Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers

Asadianfam, Shiva; Shamsi, Mahboubeh; Kenari, Abdolreza Rasouli

doi:10.1007/s11042-020-09099-8

Cited by 17 publications

(6 citation statements)

References 51 publications

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“…Recent improvements in Big Data cloud computing have provided several opportunities for cryptography experts to increase security by utilizing authentication and permission for efficient data access (Khashan, 2021; Asadianfam et al, 2020). Cloud computing eliminates the need for computing hardware, storage space, and software maintenance.…”

Section: Literature Surveymentioning

confidence: 99%

“…Cloud computing eliminates the need for computing hardware, storage space, and software maintenance. The proposed solution is used to safeguard data in Big Data clouds and address a variety of security difficulties (Asadianfam et al, 2020). The suggested solution ensures many layers of security services such as authentication, secrecy, and data integrity.…”

Section: Literature Surveymentioning

confidence: 99%

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Fusion-based advanced encryption algorithm for enhancing the security of Big Data in Cloud

Vidhya¹,

Kumar²

2022

Concurrent Engineering

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Every organization in this digital age is expected to exponentially increase its digital data due to generations from machines. The advanced computations of Big Data are now showing various opportunities for the researchers who work on security enhancements to ensure the efficient accessibility of the data stores. Our research work aims to derive a Fusion-based Advanced Encryption Algorithm (FAEA) for a cost-optimized satisfiable security model toward the usage of Big Data in the cloud. The FAEA method is evaluated for its performance toward efficiency, scalability, and security and proved to be 98% ahead of the existing methods of Security Hadoop Distributed File System Sec (HDFS) and Map Reduce Encryption Scheme (MRE). On the other hand, this work aims to address the problems of usage of Big Data in the cloud toward the sole solution, cost-effective solutioning, and proof of ownership. The outcome analysis of FAEA revolves around addressing these three major problems. This research work would be much helpful for the IT industries to manage Big Data in Cloud with security aspects for the decade.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Fusion-based advanced encryption algorithm for enhancing the security of Big Data in Cloud

Vidhya¹,

Kumar²

2022

Concurrent Engineering

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“…The proposed 3D-CNN model takes an input sequence of 20 frames having (20,128,128,3) dimensions the sequence of 20 frames input to 3D-CNN architecture for training. The network consists of 4 Conv3D and MaxPooling3D layers followed by one fully connected layer with a 0.5% dropout and dense SoftMax output layer.…”

Section: D-cnn Architecturementioning

confidence: 99%

“…Unusual activities are different from normal routine activities and vary in specific situations, known as suspicious activities. A lot of work has been done for the detection of suspicious activities in different situations such as observing an abandoned object, theft, health monitoring of patients at the hospital and home (i.e., Fall) [1], road accidents [2], traffic rules violation [3], driver drowsiness [4,5] etc. Nowadays, terrorist activities [6] are happening in crowded and sensitive places such as religious places like mosques, churches, educational institutes, airports, bus stations, government buildings, and shopping malls.…”

Section: Introductionmentioning

confidence: 99%

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Ramzan¹,

Abid²,

Awan³

2022

Computers, Materials &Amp; Continua

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In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

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“…A study from Spain explored the construction of a social media big data platform, which can monitor early signs of depression in 2020 [ 5 ]. In the field of transportation, some scholars have realized the dynamic benefits of monitoring of traffic violations through a big data platform [ 6 ]. Moreover, the education big data platform achieved the informational management of student courses and effectively improved the teaching quality in the field of education [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Gao

Qing

et al. 2023

Computational Intelligence and Neuroscience

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Objective. This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. Methods. Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem. Results. With the concept of “Internet + medical integration,” the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements. Conclusion. The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.

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Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers

Cited by 17 publications

References 51 publications

Fusion-based advanced encryption algorithm for enhancing the security of Big Data in Cloud

Fusion-based advanced encryption algorithm for enhancing the security of Big Data in Cloud

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

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