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Item type:Publication, Light-field imaging reconstruction using deep learning enabling intelligent autonomous transportation system(Institute of Electrical and Electronics Engineers Inc., 2021-05-27)Light-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Light field image quality enhancement by a lightweight deformable deep learning framework for intelligent transportation systems(MDPI AG, 2021-05-02)Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms(Wiley, 2021-12-11)With the substantial industrial growth, the industrial internet of things (IIoT) and many IoT avenues have emerged. However, the existing industrial architectures are still inefficient to deal with advanced security issues due to the distributed and distensible nature of the network IIoT communication networks. Therefore, solutions for improving intelligent decision‐making actions to the IIoT are sorely necessary. Thus, in this paper, the main cybersecurity attacks are predicted by applying a deep learning model. The various security and integrity features such as the DoS, malevolent operation, data type probing, spying, scanning, intrusion detection, brute force, web attacks, and wrong setup is analysed and detected by a novel sparse evolutionary training (SET) based prediction model. To scrutinize the conduct of the proposed SET‐based prediction model, evaluation parameters, such as, precision, accuracy, recall, and F1 score are measured and compared to other state‐of‐the‐art algorithms, in which the proposed SET‐based model achieved an average accuracy of 0.99% for an average testing time of 2.29 ms. Results reveal that the proposed model improved the attack detection accuracy by an average of 6.25% when compared with the other state‐of‐the‐art machine learning models in a real scenario of IoT security in Industry 4.0. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Big data analytics for critical information classification in online social networks using classifier chains(Springer, 2022-01-01)Industrial and academic organizations are using online social network (OSN) for different purposes, such as social and economic aspects. Now, OSN is a new mean of obtaining information from people about their preferences, and interests. Due to the large volume of user-generated content, researchers use various techniques, such as sentiment analysis or data mining to evaluate this information automatically. However, the sentiment analysis of OSN content is performed by different methods, but there are some problems to obtain highly reliable results, mainly because of the lack of user profile information, such as gender and age. In this work, a novel dataset is built, which contains the writing characteristics of 160,000 users of the Twitter OSN. Before creating classification models with Machine Learning (ML) techniques, feature transformation and feature selection methods are applied to determine the most relevant set of characteristics. To create the models, the Classifier Chain (CC) transformation technique and different machine learning algorithms are applied to the training set. Simulation results show that the Random Forest, XGBoost and Decision Tree algorithms obtain the best performance results. In the testing phase, these algorithms reached Hamming Loss values of 0.033, 0.033, and 0.034, respectively, and all of them reached the same F1 micro-average value equal to 0.976. Therefore, our proposal based on a multidimensional learning technique using CC transformation overcomes other similar proposals.