Neutrosophic C-means Research Articles

Retraction Note: A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering

Retraction Note: A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering

A new approach to finding overlapping community structure in signed networks based on Neutrosophic theory

Abstract One of the longstanding challenges in network science is the identification of overlapping community structures. Real-world networks often exhibit a complex interplay of positive and negative relationships, making the recognition of overlapping communities a crucial area of research. Current community detection methods in signed networks primarily focus on discovering disjoint communities, where each node belongs exclusively to a single community. However, these algorithms often fail to detect overlapping communities, where nodes can belong to multiple communities simultaneously. To address this limitation, we propose a novel approach called Neutrosophic c-means Overlapping Community Detection (NOCD) based on neutrosophic set (NS) theory. By incorporating NS theory, our approach effectively handles the uncertainty associated with ambiguous community boundaries and appropriately handles nodes on the community boundaries and isolated nodes. The NOCD method comprises two phases: firstly, a signed graph convolutional neural network is employed to learn the structural features of the signed network in a lower-dimensional representation; secondly, overlapping communities are detected using the neutrosophic c-means algorithm applied to the embedded network. To evaluate the effectiveness of our proposed NOCD method, we conducted comprehensive experiments on both real and artificial networks. The experimental results demonstrate the effectiveness and robustness of NOCD in identifying overlapping communities, outperforming existing methods. [Received on 3 August 2023; editorial decision on 13 December 2023; accepted on 19 December 2023]

Optimizing Workload Scheduling in Cloud Paradigm using Robust Neutrosophic C-Means Clustering Boosted with Fish School Search

In the present internet world, accessing cloud resources for a low cost, according to their needs, is available to all users. Sharing resources is becoming increasingly necessary as people complete their activities in the cloud. It becomes essential for distributed workloads to be optimized to perform efficient workload scheduling and progressing resource utilization in a cloud environment. Scheduling cloud resources considerably benefits from the invention of machine learning and metaheuristic models to address this scenario. Though many existing algorithms are developed in cloud-based task scheduling using unsupervised clustering methods, the problem of unknown task requirements or resource availability in adverse conditions is still challenging. In this study, an uncertainty-based unsupervised technique is constructed to group incoming tasks according to the required resources, and it is scheduled to the most suitable resources more prominently. This paper introduced a Robust Neutrosophic C-Means Clustering boosted with the fish school search algorithm (RNCM-FSSA) for clustering the incoming tasks and the resources based on their requirement and availability. With the degree of indeterminacy, neutrosophic C-means discriminating the deterministic and indeterministic schemes and scheduling them to the optimal resources more effectively. Using the fitness value computed by FFSA, the potential cluster centroids are utilized for clustering, thus avoiding the early convergence in the grouping process. The simulation results explore that the robustness of the proposed RCNM-SSA achieves better resource utilization, the degree of imbalance is minimal, and computation complexity is also considerably decreased compared with other unsupervised models.

DALMIG: Matching-Based Data Center Allocation and Dual Live VM Migration in Cluster-Based Federated Cloud

The modern era has been ruled by federated cloud extensively owing to the upward popularity of real-time applications. However, balancing the load among the different service providers in the federation is a challenging task. This is because of the enlarged load of each service provider and the demand for Service Level Agreement (SLA) requirements. Live VM migration is an effective mechanism to balance the load among the different Cloud Service Providers (CSP). Yet, none of the works has focused on both inter- and intra- CSP-based live VM migration in the federated cloud. Besides, they utilized traditional techniques (Pre and Post-copy) to perform live VM migration. This increases migration and downtime in a federated cloud environment. Motivated by this, we propose matching-based data center allocation and Duple live VM migration in clustered federated cloud environment (DALMIG). The proposed DALMIG approach tackles the downsides faced in the federated cloud as yet. In this regard, three processes are contributed that are Matching-based Data Center Allocation, Two-Fold Clustering, and Duple live VM migration. Initially, brokers allocate apt data center to each incoming task using Auction-based Bipartite One to N (A-BON) matching algorithm. And then, we execute two-fold clustering process: Here, the data center is clustered using the Kernel Neutrosophic C-Means (kernel NCM) algorithm. And, we cluster VMs in each data center using the Credal C-Means (CCM) algorithm. To maintain the load of each CSP, we maintain two entities, such as a load monitor and a migrator. Here, the load monitor estimates a load of each data center and the migrator performs a live VM migration process. Here, the live VM migration process is performed using the Hybrid Copy technique. We execute both intra- and inter-CSP live VM migration. Here, intra-live VM migration is performed using the State Action Reward State Action (SARSA) algorithm. Inter-CSP live VM migration is accomplished using the Black Widow Optimization (BWO) algorithm. We show our DALMIG performance in the federation using the Cloudsim tool. The simulation results acquired from Cloudsim are auspicious in the context of metrics including latency, response time, number of VM migrations, Migration time, load fairness, and communication overhead.

Heart Disease Prediction using Neutrosophic C-Means Clustering Algorithm

Heart disease, often known as cardiovascular illness, encompasses a broad range of heart-related disorders and has emerged as the leading cause of mortality during the last few decades everywhere in the globe. Numerous hazards are linked to cardiovascular disease, and timely, effective, and practical methods for making an early diagnosis are required for effective and efficient treatment. In this study, we describe a novel clustering technique for data that is unreliable clustering called neutrosophic c-means (NCM), which draws inspiration from both fuzzy c-means and the neutrosophic set architecture. The NCM is used to predict heart disease. There are four different databases included in the collection, all of which were created in 1988: Cleveland, Hungary, Switzerland, and Long Beach V. There are 76 qualities total, such as the anticipated characteristic, however only 14 have been used in any of the published trials.

Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms

Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC.

Emerging neutrosophic C means clustering based segmentation for rice plant leaf disease detection

One of the main determinations of India is agriculture and the quantity of production is very essential in the field of cultivation. The rice crops are crucially admitted as one of the powerful energy streams for the production of resources. But rice plants suffer from various diseases due to infections that occur in leaf, fruit and stem. Hence, the production of rice plant is decreased and reflects severe issues in economic, agriculture and communal loss. The usage of image processing in leaf disease detection plays a vital role to diagnose and control it effectively. Though there are many existing works are done too detect rice plant leaf disease, still the presence of vagueness, impreciseness due to noise and outliers are primary challenges. The main aim of this paper is to optimize rice plant leaf disease detection by developing neutrosophic C-means clustering for segmentation. The features are extracted and their similarity is evaluated using neutrosophic Hausdorff distance. The degree of similarity is done based on truthiness, indeterminacy and falsity. The simulation results proved that enhance neutrosophic C-means clustering achieves highest rate of accuracy in rice plant leaf disease detection with less error rate compared to K-means, DBSCAN and FCM

Overlapping community detection in networks based on Neutrosophic theory

Discovering community structure is one of the most intensively studied problems in network science. Many real networks are composed of nodes belonging to multiple communities. In this manuscript, a new overlapping community detection algorithm is proposed based on neutrosophic set (NS) theory. The proposed community detection method manages uncertainty arisen from imprecise definition of communities, by handling boundary and outlier nodes. In the first step, the proposed algorithm calculates the dissimilarity index between each pair of nodes in the network. Then, in order to keep the original distance between nodes as much as possible, the network structure is mapped into a low-dimensional space by multidimensional scaling. Finally, the neutrosophic c-means algorithm is employed to find communities in the network. The experimental results show that the proposed algorithm can detect communities on real and artificial datasets effectively and accurately.

An effective color image segmentation approach using superpixel-neutrosophic C-means clustering and gradient-structural similarity

Due to the insufficient expression of uncertain information in fuzzy sets, the fuzzy C-means clustering algorithm is difficult to deal with clustered boundary and outliers. Therefore, this paper proposes a color image segmentation method based on neutrosophic C-means clustering. First, we improved the simple linear clustering algorithm to obtain accurate and natural adaptive local spatial neighborhoods. Secondly, local neighborhood information is added to the objective function of neutrosophic C-means clustering to obtain a more accurate membership. According to the membership, superpixels are divided into certainty group and uncertainty group. Finally, the certainty group is classified by the maximum membership, and the superpixels of the uncertain group are classified by the structural similarity. Experimental results show that the proposed method has better performance on clean and noise images.

Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification

Early detection and classification of skin lesions using dermoscopic images have attracted significant attention in the healthcare sector. Automated skin lesion segmentation becomes tedious owing to the presence of artifacts like hair, skin line, etc. Earlier works have developed skin lesion detection models using clustering approaches. The advances in neutrosophic set (NS) models can be applied to derive effective clustering models for skin lesion segmentation. At the same time, artificial intelligence (AI) tools can be developed for the identification and categorization of skin cancer using dermoscopic images. This article introduces a Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification (NCCOML-SKSC) model. The proposed NCCOML-SKSC model derives a NCC-based segmentation approach to segment the dermoscopic images. Besides, the AlexNet model is exploited to generate a feature vector. In the final stage, the optimal multilayer perceptron (MLP) model is utilized for the classification process in which the MLP parameters are chosen by the use of a whale optimization algorithm (WOA). A detailed experimental analysis of the NCCOML-SKSC model using a benchmark dataset is performed and the results highlighted the supremacy of the NCCOML-SKSC model over the recent approaches.

An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization

Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier. Here the Kernel Neutrosophic C-Means Clustering is used as Feature Weight, which allocates greater weights to applicable features and smaller weights to less applicable features. Then Random Decision Forest classifier model are optimized with the help of the Bayesian Optimization algorithm to obtain optimal hyper tuning parameters. By this, the accurate classification of breast cancer is successfully achieved. Then the efficiency of the proposed system is executed in python. The performance analysis are executed in Wisconsin prognostic Breast Cancer (WPBC) dataset, 70 % training and remaining 30 % testing is compared with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of proposed feature weight and Random Decision Forest Classifier with Bayesian Optimization (FW + BOA-RDF) in Breast Cancer Wisconsin (Prognosis) Data Set is 6.66 %, 12.659 % and 37.618 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively. The performance analysis in Wisconsin prognostic Breast Cancer (WPBC) dataset, 75 % training and the remaining 25 % testing is compared at Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of FW + BOA-RDF in Breast Cancer Wisconsin (Prognosis) Data Set is 3.7146 %, 5.27398 % and 4.4413 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively.

Human brain tumors detection using neutrosophic c-means clustering algorithm

For the last several decades, detecting human brain tumors has evolved into one of the most difficult problems in the field of medical research. In the realm of medical image processing, the categorization of brain tumors is a difficult job to do. In this research, we offer a model for the detection of human brain tumors in magnetic resonance imaging (MRI) images that makes use of the template-depend neutrosophic c-means and is compared with the fuzzy C means method. This model is referred to as the NCM method. In this suggested method, well first of all, the pattern K-means method is used to initialize segmentation markedly through the ideal choice of a template, depending on the gray-level intensity of the image; besides which, the revised membership is calculated by the ranges from the closest centroid to cluster pieces of data by using neutrosophic C-means (NCM) method while it approaches its perfect outcomes; and at last, the NCM clustering method is used for sensing tumor positron emission tomography (PET) imaging The findings of the simulation reveal that the suggested method can produce improved identification of pathological and normal cells in the human brain despite a little separation in the intensity of the grey level.

NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering

BackgroundSingle individual haplotype problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task.ResultsIn this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods.ConclusionThe proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

Optimal Cryptography Scheme and Efficient Neutrosophic C-Means Clustering for Anomaly Detection in Cloud Environment

This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods have used complex and large numerical computations for the anomaly detection. Also, a single key used by the symmetric key cryptographic scheme to encrypt and decrypt the data has faced large computational complexity because the multiple users can access the original data simultaneously using this single shared secret key. Hence, a classical public key encryption technique called RSA is adopted to perform encryption and decryption of secure data using different key pairs. Furthermore, the random generation of public keys in RSA is controlled in the proposed cloud-based privacy preserving model through optimizing a public key using a new hybrid local pollination-based grey wolf optimizer (LPGWO) algorithm. For ease of convenience, a single private server handling the organization data within a collaborative public cloud data center when requiring the decryption of secure sensor data are allowed to decrypt the optimal secure data using LPGWO-based RSA optimal cryptographic scheme. The data encrypted using an optimal cryptographic scheme are then encouraged to perform data clustering computations in collaborative public servers of cloud platform using Neutrosophic c-Means Clustering (NCM) algorithm. Hence, this NCM algorithm mainly focuses for data partitioning and classification of anomalies. Experimental validation was conducted using four datasets obtained from Intel laboratory having publicly available sensor data. The experimental outcomes have proved the efficiency of the proposed framework in providing data privacy with high anomaly detection accuracy.

OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation

Automated skin lesion segmentation is one of the most crucial stages in dermoscopic images based diagnosis. To guarantee efficient unsupervised clustering-based segmentation, a histogram-based clustering estimation (HBCE) algorithm can be used to obtain the initial number of clusters with their corresponding centroids. Accordingly, the present work introduced a novel skin lesion segmentation algorithm, called optimized clustering estimation for neutrosophic graph cut algorithm (OCE-NGC). Firstly, the genetic algorithm (GA) is used to optimize the HBCE procedure by finding its optimal threshold values which are functions of a factor, called β to be optimized. This optimization process guarantees the optimal determination of the initial number of clusters and their corresponding centroids for further use in the proposed clustering process. Thus, the skin lesion dermoscopic images are then mapped into the neutrosophic set (NS) domain which is computed by the neutrosophic c-means (NCM). The NCM groups the pixels in the dermoscopic images using the pre-determined optimal number of clusters obtained by the optimized HBCE. Finally, a cost function of the graph cut (GC) algorithm is defined in the NS domain for the segmentation process. The experimental results established the superiority of the proposed OCE-NGC approach in comparison with the traditional HBCE with NCM only, the traditional HBCE with the NGC, and the typical GC. In a public dataset, the proposed approach achieved 97.12% and 86.28% average accuracy and average Jaccard (JAC) values, respectively.

Evolving Optimized Neutrosophic C means clustering using Behavioral Inspiration of Artificial Bacterial Foraging (ONCMC-ABF) in the Prediction of Dyslexia

Precise prediction of risk for dyslexia among children’s in earlier stages is a significant long-term aim in the field of cognitive computing. Producing such accurate results for detection of dyslexia from a dataset which consist of low-quality dataset and the presence of vague information is the toughest challenge among researchers. This paper aims at developing an evolving model to handle the impreciseness in the detection of dyslexia more intelligently. In this work, each instance is described in a neutrosophic domain by defining a membership degree of truthiness, indeterminacy, and falsity. These instances are neutrosophically clustered by applying Neutrosophic C-Means clustering (NCM) which forms four different clusters namely dyslexia, no dyslexia, control/revision and hyperactivity or other issues. The outlier and noise are the special categories of indeterminacy which often occurs in real datasets are promptly discovered and clustered. NCM is optimized by introducing Artificial Bacterial Foraging (ABF), especially when there is vagueness or imprecision in the selection of cluster centroids. With the merits of global searching, ABF selects more promising clustering during cluster re-computation. The interpreted results confirm that the role played by proposed ONCMC-ABF algorithm produces better results in the prediction of dyslexia with the low-quality dataset.

Neutrosophic C-means clustering with local information and noise distance-based kernel metric image segmentation

Neutrosophic C-means clustering with local information and noise distance-based kernel metric image segmentation

Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method

This work presents a novel digital modulation signal classification model by combining Neutrosophic c-means (NCM) based feature weighting (NCMBFW) and classifier algorithms. As the digital modulation signal, the multi-carrier amplitude shift keying (MC-ASK), frequency shift keying (MC-FSK), and phase shift keying (MC-PSK) modulation types are employed. In the first step, the feature extraction process has been conducted from the raw digital modulation signals and thereby extracted time, frequency, and time-frequency domain features from the multi-carrier ASK, FSK, and PSK signals. After that, these features have been weighted by using NCMBFW. Finally, classifier algorithms including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (k-NN), AdaBoostM1, and Random Forest, have been used to determine the types of digital modulation signals automatically. Many metrics are used to evaluate the performance in the experiments. The proposed method in the classification of MC digital modulation signals is the first work with respect to the classification of MC modulation signals in the literature. For worst case (in 5 dB), while the obtained f-measure values are 0.842, 0.848, 0.863, 0.842, and 0.894 using LDA, SVM, k-NN, AdaBoostM1, and Random Forest classifiers without NCMBFW, respectively, while the f-measure values by combining NCMBFW with classifier algorithms are 0.983, 0.976, 0.992, 0.988, and 0.991, respectively. The experimental results show that the proposed NCMBFW can be considered as a promising tool to improve the classification performance of digital multi-carrier modulation signals.

Neutrosophic C-means clustering with local information and noise distance-based kernel metric image segmentation

The traditional FCM algorithm is developed on the basis of classical fuzzy theory, though the classical fuzzy theory has its own limitations. The lack of expressive ability of uncertain information makes it hard for FCM algorithm to handle clustered boundary pixels and outliers. This paper proposes a Neutrosophic C-means Clustering with local information and noise distance-based kernel metric for image segmentation (NKWNLICM). At first, noisy distance and fuzzy spatial information are introduced to NCM model to improve the robustness of noise image segmentation. Then, the kernel function is used to measure the distance between pixels. By mapping low-dimensional data into high-dimensional data, the classification performance is further improved. At last, the fuzzy factor is redefined based on the distance between the center pixel and its neighborhood. The new fuzzy factor can excellently reflect the influence of neighborhood pixels on central pixels and improve the classification accuracy much better. The experimental results on Berkeley Segmentation Database demonstrates the excellent performance of the proposed method for noisy image segmentation.

Generalization of Fuzzy C-Means Based on Neutrosophic Logic

This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutrosophic logic (NL), to generalize the Fuzzy C-Means (FCM) clustering system. The NNCMs system assigns objects to clusters using three degrees of membership: a degree of truth, a degree of indeterminacy, and a degree of falsity, rather than only the truth degree used in the FCM.