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A Review of Aspect Level Sentiment Analysis 20

A Review of Aspect Level Sentiment Analysis

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Wang, Guangshuo Li, Jianping University of Electronic Science and Technology of China Department of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350318982

Aspect-level sentiment analysis is an important text sentiment analysis technology. It can perform fine-grained calculation and analysis of the sentiment of specific aspects in the text, thereby helping people understand the sentiment of the text more comprehensively. This technology finds extensive application in real-life scenarios such as recommender systems and question answering systems. This article first introduces the application background and application scenarios of aspect-level sentiment analysis, then discusses the importance of data set selection and factors that need to be considered when selecting data sets, and then explains the specific process of aspect-level sentiment analysis, including data preprocessing, Steps such as aspect extraction and sentiment classification are also introduced. Common analysis techniques, such as dictionary matching, are also introduced. Through the application of these methods, aspect-level sentiment analysis can provide richer and more fine-grained sentiment information, thereby providing strong support for research and applications in related fields. However, aspect-level sentiment analysis also faces many challenges, such as how to deal with negative emotional expressions and how to deal with non-emotional expressions. This article finally discusses the development trends and opportunities of aspect-level sentiment analysis. © 2023 IEEE.

关键词： Sentiment analysis

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Risk Assessment & Mitigation for Core Security Capabilities 19

Risk Assessment & Mitigation for Core Security Capabilities

引用

19th Annual conference on APWG Symposium on electronic Crime Research, eCrime 2024

作者： Dupuis, Marc Renaud, Karen School of Science Technology Engineering & Mathematics University of Washington Bothell United States University of Strathclyde Computer and Information Science Glasgow United Kingdom University of South Africa South Africa

ISBN: (纸本)9798331524494

Efforts to assure the cybersecurity of an organization's information and systems rely on industry metrics to monitor their current state of play. These, when monitored over time, could also help organizations to determine whether they are improving their stance or lagging behind. We reviewed the literature on metrics and consulted 12 cybersecurity professionals, working in industry, to take a snapshot of the status quo of metric and framework usage. We report on what our respondents told us and conclude by explaining that, although they were aware of metrics, many only used minimal metrics, and few used any existing frameworks. This was primarily due to resource and other business constraints. It seems that we have to encourage and engender more metric usage, and that an automated approach, with an associated dashboard to support reporting, would be the best way to help organizations to benefit from this helpful mechanism. © 2024 IEEE.

关键词： assessment cybersecurity management mitigation risk metrics

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Person Re-Identification Based on Instance Segmentation and Pose Estimation 6

Person Re-Identification Based on Instance Segmentation and ...

引用

6th IEEE International conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Yao Qin, Yusong Wang, Song Huang, Zengxi Zhang, Dazhi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China Sichuan Police College Criminal Science and Technology Department Luzhou China

ISBN: (纸本)9798350325485

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively. © 2023 IEEE.

关键词： Deep learning

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Blockchain and Quantum Cryptography-Based Hybrid Security for Healthcare 5.0 Systems 3

Blockchain and Quantum Cryptography-Based Hybrid Security fo...

引用

3rd International conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Agarwal, Nishit Kankanampati, Phanindra Kumar Chamarthy, Shyamakrishna Siddharth Khan, Imran Jain, Arpit Almusawi, Muntather Northeastern University Jersey CityNJ07307 United States Binghamton University Glenmallen Ln RichmondTx77407 United States Columbia University BrooklynNY11226 United States Visvesvaraya Technological University India Department of Computer Science and Engineering A.P. Vadeshawaram India The Islamic University Najaf Iraq

ISBN: (纸本)9798331528386

Healthcare systems can be simplified and enhanced by integrating digital databases and insurance agencies. However, as healthcare data increasingly travels over the internet, concerns around security and privacy—such as data breaches and unauthorized data modifications—become more pressing. The General Data Protection Regulation (GDPR) highlights the importance of secure data management in building trust in the protection of individuals' personal information. Blockchain technology presents an opportunity to make healthcare systems more efficient, secure, and private. This paper examines current security architectures designed to protect electronic health records (EHRs) and proposes the addition of Quantum Cryptography (QC) to reinforce traditional encryption methods. We explore blockchain-based healthcare architectures that integrate role-based access controls and quantum blind signatures within Hyperledger Fabric blockchains. Our findings demonstrate that the proposed framework outperforms existing systems in transaction handling, resource efficiency, and network traffic management while also enhancing scalability and reducing latency. © 2024 IEEE.

关键词： electronic health record

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IoT Revolution: Exploring the Impact of loT Based Embedded System Technology on Healthcare, Agriculture and Business Startups in Smart Village 2

IoT Revolution: Exploring the Impact of loT Based Embedded S...

引用

2nd International conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Gupta, Rittik Das Debnath, Sourav Gupta, Riva Das Chowdhury, Mrithika Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Economics Chattogram Bangladesh Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798331517984

The paper illustrates the development of an integrated Internet of Things (IoT) gadget with smart agricultural and healthcare systems, as well as the possibility for their startup firms to build a smart village. The system comprises a carefully designed front end powered by Arduino and NodeMCU microcontrollers. It has a number of high-precision sensors, including the DS18B20 for body temperature, the Max30102 for heart rate and oxygen level, the FC-28 for soil moisture, the DHT-11 for temperature of the environment and maintaining humidity, the dust sensor for air quality detection, and calibrated MQ sensors for gas detection of CH4, H2S, and NH3. The back end simply interfaces with the Blynk application, ThingSpeak, and Google spreadsheet as data storage for detailed data visualization and real-time monitoring via smart devices. A rigorous sensor validation process that was carried out, indicating that this device's measurements align statistically with those of commercially available sensors, as validated by paired sample t-tests, Q-Q plot and descriptive data analysis in SPSS software. The prototypic data analysis system is also a key aspect of this research. Our proposed research not only supports the usefulness of our technology, but also gives a direction for future users to correctly evaluate their data. The integrated IoT device exhibits its utilization for smart healthcare and precision agriculture systems for the purpose of smart village. The combination of IoT along with startup enterprises is extending unique notions that could improve global food sustainability and individual well-being. This paper displays the potential for startups to develop innovative IoT, health, and agriculture solutions that will shape a future where technology improves lives and ensures food security. © 2024 IEEE.

关键词： Smart agriculture

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Classification of Hoax News Using Machine Learning and Neural Networks with BERT Embeddings 3

Classification of Hoax News Using Machine Learning and Neura...

引用

3rd International conference on electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Juarto, Budi School of Computer Science Bina Nusantara University Computer Science Department Bandung Indonesia

ISBN: (纸本)9798350327762

In the digital era, hoax news poses a serious threat, especially during elections, impacting unity and causing conflicts. With 8.7 million fake news articles circulating, public susceptibility to hoaxes, often fueled by emotional language and lack of source verification, adds to the risk. Deep learning offers a potential solution by utilizing advanced neural networks for in-depth text analysis, effectively distinguishing valid news from hoaxes based on content patterns. This approach holds promise in eradicating hoax news, raising awareness, and safeguarding the nation' s unity. This study focuses on detecting hoax news using machine learning and deep learning techniques. The researchers used a general dataset containing 44,898 news articles, with 23,481 labeled as hoaxes and 21,417 as factual. Preprocessing involved tokenization, stopword removal, and lemmatization/stemming before converting text into vectors using TF-IDF. For deep learning, the BERT method was employed to extract contextual embeddings. The models were evaluated using different combinations of news titles and content. Among the machine learning models, Support Vector Machine (SVM) performed best, with an F1-score of 94% when using only news titles. However, the decision tree method outperformed others with a 99.6% F1 score when using titles and content. Nevertheless, all machine learning models were outperformed by BERT + NN, achieving an F1-score of 98.72% with titles only and 99.9% with both titles and content. © 2023 IEEE.

关键词： Embeddings

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HEALTH-ML: A Machine Learning Framework for Equity-Driven Public Health Outcome Prediction 15

HEALTH-ML: A Machine Learning Framework for Equity-Driven Pu...

引用

15th IEEE Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Ekoniak, Jake Asadinia, Marjan University of California Department of Mathematics Los Angeles United States California State University Computer Science Department Northridge United States

ISBN: (纸本)9798331507695

Significant health disparities persist across U.S. counties due to socioeconomic inequalities, environmental challenges, and unequal access to healthcare. Leveraging large health datasets, this study employs machine learning and advanced analytics to classify county health levels-categorized as low, medium, or high-based on environmental, health, and social factors. Emphasizing fairness, we incorporate bias mitigation techniques, ensuring the models avoid reinforcing existing disparities. Our approach includes comprehensive data preprocessing, feature engineering, and employing diverse machine learning algorithms, including Neural Networks, tree-based models, Support Vector Machines, and logistic regression, to capture complex relationships within the data. Results show accuracy scores exceeding 87% and AUC scores over 95%, highlighting the predictive strength of our models. Model performance is evaluated through accuracy, precision, recall, F1-score, and fairness metrics such as Equal Opportunity. Additionally, we assess computational efficiency and interpretability, ensuring the models are both practical and actionable. By providing accurate, fair, and interpretable predictions, this research offers a foundation for data-driven interventions, guiding policymakers toward reducing health inequalities across counties. Our study underscores the ethical imperative of fairness in public health analytics, setting a precedent for prioritizing social equity in future health data research. © 2025 IEEE.

关键词： electronic health record

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Uncertainty Aware Interest Point Detection and Description

Uncertainty Aware Interest Point Detection and Description

引用

2025 IEEE/CVF Winter conference on Applications of computer Vision, WACV 2025

作者： Zeng, Jingbo Gu, Zaiwang Liu, Weide Cai, Lile Cheng, Jun School of Electrical and Electronic Engineering Nanyang Technological University Singapore A∗star Singapore Boston Children's Hospital Harvard Medical School BostonMA United States

ISBN: (纸本)9798331510831

Interest point detection and description play an important role in many visual tasks, including image registration, pose estimation, 3D reconstruction, and more. State-of-the-art interest point detection techniques are based on deep neural networks (NNs), which are prone to produce overconfident predictions. However, calibrated and ro-bust uncertainty measurement is crucial when deploying deep NN models in safety critical applications. In this work, we propose a novel Uncertainty-Aware interest Point (UAPoint) detection method to address this problem. Our method leverages evidential learning to learn both aleatoric and epistemic uncertainty. We further propose a constrained sampling scheme to construct more efficient training pairs for the descriptor decoder. We evaluate our method on a wide range of benchmarks and show that our method achieves state-of-the-art performance. Code will be released in https://***/JingboZeng/UAPoint. © 2025 IEEE.

关键词： Deep neural networks

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Network environment-adaptive erasure coding scheme for decentralized ubiquitous storage

Network environment-adaptive erasure coding scheme for decen...

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2022 International conference on computer Application and Information Security, ICCAIS 2022

作者： Song, Gang Zhang, Quan Xu, Xin School of Communication Engineering Hangzhou Dianzi University Zhejiang Hangzhou310018 China School of Electronic Science and Engineering National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9781510663459

The adaptive erasure coding scheme for decentralized ubiquitous storage is investigated in this paper to improve the level of durability and reduce repair bandwidth cost and repair time. First, the extended parameters Reed-Solomon (RS) codes are proposed, and the relationship equations relating to durability, repair bandwidth cost, encoding parameters and peers fluctuation rate are set. Subsequently, the Environment-adaptive Parameters Algorithm (EAPA) is proposed. In a variety of network environments, EAPA can adjust encoding parameters adaptively to retain the desired level of durability without increasing storage cost. The Optimal Repair Cost Parameters Algorithm (ORCPA) is proposed for different scenarios, which leads to the optimal repair bandwidth cost and achieves a desired level of durability. ORCPA adapts parameters in accordance with the ratio of slow peers in the network to reduce the repair time. Experiments show that EAPA and ORCPA are capable of significantly increasing level of durability and reducing repair bandwidth cost and repair time compared with several erasure codes with wide application in multi-peer failure scenarios. © 2023 SPIE.

关键词： Reed-Solomon codes

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A Computationally efficient CNN-based Deep Learning Technique for Sickle Cell Detection

A Computationally efficient CNN-based Deep Learning Techniqu...

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2024 IEEE International conference on electronic Systems and Intelligent Computing, ICESIC 2024

作者： Hemavarshini, S. Sathya Shree, S. Arumuga Arun, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331522988

An amino acid change in the hemoglobin protein causes sickle cell disease (SCD), a prevalent hereditary illness that causes red blood cells to take on a sickle shape. These malformed cells cause severe health complications, including painful episodes, organ damage, increased infection risk, and a significantly reduced lifespan. Accurate and timely diagnosis is essential for timely therapy and better results for patients. This study investigates the potential of various lightweight deep learning models, MobileNetV1, MobileNetV2, MobileNetV3 Small, and EfficientNetV0 as an efficient and accurate underlying model for SCD detection. All of these trained them on the publicly available Sickle Cell Disease Dataset, where various augmentation methods are used to enhance the resilience and generalization of the models, such as rotating, shifting, shearing, zooming, brightness, and contrast adjustments. The MobileNetV1 has achieved better performance with an accuracy of 93.2% and F1Score of 95.43% with a lesser complexity with computational parameters of 4.855 Million and a model size of 18.52 MB. These findings highlight the potential of these lightweight models to revolutionize SCD screening and diagnosis in resource-constrained environments, offering faster, more accessible, and cost-effective diagnostic tools, ultimately improving healthcare outcomes for individuals with SCD. © 2024 IEEE.

关键词： Patient treatment

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