Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Raza, Muhammad Raheel; Hussain, Walayat; Merigó, José M.

doi:10.1109/asyu52992.2021.9599044

Cited by 23 publications

(11 citation statements)

References 28 publications

(17 reference statements)

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“…It also mixes the cell state and the hidden state, plus some other detailed changes; the final model is simpler than the standard LSTM model. According to the experimental results of the literature [28][29][30], LSTM and GRU have little difference in performance on datasets with time series correlation, and GRU models even have higher performance evaluations in some datasets. From the earliest GRU literature [25], it is shown that the number of parameters calculated by GRU element is less than that by LSTM, which indicates that the time of model training and inference can be reduced.…”

Section: Can Message Classification and Model Optimizationmentioning

confidence: 99%

A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time

Cao

et al. 2022

Security and Communication Networks

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With the rapid development of vehicular networking and intelligence, more interfaces are adopted by cars to interact with the external world. Accordingly, this also brings enormous security risks, which are potentially catastrophic due to communication loopholes. Since the Controller Area Network (CAN) is critical to the transmission of commands among vehicular components, it has become a prime target for hacker research and attack. Considering that the CAN bus is commonly used and its protocol is always flawed, how to efficiently detect the intrusions against it has become an evitable problem. In this paper, we presented an intrusion detection system that can be rapidly deployed inside the vehicle. Aiming at achieving the goal of real-time detection, we devised a feature extraction algorithm with low complexity and thoroughly exploited its advantages via a GRU-based lightweight neural network. The experiment was physically conducted on in-vehicle embedded devices using publicly available datasets. Experiment results illustrated that our intrusion detection system could be rapidly deployed with high classification and real-time performance. Moreover, we also discussed how an intrusion detection system could work with OTA services to improve the intelligence of vehicular operating systems and prevent potential attacks.

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Section: Can Message Classification and Model Optimizationmentioning

confidence: 99%

A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time

Cao

et al. 2022

Security and Communication Networks

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“…The approach enables the decision-maker to address the nonlinear relationship of selection criteria. In Raza, Hussain & Merigó (2021) , the researchers analyzed sentiment using deep learning methods. The authors compared RNN, LSTM and GRU approaches on cloud consumer sentiment and found that GRU outperforms all other methods.…”

Section: Literature Reviewmentioning

confidence: 99%

A word embedding technique for sentiment analysis of social media to understand the relationship between Islamophobic incidents and media portrayal of Muslim communities

Ali

Asif

Hamid

et al. 2022

PeerJ Computer Science

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Islamophobia is a sentiment against the Muslim community; recently, atrocities towards Muslim communities witnessed this sentiment globally. This research investigates the correlation between how news stories covered by mainstream news channels impede the hate speech/Islamophobic sentiment. To examine the objective mentioned above, we shortlisted thirteen mainstream news channels and the ten most widely reported Islamophobic incidents across the globe for experimentation. Transcripts of the news stories are scraped along with their comments, likes, dislikes, and recommended videos as the users’ responses. We used a word embedding technique for sentiment analysis, e.g., Islamophobic or not, three textual variables, video titles, video transcripts, and comments. This sentiment analysis helped to compute metric variables. The I-score represents the extent of portrayals of Muslims in a particular news story. The next step is to calculate the canonical correlation between video transcripts and their respective responses, explaining the relationship between news portrayal and hate speech. This study provides empirical evidence of how news stories can promote Islamophobic sentiments and eventually atrocities towards Muslim communities. It also provides the implicit impact of reporting news stories that may impact hate speech and crime against specific communities.

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“…Privacy assurance is one of the key features in SC websites that help build a trusted relationship between stakeholders. For adopting an onsite or offsite SC, the decision-makers need to assess which option gives better security and privacy to win a consumer's trust, consequently increasing product purchase likelihood (Wang and Herrando 2019) Reliability Reliability defines the accuracy degree of consumer's feedback and their sentiment regarding offered services and products (Raza et al 2021a, b) Scalability Scalability defines the ability to compute, process, store, communicate and transfer multiple types of data across the network when the number of consumers or offered services increases (Hussain et al 2015)…”

Section: Linguistic Approach For Criteria Weights and Aggregation Ope...mentioning

confidence: 99%

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

Hussain

Merigó

2022

J Ambient Intell Human Comput

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There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.

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Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Cited by 23 publications

References 28 publications

A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time

A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time

A word embedding technique for sentiment analysis of social media to understand the relationship between Islamophobic incidents and media portrayal of Muslim communities

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

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