Anomaly Detection Method for Online Discussion

Krammer, Peter; Habala, Ondrej; Mojžiš, Ján; Hluchý, Ladislav; Jurkovič, Marek

doi:10.1016/j.procs.2019.08.045

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…This algorithm was used to detect anomalies in communication platforms by differentiating unwanted users in the platforms [4]. The algorithm consists of three phases-multiple canopy clustering, cluster membership analysis, and classification model training.…”

Section: Anomaly Detection In Online Detectionmentioning

confidence: 99%

“…Streaming data produced daily [1] are stored as big data [3]. Since big data consists of volume, variety, and velocity, data have to be autonomously processed for information and knowledge [4] to benefit the users. Most surveillance cameras or sensor data are classified as normal data behavior.…”

Section: Introductionmentioning

confidence: 99%

“…It is because supervised learning, like classification, needs labeled data [16]. The supervised and unsupervised method is usable in the semi-automated identification of potential threats [4].…”

mentioning

confidence: 99%

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Empowering anomaly detection algorithm: a review

Iqbal Basheer,

Mohd Ali,

Osman

et al. 2024

IJ-AI

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<span lang="EN-US">Detecting anomalies in a data stream relevant to domains like intrusion detection, fraud detection, security in sensor networks, or event detection in internet of things (IoT) environments is a growing field of research. For instance, the use of surveillance cameras installed everywhere that is usually governed by human experts. However, when many cameras are involved, more human expertise is needed, thus making it expensive. Hence, researchers worldwide are trying to invent the best-automated algorithm to detect abnormal behavior using real-time data. The designed algorithm for this purpose may contain gaps that could differentiate the qualities in specific domains. Therefore, this study presents a review of anomaly detection algorithms, introducing the gap that presents the advantages and disadvantages of these algorithms. Since many works of literature were reviewed in this review, it is expected to aid researchers in closing this gap in the future.</span>

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Section: Anomaly Detection In Online Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empowering anomaly detection algorithm: a review

Iqbal Basheer,

Mohd Ali,

Osman

et al. 2024

IJ-AI

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“…Vijayakumar and Muhammad ( 2019 ) employed SVM, NB and maximum entropy with natural language processing methods to indentify sapm comments on the online forum. Krammer et al ( 2019 ) utilized SVM, RBF, and MLP to analyze online comments for abnormal behavior. To sum up, SVM has an excellent performance on text classification, so this study will use SVM to evaluate the performance of selected candidate feature subset.…”

Section: Literature Reviewmentioning

confidence: 99%

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach

Chen

Chang

Chen

et al. 2021

J Ambient Intell Human Comput

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The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define “content features” and build lexicons to determine the attributes’ values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine–recursive feature elimination (SVM–RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

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“…In addition to the classification performance of news articles, outlier detection is also the focus of this research since outlier detection is very closely related to the text classification process [10]. Outliers are abnormal patterns or events that do not match the expected events or patterns [11]. Outlier detection is used to detect news that does not fit the category of news articles.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling to Classify and Detect Outliers on Multilabel Dataset based on Content and Context

Efrizoni¹,

Defit²,

Tajuddin³

2022

IJACSA

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Due to the linked various matching categories, news article categorization are a rapidly increasing field of interest in text classification. However, the low-reliability indices and ambiguities related to frequently used province classifiers restrict success in this field. Most of the existing research uses traditional machine learning algorithms. It has weaknesses in training large-scale datasets, and data sparseness often occurs from short texts. Therefore, this study proposed a hybrid model consisting of two models, namely the news article classification and the outlier detection model. The news article classification model used a combination of two deep learning algorithms (Long Short-Term Memory dan Convolutional Neural Network) and outlier classifier model, which was intended to predict the outlier news using a decision tree algorithm. The proposed model's performance was compared against two widely used datasets. The experimental results provide useful insights that open the way for a number of future initiatives.

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Anomaly Detection Method for Online Discussion

Cited by 6 publications

References 12 publications

Empowering anomaly detection algorithm: a review

Empowering anomaly detection algorithm: a review

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach

Hybrid Modeling to Classify and Detect Outliers on Multilabel Dataset based on Content and Context

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