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- ItemA Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process(Springer Science and Business Media Deutschland GmbH, 2022)The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- ItemLoRa-enabled GPU-based CubeSat Yolo Object Detection with Hyperparameter Optimization(Institute of Electrical and Electronics Engineers Inc., 2022)This paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability. © 2022 IEEE.
- ItemComparative heat transfer analysis of γ - A l 2 O 3 - C 2 H 6 O 2 and γ - 2 O 3 - H 2 O electroconductive nanofluids in a saturated porous square cavity with Joule dissipation and heat source/sink effects(American Institute of Physics Inc., 2022-07-01)Inspired by the applications in electromagnetic nanomaterials processing in enclosures and hybrid fuel cell technologies, a mathematical model is presented to analyze the mixed convective flow of electrically conducting nanofluids (γ- A l 2 O 3 - H 2 O and γ- A l 2 O 3 - C 2 H 6 O 2) inside a square enclosure saturated with porous medium under an inclined magnetic field. The Tiwari-Das model, along with the viscosity, thermal conductivity, and effective Prandtl number correlations, is considered in this study. The impacts of Joule heating, viscous dissipation, and internal heat absorption/generation are taken into consideration. Strongly nonlinear conservation equations, which govern the heat transfer and momentum inside the cavity with associated initial and boundary conditions, are rendered dimensionless with appropriate transformations. The marker-and-cell technique is deployed to solve the non-dimensional initial-boundary value problem. Validations with a previous study are included. A detailed parametric study is carried out to evaluate the influences of the emerging parameters on the transport phenomena. When 5 % γ- A l 2 O 3 nanoparticles are suspended into H 2 O base-fluid, the average heat transfer rate of γ- A l 2 O 3 - H 2 O nanoliquid is increased by 25.63 % compared with the case where nanoparticles are absent. When 5 % γ- A l 2 O 3 nanoparticles are suspended into C 2 H 6 O 2 base-fluid, the average heat transfer rate of γ- A l 2 O 3 - C 2 H 6 O 2 nanofluid is increased by 43.20 % compared with the case where nanoparticles are absent. Furthermore, when the heat source is present, the average heat transfer rate of γ- A l 2 O 3 - C 2 H 6 O 2 nanofluid is 194.92 % higher than that in the case of γ- A l 2 O 3 - H 2 O nanofluid. © 2022 Author(s).
- ItemA new watermarking scheme for digital videos using DCT(Walter de Gruyter GmbH, 2022-01-01)With the advent of high-speed broadband Internet access, the need to protect digital videos is highly recommended. The main objective of this study is to propose an adaptive algorithm for watermarked digital videos in the frequency domain based on discrete cosine transform (DCT). The watermark signature image is embedded into the whole frame of the video. The green channel of the RGB frame is selected for the embedding process using the DCT algorithm as it shows the recommended quality of the watermarked frames. The experiment results indicate that the proposed algorithm shows robustness and high quality of the watermarked videos by testing various strength values Δ for different videos. It offers resistance against different types of attacks. © 2022 Ahmed Al-Gindy et al., published by De Gruyter.
- ItemNumerical computing in engineering mathematics(Institute of Electrical and Electronics Engineers Inc., 2022)The rapid advances in technology over the last decade have significantly altered the nature of engineering knowledge and skills required in the modern industries. In response to the changing professional requirements, engineering institutions have updated their curriculum and pedagogical practices. However, most of the changes in the curriculum have been focused on the core engineering courses without much consideration for the auxiliary courses in mathematics and sciences. In this paper, we aim to propose a new, augmented mathematics curriculum aimed at meeting the requirements of the modern, technology-based engineering workplace. The proposed updates require minimal resources and can be seamlessly integrated into the existing curriculum. © 2022 IEEE.