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检索条件"丛书名=Communications in Computer and Information Science"

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EmoBART: Multi-label Emotion Classification Method Based on Pre-trained Label Sequence Generation Model 5th

EmoBART: Multi-label Emotion Classification Method Based on ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Chen, Sufen Chen, Lei Zeng, Xuegiang Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Sequence-to-sequence models for generating emotion labels have been identified as an effective method for multi-label emotion classification, utilizing RNNs to capture emotion relationships. However, while these sequence generation models implicitly learn label correlations, they may not adequately capture the strong correlations that exist among nuanced emotions. Moreover, traditional RNN-based sequence generation models may struggle to extract textual semantic information as effectively as pre-trained language models. To address the above problems, this paper proposes a novel Multi-Label Emotion Classification Based on Pre-trained Sequence Generation Model (EmoBART). EmoBART leverages a pre-trained generative language model, BART, as the network skeleton for generating emotion labels and incorporates a correlation network to explicitly model fine-grained emotion correlations. The EmoBART architecture comprises an encoding module for semantic information extraction, a decoding module for predicting emotion label sequences through generative labeling chains, and a correlation network module that integrates the CorNet network to learn emotion correlations and predict emotion labels. Extensive comparative experimental results show that EmoBART outperforms existing models in terms of multi-label emotion recognition accuracy.

关键词： Multi-Label Emotion Classification Sequence Generation Model Sequence to Sequence Emotion Correlation Learning

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Deidentification and Temporal Normalization of the Electronic Health Record Notes Using Large Language Models: The 2023 SREDH/AI-Cup Competition for Deidentification of Sensitive Health information

Deidentification and Temporal Normalization of the Electroni...

引用

2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Mir, Tatheer Hussain Yang, Hao-Ping Chou, Yi-Yun Teng, Yu-Chin Liao, Wei-Hsiang Lin, Yu-Chuan Gupta, Shalini Panchal, Omkar Jonnagaddala, Jitendra Chen, Ching-Tai Dai, Hong-Jie Natl Kaohsiung Univ Sci & Technol Intelligent Syst Lab Coll Elect Engn & Comp Sci Kaohsiung Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung Taiwan Asia Univ Ctr Precis Hlth Res Taichung Taiwan Univ New South Wales Sydney NSW Australia NMC Royal Hosp Abu Dhabi U Arab Emirates Natl Inst Canc Res Natl Hlth Res Inst Tainan Taiwan Kaohsiung Med Univ Sch Post Baccalaureate Med Coll Med Kaohsiung Taiwan Kaohsiung Med Univ Ctr Big Data Res Kaohsiung Taiwan CGD Hlth Pvt Ltd Hyderabad Telangana India

ISBN: (纸本)9789819779659;9789819779666

Electronic Medical Records (EMR) implementation benefits the medical industry with streamlined data analysis, increased patient medication safety, reduced expenses for pathology report storage, and improved medical care efficiency. The EMR text notes hold a patient's clinical record, comprising notes written by the medical staff and further analyzed by the doctor following diagnosis. However, utilizing the EMR text note in its raw form can expose sensitive personal data belonging to patients and medical personnel. Therefore, safeguarding this private information is of utmost importance. Furthermore, the expression of time information in EMR text notes varies across institutions, which can significantly impact the accuracy and reliability of temporal information analysis. Therefore, normalizing temporal information is also a critical issue. The study presents a competition titled Privacy Protection and Standardization of Electronic Medical Record Competition that addresses recognizing Sensitive health information (SHI) recorded in EMR text notes and normalizing temporal information that poses a risk of identity theft. The competition released a corpus containing synthesized SHIs and normalized temporal information. The highest performance for the SHI recognition (subtask 1) and temporal information normalization (subtask 2) are micro-/macro-F of 0.949/0.912 and 0.844/0.869, respectively. Overall, the average micro/macro score for subtasks 1 and 2 were 0.666/0.496 and 0.6/0.394.

关键词： Electronic medical record text notes sensitive health information natural language processing de-identification temporal information normalization

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Harvesting Insights: Unrevealing Paddy Crop Yields Through Advanced Modeling Techniques 2nd

Harvesting Insights: Unrevealing Paddy Crop Yields Through A...

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2nd International Conference on Agriculture-Centric Computation

作者： Tummala, Mounika Aridoss, Manimaran Babu, Khadar S. K. VIT AP Univ Sch Adv Sci Amaravati 522237 India Vellore Inst Technol Dept Math Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783031744396;9783031744402

This research examines on the effectiveness of various Machine Learning and Deep Learning models for predicting the yield of paddy crop in East Godavari District, Andhra Pradesh, India. Dataset consist of 91 entries representing 91-day paddy crop growth cycle from 2000 to 2009, this study forecast yield for the years 2017 and 2018 using Machine Learning models such as Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Support Vector Machines (SVM) and Deep Learning models such as 1D Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Multilayer Perceptron's (MLP), Feedforward Neural Networks (FNN), and Gated Recurrent Units (GRU). Performance of the Model is evaluated by using R-square, Mean Square Error (MSE) and Mean Absolute Error (MAE).

关键词： Agriculture Yield Machine Learning and Deep Learning

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Empowering Breast Cancer Detection: A Novel Hybrid Transfer Learning Approach with Aquila Optimizer 4th

Empowering Breast Cancer Detection: A Novel Hybrid Transfer ...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Prova, Nuzhat Noor Islam Pace Univ Seidenberg Sch CSIS 1 Pace Plaza New York NY 10038 USA

ISBN: (纸本)9783031734762;9783031734779

Breast cancer (BC) is a condition where aberrant breast cells proliferate uncontrollably, resulting in tumors, and the most hazardous disease for women worldwide is BC. It frequently results in severe psychological suffering, including elevated rates of anxiety and depression. Machine learning (ML) and computer-aided diagnosis (CAD) have demonstrated potential in enhancing the identification of BC, specifically in the area of Deep Learning (DL) methods for the classification of histopathological images (HI). These techniques depend on huge amounts of labeled data, which can be challenging to gather. This study presents a new method called Aquila Optimizer (AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification (HRD-BC2HI), combining ResNet and DenseNet models for feature extraction from histopathological images, employing DenseNet and ResNet for feature extracting. The Denoising Sparse AutoEncoder (DSAE) model, improved by the AO algorithm, is employed for BC detection and classification. The trials were carefully carried out with various dataset magnifications, including resolutions at 220px and 400px. Our suggested approaches and techniques could be thoroughly assessed through careful investigation, guaranteeing their resilience and adaptability to different image resolutions.. The HRD-BC2HI technique outperformed other current methods in BC classification when tested on a benchmark dataset. It obtained remarkable results with an average accuracy of 98.92%. The outcomes suggest that the HRD-BC2HI method performs better for BC classification than other contemporary techniques.

关键词： Breast cancer Histopathological images Deep learning Aquila optimizer Hybrid Model

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Catalan's Conjecture and Elliptic Curve Cryptography (CCECC) Algorithm for Enhancing Data Security During Data Transmission in MANET 5th

Catalan's Conjecture and Elliptic Curve Cryptography (CCECC)...

引用

5th International Conference on Computing science, Communication and Security

作者： Pauline, D. Eben Angel Womens Christian Coll Chennai India

ISBN: (纸本)9783031751691;9783031751707

Security plays a major role in the processes of secured storage and secured data transmission in MANET due to the huge volume of users and their data. Moreover, MANET provides distributed services to their clients and their users anytime, anywhere. This is becoming a very challenging problem to provide secure storage and data communications. This is very important in this fast-paced internetworld. Data security is provided through cryptographic algorithms such as key generation, encryption, and decryption. The prime numbers are determining the size of the secret key that is capable of providing more security to the user's data effectively. The enhancement of the prime number is helpful for encrypting the data securely. So that this research work concentrates on the prime number selection process by applying Catalan's conjecture and using the standard Elliptic Curve Cryptographic (CCECC) algorithm to achieve a better security level and efficiency in terms of encryption time, decryption time, and key generation time.

关键词： MANET Routing Protocol Elliptic Curve Cryptography Cryptography Cryptanalysis

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Deep-MFR: A Deep Learning Ensemble Approach for Improved Masked Face Recognition 4th

Deep-MFR: A Deep Learning Ensemble Approach for Improved Mas...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Sharma, Shivani Lamba, Sonu Gaddu, Arshdeep Kaur Saini, Jaskarandeep Thakur, Aaryaman Dhawan, Mayank Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

ISBN: (纸本)9783031837890;9783031837906

Masked face recognition is a biometric authentication method widely utilized in smart devices, such as surveillance and attendance systems. The post-pandemic period has reinforced the use of masks in public places as an effective measure for preventing the spread of infections. Earlier face recognition systems were primarily trained on datasets of unmasked human faces. However, the COVID-19 pandemic has necessitated the adaptation of these programs to effectively identify individuals wearing masks. Detecting masked faces presents significant challenges due to the reduced visibility of key facial features. With much of the face obscured, the process becomes more complex, making it harder for algorithms to accurately identify individuals. In this work, we propose an approach that integrates the distinctive features of deep learning models into a unified framework for masked face recognition. This integration aims to enhance the accuracy and effectiveness of recognizing individuals wearing masks by leveraging the strengths of various deep learning techniques. An ensemble model is proposed where deep learning models (SSD and MTCNN) are used for face detection, and facial features are combined with FaceNet embeddings derived from available landmarks such as the eyes, brows, forehead, and other visible areas. A reproducible set of experiments was conducted on benchmark datasets, achieving an accuracy of approximately 94%, representing a significant improvement over the existing state-of-the-art methods.

关键词： Masked face Ensemble Models Face Detection COVID-19 Face Embedding

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Detection of Leaks in the Water Distribution Network 2nd

Detection of Leaks in the Water Distribution Network

引用

2nd International Conference on information Technologies and Their Applications

作者： Aida-Zade, Kamil Ashrafova, Yegana Inst Control Syst Baku Azerbaijan Azerbaijan Univ Architecture & Construct Baku Azerbaijan Baku State Univ Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

Numerical methods are employed to address an inverse problem associated with a water distribution network characterized by a complex loopback structure. The problem is to ascertain the locations and magnitudes of leaks based on measurements of unsteady flow characteristics at certain points in the pipeline. Key aspects of the problem include the involvement of impulse functions within a system of hyperbolic differential equations, the lack of traditional initial conditions, and the specification of nonseparated boundary conditions between states at the endpoints of adjacent pipeline segments. The problem is transformed into a parametric optimal control problem, devoid of initial conditions but featuring nonseparated boundary conditions. The latter problem is tackled using first-order optimization methods. The paper presents the outcomes of numerical experiments. Notably, this research distinguishes itself from others by addressing the inverse problem of determining leak locations and magnitudes within an unsteady flow scenario in a water distribution network with a complex (loopback) structure, as opposed to studies focusing on steady flow or transient flow in simpler pipeline configurations.

关键词： water distribution network locations of water leaks amounts of water leaks an inverse problem impulse functions hyperbolic differential equations nonseparated boundary conditions

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A Semi-automatic Pipeline for the Decay Mapping and the State of Conservation Assessment of Architectural Heritage Through Point Clouds

A Semi-automatic Pipeline for the Decay Mapping and the Stat...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Lasorella, Margherita Cantatore, Elena Rondinelli, Maria Felicia Letizia Fatiguso, Fabio Politecn Bari Via Edoardo Orabona 4 I-70125 Bari Italy Univ Basilicata Via Ateneo Lucano 10 I-85100 Potenza Italy

ISBN: (纸本)9783031834318;9783031834325

The integration of automation and artificial intelligence has revolutionized the documentation, conservation, and restoration of architectural cultural heritage. This research presents a pipeline leveraging Machine Learning (ML) algorithms for the automatic decay mapping of digitalized architectures, combined with informative system to support experts in the assessment of a synthetic index of the conservation state. Moreover, the setup of a web-based system as a platform for the expert acknowledgment and management of decays and properties helps them in a fast and coherent final assessment. The proposed pipeline combines such technologies to the architectural recovery theories and ensures a standardized procedure compliant with international and national regulations and standards such as ICOMOS-ISCS Glossary and UNI 11182:2006, UNI 8290-1:1981 and UNI CEN/TS 17385:2019. Method and tools are applied to the Labriola Palace in Tursi, Italy, demonstrating its efficacy in assessing the conservation state of architectural heritage, as well as the higher collaborative levels to reach also among technicians with different levels of computer science skill.

关键词： Digital model Historic Building Informative System Condition Assessment Machine Learning Technical users

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ReVisE: Emulated Visual Outfit Generation from User Reviews Using Generative-AI 33rd

ReVisE: Emulated Visual Outfit Generation from User Reviews ...

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33rd International Conference on Software and Data Engineering

作者： Rosas, Samar Rahimi Neupane, Subash Mitra, Shaswata Mittal, Sudip Mississippi Sch Math & Sci Columbus MS 39701 USA Mississippi State Univ Dept Comp Sci & Engn Starkville MS USA

ISBN: (纸本)9783031752001;9783031752018

The fashion industry faces significant challenges due to overproduction and waste, often driven by uncertainty about consumer preferences. This paper presents ReVisE, a novel framework leveraging generative AI to address this issue by emulating outfit generation from user reviews. ReVisE combines a text-to-text Large Language Model (LLM) and a text-to-image Stable Diffusion (SD) model to create virtual outfits based on customer feedback. The LLM consolidates user reviews to extract desired improvements and feedback, and the SD model utilizes these insights to produce realistic visual representations of the improved product. Our framework allows designers to evaluate potential designs and identify areas for improvement without physically producing multiple prototypes, thereby reducing waste and accelerating the design process. Experimental results conducted on the Amazon fashion item reviews demonstrate the effectiveness of ReVisE, showing promising results with both multimodal and human evaluations.

关键词： Outfit generation Generative AI LLM Text-to-Text Text-to-Image Stable diffusion

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The Specialization of AGI: Exploration of Industrial Applications for General Artificial Intelligence 4th

The Specialization of AGI: Exploration of Industrial Applica...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Zhang, Tao Sun, Changxin Wang, Tao Wu, Zhenyu Mo, Ketao Shen, Weilin Wang, Pei Liu, Kai Bohai Univ Dept Educ Sci Jinzhou 121013 Peoples R China Hubei Univ Econ Sch Informat Engn Wuhan 430205 Peoples R China South China Normal Univ Guangzhou 510006 Peoples R China Guangdong Sanpin Software Technol Co Ltd Guangzhou 510145 Peoples R China UNIDT Shanghai Co Ltd Shanghai 200436 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Bohai Univ Inst Gen Artificial Intelligence Jinzhou 121013 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

In the wake of Industry 4.0's progression, General Artificial Intelligence (AGI) systems, such as OpenNARS, have emerged as pivotal in both augmenting and potentially supplanting domain-specific AI in industrial contexts. Since AGI is not inherently designed for industrial tasks, it is necessary to tailor them from general-purpose to specialized applications to address their efficiency shortcomings. This paper, based on the transformation of AGI's priors design and educational guidance undertakes a specialized optimization of OpenNARS for an industrial scenario using the real-world case of steel surface defect detection. The findings demonstrate that the resultant specialized OpenNARS system manifests a marked enhancement in both reasoning efficiency and accuracy, which is a significant advancement for the broader adoption of AGI in industrial settings.

关键词： Industry 4.0 AGI OpenNARS SAI

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