariance matrix; DWT: discrete wavelet transform; EABC: enhanced artificial bee colony; EME: enhanced measurement error; FDR: false discovery rate; FLS: forward-looking sonar; FPR: false positive rate; FS: forward scattering; GA: genetic algorithm; KNN: k-nearest neighbors; MCC: Mathew’s correlation coefficient; MOE: measure of entropy; NMI: normalized mutual information; NN: neural network; NPV: negative prediction value; PCA: principal component analysis; PD: probability distribution; PSO: particle swarm optimization; RGB: red green blue; SD: standard deviation; SRC: Spearman rank correlation; SURF: speeded up robust feature; SVM: support vector machine; SWT: stationary wavelet transform; UIE: underwater image enhancement; WVD: Wigner-Ville distribution

A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System.

Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches. To evaluate spasticity conveniently, a novel spasticity evaluation method based on surface electromyogram (sEMG) signals and adaptive neuro fuzzy inference system (i.e., the sEMG-ANFIS method) was presented in this paper. Eleven stroke patients with spasticity and four healthy subjects were recruited to participate in the experiment. During the experiment, the Modified Ashworth scale (MAS) scores of each subject was obtained and sEMG signals from four elbow flexors or extensors were collected from several times (4–5) repetitions of passive stretching. Four time-domain features (root mean square, the zero-cross rate, the wavelength and a 4th-order autoregressive model coefficient) and one frequency-domain feature (the mean power frequency) were extracted from the collected sEMG signals to reflect the spasticity information. Using the ANFIS classifier, excellent regression performance was achieved [mean accuracy = 0.96, mean root-mean-square error (RMSE) = 0.13], outperforming the classical TSRT method (accuracy = 0.88, RMSE = 0.28). The results showed that the sEMG-ANFIS method not only has higher accuracy but also is convenient to implement by requiring fewer repetitions (4–5) of passive stretches. The sEMG-ANFIS method can help stroke patients develop proper rehabilitation training programs and can potentially be used to provide therapeutic feedback for some new spasticity interventions, such as shockwave therapy and repetitive transcranial magnetic stimulation.

Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments

Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.

Neural correlates of action video game experience in a visuospatial working memory task

Studies have shown improved cognitive performance of action video game players (AVGPs); however, the corresponding neural correlates are still unclear. The present study aims to investigate whether the unsupervised training on action video games contributes to the visuomotor adaptation of working memory or not, considering neural measures. Twenty-seven-Channel electroencephalogram (EEG) signals from 35 participants are recorded while performing Corsi block-tapping task (CBTT), a working memory task incorporating recall of visual and sequential–spatial information, and are analyzed employing wavelet transform-based feature extraction techniques. For removing artifacts, we have used the wavelet denoising method. Different mother wavelet functions, with different thresholding algorithms, are compared. The dmeyer mother wavelet function along with Rigorous SURE soft thresholding emerged as the best combination for denoising. Comparison of wavelet energy features has revealed improved rhythmic activity in AVGPs. Also, two measures of engagement index β/(α + θ) and β/α are computed which has provided good classification accuracies with ANFIS classifier. Higher engagement indexes are found in the frontal, right parietal and occipital cortex of AVGPs. These results evidence the neurocortical modulation among the players that may attribute to the effective cognitive processes in AVGPs related to the task.

Computer Aided Detection and Screening of Thyroid Cancer Using ANFIS Classifier

Computer Aided Detection and Screening of Thyroid Cancer Using ANFIS Classifier

Multiple Criteria Decision Analysis Based Overlapped Latent Fingerprint Recognition System Using fuzzy Sets

Latent fingerprints have attracted considerable attention from researchers in the fields of forensics and law enforcement applications. Public demand for these applications may be the driving force behind further progress in biometrics research. Although great effort has been taken to devise algorithms for overlapped latent fingerprint classification system, there are still many challenging problems involved in fingerprint classification systems. Most of the fingerprint-based applications will prolong with fingerprint recognition because of its proven performance, the existence of large databases and the availability of the fingerprint devices with minimum cost. There are various issues that need to be addressed to develop fingerprint classification system. In this connection, there are some designing challenges such as nonlinear distortion, low-quality image, segmentation, sensor noise, skin conditions, overlapping, inter-class similarity, intra-class variations and template ageing. In crime scenes, the latent images can be overlapped with some background images or more number of fingerprint images from same person or different person. An overlapped fingerprint image should be processed for fingerprint classification. This proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints. The proposed multiple criteria such as first-order, second-order and third-order features are used to classify the overlapped latent fingerprints. The proposed system designs the novel classification system for overlapped latent fingerprint images using ANFIS classifier. Extensive experiments are performed on the simultaneous latent fingerprint databases, and National Institute of Standards and Technology-Special Database 27, Fingerprint Verification Competition 2006 Database1-A and Database2-A databases. The planned work enables accurate and fast data retrieval by using one-to-N fingerprint classification for overlapped latent images. The experimental results are highly promising, and they outperform the existing systems in classifying overlapped images. The performance of adaptive neuro fuzzy inference system classifier is evaluated by applying the k-fold cross-validation technique. The outcome of the work shows that the overlapped fingerprint is classified in a successful manner, and the results are compared with Bayes, SVM and MLP classifiers. The obtained results show that the proposed system achieves a better classification rate of 90.66% with 5 s, 86.66% with 12 s compared to the existing system.

An Innovative System for Classifying Cervical Cancer Using Features Based ANFIS Classifier

An Innovative System for Classifying Cervical Cancer Using Features Based ANFIS Classifier

An Enhanced Medical Diagnosis Sustainable System for Brain Tumor Detection and Segmentation using ANFIS Classifier

An Enhanced Medical Diagnosis Sustainable System for Brain Tumor Detection and Segmentation using ANFIS Classifier

Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations.

An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels. For this purpose, source localization/extraction and noise reduction approaches based on Independent Component Analysis were used prior to correlation analysis. Feature extracted segments were then classified in one of the five classes including WAKE, STAGE1, STAGE2, SWS and REM via an ensemble neuro-fuzzy classifier. Some techniques were employed to improve the classifier's performance including Scaled Conjugate Gradient Method to speed up learning the ANFIS classifiers, a pruning algorithm to eliminate irrelevant fuzzy rules and the 10-fold cross-validation technique to train and test the system more efficiently. The performance of classification for two strategies including (1) feature extraction from effective cerebral sources and (2) feature extraction from selected channels of denoised EEG signals was compared and contrasted in terms of training errors and test accuracies. The first and second strategies achieved 92.23 and 88.74% agreement with human expert respectively which indicates the effectiveness of the staging system based on cerebral sources of activity. Our results further indicate that the misclassification rates were almost below 10%. The proposed automated sleep staging system is reliable due to the fact that it is based on the underlying dynamics of sleep staging which is less likely to be affected by sleep fragmentations occurred repeatedly in OSA.

Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier

ABSTRACTAbnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues.