Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering

2021 ◽

pp. 95-118

Author(s):

Hadeer Elziaat ◽

Nashwa El-Bendary ◽

Ramadan Moawad

Keyword(s):

Machine Learning ◽

Early Detection ◽

Feature Fusion ◽

Freezing Of Gait ◽

Feature Learning ◽

Feature Engineering ◽

Common Symptom ◽

Fusion Model ◽

Feature Sets ◽

Detection Model

Freezing of gait (FoG) is a common symptom of Parkinson's disease (PD) that causes intermittent absence of forward progression of patient's feet while walking. Accordingly, FoG momentary episodes are always accompanied with falls. This chapter presents a novel multi-feature fusion model for early detection of FoG episodes in patients with PD. In this chapter, two feature engineering schemes are investigated, namely time-domain hand-crafted feature engineering and convolutional neural network (CNN)-based spectrogram feature learning. Data of tri-axial accelerometer sensors for patients with PD is utilized to characterize the performance of the proposed model through several experiments with various machine learning (ML) algorithms. Obtained experimental results showed that the multi-feature fusion approach has outperformed typical single feature sets. Conclusively, the significance of this chapter is to highlight the impact of using feature fusion of multi-feature sets through investigating the performance of a FoG episodes early detection model.

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Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering

Journal Européen des Systèmes Automatisés ◽

10.18280/jesa.540312 ◽

2021 ◽

Vol 54 (3) ◽

pp. 487-493

Author(s):

Rakhi Yadav ◽

Yogendra Kumar

Keyword(s):

Machine Learning ◽

Power Plants ◽

Distribution Networks ◽

Research Area ◽

Feature Engineering ◽

Detection Model ◽

Electric Distribution Network ◽

Proposed Model ◽

Electrical Engineers ◽

Electrical Distribution Networks

Non-technical losses (NTL), which occur up to 40% of the total electric transmission and distribution power, create many challenges worldwide. These losses have a severe impact on distribution utilities and adversely affect the performance of electrical distribution networks. Furthermore, the depreciation of these NTL reduces the requirement of new power plants to fulfill the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper proposed a model for the detection of non-technical losses based on machine learning and feature engineering. Experimental results check the performance of the proposed model. These results clearly show that this proposed detection model has better accuracy, precision, recall, F1 score, and AUC score than other existing approaches.

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An End-to-End Rumor Detection Model Based on Feature Aggregation

Complexity ◽

10.1155/2021/6659430 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Aoshuang Ye ◽

Lina Wang ◽

Run Wang ◽

Wenqi Wang ◽

Jianpeng Ke ◽

...

Keyword(s):

Machine Learning ◽

Social Network ◽

Language Processing ◽

Domain Knowledge ◽

Learning Ability ◽

Feature Engineering ◽

Learning Technology ◽

Detection Model ◽

Feature Aggregation ◽

Rumor Detection

The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works.

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Estimating the Valence of Single Stimuli: A New Variant of the Affective Simon Task

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1026//1618-3169.50.2.86 ◽

2003 ◽

Vol 50 (2) ◽

pp. 86-96 ◽

Cited By ~ 21

Author(s):

Andreas Voß ◽

Klaus Rothermund ◽

Dirk Wentura

Keyword(s):

Simon Task ◽

Emotional Expressions ◽

Evaluation Task ◽

New Variant ◽

Game Context

Abstract. In this article, a modified variant of the Affective Simon Task (AST; De Houwer & Eelen, 1998 ) is presented as a measure of implicit evaluations of single stimuli. In the AST, the words “good” or “bad” have to be given as responses depending on the color of the stimuli. The AST was combined with an evaluation task to increase the salience of the valence of the presented stimuli. Experiment 1 investigated evaluations of schematic faces showing emotional expressions. In Experiment 2 we measured the valence of artificial stimuli that acquired valence in a game context during the experiment. Both experiments confirm the validity of the modified AST. The results also revealed a dissociation between explicit and implicit evaluations.

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