Existing research has shown that vocal and non-vocal human cues correlate with human trust and distrust behaviours, suggesting their potential to measure human trust in robots in real-time. However, there is a lack of...
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Autism Spectrum Disorder is a neurodevelopmental disorder that presents with persistent deficits in social communication and social or emotional reciprocity. Timely intervention may result from an early and precise di...
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Unified modeling Language (UML) is a visual modeling language for expressing the architecture and dynamics of software systems. Traditionally, creating UML diagrams required significant expertise and effort to transla...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Unified modeling Language (UML) is a visual modeling language for expressing the architecture and dynamics of software systems. Traditionally, creating UML diagrams required significant expertise and effort to translate user stories into visual representations. However, advancements in tools like PlantUML and Natural Language Processing (NLP) have revolutionized this process. This paper explores how these technologies can automatically generate UML diagrams, simplifying the transformation of natural language user stories into structured visual models. By focusing on the integration of NLP and UML generation tools, the paper aims to demonstrate their potential to streamline software development, improve accuracy, and enhance scalability in modeling processes.
The proceedings contain 52 papers. The special focus in this conference is on Trends in Electronics and Health Informatics. The topics include: An Effective Combination of Deep and Machine Learning Models for Monkeypo...
ISBN:
(纸本)9789819739363
The proceedings contain 52 papers. The special focus in this conference is on Trends in Electronics and Health Informatics. The topics include: An Effective Combination of Deep and Machine Learning Models for Monkeypox Detection from Dermatographic Image;a Time-Efficient and Effective Image Contrast Enhancement Technique Using Fuzzification and Defuzzification;An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features;The Multi-class Paradigm: How Transformers Are Reshaping Language Analysis in NLP;deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection;Deep Learning Solutions for Detecting Bangla Fake News: A CNN-Based Approach;a Two-Stage Stacking Ensemble Learning for Employee Attrition Prediction;ensemble Learning Approaches for Alzheimer’s Disease Classification in Brain Imaging Data;pseudo-Knighted Cocktail Shaker Sort;sentiment Analysis in Twitter Data Using Machine Learning-Based Approach;road Object Detection for Visually Impaired People in Bangladesh;newBreeze: A Comprehensive Solution to a Beginner-Friendly Arch Linux Distribution with Zen Kernel;deep Ensemble Learning Approach for Multimodal Emotion Recognition;Tri Focus Net: A CNN-Based Model with Integrated Attention Modules for Pest and Insect Detection in Agriculture;detection and Classification of Spam Email: A Machine Learning-Based Experimental Analysis;predictive modeling and Early Detection of White Spot Disease in Shrimp Farming Using Machine Learning: A Case Study in Bangladesh;bangla License Plate Detection and Recognition Approach Based on computer Vision for Authentic Vehicle Identification;feature Techniques with a Custom Convolutional Model for Breast Tumor Surveillance in Mammograms;An AI-Based Clinical Recommendation System Using Ensemble-Based Soft Voting Classifier;machine Learning-Based Approach to Predict Heart Diseases Using Fused Dataset;an Optimal Feature Selection-Based Approach to P
The proceedings contain 92 papers. The special focus in this conference is on Intelligent Data Engineering and Automated Learning. The topics include: Model-Based Meta-reinforcement Learning for Hyperparameter Op...
ISBN:
(纸本)9783031777370
The proceedings contain 92 papers. The special focus in this conference is on Intelligent Data Engineering and Automated Learning. The topics include: Model-Based Meta-reinforcement Learning for Hyperparameter Optimization;towards Sustainable Precision: Machine Learning for Laser Micromachining Optimization;association Rules Mining with Auto-encoders;using Contrastive Learning to Map Stylistic Similarities in Narrative Writers;automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks;how Resilient are Language Models to Text Perturbations?;emotional Sequential Influence modeling on False Information;CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability;padel Two-Dimensional Tracking Extraction from Monocular Video Recordings;drowsiness Detection Using Vital Sign Sensors and Deep Learning on Smartwatches;Benchmarking Out of the Box Open-Source LLMs for Malware Detection Based on API Calls Sequences;multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit;MetaLIRS: Meta-learning for Imputation and Regression Selection;pipeline for Semantic Segmentation of Large Railway Point Clouds;preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running;efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization;towards a Communication Specification Language for Heterogeneous Service Orchestration Based on Process Calculus and Holonic Multi-agent systems;counterfactual Explanations for Sustainable Tourism Indicators;Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation;low Consumption Models for Disease Diagnosis in Isolated Farms;fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation;Federated Learning for Vietnamese SMS Spam Detection Using Pre
In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution measurement Ana...
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ISBN:
(数字)9798331527518
ISBN:
(纸本)9798331527525
In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.
The proceedings contain 92 papers. The special focus in this conference is on Intelligent Data Engineering and Automated Learning. The topics include: Model-Based Meta-reinforcement Learning for Hyperparameter Op...
ISBN:
(纸本)9783031777301
The proceedings contain 92 papers. The special focus in this conference is on Intelligent Data Engineering and Automated Learning. The topics include: Model-Based Meta-reinforcement Learning for Hyperparameter Optimization;towards Sustainable Precision: Machine Learning for Laser Micromachining Optimization;association Rules Mining with Auto-encoders;using Contrastive Learning to Map Stylistic Similarities in Narrative Writers;automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks;how Resilient are Language Models to Text Perturbations?;emotional Sequential Influence modeling on False Information;CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability;padel Two-Dimensional Tracking Extraction from Monocular Video Recordings;drowsiness Detection Using Vital Sign Sensors and Deep Learning on Smartwatches;Benchmarking Out of the Box Open-Source LLMs for Malware Detection Based on API Calls Sequences;multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit;MetaLIRS: Meta-learning for Imputation and Regression Selection;pipeline for Semantic Segmentation of Large Railway Point Clouds;preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running;efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization;towards a Communication Specification Language for Heterogeneous Service Orchestration Based on Process Calculus and Holonic Multi-agent systems;counterfactual Explanations for Sustainable Tourism Indicators;Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation;low Consumption Models for Disease Diagnosis in Isolated Farms;fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation;Federated Learning for Vietnamese SMS Spam Detection Using Pre
The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Genera...
ISBN:
(纸本)9789819794393
The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large Language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models;CKF: Conditional Knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models;model-Agnostic Knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU
The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Genera...
ISBN:
(纸本)9789819794331
The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large Language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models;CKF: Conditional Knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models;model-Agnostic Knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU
This paper proposes an innovative flood control simulation and early warning system integrating computer simulation and building information modeling (BIM). The system combines deep learning technology with water flow...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
This paper proposes an innovative flood control simulation and early warning system integrating computer simulation and building information modeling (BIM). The system combines deep learning technology with water flow simulation, uses long short-term memory network (LSTM) to model and predict historical water level data, and combines the three-dimensional terrain information provided by the BIM model to dynamically evaluate the water flow and flood spread risks. Through this innovative algorithm, the system can achieve accurate flood prediction according to different scenarios and conditions, and improve the timeliness and accuracy of early warning. In the experimental part, a reservoir was used as a case for verification. The results showed that the LSTM-based algorithm improved the prediction accuracy by $\mathbf{2 0 \%}$ compared with the traditional regression prediction method, and the computational efficiency was improved by 15 %. Compared with traditional methods, the water conservancy flood control early warning system integrating BIM and deep learning is more stable and efficient under complex terrain. This study provides new ideas for intelligent decision-making in the field of water conservancy and flood control. It is not only highly practical, but also provides technical support for further promoting intelligent water conservancy management and disaster prevention and control.
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