The number of research papers on Motion Capture technologies published in conferences and journals has been rapidly increasing due to the emerging of new technologies, software and hardware which create new challenges and opportunities for Martial Arts research. Current trend of the Martial Arts using Motion Capture technologies (MAMoCap) researches consists of phases of MoCap-Processing and Post-MoCap-Processing; contexts of algorithms, performance and system development; and feedbacks of intrinsic and extrinsic. The purpose of this paper is to study and explore the potential future trend of research and publications pertaining to MAMoCap researches. A systematic survey of research publications was conducted through the topic of Martial Art (MA) and Motion Capture (MoCap) in order to retrieve the scientific articles published in FOUR (4) established publishers including SPRINGERLINK, SCIENCEDIRECT, IEEE and ACM. Search refinements were done by the inclusions criteria of document types of academic journals and conference proceedings; and by the exceptions criteria of letters, editorials and book reviews. The findings show that only 27% of the publications have been selected while other 73% have been classified as irrelevant contents due to none significance and relevance to the MAMoCap researches. Analysis on the research phases, contexts and feedbacks has been conducted and discussed in detailed for pertaining knowledge gaps and future research agenda. Based on the preliminary study, a framework of EFs-Based Automated Evaluation System for the martial arts should be proposed.
A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.
Abstract. The increasing amount of digital images available on the Internet has made searching, browsing, and organizing such resources a major challenge. This paper proposes a semantic approach to text-based image retrieval of manually annotated digital images. The approach uses statistical models based on Semantic DNA (SDNA) extracted from the structure of a lexical ontology called OntoRo. The approach involves three main techniques: (a) SDNA extraction, (b) word sense disambiguation using statistical models based on the extracted SDNA, and (c) applying semantic similarity measures using SDNA. The experiments performed show that the proposed approach retrieves images based on their conceptual meaning rather than the use of specific keywords in their annotations.
Measuring the similarity of documents is of great importance and has influences on many areas and subjects such as Information Retrieval (IR), redrafting discovered, classified documents, and conversational agent programs. Recently, several scientific studies have been interested in implementing the similarity of documents in Arabic language applications. In this study, we review most of the techniques used in measuring the similarity between documents in Arabic; these techniques are classified into three types, mainly lexical, semantic, and finally hybrid. Hence, most of the techniques and methods adopted to measure the similarity between documents were reviewed to determine the most appropriate ones to measure the similarity in Arabic. However, the measurement of the percentage of similarity in documents in the Arabic language represents a major challenge faced by most of the researchers due to the nature of the Arabic language, such as complex morphological processes, ambiguity, and a general lack of resources.
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