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CLEO: Science and Innovations, S and I 2022

作者： Hassan, Sakib Zhao, Xuan Schreib, Christian C. Duret, Guillaume Veiseh, Omid Robinson, Jacob T. Electrical & Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Bioengineering Rice University HoustonTX77005 United States

Here we show how photonic microring resonators enable real-time detection of Nitric Oxide (NO) in vivo, which is critical for physiological monitoring particularly during the wound healing process. © Optica Publi... 详细信息

ISBN: (纸本)9781557528209

关键词： Nitric oxide

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer Science 2021年第1期2卷 1-11页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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State of The Art: Fragility Function of electrical Power System

State of The Art: Fragility Function of Electrical Power Sys...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Elvy Sahnur Nasution Syahrial Syahrial Ira Devi Sara Yuwaldi Away Doctoral Program Postgraduate School of Engineering Universitas Syiah Kuala Universitas Muhammadiyah Sumatera Utara Banda Aceh Indonesia Dept. Electrical and computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

Natural disasters, particularly earthquakes, can cause the electric power system to collapse, which can be brought on by one of the infrastructure breakdowns in the power system. Damaged power system can cause losses to electricity providers, electricity users, so it is necessary to understand the vulnerability of electric power system assets to earthquakes. The research fragility function presents the concept of fragility function in power system of the entire power system, showed fragility of power plant, transmission line, tower transmission, transformers, generator, and substation. Substation is more fragile than the others because substation consist of circuit breaker and disconnecting switch the most fragile asset. In addition, this paper also presents how to creating the fragility function and several solutions to increase the resilience of the electric power system to earthquakes.

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Physics-based learned design for Fourier DiffuserScope

Physics-based learned design for Fourier DiffuserScope

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Computational Optical Sensing and Imaging, COSI 2021 - Part of OSA Imaging and Applied Optics Congress 2021

作者： Markley, Eric Liu, Fanglin Linda Kellman, Michael Antipa, Nick Waller, Laura UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Pharmaceutical Chemistry University of California San Francisco United States Department of Electrical and Computer Engineering University of California San Diego United States

We couple a differentiable forward model of diffuser-based single-shot 3D microscopy (Fourier DiffuserScope) with an invertible and differentiable reconstruction algorithm to jointly optimize the imaging system and th... 详细信息

ISBN: (纸本)9781557528209

关键词： Imaging systems

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers

Observing Generator Performance by Finite Element Analysis C...

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Technology and Policy in Energy and Electric Power (ICT-PEP), International Conference on

作者： Muhammad Afnan Habibi Mochamad Farhan Ali Irfani Langlang Gumilar Arya Kusumawardana Aripriharta Angger Baskoro Study Program of Power Plant Engineering Technology Universitas Negeri Malang Malang Indonesia Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Dept. Electrical Engineering and Computer Sciences National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies.

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The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection 18

The KIND Dataset: A Social Collaboration Approach for Nuance...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Yamani, Asma Z. Alziyady, Raghad AlYami, Reem Albelali, Salma A. Abouhagar, Leina Almulhim, Jawharah Alsulami, Amjad Alfarraj, Motaz Al-Zaidy, Rabeah Department of Information and Computer Science King Fahd University of Petroleum & Minerals Saudi Arabia Center for Integrative Petroleum Research CIPR King Fahd University of Petroleum & Minerals Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum & Minerals Saudi Arabia Preparatory Year Program King Fahd University of Petroleum & Minerals Saudi Arabia Department of Computer Science Imam Abdulrahman Bin Faisal University Saudi Arabia Saudi Data & AI Authority Saudi Arabia

ISBN: (纸本)9798891760905

Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. One of the main challenges is the limited amount of datasets to enable extensive research and experimentation. We propose an approach for efficiently collecting nuanced dialectal datasets that are not only of high quality, but are versatile enough to be multipurpose as well. To test our approach we collect the KIND corpus, which is a collection of fine-grained Arabic dialect data. The data is short texts, and unlike many nuanced dialectal datasets, it is curated manually through social collaboration efforts as opposed to being crawled from social media. The collaborative approach is incentivized through educational gamification and competitions for which the community itself benefits from the open source dataset. Our approach aims to achieve: (1) coverage of dialects from under-represented groups and fine-grained dialectal varieties, (2) provide aligned parallel corpora for translation between Modern Standard Arabic (MSA) and multiple dialects to enable translation and comparison studies, (3) promote innovative approaches for nuanced dialect data collection. We explain the steps for the competition as well as the resulting datasets and the competing data collection systems. The KIND dataset is shared with the research community. © 2024 Association for Computational Linguistics.

关键词： Data acquisition

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System

Dental Imaging Using a Higher Signal-to-Noise Ratio Optical ...

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electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Akeem. A. Azeez Biniyam Mezgebo Rodrigo França Sherif S. Sherif Biomedical Engineering Graduate Program University of Manitoba Winnipeg Canada Dept. of Electrical andComputer Engineering University of Manitoba Winnipeg Canada Dept. of Restorative Dentistry University of Manitoba Winnipeg Canada Dept. of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

This paper presents an optical coherence tomography (OCT) system in conjunction with a novel image reconstruction technique employed for in vitro imaging of human teeth. The primary goal is to enhance the signal-to-noise ratio (SNR) in the obtained images. The study entails a comparative analysis between the conventional Fast Fourier Transform (FFT) OCT image reconstruction method and a newly introduced scaled nonuniform discrete Fourier transform (NDFT) approach. The findings reveal that the NDFT method consistently delivers superior results in terms of peak signal-to-noise ratio (PSNR) and overall image quality, even when dealing with redundant and nonuniform frequency domain samples. In light of these results, this paper concludes that integrating NDFT into OCT procedures has the potential to significantly enhance the quality of image reconstructions, thereby fostering its broader application in the field of dental imaging.

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Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

arXiv

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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