Neutrosophic C-means Clustering 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

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.

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.

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.

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.

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

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.

Modified spatial neutrosophic clustering technique for boundary extraction of tumours in B‐mode BUS images

Breast ultrasound (BUS) images are of poor quality, contain inherent noise and shadow regions. Consequently, the task of tumour segmentation from these images becomes more difficult. In this study, a modified spatial neutrosophic clustering technique has been proposed for automatic boundary extraction of tumours in B-mode BUS images. The contributions of the work are two-fold: (i) spatial information is incorporated in the neutrosophic l -means (NLM) clustering method for better cluster formation and (ii) membership values are updated by using type-2 membership function, which helps in converging the cluster centres to more desirable locations than ordinary fuzzy membership functions. BUS images with manually marked lesions by an experienced radiologist have been used as gold standard/reference images for quantitative comparison. The proposed method has been applied to 60 BUS images and results are recorded in the form of area and boundary error metrics. The performance of the proposed method has been compared with the region growing, fuzzy c-means clustering, watershed segmentation, neutrosophic c-means clustering and NLM clustering methods. From the quantitative and visual results, it has been observed that the proposed method can extract the tumour boundaries more precisely as compared with the other state-of-the-art clustering techniques.

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm.

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

This paper presents a novel image segmentation algorithm based on neutrosophic c-means clustering and indeterminacy filtering method. Firstly, the image is transformed into neutrosophic set domain. Then, a new filter, indeterminacy filter is designed according to the indeterminacy value on the neutrosophic image, and the neighborhood information is utilized to remove the indeterminacy in the spatial neighborhood. Neutrosophic c-means clustering is then used to cluster the pixels into different groups, which has advantages to describe the indeterminacy in the intensity. The indeterminacy filter is employed again to remove the indeterminacy in the intensity. Finally, the segmentation results are obtained according to the refined membership in the clustering after indeterminacy filtering operation. A variety of experiments are performed to evaluate the performance of the proposed method, and a newly published method neutrosophic similarity clustering (NSC) segmentation algorithm is utilized to compare with the proposed method quantitatively. The experimental results show that the proposed algorithm has better performances in quantitatively and qualitatively.