The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the sub...

Intra-class testing refers to the testing of the interaction among methods and data structures encapsulated within a single class. Our approach to intra-class testing is to execute sequences of instance methods that are derived from sequencing constraints and evaluate their results for correctness. These constraints impose restrictions on method behaviors and can be derived from a formal or inf...

  Objective(s): Reliability measures precision or the extent to which test results can be replicated. This is the first ever systematic review to identify statistical methods used to measure reliability of equipment measuring continuous variables. This studyalso aims to highlight the inappropriate statistical method used in the reliability analysis and its implication in the medical practice.  ...

Along with the popularity of Internet Things (IoT) techniques several computational paradigms, such as cloud and edge computing, microservice has been viewed a promising architecture in large-scale application design deployment. Due to limited computing ability devices distributed IoT, only small scale data can be used for model training. In addition, most machine-learning-based intrusion detec...

A method of generating complementary eigenspaces optimised for interclass and intra-class separability respectively is presented. The objective of creating these spaces is to improve the efficiency of eigenspace search algorithms. The inter-class optimised space may also be used to improve classification and a quantitative evaluation of this against conventional Principal Component Analysis and...

A method of generating complementary eigenspaces optimised for inter-class and intra-class separability respectively is presented. The objective of creating these spaces is to improve the efficiency of eigenspace search algorithms. The inter-class optimised space may also be used to improve classification and a quantitative evaluation of this against conventional Principal Component Analysis an...

Feature extraction is one of the major components in traditional pattern recognition. There are many methods for extracting the features, either structural approach or global approach. In this paper, we present integrated formulation of Zernike Moments and United Moment Invariant for extracting the character images accordingly. The extraction values are validated by measuring the Inter-class an...

The cross-sectional area (CSA) of the ulnar nerve is thought to be indicative of ulnar nerve entrapment. The purpose of the study was to determine reproducibility of ultrasonographic measurements of CSA of the ulnar nerve at the cubital tunnel in healthy adults. Two sonographers tested 69 participants using a standardized protocol. The inter-rater reliability intra-class correlation coefficient...