Cricket Match Analytics Using the Big Data Approach

Awan, Mazhar Javed; Gilani, Syed Arbaz Haider; Ramzan, Hamza; Nobanee, Haitham; Yasin, Awais; Zain, Azlan Mohd; Javed, Rabia

doi:10.3390/electronics10192350

Cited by 30 publications

(21 citation statements)

References 20 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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“…Blockchain-based IoT in SCM as a future research direction is not comprehensible, but fitter away time mainly notion and orchestrations understandable and move to the work. On the other hand, the extension of hardware architecture under the suitable proposed framework for implementations comprising real IoT gadgets and gateways will obtain the highest performances in the future for different industries prospective of big data [119][120][121][122][123][124][125][126][127].…”

Section: Future Work and Limitationsmentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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Through recent progress, the forms of modern supply chains have evolved into complex networks. The supply chain management systems face a variety of challenges. These include lack of visibility of the upstream party (Provider) to the downstream party (Client); lack of flexibility in the face of sudden variations in demand and control of operating costs; lack of reliance on safety stakeholders; ineffective management of supply chain risks. Blockchain (BC) is used in the supply chain to overcome the growing demands for items. The Internet of Things (IoT) is a profoundly encouraging innovation that can help companies observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to answer IoT gadgets in supply chain management. This paper presents orderly writing on and reviewing of Blockchain-based IoT advances and their current usage. We discuss the smart devices used in this system and which device is the most appropriate in the supply chain. This paper also looks at future examination themes in blockchain-based IoT, referred to as the executive’s framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable publications between 2016 and 2021. Lastly, current issues and challenges are present to provide researchers with promising future directions in IoT supply chain management systems.

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Cricket Match Analytics Using the Big Data Approach

Cited by 30 publications

References 20 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

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

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