Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

Latif, Rana M. Amir; Brahim, Samir Brahim; Saeed, Saqib; Imran, Laiqa Binte; Sadiq, Mazhar; Farhan, Muhammad

doi:10.1109/access.2019.2963590

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Okumura Hata intelligent model is the most widely used in the big data environment. In [20], the most important aspect of the model is that it has a high resistance to interference from external influences, which may assure the correctness of the system design. Okumura Hata's intelligent model is capable of formulating acquired data in a systematic manner and analyzing and interpreting the data properties in real time.…”

Section: Evaluation Algorithm Of Badminton Players' Pacementioning

confidence: 99%

Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

Zhi

Zhu³

et al. 2022

Security and Communication Networks

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In view of the poor effect of badminton players’ physical fitness and response training, this paper puts forward the construction method of badminton players’ pace training model based on big data, constructs badminton players’ pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players’ pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete’s pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements.

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Section: Evaluation Algorithm Of Badminton Players' Pacementioning

confidence: 99%

Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

Zhi

Zhu³

et al. 2022

Security and Communication Networks

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“…The computing culture in vision has been researching videos for decades. Different challenges have been addressed over the years, including intervention recognition [20], incident identification [31], video recovery [29], tracking of incidents and activities, and many more. Video depictions are a large part of these productions.…”

Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

“…Given these limits, many CNN [28] extensions have been investigated in the video domain. Paper [29] and [6] expand the CNN picture to visual realms by considering space and time as the corresponding input dimensions and conducting time and space convolutions. These extensions are known to be just one logical generalization.…”

Section: D Convolutions For Video Datamentioning

confidence: 99%

Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning

Syed¹,

Aldhahri²,

Iqbal³

et al. 2022

Preprint

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In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand space-time association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.

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“…Latif et al analyzed the big data according to 70 attributes that may affect the delayed graduation of college graduates and obtained the prediction accuracy of delayed graduation events greater than 98% based on convolutional neural network (CNN) algorithm. Based on this result, it has good social significance for college graduates to change their strategies according to the current learning situation and improve their school efficiency [11]. Dijk et al proposed to establish the largest information database for college graduates, which aims to use big data technology to predict the whereabouts of college graduates, so as to improve the statistical efficiency of the whereabouts of graduates [12].…”

Section: Related Workmentioning

confidence: 99%

Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

Yang

2022

Discrete Dynamics in Nature and Society

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With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates’ behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates’ entrepreneurship.

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Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

Cited by 16 publications

References 36 publications

Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning

Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

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