Machine learning based distributed big data analysis framework for next generation web in IoT

Singh, Sushil Kumar; Cha, Jeonghun; Kim, Tae Woo; Park, Jong Hyuk

doi:10.2298/csis200330012s

Cited by 24 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be considered that the load patterns usually have different probabilities so that we should not treat them equally in the incremental clustering. Thus, an optimized distance measure with probability strategy is proposed for Algorithm 1, in which Euclidean distance measure is replaced with the proposed measure when performing both K-means clustering and SSWC calculation shown in Equation (3). It is assumed that this probability strategy can optimize Algorithm 1 to achieve an ideal A 1 .…”

Section: Optimization Via Probability Strategymentioning

confidence: 99%

See 1 more Smart Citation

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

View full text Add to dashboard Cite

The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.

show abstract

Section: Optimization Via Probability Strategymentioning

confidence: 99%

“…The smart grid system has been developing with the integration of massive new technologies, such as Internet of Things (IoT), Blockchain, and Artificial Intelligence (AI) [ 1 , 2 , 3 ]. Diverse IoT devices and frameworks are applied on smart grid to support data collection, transmission [ 4 ], real-time monitoring [ 5 ], etc.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The first view [ 10 , 14 , 20 , 26 ] is to predict if a tweet will get a retweet or not? The second view [ 35 , 36 , 38 , 44 ] is why tweets of some users get more retweets than other user’s tweets? The third view [ 18 , 31 , 46 , 47 ] is to predict which user will retweet a post and why?…”

Section: Related Workmentioning

confidence: 99%

“…To study the activities of online users and to understand the behavioral pattern of users in various research domains noteworthy efforts are being made in the past few years [ 29 , 35 , 36 , 38 , 44 ]. The online activities make every user unique from other users which will become visible as strong patterns in time.…”

Section: Introductionmentioning

confidence: 99%

Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

View full text Add to dashboard Cite

To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely “Author total activity”, “Author total activity per year”, and “Author tweets per year”. The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

show abstract

“…Jin et al [ 17 ] built an attention based RNN model which mines the textual, visual and social context features, and combines them by using attention mechanism. Singh et al [ 42 ] designed an extreme learning machine (ELM) model for various internet-of-things (IoT) applications. Yang.K et al [ 52 ] analysed text and images and then derived user interested tags using adaptive tag (AT) algorithm.…”

Section: Related Workmentioning

confidence: 99%

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Palani

Elango

K³

2021

Multimed Tools Appl

View full text Add to dashboard Cite

The progressive growth of today’s digital world has made news spread exponentially faster on social media platforms like Twitter, Facebook, and Weibo. Unverified news is often disseminated in the form of multimedia content like text, picture, audio, or video. The dissemination of such false news deceives the public and leads to protests and creates troubles for the public and the government. Hence, it is essential to verify the authenticity of the news at an early stage before sharing it with the public. Earlier fake news detection (FND) approaches combined textual and visual features, but the semantic correlations between words were not addressed and many informative visual features were lost. To address this issue, an automated fake news detection system is proposed, which fuses textual and visual features to create a multimodal feature vector with high information content. The proposed work incorporates the bidirectional encoder representations from transformers (BERT) model to extract the textual features, which preserves the semantic relationships between words. Unlike the convolutional neural network (CNN), the proposed capsule neural network (CapsNet) model captures the most informative visual features from an image. These features are combined to obtain a richer data representation that helps to determine whether the news is fake or real. We investigated the performance of our model against different baselines using two publicly accessible datasets, Politifact and Gossipcop. Our proposed model achieves significantly better classification accuracy of 93% and 92% for the Politifact and Gossipcop datasets, respectively, compared to 84.6% and 85.6% for the SpotFake+ model.

show abstract

Machine learning based distributed big data analysis framework for next generation web in IoT

Cited by 24 publications

References 42 publications

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Role of twitter user profile features in retweet prediction for big data streams

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

Contact Info

Product

Resources

About