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Rehabilitation Training Program Recommendation System Based on ALBERT-LDA Model 5th

Rehabilitation Training Program Recommendation System Based ...

引用

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

作者： Zhu, Xiaozhuang Xu, Qianqian Gao, Nuo Shandong Jianzhu Univ Informat & Elect Engn Dept Jinan 250101 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

The recommendation system that can assist rehabilitation doctors to provide objective and effective rehabilitation training programs for the huge patients with physical disability is a hot spot of current research. Traditional rehabilitation training program recommendation methods often ignore the contextual semantic information and potential semantic information in physical evaluation, resulting in inaccurate semantic expression and low accuracy of training program recommendation effect. Based on this, this paper proposes a deep learning network model (ALBERT-LDA model) based on ALBERT and LDA, and constructs a recommendation system for physical exercise rehabilitation training based on this model. The model is applied to construct a rehabilitation training program recommendation system. The recommendation system firstly uses the LDA topic model to obtain the document level topic information and the ALBERT model to obtain the word level semantic representation. Secondly, TextCNN is adopted to achieve feature extraction and dimension reduction of the word level semantic of the body evaluation text, and then hierarchical attention mechanism is used to reconstruct the dimensionality reduction features. Finally, the reconstructed features are fused with the subject features through certain fusion strategies to realize the recommendation of training schemes. A series of experimental results show that the limb movement rehabilitation training recommendation system constructed in this paper can not only obtain deeper semantic features through the fusion model, but also understand the context semantics more comprehensively. Therefore, it can describe complex semantic information in rehabilitation evaluation more accurately and improve the performance of multi-label classification. Thus, it can provide patients with more objective and effective rehabilitation training programs and improve the performance of medical recommendation system.

关键词： Recommendation of rehabilitation training program ALBERT model LDA topic model Feature fusion

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for Image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci M2PA Lab ENS Fes 30030 Morocco

ISBN: (纸本)9783031821523;9783031821530

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning Image processing Image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Advancing Financial Inclusion and Data Ethics: The Role of Alternative Credit Scoring 4th

Advancing Financial Inclusion and Data Ethics: The Role of A...

引用

4th International Conference on Society 5.0

作者： Machikape, Keoitshepile Oluwadele, Deborah Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9783031714115;9783031714122

Alternative credit scoring plays a vital role in advancing the goals of balancing economic progress with resolving social issues by promoting financial inclusion, supporting data-driven decision-making, fostering innovation, enhancing risk management, and addressing ethical considerations. This research investigates alternative data sources and credit-scoring algorithms to evaluate the creditworthiness of individuals with limited credit histories. The study conducts a systematic literature review and meta-analysis to explore and evaluate alternative data sources and advanced credit scoring algorithms used in assessing the creditworthiness of individuals. Alternative data such as Online Behaviour and E-commerce, Social Networks and Relationships, Location, and Property Details were identified, while Machine Learning techniques such as Gradient-Boosted Decision Trees and Light Gradient-Boosting Machine demonstrate superior performance. However, ethical and privacy concerns regarding alternative data sources are paramount. The study's implications extend to financial institutions and creditscoring agencies, offering insights to enhance credit assessment processes and stressing the importance of ethical data handling and privacy. This research contributes to understanding the dynamics between alternative data and credit-scoring algorithms, providing a roadmap for their responsible integration in credit assessments, thereby fostering financial inclusion and ethical data practices in the fut. Juristic society.

关键词： alternative data credit scoring machine learning financial inclusion ethical data handling

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Monitoring the Concentration of Air Pollutants and Its Health Hazards Using Machine Learning Models 5th

Monitoring the Concentration of Air Pollutants and Its Healt...

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5th International Conference on Computing science, Communication and Security

作者： Jain, Aditi Shenoy, Aditya Adiga, Ananya Anekal, Anirudha Prajwal, Saritha PES Univ Bengaluru 560085 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

With the world moving rapidly towards industrialization driven by economic growth and technological advancements, there is an alarming surge of air pollution leading to significant health concerns. In response, this work introduces a research-driven approach for a continuous air quality monitoring system, designed to continuously track air quality in real-time in the proximity and proactively predict potential health hazards for the user. Central to the system's efficacy is a state-of-the-art hybrid Machine Learning model, seamlessly amalgamating the strengths of Adaptive Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) models, renowned for their ability in handling intricate time series data. This model is securely deployed on a cloud platform, ensuring not only accessibility but also scalability to meet current and future technological standards. The system primarily concentrates on the monitoring of air pollutants, such as PM2.5, PM10, and CO, ensuring that users have access to immediate and up-to-date insights into the air quality in their surroundings. Beyond this, the system goes a step further by employing this data to assess users' potential risk of developing lung cancer. Through the use of Internet of Things (IoT) sensors, the system can issue timely and potentially life-saving insights, providing users with valuable information for decision-making improving their well-being. In a world, where the link between air quality and health is increasingly evident, our research-based initiative serves as a beacon for a healthier future, while also fostering environmental consciousness and public well-being.

关键词： Lung Cancer IOT Adaptive LSTM ARIMA AQI Air Pollutants

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Optimization of Hydro Generation and Load Forecasting Based On LSTNet 7th

Optimization of Hydro Generation and Load Forecasting Based ...

引用

7th International Conference on Optimization and Learning

作者： Du, Xingbang Zhang, Enzhi Hokkaido Univ Sapporo Hokkaido 0600808 Japan

ISBN: (纸本)9783031779404;9783031779411

Renewable energy generation and power load forecasting are important in both advanced smart grid and sustainable development. Many RNN related methods were used in prediction of power generation time series data but they often fails to capture very long-term correlations in practice due to the vanishing gradient problem. We introduced a modified version of the LSTNet model, which incorporates CNN, LSTM, SKIP-LSTM and Dense components. This model captures both shortterm patterns and addresses the issue of vanishing gradients when capturing long-term patterns. We applied this model to predict hydro power and grid load, and by comparing the MAPE, MSE, and MAE metrics, it is evident that the performance is superior to the commonly used LSTM, GRU, and SimpleRNN models in time series analysis of smart grid. The integrated model can be applied to the intelligence of renewable energy grids.

关键词： LSTNet time series Hydro Generation Load Smart Grid

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On Capability Needs for AI Utilization in Innovation Networks: Critical Literature Review 5th

On Capability Needs for AI Utilization in Innovation Network...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Hanninen, Pasi Vilko, Jyri LUT Univ Dept Ind Engn & Management Kauppalankatu 13 Kouvola 45100 Finland

ISBN: (纸本)9783031807749;9783031807756

The potential of AI has been a growing trend in research in recent years. From a business perspective, the use of AI places new demands on skills and resources. Innovation networks are one option to tap into resources that companies themselves do not have. Although there has been research on combining AI and innovation, there is not enough research on the capabilities needed for an innovation network to use AI. This study aims to present a snapshot of the research on the topic and to show how the capabilities of the network for using AI are formed. The findings of the study are that research on innovation network capabilities for using AI does not match the requirements of using AI, and it is argued that innovation network capability needs remain the same in terms of the terms presented, but that the use of AI places entirely new requirements on them.

关键词： Artificial intelligence Innovation network capabilities Innovation management Open Innovation

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Deployment and Evaluation of Intelligent DICOM Viewers in Low-Resource Settings: Orthanc Plugin for Semi-automated Interpretation of Medical Images 1st

Deployment and Evaluation of Intelligent DICOM Viewers in Lo...

引用

1st MICCAI Meets Africa Workshop

作者： Shawa, Andrew Phiri, Lighton Univ Zambia Dept Comp Sci Lusaka Zambia Univ Zambia Dept Lib & Informat Sci Lusaka Zambia

ISBN: (纸本)9783031791024;9783031791031

Medical image interpretation is a crucial part of radiological workflows, providing valuable input to the final output of the process: medical image interpretation reports. Radiologists typically employ Digital Imaging and Communication in Medicine (DICOM) Viewers. While numerous types of DICOM Viewers have been implemented, there has been arguably little focus on how such software tools can be made more effective by integrating them with Artificial Intelligence (AI) services. This paper presents a study conducted to design and implement an OrthancWeb-based Picture Archiving and Communication System (PACS) plugin DICOM Viewer for facilitating the semi-automated interpretation of medical images using AI. An Orthanc DICOM Viewer plugin, interoperable with two (2) models-Pneumonia Classification and Detection models-was implemented using the Python programming language. The plugin was evaluated with Radiologist Residents at a large University Teaching Hospital in a controlled setting. TAM 2 instrument was used to assess its perceived usefulness and ease of use. The TAM 2 constructs were rated positively, with study participants expressing a desire to incorporate other pathologies to the plugin and, additionally, integrating similar tools in more widely used DICOM Viewers such as RadiAnt and Weasis. The integration of AI models has the potential to reduce the turnaround time and workload involved in interpreting medical images. More significantly, the positive responses related to perceived usefulness of the tool suggest a potential that such tools have in improving delivery of services at the point of clinical care.

关键词： Artificial Intelligence DICOM Viewer Pneumonia

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Digital Twin Data Broker with Assisted Mapping into a Knowledge Base 5th

Digital Twin Data Broker with Assisted Mapping into a Knowle...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Schmeyer, Thomas Kraemer, Kai Peh, Anna-Lena Brandherm, Boris Chikobava, Margarita Kiefer, Gian-Lucca German Res Ctr Artificial Intelligence Cognit Assistence Syst D-66123 Saarbrucken Germany Fraunhofer IOSB Angew Syst Tech D-98693 Ilmenau Germany

ISBN: (纸本)9783031807596;9783031807602

The frequent usage of digital twins to communicate between physical objects is resulting in more complex cyber-physical systems. To simplify the individual components' integration and to optimize their usage, a data broker is being developed. Therefore, digital twins need to be semantically organized in an ontology that provides the advantage of reasoning methods. An assisted workflow is being developed to automatically enter subgraphs into an ontology. As a digital twin representation, the Asset Administration Shell format is used to have an international standard technology. Based on this, a new domain-specific language is developed, allowing experts to configure the generation process. This process maps the digital twin's information into a graph representation of the ontology. The preconfigured generation process enables the user to efficiently register new digital twins without having expert knowledge of the underlying ontology. Additionally, a Large Language Model vector embedding and text reasoning support is implemented analysing the digital twin to create entity suggestions. The presented data broker is an automation tool for bridging the gap between semantic descriptions and digital twin formats in order to unite the advantages of both representations.

关键词： Digital twin Asset administration shell Ontology Automation Cyber-physical system

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Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer 4th

Semantic Segmentation of Unmanned Aerial Vehicle Remote Sens...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Spasev, Vlatko Dimitrovski, Ivica Chorbev, Ivan Kitanovski, Ivan Univ Ss Cyril & Methodius Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9783031821554;9783031821561

The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. The advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remote sensing images. This paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. The research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model's performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance.

关键词： Semantic segmentation Deep learning SegFormer UAV images

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'Gamified Pathway'- A Framework for Enhancing User Journeys 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Sankar, Abirami Athavale, Sandeep Tata Consultancy Serv Ltd TCS iON GAMELab Thana Maharashtra India Tata Consultancy Serv Ltd TCS iON GAMELab Pune Maharashtra India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Designing engagement features at several touchpoints across the user journey helps businesses enrol, excite, and retain its users. Gamification is an emerging popular approach to help enhance user engagement. There are several gamification frameworks that help designers to gamify their product/services effectively. However, most frameworks lack specific guidance and applicability across the length of user journey. In this paper we develop and propose, 'Gamified Pathway', a framework that enables quick gamification of every step of the user journey. It is a two-dimensional framework with a seven-stage user journey as the first and gamification elements as second dimension. The intersections of the dimensions provide specific guidance to designers for selecting actions of significance. We test the applicability of the framework through a case study approach. We observe that the 'Gamified Pathway' can help in designing compelling user journeys.

关键词： User Engagement User Journey Gamification

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