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检索条件"机构=School of Data Science and Computer Science"

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Learning Medical Subject Headings in PubMed Articles to Enhance Deep Predictions 11

Learning Medical Subject Headings in PubMed Articles to Enha...

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11th IEEE International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Dashdorj, Zolzaya Jargalsaikhan, Zoljargal Grigorev, Stanislav Trufanov, Andrey Kang, Tae-Koo Altangerel, Erdenebaatar Mongolian University of Science and Technology Computer Science Ulaanbaatar Mongolia School of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk Russia Sangmyung University Department of Human Intelligence and Robot Engineering Seoul Korea Republic of

ISBN: (纸本)9798350350142

MeSH is a vocabulary of terms used in the life sciences and biomedical fields. A large amount of Medical Subject Headings (MeSH) occur in PubMed articles with different weights. In this research, we analyze a disease-symptom network exploiting MeSH metadata co-occurring in 7 million PubMed articles and configure a deep model in the context of biomedical and health care data. Optimized and efficient data networks are important to compile machine learning models. We optimize the deep model by reducing important features. Our result highlights that the degree of the severity of symptoms associated with a disease improves the accuracy of disease prediction models. The network was well optimized by reducing the number of symptoms. This finding may have important implications in healthcare: healthcare chatbots, clinical significance, practical implications. © 2024 IEEE.

关键词： data Analytics

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Decentralized Metaverse: Towards a Secure, Autonomous, and Inclusive Virtual World

Decentralized Metaverse: Towards a Secure, Autonomous, and I...

引用

2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Kumar, Santosh Porwol, Lukasz Curry, Edward Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Computer Science and Engineering Iiit Naya Raipur Chhattisgarh India

ISBN: (纸本)9798350327816

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration. © 2023 IEEE.

关键词： Blockchain Cyber-Physical-Social Systems Decentralization Metaverse Situation Awareness

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The 32nd AIAI Irish Conference on Artificial Intelligence and Cognitive science 32

The 32nd AIAI Irish Conference on Artificial Intelligence an...

引用

32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Pakrashi, Arjun Rushe, Ellen Namee, Brian Mac Brennan, Rob Yaman, Bezya School of Computer Science University College Dublin BelfieldDublin 4 Ireland School of Computer Science Dublin City University Collins Ave Ext Whitehall Dublin 9 Ireland The Insight Centre for Data Analytics Ireland SFI Centre for Research Training in Machine Learning Ireland ADAPT Centre Ireland School of Computer Science Trinity College Dublin College Green Dublin 2 Ireland

The 32nd Irish Conference on Artificial Intelligence and Cognitive science (AICS 2024), hosted by University College Dublin (UCD) in collaboration with Dublin City University (DCU), featured high-quality research in AI and cognitive science, with a focus on trustworthy AI, fairness, and bias mitigation. Of 82 submissions, 44 papers were accepted, covering applications in healthcare, smart mobility, financial forecasting, anomaly detection, and sports analytics, alongside fundamental machine learning advancements. Keynote speakers Dr. Susan Leavy (UCD) discussed AI’s societal impact, while Prof. Eamonn Keogh (UC Riverside) critiqued research in time series anomaly detection. AICS 2024 reaffirmed its role as Ireland’s leading AI research forum, fostering discussions on cutting-edge advancements and their societal implications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination 20

On-Line System of Garbage Image-Orientated Intelligent Class...

引用

20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Tian, Jiayin Wang, Yaozhi Liu, Jiaxin Chen, Yan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350365856

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described. © 2024 IEEE.

关键词： Zero-shot learning

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A Blockchain-Based Federated Learning for Smart Homes 4

A Blockchain-Based Federated Learning for Smart Homes

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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Zhu, Boqi Lu, Kezhong Tao, Tao School of Computer Science and Technology Anhui University of Technology Maanshan China School of Big Data and Artificial Chizhou University Chizhou China

ISBN: (纸本)9798350337181

Smart home devices generate a substantial amount of local data, and finding effective ways to utilize this data while ensuring privacy has become an increasingly pressing concern. Technologies such as Smart Homes, Federated Learning and Blockchain offer promising solutions to address this challenge. We introduce a blockchain-based federated learning approach that leverages edge nodes to maintain a decentralized blockchain, thus mitigating the risks associated with single points of failure. Furthermore, this method utilizes local data from home IoT devices for model training, ensuring efficient learning while preserving data privacy. To address the challenges posed by non-independent and homogeneous data distribution, we propose a clustering method. This strategy effectively tackles the issues arising from non-homogeneous data distribution, consequently improving model accuracy. Finally, experimental results demonstrate that our proposed approach significantly enhances model accuracy and generalization while safeguarding user privacy. © 2023 IEEE.

关键词： Blockchain

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A Survey on Energy Saving Technology of Wireless Sensor Network 4th

A Survey on Energy Saving Technology of Wireless Sensor Netw...

引用

4th International Conference on Wireless Communications and Applications, ICWCA 2020

作者： Zhang, Huanan School of Data and Computer Science Guangdong Peizheng College Guangzhou China

ISBN: (纸本)9789811651670

Wireless sensor networks (WSNs) have important applications in many fields such as medical treatment and industry. A WSN is typically consists of a large number of sensor nodes that rely on a limited supply of power in many applications. Therefore, improving the energy efficiency of wireless sensor networks has become an important research topic. This paper analyzes the principle of energy consumption of each component unit of wireless sensor node, and studies the energy saving technology and energy saving design idea of MAC protocol from the data link layer and network layer of network model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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An Improved Algorithm of Detecting Abnormal data in Geological Disaster System 6

An Improved Algorithm of Detecting Abnormal Data in Geologic...

引用

6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

引用

Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis 25

Mining Evidence about Your Symptoms: Mitigating Availability...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zhang, Junti Zhu, Zicheng Li, Jingshu Lee, Yi-Chieh Institute of Data Science National University of Singapore Singapore Singapore School of Computing National University of Singapore Singapore Singapore Computer Science School of Computing National University of Singapore Singapore Singapore Computer Science National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713941

People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis. © 2025 Copyright held by the owner/author(s).

关键词： Diagnosis

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Adaptive weighted broad learning system for imbalanced classification 6

Adaptive weighted broad learning system for imbalanced class...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Yanting Gao, Yiping Jin, Junwei Chen, C. L. Philip Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350316520

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods. © 2024 IEEE.

关键词： Classification (of information)

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