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Oil Spill Segmentation Using Enhanced U-NET and Fuzzy System Hybrid

Oil Spill Segmentation Using Enhanced U-NET and Fuzzy System...

引用

Machine Learning and Applied Network Technologies (ICMLANT), ieee international conference on

作者： Shreya Ranjan S. Babeetha Prince Kumar Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350389180

ISBN: (纸本)9798350389197

The suggested method integrates an advanced U-Net architecture with a fuzzy logic system to improve segmentation outcomes for accuracy and interpretability. The Enhanced U-Net incorporates additional convolutional layers, enhanced dropout rates, batch normalization, and improved skip connections, facilitating superior detection of complex features in oil spill photos while reducing overfitting. Significant advances comprise Leaky ReLU activation, dynamic learning rate scheduling, and a bespoke weighted loss function that together address the class imbalance problem and enhance the model's training efficiency. data supplementation and improved preprocessing enhance the model's generalization capability. Fuzzy logic introduces subtleties into the segmentation process, significantly diminishing false positives and negatives while yielding more dependable and interpretable outcomes. The hybrid approach enhances oil spill detection accuracy, hence significantly augmenting its utility for environmental monitoring.

关键词： Fuzzy logic Image segmentation Accuracy Oils Feature extraction Dynamic scheduling Real-time systems data models Satellite images Environmental monitoring

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Application and Performance Analysis of Physics-Informed Neural Networks in Solving complex Physical Systems

Application and Performance Analysis of Physics-Informed Neu...

引用

Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Xuyang Chen Letters and Science University of California Davis California United States of America

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study introduces a quantum-accelerated framework that integrates quantum optimization with physics-informed neural networks (PINNs) to solve PDEs more efficiently. By leveraging the Quantum Approximate Optimization Algorithm (QAOA), the proposed framework enhances the initialization and boundary parameter optimization of PINNs, addressing key challenges such as slow convergence and high computational cost. The hybrid approach combines the data efficiency and physical constraint embedding capabilities of PINNs with the computational advantages of quantum optimization, enabling faster and more accurate solutions for complex PDEs. The framework was validated using the Shuhao Cao PDE dataset, demonstrating significant improvements in training efficiency and accuracy compared to traditional numerical solvers and classical PINNs. Experimental results highlight that quantum optimization effectively accelerates convergence by optimizing initial conditions and boundary constraints, reducing the computational overhead associated with classical training processes. Despite its reliance on emerging quantum hardware and the sensitivity of PINNs to hyperparameter selection, this approach offers a scalable and robust solution for solving high-dimensional PDEs. The findings underscore the potential of hybrid quantum-classical methods in advancing scientific computing, providing a foundation for future research in stochastic PDEs, multi-physics problems, and other real-world applications.

关键词： Training Quantum computing Accuracy Sensitivity Scientific computing Approximation algorithms Hardware Computational efficiency Optimization Convergence

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Mitigating Information Overload in Scientific Text Analysis: Automated Extraction of Important Concepts Using Dependency Parsing and Semantic Similarity

Mitigating Information Overload in Scientific Text Analysis:...

引用

international conference on computing, Engineering, and Design (ICCED)

作者： Faisal Fahmi Zulfatun Sofiyani Imam Yuadi Yunus Abdul Halim Ragil Tri Atmi Department of Library and Information Science Airlangga University Surabaya Indonesia

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

Information overload poses a significant challenge in the scientific community, where researchers are often confronted with vast amounts of textual data. This overload can hinder effective information retrieval and decision-making, particularly when analyzing large volumes of scientific texts. To address this issue, a novel approach that combines dependency parsing with advanced semantic techniques to automate the extraction and evaluation of important concepts from scientific abstracts is proposed. By focusing on grammatical relationships within the words in each sentence, our method can identify key information accurately, which is then evaluated using Sentence-BERT to calculate semantic similarity between the extracted concepts and provided keywords. The evaluation is applied to a dataset of Scopus-published papers that are selected based on specific query string. The results demonstrate that while our approach effectively mitigates information overload by enhancing the relevance and accuracy of information retrieval, the average precision and F1-scores indicate room for improvement, particularly in complex domains. This paper contributes to the field of information sciences by offering a practical foundation for managing the cognitive loads associated with analyzing vast amount of scientific literature, with potential for further improvement and application across different research domains.

关键词： Accuracy Text analysis Scalability Semantics Education Decision making Focusing Information retrieval cognitive load complexity theory

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Achieving High-Accuracy Human Activity Recognition Using BERT-Based Classwise Ensemble Models

Achieving High-Accuracy Human Activity Recognition Using BER...

引用

computing, Applications and Systems (COMPAS), ieee international conference on

作者： K.M Nafiur Rahman Fuad Mumtahina Ahmed Md. Golam Rabbani Abir Md Anisur Rahman Md Zakir Hossain Zamil M. F. Mridha Jungpil Shin Bangladesh University of Business and Technology Dhaka Bangladesh Western Illinois University Illinois USA American International University - Bangladesh Dhaka Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Human Activity Recognition (HAR) plays a vital role in ubiquitous computing and has a wide range of applications in healthcare, sports, and human-computer interaction. The study presents an innovative way of leveraging the BERT (Bidirectional Encoder Representations from Transformers) architecture to enhance the accuracy of HAR systems. By utilizing a classwise ensemble method, we effectively harness the capabilities of BERT for sequence classification, adapting it to the multi-dimensional time-series data characteristic of HAR tasks. Our model was trained and evaluated on the UCI HAR dataset, which consists of data collected with sensors from accelerometers and gyroscopes which recorded activities like walking, walking upstairs, sitting, walking downstairs, sitting, standing, and lying down from 30 participants. Our approach achieved outstanding performance through extensive experimentation, attaining 98% accuracy, 97% precision, 98% recall, and 97% F1-score. The results portray the robustness of the BERT-based classwise ensemble method in capturing complex patterns of human activities, outperforming traditional HAR models. This progress opens the door to more precise and dependable HAR systems, carrying substantial potential to improve user experiences across diverse application areas.

关键词： Legged locomotion Accuracy Computational modeling Transfer learning Bidirectional control data models Encoding Robustness Human activity recognition Ensemble learning

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Investigating the Role of Metaphors in Human-AI Conversation and Engagement

Investigating the Role of Metaphors in Human-AI Conversation...

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ieee international conference on data Mining Workshops (ICDM Workshops)

作者： Pragati Maheshwary Qiawen Liu Rui Mao Human-Computer Interaction Institute Carnegie Mellon University United States Department of Psychology University of Wisconsin-Madison United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages.

关键词： Analytical models conferences Oral communication Linguistics Chatbots Regression analysis data mining Artificial intelligence

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks Three-dimensional displays Buildings Psychology data visualization cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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Key Object Detection and Recognition Method for Abnormal Events in Waiting Halls

Key Object Detection and Recognition Method for Abnormal Eve...

引用

international conference on Signal Processing Proceedings (ICSP)

作者： Feng Gao Institute of Computing Technology China Academy of Railway Sciences Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

In recent years, with the enhancement of the domestic railway network and the widespread adoption of high-speed rail, the issue of safety management within waiting areas has become increasingly prominent. This challenge is characterized by high passenger mobility and diverse behaviors. Traditional manual inspection methods are inefficient and struggle to provide comprehensive coverage. Meanwhile, the application of generic visual technology in complex waiting environments is limited due to the scarcity of anomalous data and class imbalance, leading to difficulties in detection. This paper proposes a method for key object detection and recognition based on object detection techniques. By optimizing single-stage detectors and employing data augmentation strategies, this method addresses the issue of insufficient samples, thereby enhancing the safety management level of waiting areas.

关键词： Visualization Safety management Object detection Manuals Detectors Signal processing Inspection data augmentation Rail transportation

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Piecewise Computation of Persistent Homology

Piecewise Computation of Persistent Homology

引用

ieee international conference on Big data

作者： Rohit P. Singh Nicholas O. Malott Philip A. Wilsey Univ of Wisconsin Milwaukee WI Interstates Cincinnati OH Univ of Cincinnati Cincinnati OH

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Persistent Homology (PH) is a widely used tool of Topological data Analysis (TDA) that measures the persistence of homological features present in data. PH is computed on a sequence of nested sub-complexes that form a filtration ${{\mathcal{K}}_{\mathcal{F}}}$ of data. In general, each nested sub-complex is defined by a scale parameter ϵ that defines the connectivity distances used to for its construction. These sub-complexes are arranged by increasing distances, ϵ 0 to ϵ ∞ , in the filtration; PH is then computed from ${{\mathcal{K}}_{\mathcal{F}}}$. However, due to its exponential space and time complexity, computing PH on big data is beyond the capabilities of contemporary machines. This paper explores the Piecewise computation of PH (PwPH) using subset constructions of the filtrations of data. PwPH is a framework for solutions that compute PH of data. In some embodiments, PwPH can be support computations of PH can be assembled into a complete picture of the homologies; in others, PwPH can only compute an approximation of the PH. The general solution of PwPH can be organized to use significantly less memory and provides a partition of computational PH elements that is, in some embodiments, embarrassingly parallel. This paper explores two complementary foundations to organize filtrations for PwPH.

关键词： data analysis Filtration Memory management Big data data mining Time complexity

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Advancements in data Quality Management in the Big data Era: A Comprehensive Review 3

Advancements in Data Quality Management in the Big Data Era:...

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3rd international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Mishra, Vaishali Darade, Harshal Seattle WA United States

ISBN: (纸本)9798350391244

This paper reviews the latest advancements in data quality within the Big data era, focusing on developments over the last five years. We explore emerging challenges in assessing data quality, particularly as organizations manage increasingly complex datasets from domains like geospatial data, healthcare, and the Internet of Things (IoT). Traditional rule-based approaches are becoming insufficient, leading to the rise of AI-powered methods for data cleansing, anomaly detection, and real-time validation. Additionally, the paper examines organizational strategies for effective data quality management, as well as the growing importance of data quality tools and frameworks in both industry and academia. Key technologies such as distributed systems (Hadoop, Spark, Snowflake) and machine learning models are evaluated in their roles for improving data accuracy, consistency, and reliability. Case studies from industries highlight the practical implementation of these technologies, demonstrating their impact on operational efficiency and decision-making. Lastly, we discuss future research directions, focusing on scalable solutions, ethical considerations, and the role of emerging technologies like quantum computing. By synthesizing insights from recent studies and industry practices, this review provides a comprehensive overview of current trends and future outlooks in data management and quality assurance. © 2024 ieee.

关键词： data reliability

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CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning

CARA: A Hybrid Framework Integrating Swarm AI Agents and Kno...

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Advancements in computing (ICAC), international conference on

作者： Nirodya Pussadeniya Ruchira Wijesinghe Udaya Wijenayake Bhagya Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.

关键词： Adaptation models Adaptive systems Decision making Training data Knowledge graphs Linguistics Cognition Artificial intelligence Standards Context modeling

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