检索结果-内蒙古大学图书馆

Performance Analysis of VADER and RoBERTa Methods for Smart Retail Customer Sentiment on Amazon Go Store

Performance Analysis of VADER and RoBERTa Methods for Smart ...

2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Rahmania, Rissa Pohan, Husni Iskandar Arrahmah, Annisa Istiqomah Wibowo, Suryo Adhi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Electrical Engineering Telkom University Ai Center Bandung40257 Indonesia

ISBN: (纸本)9798350369359

The implementation of intelligent systems in the retail industry is still limited in various regions of the world. Amazon Go currently has four stores, located in the Seattle area, and has received customer reviews based on ratings that can be seen in the Google Maps feature. This paper aims to analyze Amazon Go customer sentiment through reviews and star ratings obtained from Google Maps reviews. First, we collect and prepare data from Google Maps Review. Second, we perform experiments and analysis of the VADER and Robustly Optimized BERT Pretraining Approach (RoBERTa) and compare both methods, which are represented by graphs. Lastly, both methods are evaluated in terms of precision, recall, F1-Score, and accuracy. Sentiment analysis from Amazon Go shows that most customers give it a rating of 5.0. Supported by text data from customer comments containing positive, neutral, and negative sentiments showing the relation between the rating given and the sentiment of the comment by the compound score from the results of the VADER method. Sentiment analysis on smart retail from the Amazon Go Store has provided insight and future challenges in the scope of smart retail implementation. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

引用

IEEE Transactions on Services Computing 2025年第03期18卷 1753-1768页

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

来源：评论

学校读者我要写书评

暂无评论

Advancements and applications of position-sensitive detector(PSD): a review

引用

Optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan Department of Electronics&Communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

来源：评论

学校读者我要写书评

暂无评论

Surface Plasmon Optoelectronics

Surface Plasmon Optoelectronics

引用

2023 Photonics North, PN 2023

作者： Berini, Pierre Nexqt Institute University of Ottawa School of Electrical Engineering and Computer Science Department of Physics Ottawa Canada

Plasmonic nanostructures and metasurfaces offer a new paradigm for the design of optoelectronic devices. We discuss nanoscale device ideas that exploit the optical field enhancement and confinement provided by such st... 详细信息

ISBN: (纸本)9798350326734

关键词： Optoelectronic devices

来源：评论

学校读者我要写书评

暂无评论

Enhancing WRSN Sustainability Through On-Demand Directional Wireless Charging With Multiple Green-Powered Mobile Vehicles

引用

IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China New Jersey Institute of Technology Advanced Networking Laboratory Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

来源：评论

学校读者我要写书评

暂无评论

Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab Data for Scarce Industry Data 22

Adversarial Domain Adaptation for Metal Cutting Sound Detect...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Mostafiz, Mir imtiaz Kim, Eunseob Li, Adrian Shuai Bertino, Elisa Jun, Martin Byung-Guk Shakouri, Ali Department of Computer Science Purdue University West Lafayette United States School of Mechanical Engineering Purdue University West Lafayette United States School of Electrical and Computer Engineering Purdue University West Lafayette United States

ISBN: (纸本)9798331527471

Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings. © 2024 IEEE.

关键词： Adversarial Domain Adaptation Cutting sound detection Feature transform Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Pairwise Alignment Improves Graph Domain Adaptation 41

Pairwise Alignment Improves Graph Domain Adaptation

引用

41st International Conference on Machine Learning, ICML 2024

作者： Liu, Shikun Zou, Deyu Zhao, Han Li, Pan Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States School of Data Science University of Science and Technology of China Hefei China Department of Computer Science University of Illinois Urbana-Champaign Champaign United States

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets. Copyright 2024 by the author(s)

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning-Based Pneumonia Detection: A Comparative Evaluation of Machine Learning Models

Transfer Learning-Based Pneumonia Detection: A Comparative E...

引用

2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Anitha, G. Priya, S.Baghavathi Laxmikant, Kumar Pattanaik, Balachandra Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Wallaga University School of Electrical and Computer Engineering Ethiopia Africa Nekemte Ethiopia

ISBN: (纸本)9798350384659

Pneumonia, a widespread respiratory infection, poses significant health risks and requires timely detection for effective treatment. This study employs transfer learning for pattern identification and predictive modeling for classification in pneumonia detection using chest radiographs. Pre-trained deep learning models are utilized to extract informative features, which are then classified using various machine learning algorithms. A comprehensive evaluation is conducted on a benchmark dataset to assess the performance of the approach. The results demonstrate the effectiveness of transfer learning-based feature extraction combined with machine learning classification in accurate pneumonia detection. The findings contribute to improving diagnostic accuracy and have the potential to enhance healthcare decision-making processes. © 2023 IEEE.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

A Robust Random Forest Model for Classifying the Severity of Partial Discharges in Dielectrics 4

A Robust Random Forest Model for Classifying the Severity of...

引用

4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz Saleh, Mohammad Alshaikh Refaat, Shady S. Olesz, Marek Guzinski, Jaroslaw Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdańsk University of Technology Department of Mechatronics and High Voltage Engineering Gdańsk Poland

ISBN: (纸本)9798350306262

Partial Discharges (PDs) are a common source of degradation in electrical assets. It is essential that the extent of the deterioration level of insulating medium is correctly identified, to optimize maintenance schedules and prevent abrupt power outages. Temporal PD signals received from damaged insulation, collected through the IEC-60270 method is the gold standard for PD detection. Temporal signals may be transformed to the frequency domain, introducing new spectral features that may be beneficial in certain circumstances. Consequently, time delays are introduced, due to the high utilization of computational resources within the signal processing pipeline. Moreover, some microprocessors struggle with the excess computational burden demanded by resource-heavy mathematical transformations. To rectify these issues, an alternative approach is utilized, where Machine learning (ML) algorithms are directly used for the classification of PD severity. Cylindrically-shaped air cavities with lengths ranging from 1mm-6mm are introduced to a resin-based polyethylene terephthalate (PET) insulation material. The cavities are partitioned based on size, to obtain different classes of PD severity. A comparative analysis is performed on various ML algorithms, to determine which algorithm correctly determined the severity of PDs, with highest efficacy. Random Forest was determined to be the most performant, with an accuracy of 98.33%. The high performance illustrates the model's potential success in accurately determining the hazard level of PDs in real-time, based on merely time-domain signals. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Fuzzy based Fall Risk Prediction in Older Adults 3

Fuzzy based Fall Risk Prediction in Older Adults

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohan, Deepika Al-Hamid, Duaa Zuhair Chong, Peter Han Joo Gutierrez, Jairo Li, Hui Auckland University of Technology Department of Electrical and Electronic Engineering Auckland New Zealand Auckland University of Technology Department of Computer Science and Software Engineering Auckland New Zealand Peking University School of Electronic & Computer Engineering Shenzhen China

ISBN: (纸本)9798350372120

The global elderly population is increasing rapidly, leading to a rise in chronic illnesses and co-existing conditions, which in turn results in higher healthcare expenses. Accidental falls are among the leading causes of injury-related deaths in elderly individuals. This study aims to create a real-time monitoring system using vital signs to foresee a future fall in older adults by identifying abnormalities through continuous monitoring. The proposed fall prediction technique, employing the Fuzzy-based Fall Prediction Algorithm, utilizes Fuzzy rules to learn and execute tasks. The obtained results are then classified according to different levels of predicted risk indicators. The developed model is tested using data from older adults sourced from a public repository and compared with the results of the theoretical evaluation. The simulated outcomes demonstrate that the proposed algorithm achieves 96% accuracy, 93.75% sensitivity, and 100% specificity. Utilizing these advancements in the proposed heterogeneous technology allows for the early prediction of falls in the elderly and can potentially save lives. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：