Social Media and Stock Market Prediction: A Big Data Approach

Awan, Mazhar Javed; Rahim, Mohd. Shafry Mohd.; Nobanee, Haitham; Munawar, Ashna; Yasin, Awais; Zain, Azlan Mohd

doi:10.32604/cmc.2021.014253

Cited by 78 publications

(28 citation statements)

References 10 publications

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“…Recently big data using Spark ML and big deep learning (BigDL) was performed very good result due to Spark framework so we can apply our work through big data in IOT malware prediction [69][70][71][72][73][74][75].…”

Section: Discussionmentioning

confidence: 99%

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

et al. 2021

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In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

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Section: Discussionmentioning

confidence: 99%

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

et al. 2021

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“…The data used in this study are limited to Pakistan, but, in the future, we plan to extend the functionalities to predict and identify suicide attacks at a global level through inspiration from recent work of big data Spark ML and big deep learning models [45][46][47][48][49][50][51][52][53].…”

Section: Discussionmentioning

confidence: 99%

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

et al. 2021

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Suicide bomb attacks are a high priority concern nowadays for every country in the world. They are a massively destructive criminal activity known as terrorism where one explodes a bomb attached to himself or herself, usually in a public place, taking the lives of many. Terrorist activity in different regions of the world depends and varies according to geopolitical situations and significant regional factors. There has been no significant work performed previously by utilizing the Pakistani suicide attack dataset and no data mining-based solutions have been given related to suicide attacks. This paper aims to contribute to the counterterrorism initiative for the safety of this world against suicide bomb attacks by extracting hidden patterns from suicidal bombing attack data. In order to analyze the psychology of suicide bombers and find a correlation between suicide attacks and the prediction of the next possible venue for terrorist activities, visualization analysis is performed and data mining techniques of classification, clustering and association rule mining are incorporated. For classification, Naïve Bayes, ID3 and J48 algorithms are applied on distinctive selected attributes. The results exhibited by classification show high accuracy against all three algorithms applied, i.e., 73.2%, 73.8% and 75.4%. We adapt the K-means algorithm to perform clustering and, consequently, the risk of blast intensity is identified in a particular location. Frequent patterns are also obtained through the Apriori algorithm for the association rule to extract the factors involved in suicide attacks.

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“…However, no method could reach the level of accuracy and scalability in terms of big data. Recently, studies have shown promising results after applying the big data framework Apache Sharp using Spark ML on various domains [24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Cricket Match Analytics Using the Big Data Approach

et al. 2021

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Cricket is one of the most liked, played, encouraged, and exciting sports in today’s time that requires a proper advancement with machine learning and artificial intelligence (AI) to attain more accuracy. With the increasing number of matches with time, the data related to cricket matches and the individual player are increasing rapidly. Moreover, the need of using big data analytics and the opportunities of utilizing this big data effectively in many beneficial ways are also increasing, such as the selection process of players in the team, predicting the winner of the match, and many more future predictions using some machine learning models or big data techniques. We applied the machine learning linear regression model to predict the team scores without big data and the big data framework Spark ML. The experimental results are measured through accuracy, the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), respectively 95%, 30.2, 1350.34, and 28.2 after applying linear regression in Spark ML. Furthermore, our approach can be applied to other sports.

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Social Media and Stock Market Prediction: A Big Data Approach

Cited by 78 publications

References 10 publications

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Cricket Match Analytics Using the Big Data Approach

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