SS-ITS: secure scalable intelligent transportation systems

Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1007/s11227-020-03582-7

Cited by 15 publications

(6 citation statements)

References 53 publications

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“…This will allow for more accurate evaluation of results from NUMERATE. Security and privacy are two important factors in AIoMT‐based applications. Security and privacy can be solved by advanced blockchain technologies (Belhadi et al, 2021; Djenouri, Srivastava, et al, 2021) and identification of hidden sensitive patterns from biomedical healthcare data (Haque et al, 2021; Lin et al, 2020; Lin, Wu, et al, 2019; Lin, Zhang, et al, 2019). NUMERATE is updated by considering these two approaches in both training and deployment phases.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

et al. 2022

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In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

et al. 2022

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“…To take full advantage of the new hardware capabilities, existing single-threaded applications must be thoroughly redesigned; hence the shift to parallelism and concurrent programming. An interesting and promising approach to running performance-critical tasks is ofered by NVIDIA and its CUDA computing platform [5,12]. It allows algorithms traditionally handled by a CPU to be run on a GPU and use advantage of its enormous of threads.…”

Section: Motivationsmentioning

confidence: 99%

“…This will enable the acceleration of the whole process and the processing of large amounts of data in real time. (5) We conduct intensive experiments with diferent collections of tweets. The results of the experiments show that the proposed solution outperforms the baseline algorithms in both runtime and accuracy.…”

Section: Contributionmentioning

confidence: 99%

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Djenouri

Belhadi

Srivastava

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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This paper proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets orpheline tweet: It is a tweet with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI (XAI) is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of \(90\% \) when analyzing complex Wikipedia data while the other algorithms did not achieve \(85\% \) when processing the same amount of data.

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“…Belhadi et al [85] presented SS-ITS-a secure scalable intelligent transportation system. The system gathers and performs Feature Extraction on urban traffic data.…”

Section: Iot and Sensorsmentioning

confidence: 99%

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

2021

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Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.

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SS-ITS: secure scalable intelligent transportation systems

Cited by 15 publications

References 53 publications

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

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