Fuzzy Local Information C-means Research Articles

Intuitionistic fuzzy local information C-means algorithm for image segmentation

Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, and pattern recognition owing to its importance in analyzing the image. The fuzzy C-means (FCM) algorithm is a popular method for image segmentation and pattern recognition. However, uncertainty and unknown noise in the data impair the effectiveness of the algorithm. Alternatively, uncertainty in real world can be addressed by the intuitionistic fuzzy set (IFS). This article presents a new approach to image representation using IFS and local information about the image. We introduce the concept of filtering into the intuitionistic fuzzy set and utilize a specially designed exponential distance for IFS. We propose the intuitionistic fuzzy local information C-means (IFLICM) algorithm. The goal of IFLICM is to increase the tolerance to noise and the maintain the details in image better than existing FCM variants. We test the performance of our algorithm on a public dataset and compare it with existing FCM methods and Double Deep-Image-Prior (Double-DIP). The experimental results demonstrate that IFLICM is highly effective in image segmentation and outperforms existing methods.

Energy‐aware power allocation using Ladybug Beetle Honey Badger Optimization in multiple‐input multiple‐output‐non‐orthogonal multiple access with multiple users

SummaryThe non‐orthogonal multiple access (NOMA) is highly capable of serving multiple users at similar times and frequencies. The power allocation (PA) is widely considered as a main factor in NOMA for efficient communication. Here, the application of multiple‐input multiple‐output (MIMO) is added to NOMA to fulfill demands of enriched spectral efficiencies and extra user data. In this research, the Ladybug Beetle Honey Badger Optimization (LBHBO) is proposed for efficient PA in MIMO‐NOMA. Initially, the received signals from the user are modulated for amplitude and frequency. Then, user grouping is conducted by fuzzy local information c‐means (FLICM) clustering followed by using PA done by proposed LBHBO. This power is then moved to the transmitter and then to the channel estimation process. Moreover, cyclic prefix (CP) removal is carried out that tends to discrete Fourier transform (DFT). Finally, quadrature amplitude modulation (QAM) demodulation is performed for reallocated output. Furthermore, LBHBO is formed by combining Ladybug Beetle Optimization (LBO) and Honey Badger Algorithm (HBA). The performance offered by the LBHBO‐PA with maximal values with energy efficiency (EE) of 25.38 Mbits/s, sum rate of 1.29 Mbits/s, and achievable rate of 100.47 Mbits/s.

Prediction Models Applying Convolutional Neural Network based Deep Learning to Cervical Cancer Outcomes

Early identification of Cervical Cancer (CC) helps in reducing the death rate of CC patients. A Pap smear/Pap test, which is also a widely used method, is used to detect CC in its early stages. The automatic detection and categorization of Pap smear images is a difficult task. So, it is important to design and implement a high-efficiency with low-cost screening system. Therefore, this paper compares the efficiency of two deep learning-based cervical cancer screening strategies. The first technique taken for comparison is the Mutation based Atom Search Optimization (MASO) optimized DenseNet 121 architecture. The MASO technique is used to adjust hyperparameters such as learning rate value, batch sizes, and the number of neurons in the dense layer in the DenseNet 121 architecture. The second technique taken is the Sooty Tern optimized CNN-based Long Short-Term Memory (ST-CNN-LSTM) classifier to identify the different types of cervical cancer stages. The areas affected by the cancer are identified using Kernel-Weighted Fuzzy Local Information C-Means clustering (KWFLICM) model. This model removes the nucleus and cytoplasm from the background area of the cell. Normal, mild dysplastic, severe dysplastic, and carcinoma are among the CC classifications offered by these two classifiers. The efficiency of these classifiers is compared in two datasets (Hervel and SIPaKMeD) via different performance metrics such as specificity, precisions, F-score, accuracy, confusion matrix, sensitivity, and recall. The ST-CNN-LSTM offers improved performance in terms of accuracy (99.80%), sensitivity (98.83%), and specificity (99%) when compared to other classifiers.

FLICM clustering with matrix factorization based course recommendation in an E-learning platform

Technology-enabled learning has progressively grown for research areas with wide application of information and communication technologies for numerous standard-compliant Learning and Open Educational Resources. This provides formidable support to users for the selection of courses when they want to develop the course with available learning materials. But selecting a course via searching learning objects is an inherently complex operation having various repositories. In an E-learning Platform, many complexities arise due to various software tools and specification formats that hinder the success of the course. In this paper, many obstacles in the E-learning platform are eradicated by utilizing Fuzzy Local Information C-Means (FLICM) clustering with matrix factorization for the selection of courses. The dataset utilized in this work is E-Khool review data, from which an agglomerative matrix is generated. Here, the agglomerative matrix consists of the learner series matrix and course series matrix along with their binary matrix. After this process, course grouping is carried out by FLICM clustering with matrix factorization. Moreover, group course bilevel matching, followed by relevant learner retrieval and group user is done by Minkowski and Chebyshev distance. From this learner’s preferred course is retrieved and then a recommendation using matrix factorization is carried out. Finally, the course is recommended for the user based on maximum rating. Furthermore, the performance of developed FLICM_matrix factorization is achieved by performance metrics, like precision, recall, and f-measure with values 0.915, 0.850, and 0.882, accordingly.

Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation

Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation

Fuzzy Local Information C -Means based clustering and Fractional Dwarf Mongoose optimization enabled deep learning for relevant document retrieval

Document Retrieval (DR) needs an innovative model to rank and retrieve documents based on their relevancy with respect to some questions that requires strong text understanding capability. The prime motive of documents retrieval is to search the relevant documents that satisfy the user's questions. However, it is a complex process because it means the natural language textual content based on the syntax, context and semantics. Conventional techniques for listing files prefer typical word and sentence encrypting to create constant length document abiding. However, the widely used bag-of-words (BoW) method failed to integrate the signify context, which is a crucial problem to comprehend the document-query relevancy. In order to overcome such issues, deep neural networks (DNNs) have been put forward to arrange search outcomes with respect to user's questions. Here, a unified solution is provided to perform relevant document retrieval using Dwarf Mongoose Optimization Fractional-based Deep Convolutional Neural network (DMOF-Deep CNN). Here, the textual content processing is done based on BERT tokenization and feature term extraction. Moreover, the cluster based indexing by elastic search is accomplished using Fuzzy Local Information C-Means (FLICM) clustering and dice coefficient is employed to perform the query matching. Finally, re-ranking based document retrieval is conducted in terms of deep CNN, which is trained using designed DMOF. In addition, the designed DMOF-Deep CNN has outperformed other existing models by delivering maximum precision of 0.854, recall of 0.913, F1-score of 0.882.

Reflectance material classification using optimized deep learning and change detection of LANDSAT surface reflectance images

The time-series changes that are affected by a specific alteration cannot be accurately found using change detection techniques. However, there are not many techniques available for identifying seasonal changes. This study develops a new deep model for identifying reflectance materials depending on optimization. At time instances “t” and “t-1” the images are accumulated in this instance. The Type 2 Fuzzy and Cuckoo Search-Based Filter (T2FCS) is used for the pre-processing of two images. Then, Fuzzy Local Information C-Means (FLICM) is used to segment both images. On the image accumulated at time “t” the feature extraction is performed to extract statistical features like mean, variance, kurtosis, energy, skewness, and standard deviation as well as imperative features like Local Optimal Oriented Pattern (LOOP), Local Ternary Pattern (LTP), Local Gabor XOR Pattern (LGXP), and others. The reflectance material is then categorized using a Deep Neuro-Fuzzy Network (DNFN) with segments created from images collected at time instance “t-1” and features collected from “t”. The proposed Jaya Student psychology-based optimization (JSPO), which was created by fusing the Jaya algorithm and the Student psychology-based optimization method(SPBO), is used here to train the DNFN. The segmented output that was collected at times “t” and “t-1” is then used to do the change detection. With the highest accuracy (94.1%) and maximum sensitivity (93.8%), the proposed JSPO-based DNFN demonstrated improved performance. For applications involving optical imagery, the Landsat program is crucial.

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R2 of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.

Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering

Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering

Convolutional Fuzzy Machine Learning based Algorithms for Mapping Chickpea Agricultural Crop Fields using Temporal Satellite Data

In this research, we employed the convolutional classifiers, namely Fuzzy Local Information c-Means (FLICM) and Adaptive Fuzzy Local Information c-Means (ADFLICM), to classify and map chickpea agricultural fields in the Nagaur region of Rajasthan. This classification was performed using Planet Scope and Sentinel-2 data. To streamline the processing of temporal data and reduce spectral information, we experimented with the Normalized Difference Vegetation Index (NDVI) indices. To optimize the performance of the FLICM and ADFLICM algorithms, we utilized the Mean Membership Difference (MMD) technique to determine the most suitable fuzziness value. We also estimated the area of the ground-truthed chickpea fields to compare the performance of both methods. For the FLICM and ADFLICM algorithms with Planet Scope data, the estimated areas were 8914 m2 and 11278 m2, respectively. The ADFLICM algorithm was also applied to classify Sentinel-2 data, demonstrating its effectiveness with medium-resolution data, and the estimated area for Sentinel-2 data was 10633 m2. Finally, we calculated the Root Mean Square Error (RMSE) for the ADFLICM algorithm, which was found to be 0.075194, using Planet Scope satellite data as the reference dataset and Sentinel-2 satellite data as the classified dataset.

Robust interval type-2 kernel-based possibilistic fuzzy clustering algorithm incorporating local and non-local information

Type-2 fuzzy set theory has certain potential advantage in processing high-order uncertainty, and has been more and more widely applied in pattern recognition, image processing, and system modeling. Although it has been introduced into robust fuzzy clustering modeling, most clustering methods based on type-2 fuzzy set theory have many obvious shortcomings, such as weak robustness against noise and high sensitivity to initial value. Therefore, this paper presents a novel single fuzzifier interval type-2 kernel-based possibilistic fuzzy local and non-local information c-means clustering, and it is driven by deep neighborhood information for image segmentation in the presence of high noise. Firstly, we construct the novel deep neighborhood window structure, which consists of local neighborhood window around current pixel and deep neighborhood window around the pixel in local neighborhood window around current pixel. Secondly, using the structural similarity between local neighborhood window and deep neighborhood window around current pixel, we construct a novel fuzzy local information factor to tune the impact of local neighborhood information on current pixel clustering. Thirdly, possibility theory and fuzzy local information factor are introduced into interval type-2 kernel-based fuzzy c-means clustering, and a novel single fuzzifier type-2 possibilistic fuzzy clustering with richer local and non-local information and kernel metric is proposed. Experimental results indicate that the proposed algorithm outperforms existing state-of-the-art robust fuzzy clustering-related algorithms, and significantly improve the robustness of robust fuzzy clustering algorithms against noise.

An Algorithm to Extract the Boundary and Center of EUV Solar Image Based on Sobel Operator and FLICM

An algorithm to extract the disk boundary and center of EUV solar image using the Sobel operator, Fuzzy Local Information C-Means Clustering algorithm (FLICM), and the least square circle fitting method is proposed in this paper. The Sobel operator can determine the solar disk boundary preliminarily, and then the image is processed further using the FLICM algorithm. After the background is removed based on the clustered image and the boundary points can be highlighted, these points are fitted using the least square circle fitting method as the final boundary circle. The solar data used in this paper was from the observation of the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) instrument. The 2523 19.3 nm solar images covering solar minimum, moderate solar activity, and more active suns were calculated using the proposed algorithm to analyze the accuracy statistically. The statistical comparison results demonstrate that the method is accurate and effective. This method can support the processing of solar EUV images and serve the operational system of a space weather forecast.

FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.

Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respiration alterritory and lungs. The new COVID is a rapidly spreading pathogen globally, and it threatens billions of humans’ lives. However, it is significant to identify positive cases in order to avoid the spread of plague and to speedily treat infected patients. Hence, in this paper, the WSCA-based RMDL approach is devised for COVID-19 prediction by means of chest X-ray images. Moreover, Fuzzy Weighted Local Information C-Means (FWLICM) approach is devised in order to segment lung lobes. The developed FWLICM method is designed by modifying the Fuzzy Local Information C-Means (FLICM) technique. Additionally, random multimodel deep learning (RMDL) classifier is utilized for the COVID-19 prediction process. The new optimization approach, named water sine cosine algorithm (WSCA), is devised in order to obtain an effective prediction. The developed WSCA is newly designed by incorporating sine cosine algorithm (SCA) and water cycle algorithm (WCA). The developed WSCA-driven RMDL approach outperforms other COVID-19 prediction techniques with regard to accuracy, specificity, sensitivity, and dice score of 92.41%, 93.55%, 92.14%, and 90.02%.

Fuzzy Local Information and Bhattacharya-Based C-Means Clustering and Optimized Deep Learning in Spark Framework for Intrusion Detection

Strong network connections make the risk of malicious activities emerge faster while dealing with big data. An intrusion detection system (IDS) can be utilized for alerting suitable entities when hazardous actions are occurring. Most of the techniques used to classify intrusions lack the techniques executed with big data. This paper devised an optimization-driven deep learning technique for detecting the intrusion using the Spark model. The input data is fed to the data partitioning phase wherein the partitioning of data is done using the proposed fuzzy local information and Bhattacharya-based C-means (FLIBCM). The proposed FLIBCM was devised by combining Bhattacharya distance and fuzzy local information C-Means (FLICM). The feature selection was achieved with classwise info gained to select imperative features. The data augmentation was done with oversampling to make it apposite for further processing. The detection of intrusion was done using a deep Maxout network (DMN), which was trained using the proposed student psychology water cycle caviar (SPWCC) obtained by combining the water cycle algorithm (WCA), the conditional autoregressive value at risk by regression quantiles (CAViaR), and the student psychology-based optimization algorithm (SPBO). The proposed SPWCC-based DMN offered enhanced performance with the highest accuracy of 97.6%, sensitivity of 98%, and specificity of 97%.

An intelligent feature selection approach with systolic tree structures for efficient association rules in big data environment

Frequent Pattern Mining (FPM) is regularly used in data mining applications for identifying objects of interest that frequent transactional databases. Traditional ARM algorithms work on minimum levels of support and confidence metrics which have to be defined and are subjective. This work attempts to solve this issue by proposing techniques for the automatic determination of these metrics. The Kernel Possibilistic Fuzzy Local Information C-Means (KPFLICM) Clustering and K-means Clustering-based Feature Selection algorithm for Association Rules (KCFSAR) is used where FPMs are based on Systolic Tree Structures (STS). These structures aim to achieve better accuracy by mimicking internal memory structures of the Frequent Pattern Growths (FPG) algorithm. Moreover, Chaotic Butterfly Optimization Method (CBOAs) is used to obtain the optimal support value and CBOA subsequently discovers global optimal solutions. This work's suggested scheme demonstrates its efficacy and superiority in experimental findings when compared to other approaches in terms of feature subset sizes, accuracies, execution times, and memory consumptions.

A modified fuzzy dual-local information c-mean clustering algorithm using quadratic surface as prototype for image segmentation

Single-valued point can not effectively describe the gray characteristic of the image region, which makes existing fuzzy clustering algorithms difficult to segment image with uneven illumination, weak edges and strong noise. Hence, fuzzy c-means clustering with quadratic polynomial surface as clustering center has become a new method to solve complex image segmentation. Aiming at the shortcoming of current latest spatial fuzzy c-means clustering algorithm with quadratic surface as clustering center, quadratic polynomial surface is firstly introduced into existing widely used fuzzy local information c-means clustering algorithm, and further modify the membership degree of fuzzy local information c-means clustering by means of local membership of neighborhood pixels, a novel fuzzy dual-local information c-means clustering with quadratic polynomial-center clusters is proposed to solve the segmentation problem of complex image in the presence of high noise. Then the convergence of the proposed algorithm is strictly proved by Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm has better segmentation performance and anti-noise robustness than existing quadratic polynomial-related QPFCM, widely used FLICM, and multiple state-of-the-art algorithms, meanwhile, it also promotes the development of robust fuzzy clustering-related image segmentation theory.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model

Image segmentation is an essential technique of brain tumor MRI image processing for automated diagnosis of an image by partitioning it into distinct regions referred to as a set of pixels. The classification of the tumor affected and non-tumor becomes an arduous task for radiologists. This paper presents a novel image enhancement based on the SCA (Sine Cosine Algorithm) optimization technique for the improvement of image quality. The improved FLICM (Fuzzy Local Information C Means) segmentation technique is proposed to detect the affected regions of brain tumor from the MRI brain tumor images and reduction of noise from the MRI images by introducing a fuzzy factor to the objective function. The SCA weight-optimized Wavelet-Extreme Learning Machine (SCA-WELM) model is also proposed for the classification of benign tumors and malignant tumors from MRI brain images. In the first instance, the enhanced images are undergone improved FLICM Segmentation. In the second phase, the segmented images are utilized for feature extraction. The GLCM feature extraction technique is considered for feature extraction. The extracted features are aligned as input to the SCA-WELM model for the classification of benign and malignant tumors. The following dataset (Dataset-255) is considered for evaluating the proposed classification approach. An accuracy of 99.12% is achieved by the improved FLICM segmentation technique. The classification performance of the SCA-WELM is measured by sensitivity, specificity, accuracy, and computational time and achieved 0.98, 0.99, 99.21%, and 97.2576 seconds respectively. The comparison results of SVM (Support Vector Machine), ELM, SCA-ELM, and proposed SCA-WELM models are presented to show the robustness of the proposed SCA-WELM classification model.

Rider Manta Ray Foraging Optimization-based Generative Adversarial Network and CNN feature for detecting glaucoma

Glaucoma is a leading and rapidly increasing disease in recent years, which affects the eye's optic nerve and causes everlasting vision loss. The early recognition of glaucoma is a significant one for decreasing the risk of permanent loss of sight. Therefore, it is necessary to detect glaucoma disease in the initial stage. Moreover, optic cup segmentation from retinal fundus images is an essential procedure for automatic glaucoma detection. In this paper, an efficient glaucoma detection approach is developed using Rider Manta-Ray Foraging Optimization-based General Adversarial Network ((Rider MRFO-based GAN). Here, the optic disc segmentation process is performed by the Fuzzy Local Information C-Means clustering (FLICM clustering). In addition, the sparking process is also employed in this glaucoma detection method for the blood vessel detection process. Several significant features in this detection model, namely mean, standard deviation, variance, kurtosis, skewness, entropy, and CNN features, are extracted to detect glaucomatous images further. The developed Rider MRFO approach is newly developed by the MRFO technique and Rider Optimization Algorithm (ROA). Additionally, the developed glaucoma detection technique performs better based on various parameters, like specificity, sensitivity, and accuracy. Hence, the developed Rider MRFO-based GAN model showed improved results with the highest accuracy of 0.96, the sensitivity of 0.94, and the specificity of 0.89.

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction. In this study, based on the FLICM algorithm, the Canny edge detection algorithm and the Fourier shape descriptor were introduced to optimize the algorithm. The difference of Jaccard coefficient, Dice coefficient, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), running time, and segmentation accuracy of the optimized FLICM algorithm and other algorithms when the brain tissue MRI images were segmented was studied. 36 patients with lacunar infarction were selected as the research objects, and they were divided into a control group (no restroke, 20 cases) and a stroke group (restroke, 16 cases) according to whether the patients had restroke. The differences in MRI imaging characteristics of the two groups of patients were compared, and the risk factors for restroke in lacunar infarction were analyzed by logistic multivariate regression. The results showed that the Jaccard coefficient, Dice coefficient, PSNR value, and SSIM value of the optimized FLICM algorithm for segmenting brain tissue were all higher than those of other algorithms. The shortest running time was 26 s, and the highest accuracy rate was 97.86%. The proportion of patients with a history of hypertension, the proportion of patients with paraventricular white matter lesion (WML) score greater than 2 in the stroke group, the proportion of patients with a deep WML score of 2, and the average age of patients in the stroke group were much higher than those in the control group (P < 0.05). Logistic multivariate regression showed that age and history of hypertension were risk factors for restroke after lacunar infarction (P < 0.05). It showed that the optimized FLICM algorithm can effectively segment brain MRI images, and the risk factors for restroke in patients with lacunar infarction were age and hypertension history. This study could provide a reference for the diagnosis and prognosis of lacunar infarction.