DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning

Wanda, Putra; Huang, Jie

doi:10.1007/s13278-021-00742-2

Cited by 41 publications

(23 citation statements)

References 36 publications

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“…For future work, methods to predict the authenticity or emotion of users can be incorporated, such as sentiment analysis, fake accounts detection [41], and malicious content detection [42]. It was proven that non-authentic users can behave differently from authentic users [41].…”

Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of Instagram Users Popularity using Regression Techniques based on Metadata, Media and Hashtags Analysis

Purba¹,

Asirvatham²,

Murugesan³

2020

Preprint

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In recent years, social media is growing at an unprecedented rate, and more people have become influencers. Understanding popularity helps ordinary users to boost popularity, and business users to choose better influencers. There were studies to predict the popularity of posted images on social media, but there was none on the user's popularity as a whole. Furthermore, existing studies have not taken hashtag analysis into consideration, one of the most useful social media feature. This research aims to create a model to predict a user's popularity, which is defined by a combination of engagement rate and followers growth. There were six machine learning regression models tested. The proposed model successfully predicted the users’ popularity, with R2 up to 0.852, using Random Forest with 10-fold cross-validation. The additional statistical analysis and features analysis results revealed factors that can boost popularity, such as actively posting and following users, completing user's metadata, and using 11 hashtags. In contrast, it was also found that having a large number of posts and following in the past will not help in growing popularity, as well as the use of popular hashtags.

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Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of Instagram Users Popularity using Regression Techniques based on Metadata, Media and Hashtags Analysis

Purba¹,

Asirvatham²,

Murugesan³

2020

Preprint

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“…Recently, Deep Learning has been in the spotlight in the development of Machine Learning. The reason is that deep learning has achieved excellent results in many areas [15] [16]. It is a branch of Machine Learning inspired by the human cortex by implementing an artificial neural network with many hidden layers.…”

Section: Related Workmentioning

confidence: 99%

“…Other classification models also have been proposed to deal with several classification issues. Current techniques explore numerous works that collaborate with conventional techniques with a temporal model to establish a classification model [3][15] [16].…”

Section: Introductionmentioning

confidence: 99%

Implementation of CNN for Plant Leaf Classification

Diqi

Mulyani²

2021

ijicom

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Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

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“…However, it remains drawbacks in the large dataset and the difficulty of manual feature extraction. Therefore, the current papers explored various deep learning approaches to deal with the issues such as CNN [24][25] [26].…”

Section: Introductionmentioning

confidence: 99%

Efficient Fruits Classification Using Convolutional Neural Network

Abidin¹,

Hamzah²,

Endah³

2021

ijicom

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Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

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DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning

Cited by 41 publications

References 36 publications

Analysis and Prediction of Instagram Users Popularity using Regression Techniques based on Metadata, Media and Hashtags Analysis

Analysis and Prediction of Instagram Users Popularity using Regression Techniques based on Metadata, Media and Hashtags Analysis

Implementation of CNN for Plant Leaf Classification

Efficient Fruits Classification Using Convolutional Neural Network

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