Deep Imitation Learning: The Impact of Depth on Policy Performance

Kebria, Parham M.; Khosravi, Abbas; Salaken, Syed Moshfeq; Hossain, Ibrahim; Kabir, H M Dipu; Koohestani, Afsaneh; Alizadehsani, Roohallah; Nahavandi, Saeid

doi:10.1007/978-3-030-04167-0_16

Cited by 16 publications

(3 citation statements)

References 24 publications

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“…Successful autonomous driving requires many calibrations [156], [157], e.g. slight differences between the odometer reading and the actual distance traveled by a vehicle normally exist [158], [159]. In this case, fusing sensor information or adjusting sensor readings requires calibrations [160].…”

Section: F Future Opportunities From Aimentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

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Section: F Future Opportunities From Aimentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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“…We propose an optimal DCNN architecture specifically tuned for gender recognition. Similar challenges are nowadays faced in akin interactive, humancentered fields, such as autonomous driving [1], that require careful design of a real-time capable network architecture [2], [3].…”

Section: Introductionmentioning

confidence: 95%

A Convolutional Neural Network for Gender Recognition Optimizing the Accuracy/Speed Tradeoff

2020

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Gender recognition has been among the most investigated problems in the last years; although several contributions have been proposed, gender recognition in unconstrained environments is still a challenging problem and a definitive solution has not been found yet. Furthermore, Deep Convolutional Neural Networks (DCNNs) achieve very interesting performance, but they typically require a huge amount of computational resources (CPU, GPU, RAM, storage), that are not always available in real systems, due to their cost or to specific application constraints (when the application needs to be installed directly on board of low-power smart cameras, e.g. for digital signage). In the latest years the Machine Learning community developed an interest towards optimizing the efficiency of Deep Learning solutions, in order to make them portable and widespread. In this work we propose a compact DCNN architecture for Gender Recognition from face images that achieves approximately state of the art accuracy at a highly reduced computational cost (almost five times). We also perform a sensitivity analysis in order to show how some changes in the architecture of the network can influence the tradeoff between accuracy and speed. In addition, we compare our optimized architecture with popular efficient CNNs on various common benchmark dataset, widely adopted in the scientific community, namely LFW, MIVIA-Gender, IMDB-WIKI and Adience, demonstrating the effectiveness of the proposed solution.

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“…These reviewed studies used univariate or bi-variate analysis to investigate the impact of one or more factors on COVID-19 severity. Several multivariate machine learning techniques [ 13 , 14 , 15 , 16 ] exist; among these are Linear discriminant analysis (LDA) and support vector machine (SVM), which are considered to have a potential predicting power [ 17 , 18 ]. Therefore, the main aim of this article is to predict the admission of COVID 19-patients in ICU based on related factors through weighted radial kernel-based SVM coupled with Recursive Feature Elimination (RFE) methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE)

Alshanbari

Mehmood

Sami

et al. 2022

Life

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Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients’ demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with (RFE). The proposed method is compared with other reference methods such as linear discriminant analysis (LDA) and kernel-based SVM variants including the linear, polynomial, and radial kernels coupled with REF for predicting ICU admissions of COVID-19 patients. An initial performance assessment indicated that the SVM with weighted radial kernels coupled with REF outperformed the other classification methods in discriminating between ICU and non-ICU admissions in COVID-19 patients. Furthermore, applying the Recursive Feature Elimination (RFE) with weighted radial kernel SVM identified a significant set of variables that can predict and statistically distinguish ICU from non-ICU COVID-19 patients. The patients’ weight, PCR Ct Value, CCL19, INF-β, BLC, INR, PT, PTT, CKMB, HB, platelets, RBC, urea, creatinine and albumin results were found to be the significant predicting features. We believe that weighted radial kernel SVM can be used as an assisting ML approach to guide hospital decision makers in resource allocation and mobilization between intensive care and isolation units. We model the data retrospectively on a selected subset of patient-derived variables based on previous knowledge of ICU admission and this needs to be trained in order to forecast prospectively.

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Deep Imitation Learning: The Impact of Depth on Policy Performance

Cited by 16 publications

References 24 publications

Autonomous Convoying: A Survey on Current Research and Development

Autonomous Convoying: A Survey on Current Research and Development

A Convolutional Neural Network for Gender Recognition Optimizing the Accuracy/Speed Tradeoff

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE)

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