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In this paper we study the fuzzy clustering problem with capacity constraints. Despite of the fact that the fuzzy clustering approach is widely encountered in the literature, the inclusion of capacity constraints is recent and has several ...

The Fuzzy C-means clustering (FCM) algorithm has gained prominence as a widely utilized technique for data partitioning and image segmentation in various applications. Nevertheless, it exhibits certain limitations in its current form, primarily ...

Medical image segmentation is crucial for understanding anatomical or pathological changes, playing a key role in computer-aided diagnosis and advancing intelligent healthcare. Currently, important issues in medical image segmentation need to be ...

Voice is perceived as a form of biometrics which communicates valuable and rich information pertinent to an individual, such as his or her identity, gender, accent, age and emotion. Speaker identification denotes the task of identifying speakers ...

Breast cancer has emerged as one of the most prevalent malignancies affecting women today, underscoring the critical need for advanced diagnostic tools. Mammography has been the conventional method for early breast cancer detection, yet recent ...

In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations ...

Adaptive-network-based fuzzy inference system (ANFIS) is a well-known neuro-fuzzy system, which is widely implemented in nonlinear system approximation, prediction, control, and pattern classification. However, either too many fuzzy sets or fuzzy ...

In order to prevent the overloading, the routing algorithm aids in building productive paths both within and between clusters. When sending information from the source Internet of Things (IoT) device to a Base Station (BS), not all IoT devices are ...

In general, a network that possesses numerous free or autonomous nodes is proffered as a Mobile Ad hoc Network (MANET). In this, to send along receive data, every single node acts as a router. The entire network’s performance is degraded with the ...

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk ...

Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care ...

• A fuzzy C-Means-inspired unit enhances CNNs for 3D ultrasound TBV estimation.
• Segmentation-free approach-no reliance on segmentation for TBV estimation.
• Model validated against three state-of-the-art methods for accuracy.
• ...

Time series clustering plays an important role in various fields such as anomaly detection and resource scheduling. With the increase of complexity and scale of time series datasets, many deep-learning-based time series clustering methods have ...

Fuzzy C-means clustering (FCM) approach is an effective method for clustering and has been successfully applied in numbers of real-world problems. In this paper, we propose an improving adaptive weighted FCM based on data divergence, with the ...

Fuzzy c-means (FCM) is a classic unsupervised clustering algorithm in machine learning fields. Euclidean distance is a frequently used distance metric in FCM, but it is only suitable for data with spherical clusters. Therefore, Mahalanobis ...

Fuzzy C-Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlapping clusters, it involves issues such as sensitivity to noise and outliers, the fuzzification parameter m, and the initial ...

• Proposes a quality predictive model for EBW process using weld surface attributes.
• Varying weld surface profiles have good correlation with mechanical properties.
• Develops a predictive model for surface attributes using SVR.
• ...

Destructive manual experiments are primarily used for quality assessment of electron beam welded components, which consume the resources and time significantly. Vision-based and other NDT-based quality monitoring in EBW is rarely reported, and ...

In this paper we present a theoretical analysis on fuzzy centroid-based clustering methods. In addition to the formulation on the classical approaches, we consider constraints that may be useful in some practical applications, such as ...

Most of the machine learning and artificial intelligence applications are data driven. When it comes to sensitive data, maintaining the data privacy principles is a big challenge. Building a machine learning model for classifying ...

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• Projects ‘Facial Paralysis classification’ as an application of federated learning.

Microscopic images have the potential to talk about much precious information about the internal structure of living organisms. But the naked eye is not always efficient enough to explore various hidden information from the microscopic ...

With the prosperity of Industry 4.0, numerous emerging industries continue to gain popularity and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the thriving intelligent industries, enjoys bright development ...