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共 1778 条记录，以下是311-320 订阅

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Digitization of Written Parliamentary Questions from the Historical Archive (1974-1977) of the Hellenic Parliament

Digitization of Written Parliamentary Questions from the His...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Fitsilis, Fotios Gatos, Basilis Palaiologos, Konstantinos Kaddas, Panagiotis Kyrkos, Charalambis Georgoulea, Maria-Eleni Armenakis, Yiannis Tasouli, Christina Mikros, George Rozenberg, Olivier Kiousi, Eleni Hellen Parliament Vas Sofias Ave 2-4 Athens 10021 Greece Natl Ctr Sci Res Demokritos Patriarchou Grigoriou E & Neapoleos 27 Aghia Paraskevi 15341 Greece Hellen OCR Team Aretis 5B Aghia Paraskevi 15343 Greece Hamad Bin Khalifa Univ Educ City Gate 8 Ar Rayyan Qatar

ISBN: (纸本)9783031706448;9783031706455

this article outlines the digitization process and methodology applied to the archive of parliamentary questions from the 1st Parliamentary Term (1974-1977) in the Hellenic Parliament. A collaborative pilot project involving parliament, academia, and a research center facilitated the conversion of printed material to open data. the main tasks of the project include capturing digital images, a custom Optical Character recognition (OCR) software solution employing machine learning, and rigorous validation for accuracy of a fragmented and of variable quality polytonic corpus in a variety of modern Greek language called Katharevousa. the article discusses the approach and challenges as well as the initial results of the digitization effort, emphasizing ongoing research steps. Overall, 1,674 images were digitally processed corresponding to 1,338 questions. Following algorithmic training, character recognition accuracy is over 98.5%. Successful implementation streamlines further similar digitalization operations in the vast parliamentary archives, while enabling in-depth studies on parliamentary control in the turbulent period of the immediate post-junta era in Greece. A preliminary comparative analysis with a corpus of newer parliamentary questions (2009-2019) provides insights and incentives for the further study of the characteristics and evolution of the Greek language.

关键词： Hellenic Parliament written questions machine learning OCR parliamentary control polytonic corpus

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Deep learning Methods for Tree Detection and Classification 4

Deep Learning Methods for Tree Detection and Classification

引用

IEEE 4th international conference on Cognitive machine Intelligence (CogMI)

作者： Zhang, Yang Wang, Yizhen Tang, Zhicheng Zhai, Zhenduo Shang, Yi Viegut, Reid Univ Missouri Dept Elect Engn & Comp Sci EECS Columbia MO 65211 USA Univ Missouri Sch Nat Resources Columbia MO USA

ISBN: (纸本)9781665474061

this paper presents the results of our deep learning methods for tree detection and classification on aerial images in the Plant recognition University Challenge sponsored by Ameren in 2021-2022. the task was to locate the trees in an aerial image and predict their family, genus, and species. For tree detection, we applied various supervised learning methods with labeled training data as well as semi-supervised learning methods with the addition of unlabeled data. Our experimental results show that the semi-supervised learning method outperformed the supervised learning methods, improving the f1-score by an average of three percent on the set of images used in the final Plant Challenge competition. For tree classification, We applied various machine learning methods and deep learning models for image classification to predict family, genus and species on the portions of images detected of trees by the detection models. By considering the relationships between family, genus and species, we developed a multi-head ResNet18-based neural network and increased mean accuracy by two percent over the baseline ResNet18. Finally, our team ranked first among all teams in the Plant Challenge competition.

关键词： aerial image RGB image multi-spectral image tree detection tree classification supervised learning semi-supervised learning deep learning

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data Augment Method for Power System Transient Stability Assessment Based on DeepSMOTE 4

Data Augment Method for Power System Transient Stability Ass...

引用

4th international conference on Power Engineering, ICPE 2023

作者： Ma, Binyu Yang, Jun Shen, Jinpeng Pei, Yangzhou Peng, Xiaotao Jiang, Kezheng Liu, Dan Cao, Kan School of Electrical Engineering and Automation School of Electrical Engineering and Automation Wuhan University Wuhan University Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Wuhan China State Grid Hubei Electric Power Research Institute Wuhan China

ISBN: (纸本)9798350340020

data-driven transient stability assessment (TSA) method has become the focus of power system stability and security research in recent years. However, in actual power system, transient unstable data only accounts for a small portion of all data. Unbalanced data is not conducive to the machine learning model's judgment of instability situations. A data augment method based on DeepSMOTE is proposed to generate high-quality unstable datasets. then, an extreme learning machine (ELM) classifier is used to select the generated dataset with the highest G-mean value, which is used to fuse with the original dataset to construct a balanced dataset with a similar number of transient stability and instability samples. Finally, TSA is implemented based on the balanced dataset. It has been verified in the modified IEEE 39-bus system simulation example containing new energy that DeepSMOTE can effectively generate data that follows the distribution characteristics of the original data of power system, thereby improving the recognition rate of unstable samples in TSA. Moreover, it has strong robustness under wide area noise and effectively balances power system instability data. © 2023 IEEE.

关键词： Classification (of information)

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Detection of Attacks in Smart Healthcare deploying machine learning Algorithms 4

Detection of Attacks in Smart Healthcare deploying Machine L...

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4th international conference for Emerging Technology, INCET 2023

作者： Sharma, Anshika Babbar, Himanshi Vats, Amit Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India ABES Institute of Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350335750

the Internet of things (IoT) is a sort of network that uses a set of protocols and data-sensing tools to link anything to the Internet. this type of network allows for data exchange as well as smart recognition, surveillance, deployment, tracking, and maintenance. the healthcare business is rapidly digitizing, and the IoT is a key factor in this. It has already had an impact on medical networks and will continue to do so soon. this paper highlights privacy concerns as well as security attacks, which have rapidly increased in recent years in the healthcare industry. An architecture is proposed for detecting IoT attacks in smart health-care systems using machine learning (ML) techniques. this study used the UNSW-NB15 dataset to simultaneously classify Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbour (KNN) classifiers for detecting the attacks (Generic, Fuzzers, Exploits, Analysis, Denial of Service (DoS), Reconnaissance, Backdoor, Worms and Shellcode) in the healthcare industry. In addition to developing the essential smart and effective online system, this research increased knowledge of privacy and security in the smart healthcare sector. Furthermore, performance measures like accuracy, precision, recall, and F1-score have been calculated using the existing ML techniques based on the mentioned UNSW-NB15 dataset. the results indicate that when comparing the mentioned ML techniques, the KNN model has the highest accuracy value, or 90%, while RF and NB provide 89% and 75% accuracy values, respectively. © 2023 IEEE.

关键词： Internet of things

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A component search method by feature Mixing based Continual learning 4

A component search method by feature Mixing based Continual ...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Fang, Xiaoyu Yuan, Liu School of Computer Science Shanxi Normal University Xian China

ISBN: (纸本)9798400709449

Component platforms are widely used in developing large web and mobile applications to improve development efficiency and test coverage. Searching for the appropriate component in the component platform and introducing it into the project for use makes development efficient and reduces redundant code. machine learning methods are now widely used in large component platforms to enhance automatic retrieval or identification. However, current machine learning models are less accurate in identifying components or icons with differences due to the difficulty of collecting a large amount of label data in online environments that can help learning. To address this problem, this paper improves the feature mixing method in active learning and embeds it into a classical continuous learning model to solve the problem of accuracy degradation when the model handles discrepant tasks and tasks with limited training data. the model proposed in this paper ensures that the accuracy and performance of the model do not degrade significantly when facing a series of tasks with less data in the dataset after training on the first few tasks. Compared with large neural network recognition tasks, the improved continuous learning model is more lightweight and can learn multiple tasks using a single model. Easy to use when combined with front-end engineering and can be added to projects through dependency libraries. Compared to various classical benchmark models in continual learning, the accuracy of the proposed model can be improved by about fifteen percent. © 2023 ACM.

关键词： Mixing

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Research on Hybrid data Mining Methods for Social Media Sentiment Analysis 4

Research on Hybrid Data Mining Methods for Social Media Sent...

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4th international conference on Big data Economy and Information Management, BDEIM 2023

作者： Li, Haoran University of Bristol Bristol United Kingdom

ISBN: (纸本)9798400716669

With the popularity of social media in modern society, online opinion analysis has become an important research field. this study proposes a novel hybrid data mining algorithm that combines sentiment analysis and topic modeling techniques, aiming to improve the accuracy and comprehensiveness of social media data analysis. this paper first introduces the important role of social media in shaping public opinion and the major challenges currently faced in data analysis, such as information overload and viewpoint polarization. this is followed by an overview of the main current research trends in online opinion analysis, especially the application of deep learning and machine learning techniques in processing large-scale social media data. this study details the theoretical foundations and implementation of sentiment analysis and topic modeling, and shows how the two techniques can be combined. through experimental results, this paper demonstrates the performance of the hybrid algorithm in sentiment analysis and topic modeling, and verifies its effectiveness in online public opinion analysis. the experimental results show that the algorithm not only exhibits high accuracy in sentiment categorization, but also demonstrates good performance in topic recognition. © 2023 ACM.

关键词： data accuracy

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Research on Intelligent Identification Method of Primary Loop Transient in Nuclear Power Plant 4

Research on Intelligent Identification Method of Primary Loo...

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4th international conference on System Reliability and Safety Engineering (SRSE)

作者： Zhang Runze Huang YongQiang Chen Xin Suzhou Nucl Power Res Inst Performance Test Ctr Shen Zhen Peoples R China

ISBN: (纸本)9781665473880

the primary loop transient of nuclear power plant is one of the important references for characterizing the fatigue of primary loop materials. For the identification and classification of transients, the common method is to determine according to the change of the value and the operation of the unit. In this paper, through the research on the thermal parameters behind various transients and the exploration of the law of transient occurrence, a complete set of automatic transient identification framework is formed. Combined with new computer technologies such as machine learning, it realizes fully automatic processing from the import of raw data to the export of recognition results. It provides ideas for the key links in the development of the whole-process automation platform for transient management.

关键词： transient machine learning intelligent identification nuclear power plant transient management

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Prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction 4th

Prototype-Guided Contrastive Knowledge Graph Representation ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Park, Wooyeol Mulyadi, Ahmad Wisnu Kang, Eunsong Suk, Heung-Il Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787012

the medical knowledge graph (KG) constructed from electronic health records (EHR) offers a comprehensive understanding of patients as it facilitates interconnections among medical codes. While KG has been proven to improve diagnostic accuracy, it inevitably contains noisy and incomplete attributes that could compromise the verdicts. Hence, direct utilization of medical KG for downstream clinical tasks may entail inadequacy, even by resorting to proper machine learning methods. Several prior studies have initiated noise filtering methods upon medical KG to relieve such issues by masking its task-irrelevant nodes and relations. However, they focused solely on a patient-wise perspective that feasibly overlooked essential shared traits from a (sub-)population viewpoint. this study proposes a novel prototype-guided predictive model called ProtoCare to predict diagnoses in EHR by unifying prototype and contrastive learning. ProtoCare leverages a set of prototypes to discover latent shared characteristics among groups of patients, while contrastive learning plays the role of noise filtering and KG refinement accounting for both learned patient and selected prototype features. Experimental results on the real-world EHR cohort exhibited that our proposed ProtoCare outperformed several baselines on the diagnosis prediction task. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Towards IoT Context data Analysis and Classification 4

Towards IoT Context Data Analysis and Classification

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Bandason, Vivian Sibanda, Khulumani Tamuka, Nyashadzashe University of Fort Hare Department of Computer Science Eastern Cape Alice South Africa Walter Sisulu University Department of Applied Informatics Eastern Cape East London South Africa

ISBN: (纸本)9798350387988

the capability of generating and acquiring data has never been as pronounced as today since the advent of Information Technology in the early 19th century. Big data is a growing trend where data processing has grown so much that it is beyond the ability of commonly used software tools to capture, manage, and analyse this data. the most fundamental challenge for the Big data applications is to explore the large volumes of data and extract useful knowledge. Another recent development furthering the accumulation of data is the Internet of things - wherein the amount of 'things' housing different sensors has multiplied. the Internet of things (IoT) is built on sensors and actuators, which send out large quantities of data about their state and the environment. data processing techniques can be utilized to extract relevant insights from this treasure of data and information that is generated in IoT environments. this research investigates how sensor data from user devices can be used to determine context information about a user. As far as the classification of daily human activities (such as walking, eating, driving and sleeping) is concerned, several studies have proposed traditional computer vision methods. Unlike traditional computer vision methods, which pose mobility limitations, computational complexity, and privacy issues, our approach leverages mobile sensor data for real-time activity recognition. three classification algorithms which are the K-Nearest Neighbours (KNN), Decision Trees, and Support Vector machines (SVM) were evaluated based on their accuracy in classifying human activities. Findings revealed that the KNN was superior to other algorithms in providing a promising approach for context-aware systems in IoT environments. this study highlights the potential of machine learning and sensor data in enhancing user-centric applications through improved context-awareness. © 2024 IEEE.

关键词： data assimilation

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Description Attribute-Enhanced Spatio-Temporal Zero-Shot Action recognition 4th

Description Attribute-Enhanced Spatio-Temporal Zero-Shot Act...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Kim, Yehna Kim, Ho-Joong Lee, Seong-Whan Department of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787043

Vision-Language Models (VLMs) have shown remarkable performance in zero-shot action recognition by learning the correlation between video embeddings and class embeddings. However, an issue arises when depending solely on action classes for semantic information due to multi-semantic words - words with multiple meanings. theses words hinder the difficulty of the model to accurately capture the intended concepts of actions. We propose a novel approach which leverages web-crawled descriptions with utilizing a large-language model for the extraction of keywords. this method reduces the reliance on human annotators and avoids the exhaustive manual process of attribute data creation. Moreover, we introduce a spatio-temporal interaction module which focuses on objects and action units to align description attributes with video content. In zero-shot experiment, our model achieves 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively, which demonstrates the transferability of our model to downstream tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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