Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order toComment: IEEE Multimedia Big Data (BigMM 2015

23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management

The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.Comment: IEEE Data Science and Advanced Analytics (DSAA'2014

Health-related quality of life in multiple sclerosis: Effects of natalizumab

Objective: To report the relationship between disease activity and health-related quality of life (HRQoL) in relapsing multiple sclerosis, and the impact of natalizumab. Methods: HRQoL data were available from 2,113 multiple sclerosis patients in natalizumab clinical studies. In the Natalizumab Safety and Efficacy in Relapsing Remitting Multiple Sclerosis (AFFIRM) study, patients received natalizumab 300mg (n = 627) or placebo (n = 315); in the Safety and Efficacy of Natalizumab in Combination with Interferon Beta-1a in Patients with Relapsing Remitting Multiple Sclerosis (SENTINEL) study, patients received interferon beta-la (IFN-\u3b2-1a) plus natalizumab 300mg (n = 589), or IFN-\u3b2-1a plus placebo (n = 582). The Short Form-36 (SF-36) and a subject global assessment visual analog scale were administered at baseline and weeks 24, 52, and 104. Prespecified analyses included changes from baseline to week 104 in SF-36 and visual analog scale scores. Odds ratios for clinically meaningful improvement or worsening on the SF-36 Physical Component Summary (PCS) and Mental Component Summary were calculated. Results: Mean baseline SF-36 scores were significantly less than the general US population and correlated with Expanded Disability Status Scale scores, sustained disability progression, relapse number, and increased volume of brain magnetic resonance imaging lesions. Natalizumab significantly improved SF-36 PCS and Mental Component Summary scores at week 104 in AFFIRM. PCS changes were significantly improved by week 24 and at all subsequent time points. Natalizumab-treated patients in both studies were more likely to experience clinically important improvement and less likely to experience clinically important deterioration on the SF-36 PCS. The visual analog scale also showed significantly improved HRQoL with natalizumab. Interpretation: HRQoL was impaired in relapsing multiple sclerosis patients, correlated with severity of disease as measured by neurological ratings or magnetic resonance imaging, and improved significantly with natalizumab. \ua9 2007 American Neurological Association. Published by Wiley-Liss, Inc

The incidence and significance of anti-natalizumab antibodies - Results from AFFIRM and SENTINEL

Objective: To determine the incidence and clinical effects of antibodies that develop during treatment with natalizumab