Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...

We investigate the problem of automatic detection of annotation errors in single-speaker read-speech corpora used for textto-speech (TTS) synthesis. Various word-level feature sets were used, and the performance of several detection methods based on support vector machines, extremely randomized trees, knearest neighbors, and the performance of novelty and outlier detection are evaluated. We sho...

Previous work has high classification accuracy when classifying music genres that are very different [1] (e.g. rock vs. pop, jazz vs. classical), but little machine learning research has been done to classify music by subgenres within a larger genre that describe temporally and stylistically similar music. In this paper, we apply machine learning to classify classical music by time period. To d...

Given a set S of points in a metric space with distance function D, the nearest-neighbor searching problem is to build a data structure for S so that for an input query point q, the point s ∈ S that minimizes D(s, q) can be found quickly. We survey approaches to this problem, and its relation to concepts of metric space dimension. Several measures of dimension can be estimated using nearest-nei...

Classification of Domestic Burning Smell using Covariance kNearest Neighbour Algorithm for Early Fire Detection Application Allan M. Andrew*, Kamarulzaman Kamarudin, Syed M. Mamduh, Ali Yeon M. Shakaff, Ammar Zakaria, Abdul H. Adom, David L. Ndzi, Santiagoo Ragunathan Centre of Excellence for Advanced Sensor Technology (CEASTech), University of Malaysia Perlis (UniMAP), Lot 1621, Pusat Pengajia...