Automatic visual detection of human behavior: a review from 2000 to 2014
Accepted Manuscript Review Automatic visual detection of human behavior: a review from 2000 to 2014 Palwasha Afsar, Paulo Cortez, Henrique Santos PII: S0957-4174(15)00351-6 DOI: http://dx.doi.org/10.1016/j.eswa.2015.05.023 Reference: ESWA 10042 To appear in: Expert Systems with Applications Please cite this article as: Afsar, P., Cortez, P., Santos, H., Automatic visual detection of human behavior: a review from 2000 to 2014, Expert Systems with Applications (2015), doi: http://dx.doi.org/10.1016/j.eswa.2015.05.023
Abstract :Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.
Keywords: Data mining, Human behavior,
Literature review, Video analysis, Video databases
Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews
ARTICLE in INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS · JANUARY 2016 Impact Factor: 1.32 · DOI: 10.14569/IJACSA.2016.070269
Abstract—Sentiment Analysis (SA) is one of hottest fields in data mining (DM) and natural language processing (NLP). The goal of SA is to extract the sentiment conveyed in a certain text based on its content. While most current works focus on the simple problem of determining whether the sentiment is positive or negative, Multi-Way Sentiment Analysis (MWSA) focuses on sentiments conveyed through a rating or scoring system (e.g., a 5-star scoring system). In such scoring systems, the sentiments conveyed in two reviews of close scores (such as 4 stars and 5 stars) can be very similar creating an added challenge compared to traditional SA. One intuitive way of handling this challenge is via a divide-and-conquer approach where the MWSA problem is divided into a set of sub-problems allowing the use of customized classifiers to differentiate between reviews of close scores. A hierarchical classification structure can be used with this approach where each node represents a different classification sub-problem and the decision from it may lead to the invocation of another classifier. In this work, we show how the use of this divide-and-conquer hierarchical structure of classifiers can generate better results than the use of existing flat classifiers for the MWSA problem. We focus on the Arabic language for many reasons such as the importance of this language and the scarcity of prior works and available tools for it. To the best of our knowledge, very few papers have been published on MWSA of Arabic reviews. One notable work is that of Ali and Atiya, in which the authors collected a large scale Arabic Book Reviews (LABR) dataset and made it publicly available. Unfortunately, the baseline experiments on this dataset had very low accuracy. We present two different hierarchical structures and compare their accuracies with the flat structure using different core classifiers. The comparison is based on standard accuracy measures such as precision and recall in addition to using the mean squared error (MSE) as a more accurate measure given the fact that not all misclassifications are the same. The results show that, in general, hierarchical classifiers give significant improvements (of more than 50% in certain cases) over flat classifiers. Keywords—multi-way sentiment analysis, hierarchical classi- fiers,support vector machine, decision tree, naive bayes, k-nearest neighbor, mean squared error
Intelligent financial fraud detection: a comprehensive review
Author: Jarrod West, Maumita Bhattacharya PII: S0167-4048(15)00126-1 DOI: http://dx.doi.org/doi:10.1016/j.cose.2015.09.005 Reference: COSE 941 To appear in: Computers & Security Received date: 11-9-2014 Revised date: 10-4-2015 Accepted date: 8-9-2015
Abstract. Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods involving manual detection are not only time consuming, expensive and inaccurate, but in the age of big data they are also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive review of financial fraud detection research using such data mining methods, with a particular focus on computational intelligence (CI)-based techniques. Over fifty scientific literature, primarily spanning the period 2004-2014, were analysed in this study; literature that reported empirical studies focusing specifically on CI-based financial fraud detection were considered in particular. Research gap was identified as none of the existing review articles addresses the association among fraud types, CIbased detection algorithms and their performance, as reported in the literature. We have presented a comprehensive classification as well as analysis of existing fraud detection literature based on key aspects such as detection algorithm used, fraud type investigated, and performance of the detection methods for specific financial fraud types. Some of the key issues and challenges associated with the current practices and potential future direction of research have also been identified. Key words: Financial fraud detection; Computational intelligence; Data mining; Anomaly detection; Classification
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