breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Introduction: Mechanisms driving the invasiveness of follicular thyroid cancer (FTC) are not fully understood. In our study, we undertook an unsupervised analysis of the set of follicular thyroid tumours (adenomas (FTA) and carcinomas) to verify whether the malignant phenotype influences major sources of variability in our dataset. Material and methods: The core set of samples consisted of 52 t...

Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers engineers have strived to refine the acquisition display of electronic signals generated. A large array solutions was then developed identify at best numerous subsets that can be delineated, notably among cells. As instruments more stable robust, focus moved...

Abstract We developed a graphical user interface (GUI) to analyse tomographic images of superconducting Nb 3 Sn wires designed for the next generation accelerator magnets. The Tomography Analysis Tool (TAT) relies on k -means algorithm, an unsupervised machine learning technique which is widely used partition into separated clusters. GUI compatible with both Linux and Windows operating systems....

Clustering is the problem of grouping data based on similarity and consists of maximizing the intra-group similarity while minimizing the inter-group similarity. The problem of clustering data sets is also known as unsupervised classification, since no class labels are given. However, all existing clustering algorithms require some parameters to steer the clustering process, such as the famous ...

This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in similar contexts are grouped together via McQuitty’s Similarity Analysis, an agglomerative clustering algorithm. The context in which a target word occurs is represented by surface lexical features such as unigrams, bigr...

In this paper we introduce a method for automatically assigning subcategorization frames to previously unseen verbs of Spanish, as an aid to syntactical analysis. Since there is not a consensus on the classes of subcategorization frames, we combine supervised and unsupervised learning. We apply clustering techniques to obtain coarse-grained subcategorization classes from an annotated corpus of ...