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检索条件"机构=Dept. of Computer & Electrical Engineering and Computer Science"

共 55306 条记录，以下是181-190 订阅

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Accidental Prone Area Detection in Dhaka Metropolitan City Using Machine Learning Models 5

Accidental Prone Area Detection in Dhaka Metropolitan City U...

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Jerin, Foyjunnesa Akhi, Amatul Bushra Tasin, Mobin Mohtadi Sultana, Naznin Akbar, Md. Atef Sharmin, Shayla Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh North South University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350351378

Dhaka, known for its status as one of the world's most densely populated areas, grapples with numerous challenges associated with urbanization and safety. This study identifies accident-prone zones within the Dhaka Metropolitan City by analyzing data collected from 2019 to 2023. Ten specific locations were investigated using various machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis encompassed both monthly and annual accident data, revealing trends and probabilities associated with different incident types. The study's implications suggest potential advancements in accident investigation by incorporating advanced machine learning techniques, integrating diverse datasets, and expanding the geographical coverage to encompass the entire metropolitan area. This comprehensive approach aims to bolster accident prevention measures and enhance urban safety. Overall, this research contributes to a deeper understanding of accident-prone regions and proposes strategies for targeted interventions to improve safety within metropolitan areas. © 2024 IEEE.

关键词： Accidents

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Reviving Software Diversity in Microservices to Optimize the Performance of Software Systems 4

Reviving Software Diversity in Microservices to Optimize the...

引用

4th IEEE International Conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Dhaliwal, Prabjot Khazaei, Hamzeh York University Dept. of Electrical Engineering & Computer Science TorontoON Canada

ISBN: (纸本)9798350337464

With the increasing popularity and complexity of microservice software systems, satisfying the performance requirements of these systems becomes a non-trivial task. While horizontal auto-scaling is a common remedy to this problem, it is not necessarily cost-effective and the proper solution for all scenarios. Also, regardless of the number of replicas, they are all prone to common bug failure. In this poster/demo, we present an agile and cost-effective approach for satisfying the performance requirements of microservice software systems without incurring extra costs on the service provider. We research how performance, i.e., response time, can be tamed by applying software diversity, aka multi-versioning, to the system's resource-heavy critical services. We use our open-source extension of the Docker framework, called DockerMV, to deploy microservice systems with multi-versioning embedded underneath. We also propose a dynamic load-balancing service that proactively adapts to the various versions depending on current and near-future performance needs. We demonstrate the efficacy of multi-versioning for satisfying the performance requirements of microservice software systems through extensive experiments on TeaStore, a microservice reference test application, and Znn, a containerized news portal. We will present our results through the poster and a live demonstration throughout the conference. A preliminary demo of our work can be accessed here11https://***/watch?v=oeMCxlDtU64. The GitHub repository of our work can be accessed here22https://***/prabjot09/nginx-dynamic-load-balancing. © 2023 IEEE.

关键词： Software testing

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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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Neural Style Transfer: A Comparative Study 15

Neural Style Transfer: A Comparative Study

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model's layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model's ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing. © 2024 IEEE.

关键词： Artistic image synthesis CNN DenseNet121 image processing MobileNetV2 Neural Style Transfer ResNet50 TensorFlow VGG-19

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Generating Random Petri Nets for GPenSIM environment 9

Generating Random Petri Nets for GPenSIM environment

引用

9th International Conference on Systems and Informatics, ICSAI 2023

作者： Davidrajuh, Reggie University of Stavanger Dept. Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9798350383706

During the development stages of the modeling processes, there is always a need to create random Petri Nets so that these Petri Nets can be used as test cases. This paper presents new functions for the automated generation of random Petri Nets. The new functions are implemented in the General-purpose Petri Net Simulator (GPenSIM), a tool that runs on the MATLAB platform. Some researchers use GPenSIM for modeling, simulation, and performance analysis of discrete systems. Newer applications of GPenSIM also include modeling and control of cyber-physical systems using distributed modules known as 'Petri Modules.' Firstly, this paper introduces GPenSIM and a few Petri Net types, such as event graphs and Petri modules. Then, the newer functions of GPenSIM for creating random Petri Nets are presented, which is the goal of this paper. © 2023 IEEE.

关键词： Petri nets

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

引用

9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

Self-supervised Registration and Segmentation of the Ossicle...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Zhang, Yike Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually annotated ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method. © 2023 SPIE.

关键词： Image segmentation

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MAD-TI: Meta-Path Aggregated-Graph Attention Network for Drug Target Interaction Prediction 31

MAD-TI: Meta-Path Aggregated-Graph Attention Network for Dru...

引用

31st International Conference on electrical engineering, ICEE 2023

作者： Shami Tanha, Reza Sadighian, Maryam Zabihian, Arash Hooshmand, Mohsen Afsharchi, Mohsen Dept. Computer Science and It Zanjan Iran Tehran Iran University of Zanjan Dept. Electrical and Computer Engineering Zanjan Iran

ISBN: (纸本)9798350312560

Computational identification of unknown drug-target interactions (DTI) is crucial in locating new drug treat-ments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Embeddings

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A Bridgless Stacked Switches-Based AC-DC Converter with Reduced Storage Capacitances and Low-Frequency Voltage Ripple for HV EV Systems

A Bridgless Stacked Switches-Based AC-DC Converter with Redu...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Derakhshan, Siamak Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350316445

This paper presents a fully soft-switched single-stage stacked switches-based AC-DC converter with significantly reduced storage capacitances and low-frequency voltage ripple for High Voltage (HV) Electric Vehicle (EV) application. In the proposed converter, a front-end duty ratio closed-loop control system based on the input current equation provides a high-quality sinusoidal input current. Moreover, a dual feedback control loops in both the primary and secondary side of the converter are responsible to minimize the low-frequency DC-link and output voltage oscillations, eliminating the need for bulky DC-link and output storage capacitors and enhancing the reliability, lifetime, and cost-effectiveness of the converter. Moreover, the output voltage of the converter is regulated through a Variable Frequency (VF) control algorithm. The performance of the proposed converter is verified through a 1kW, 120Vrms/800Vdc, 150kHz proof-of-concept hardware prototype. © 2023 IEEE.

关键词： closed-loop control low capacitance size minimum low-frequency ripple Soft-switching

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