An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

Ismail, Abdul Azim; Yusoff, Marina

doi:10.14569/ijacsa.2022.0130999

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Analysing and classifying text from online forums and social media can be challenging and many methods have been proposed to automatically classify this type of text ( [15], [22], [26], [44], [51], [59], [60]). One of our objectives is to find the best method to predict cryptocurrency investment scam advertisements.…”

Section: Methodsmentioning

confidence: 99%

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“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

Siu,

Hutchings

2023

2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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This work compares machine learning methods using supervised, semi-supervised and unsupervised learning, to classify advertisements for cryptocurrency related investment scams found in the online forum Bitcointalk, and the social media platform Reddit. We extract more than 24.2 million posts from Bitcointalk and use Reddit's API to collect 2,108 submissions. We train and compare several multiclass text classification approaches and use the models with highest accuracy and F-measure to identify cryptocurrency investment scam advertisements found on both platforms. We discover around five percent of all posts collected on both sites are potential scams. We then use another text classifier to identify the scam actors involved in these investment scam advertisements. We also discover the lures used within these fraudulent adverts and find the main differences in luring techniques used between Bitcointalk and Reddit. We identify that the most prevalent lure type uses the financial principle, followed by the distraction principle in Bitcointalk, and by the authority principle in Reddit. Finally, we use subreddits as communities' proxies and compare scam advertisements within them to identify whether pensioners are being specifically targeted by cryptocurrency scam adverts. Our results show that retirement subreddits do not contain a higher number of cryptocurrency investment scam adverts compared to other investment focused subreddits.

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Section: Methodsmentioning

confidence: 99%

“…They find that a classifier trained using multiple data sources does not always outperform classifiers that use a single data source. Ismail and Yusoff [26] focus on multiclass categorisation of gender violence and implement a hybrid LSTM-CNN model with GloVe word embeddings on Twitter posts.…”

Section: Automated Text Classification Of Social Media Postsmentioning

confidence: 99%

“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

Siu,

Hutchings

2023

2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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show abstract

“…On the other hand, the number of dense and LSTM units should be chosen carefully to avoid overfitting or underfitting the data. Therefore, network architecture design, including the composition of dense and LSTM units, is significantly important [30]. It is advisable to experiment with different combinations of dense and LSTM units to find the optimal architecture that yields the best performance of forecasting accuracy.…”

Section: A Effect Of Lstm and Dense Unitsmentioning

confidence: 99%

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Yusoff,

Ehsan,

Sharif

et al. 2024

IJACSA

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Forecasting crude oil prices hold significant importance in finance, energy, and economics, given its extensive impact on worldwide markets and socio-economic equilibrium. Using Long Short-Term Memory (LSTM) neural networks has exhibited noteworthy achievements in time series forecasting, specifically in predicting crude oil prices. Nevertheless, LSTM models frequently depend on the manual adjustment of hyperparameters, a task that can be laborious and demanding. This study presents a novel methodology incorporating Particle Swarm Optimization (PSO) into LSTM networks to optimize the network architecture and minimize the error. This study employs historical data on crude oil prices to explore and identify optimal hyperparameters autonomously and embedded with the star and ring topology of PSO to address the local and global search capabilities. The findings demonstrate that LSTM+starPSO is superior to LSTM+ringPSO, previous hybrid LSTM-PSO, conventional LSTM networks, and statistical time series methods in its predictive accuracy. LSTM+starPSO model offers a better RMSE of about +0.16% and +22.82% for WTI and BRENT datasets, respectively. The results indicate that the LSTM model, when enhanced with PSO, demonstrates a better proficiency in capturing the patterns and inherent dynamics data changes of crude oil prices. The proposed model offers a dual benefit by alleviating the need for manual hyperparameter tuning and serving as a valuable resource for stakeholders in the energy and financial industries interested in obtaining dependable insights into fluctuations in crude oil prices.

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“…The entropy method has been used in many applications such that malware detection [4], lung sound classification [5], gender violence classification [6], Arabic text classification [7], image steganography [8], and sediments quality evaluation [9].…”

Section: Introductionmentioning

confidence: 99%

A Fast Multicore-based Window Entropy Algorithm

Shokr¹,

Bahig²

2022

IJACSA

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Malware analysis is a major challenge in cybersecurity due to the regular appearance of new malware and its effect in cyberspace. The existing tools for malware analysis enable reverse engineering to understand the origin, purpose, attributes, and potential consequences of malicious software. An entropy method is one of the techniques used to analyze and detect malware, which is defined as a measure of information encoded in a series of values based upon the probability of those values appearing. The window entropy algorithm is one of the methods that can be applied to calculate entropy values in an effective manner. However, it requires a significant amount of time when the size of the file is large. In this paper, we solve this problem in two ways. The first way of improvement is determining the best window size that leads to minimizing the running time of the window entropy algorithm. The second way of improvement is by parallelizing the window entropy algorithm on a multicore system. The experimental studies using artificial data show that the improved sequential algorithm can reduce the window entropy method's running time by 79% on an average. Also, the proposed parallel algorithm outperforms the modified sequential algorithm by 77% and has super-linear speed up.

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An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

Cited by 6 publications

References 16 publications

“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

A Fast Multicore-based Window Entropy Algorithm

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