Fuzzy Neural Network Classifier Research Articles

Feature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimization

Feature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimization

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min-max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min-max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min-max neural network classifiers mentioned in the literature.

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

PurposeNeuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. DesignIn this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). FindingsResults showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. OriginalityTo the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.

GAN-FuzzyNN: Optimization Based Generative Adversarial Network and Fuzzy Neural Network Classification for Change Detection in Satellite Images

GAN-FuzzyNN: Optimization Based Generative Adversarial Network and Fuzzy Neural Network Classification for Change Detection in Satellite Images

A Fuzzy Neural Network Classifier and Its Dual Network for Adaptive Learning of Structure and Parameters

A Fuzzy Neural Network Classifier and Its Dual Network for Adaptive Learning of Structure and Parameters

Study on Music Emotion Recognition Based on the Machine Learning Model Clustering Algorithm

In recent years, the explosive growth of online music resources makes it difficult to retrieve and manage music information. To efficiently retrieve and classify music information has become a hot research topic. Thayer’s two-dimensional emotion plane is selected as the basis for establishing the music emotion database. Music is divided into five categories, the concept of continuous emotion perception is introduced, and music emotion is regarded as a point on a two-dimensional emotional plane, together with the two sentiment variables to determine its location. The artificial labeling method is used to determine the position range of the five types of emotions on the emotional plane, and the regression method is used to obtain the relationship between the VA value and the music features so that the music emotion classification problem is transformed into a regression problem. A regression-based music emotion classification system is designed and implemented, which mainly includes a training part and a testing part. In the training part, three algorithms, namely, polynomial regression, support vector regression, and k-plane piecewise regression, are used to obtain the regression model. In the test part, the input music data is regressed and predicted to obtain its VA value and then classified, and the system performance is considered by classification accuracy. Results show that the combined method of support vector regression and k-plane piecewise regression improves the accuracy by 3 to 4 percentage points compared to using one algorithm alone; compared with the traditional classification method based on a support vector machine, the accuracy improves by 6 percentage points. Music emotion is classified by algorithms such as support vector machine classification, K-neighborhood classification, fuzzy neural network classification, fuzzy K-neighborhood classification, Bayesian classification, and Fisher linear discrimination, among which the support vector machine, fuzzy K-neighborhood, and the accuracy rate of music emotion classification realized by Fisher linear discriminant algorithm are more than 80%; a new algorithm “mixed classifier” is proposed, and the music emotion recognition rate based on this algorithm reaches 84.9%.

Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network

Adjusting tillage parameters according to soil conditions can reduce energy consumption. In this study, the working parameters and soil physical parameters of plowing were determined using a designed electric suspension platform and soil instrument. The soil conditions were classified into three physical states, namely ‘hard’, ‘zero’, and ‘soft’ using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi–Sugeno (T–S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil’s physical state. The results show that when 280 groups of test data were used to verify the model, 264 groups were correctly identified, indicating a soil physical state identification accuracy of 94.29%. The T–S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.

Ransomeware classification using fuzzy neural network algorithm

Traditional malware classification relies on known malware types and significantly large datasets labelled manually which limits its ability to recognize new malware classes. For unknown malware types or new variants of existing malware containing only a few samples each class, common classification methods often fail to work well due to severe over fitting. In this paper, we propose a new neural network structure called fuzzy neural network based algorithm for ransom ware classification. Malware classification with fuzzy neural network classifier yields 98% accuracy which means greater performance than classification with neural network.

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

AbstractWith recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

Modeling of false information on microblog with block matching and fuzzy neural network

The detection method of microblog false information has been constructed based on block matching and fuzzy neural network to improve the detection accuracy of microblog false information effectively. With this method, we can calculate the rank distance and sample entropy of microblog data according to the evaluation word rank vector of microblog false information, carry out the block matching of false information in fuzzy data set and input the characteristic quantity of microblog false information extracted into the fuzzy neural network classifier for data classification and recognition. So that it has achieved the optimized detection of false information and improved the judgment ability of false information. Finally, the key factors that affect the algorithm are deeply studied through simulation experiments according to the real data of Sina microblog, and the performance state between the proposed algorithm and Fuzzy C-means and Spectral Analysis algorithms is compared and analyzed correspondingly. The results show that the algorithm has good adaptability.

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

Design of Reinforced Fuzzy Radial Basis Function Neural Network Classifier Driven With the Aid of Iterative Learning Techniques and Support Vector-Based Clustering

In this article, a reinforced fuzzy radial basis function neural network (R-FRBFNN) classifier is proposed. It focuses on the development of methodologies of reinforced architecture to improve classification accuracy and enhance the robust capability based on two learning strategies. The two learning strategies are summarized: 1) R-FRBFNN designed via support vector (SV)-based fuzzy C-means (FCM) clustering and softmax-based iterative reweighted least square (IRLS), which concentrate on improving the classification performance of R-FRBFNN; and 2) R-FRBFNN designed via SV-based FCM and softmax-based iterative quadratic programming (IQP), which focus on improving the robust abilities of the R-FRBFNN and reducing the effects of noise and outliers. The essential points of the proposed R-FRBFNN classifier are summarized as follows. a) The proposed R-FRBFNN consists of three phases: condition, conclusion, and inference. b) An SV-based FCM is considered for prioritizing the classification boundary and improving the classification performance of the proposed classifier. c) Three types of polynomials construct the conclusion phase. Two learning techniques are designed to update the coefficients of the polynomials. Softmax-based IRLS is a type of iterative learning technique based on Newton's method. Softmax-based IQP is more robust and avoids the degradation of generalization capabilities caused by outliers and noisy data. d) In the concept of reinforced architecture, SV-based FCM imposes compensation (membership degrees) on learning techniques according to the data characteristics encountered in the inference phase. Experimental results reported for benchmark data and outliers/noisy datasets demonstrate that the proposed classifier shows improved classification performance compared with other previously studied methods.

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

Classification of Ultrasound Images for Thyroid Detection using Fuzzy C-Means and Artificial Neural Network (ANN) Classifier Technique

Classification of Ultrasound Images for Thyroid Detection using Fuzzy C-Means and Artificial Neural Network (ANN) Classifier Technique

Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.

Logic-Enhanced Adaptive Network-Based Fuzzy Classifier for Fall Recognition in Rehabilitation

Currently, the most widely adopted fall prediction methods use kinematic sensors for data collection and set thresholds to identify a fall based on artificial experience or neural network learning algorithms. These methods require classification and calibration in advance. Striking a balance between sensitivity and specificity is difficult when setting thresholds. Backpropagation (BP), support vector machines (SVMs) and other neural network algorithm models require professional expertise and are too complicated, difficult to understand, and computationally expensive, which renders embedded development with these models difficult. In response to these problems, this paper builds a fuzzy logic-enhanced adaptive neural network classifier (FLEANN); uses the prior knowledge of experts to construct fuzzy rules; employs neural networks to drive the underlying data to confirm and optimize logical rules; and utilizes an adaptive fuzzy neural network. The parameters of the algorithm are modified and optimized using a large amount of data training to improve the heuristics, transparency, and robustness of the neural network. The enhanced adaptive fuzzy neural network is applied in rehabilitation training for the classification of imprecise, uncertain, and nonlinear falls; abnormal gait; and normal gait. The experimental results confirm that the accuracy of this classifier is better than that of conventional neural network classifiers and that its logical structure is clear, which enables its application to embedded development. The reduction in computational demand enables the elimination of high-configuration PCs and reliance on a single chip microcomputer.

Reinforced Fuzzy Clustering-Based Ensemble Neural Networks

In this paper, we propose reinforced fuzzy clustering-based ensemble neural networks (FCENNs) classifier. The objective of this paper is focused on the development of the design methodologies of ensemble neural networks classifier for constructing the network structure and enhancing the learning methods of fuzzy clustering-based neural networks through the combination of the probabilistic model and its learning mechanism. The proposed FCENNs classifier takes into consideration a cross-entropy error function to improve learning while $L_2$ -norm regularization is used to reduce overfitting as well as enhance generalization abilities. The essential points of the proposed reinforced FCENNs classifier can be enumerated as follows: First, in the proposed classifier, the cross-entropy error function is used as a cost function; to do this, a softmax function is applied to represent a categorical distribution located at the nodes of the output layer. Second, the learning mechanism is composed of two parts. First, fuzzy C-means clustering forms the connections (weights) of the hidden layer while the connections of the output layer are adjusted with the aid of the nonlinear least squares method using Newton's method-based learning. Third, $L_2$ norm-regularization is considered to avoid the degradation of generalization ability caused by overfitting. The learning mechanism similar to ridge regression is realized by adding $L_2$ penalty term to the cross-entropy error function. From the viewpoint of performance improvement achieved through the proposed novel learning method, the design methodology for the ensemble neural networks classifier is discussed and analyzed with the aid of a diversity of two-dimensional synthetic data and machine learning datasets.

Efficient multimedia content storage and allocation in multidimensional cloud computing resources

Optimal management of the cloud resources for multimedia contents is the important aim of this research. In our previous work, multiple kernel learning with support vector machine (MKL-SVM) is introduced, which can achieve a balanced resource usage with multimedia user request. However, existing work do not concentrate on caching mechanism which might lead to more computational overhead. To solve this problem, new method is proposed namely improved storage and scheduling of multimedia contents in cloud storage (ISS-MCCS). In this work, fuzzy neural network classification is utilised for handling the server clusters with unevenness. Then task scheduling is done using hybrid genetic-cuckoo search algorithm where hybrid fuzzy weighting scheme is used for the fitness evaluation. Finally, adaptive replacement cache (ARC) is integrated to optimise memory. The overall assessment of the research work is done in cloudsim environment which proves it can manage the multimedia contents with efficiently.

Efficient multimedia content storage and allocation in multidimensional cloud computing resources

Optimal management of the cloud resources for multimedia contents is the important aim of this research. In our previous work, multiple kernel learning with support vector machine (MKL-SVM) is introduced, which can achieve a balanced resource usage with multimedia user request. However, existing work do not concentrate on caching mechanism which might lead to more computational overhead. To solve this problem, new method is proposed namely improved storage and scheduling of multimedia contents in cloud storage (ISS-MCCS). In this work, fuzzy neural network classification is utilised for handling the server clusters with unevenness. Then task scheduling is done using hybrid genetic-cuckoo search algorithm where hybrid fuzzy weighting scheme is used for the fitness evaluation. Finally, adaptive replacement cache (ARC) is integrated to optimise memory. The overall assessment of the research work is done in cloudsim environment which proves it can manage the multimedia contents with efficiently.

Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy

As large amounts of plastics are widely used in diverse areas of industry, the amount of plastic waste, including black plastics, continues to increase. In this situation, the necessity of useful recycling having limited resources gradually increases. The design of plastic classification systems for plastics recycling becomes more important to effectively address recycling activities. Until now, conventional sorting systems based on the near infrared ray technology have been used to classify plastic wastes. However, the classification of black plastic waste still remains a challenge because such materials do not reflect sufficient signals due to the absorption of laser light coming from the NIR spectrometer. In order to solve such problems, this research is focused on an efficient way to identify black plastics. Attenuated Total Reflectance (ATR) Fourier Transform Infrared Radiation (FT-IR) and a Raman spectrometer are used to carry out qualitative and quantitative analysis for the effective as well as efficient classification of black plastic wastes. In this study, to effectively classify the black plastic waste, data processing and Fuzzy Transform (F-Transform) as well as PCA-based Fuzzy Radial Basis Function Neural Networks (FRBFNNs) classifier is proposed. Input variables extracted on a basis of chemical characteristic peaks as well as interval range positioned near the chemical characteristic peaks were exploited as a way to improve the classification performance of the FRBFNN classifier. In order to evaluate the performance of the classifier, a suite of techniques including F-Transform-based as well as Principal Component Analysis (PCA)-based FRBFNNs classifier designed with the aid of Particle Swam Optimization are developed to analyze and classify black plastics.