An extended approach of weight collective influence graph for detection influence actor

Martono, Galih Hendro; Azhari, Azhari; Mustofa, Khabib

doi:10.26555/ijain.v8i1.800

Cited by 3 publications

(5 citation statements)

References 30 publications

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“…The analysis of the accuracy values of the test dataset with different types of convolutional layers showed that the model of a graph neural network with GINConv convolution layer gave the best results relative to other models (89%). We performed calculations with different numbers (1,2,3,4) Moreover, a receiver operating characteristic curve (ROC-curve) was built for each model of a graph neural network (Fig. 5b).…”

Section: Resultsmentioning

confidence: 99%

Online social network user performance prediction by graph neural networks

Gafarov

Berdnikov²,

Ustin³

2022

Int. J. Adv. Intell. Informatics

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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use of graph convolutional neural networks with different convolution layers (GCNConv, SAGEConv, GraphConv, GATConv, TransformerConv, GINConv) for predicting the user’s professional success in VKontakte online social network, based on data obtained from his profiles. We have used various parameters obtained from users’ personal pages in VKontakte social network (the number of friends, subscribers, interesting pages, etc.) as their features for determining the professional success, as well as networks (graphs) reflecting connections between users (followers/ friends). In this work we performed graph classification by using graph convolutional neural networks (with different types of convolution layers). The best accuracy of the graph convolutional neural network (0.88) was achieved by using the graph isomorphism network (GIN) layer. The results, obtained in this work, will serve for further studies of social success, based on metrics of personal profiles of OSN users and social graphs using neural network methods.

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Section: Resultsmentioning

confidence: 99%

Online social network user performance prediction by graph neural networks

Gafarov

Berdnikov²,

Ustin³

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

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“…The recall is the proportion of actual positives which are predicted positive [38]. The formula for the recall rate is shown in (2). Precision is also a positive predictive value indicating the algorithm's accuracy for each model that detects hate speech [26].…”

Section: Discussionmentioning

confidence: 99%

“…Hate speech is one of the important topics of discussion related to social media analysis. It is mainly associated with the freedom of users to share content and opinions on existing social media platforms [2]. Freedom of opinion in social media has also led to increased hate speech through social media.…”

Section: Introductionmentioning

confidence: 99%

“…The study can be used to make an experimental approach to detecting hate speech and abusive language. Zhang et al [2] observed that extremist violence tends to increase online hate speech, particularly on messages directly advocating violence [6]. Sinyangwe established that in the fore model, to detect hate speech and offensive language on online social media platforms, the data set must be categorized and presented in statistical form after running the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Alkaff,

Miqdad,

Fachrurrazi

et al. 2023

matrik

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Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately.

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“…All interactions can be described as knowledge domains at the macroscopic level. A GNN application with weights on each attribute is required for input graphs with various contexts [39]. This method's goals are for semi-supervised categorization in daily activities related to objects.…”

Section: Discussion On Macroscopic Levelmentioning

confidence: 99%

Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system

Besari¹,

Saputra

Chin

et al. 2023

Int. J. Adv. Intell. Informatics

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Computer vision-based cyber-physical-social systems (CPSS) are predicted to be the future of independent hand rehabilitation. However, there is a link between hand function and cognition in the elderly that this technology has not adequately supported. To investigate this issue, this paper proposes a multiscopic CPSS framework by developing hand–object interaction (HOI) based on visual attention. First, we use egocentric vision to extract features from hand posture at the microscopic level. With 94.87% testing accuracy, we use three layers of graph neural network (GNN) based on hand skeletal features to categorize 16 grasp postures. Second, we use a mesoscopic active perception ability to validate the HOI with eye tracking in the task-specific reach-to-grasp cycle. With 90.75% testing accuracy, the distance between the fingertips and the center of an object is used as input to a multi-layer gated recurrent unit based on recurrent neural network architecture. Third, we incorporate visual attention into the cognitive ability for classifying multiple objects at the macroscopic level. In two scenarios with four activities, we use GNN with three convolutional layers to categorize some objects. The outcome demonstrates that the system can successfully separate objects based on related activities. Further research and development are expected to support the CPSS application in independent rehabilitation.

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An extended approach of weight collective influence graph for detection influence actor

Cited by 3 publications

References 30 publications

Online social network user performance prediction by graph neural networks

Online social network user performance prediction by graph neural networks

Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Hand–object interaction recognition based on visual attention using multiscopic cyber-physical-social system

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