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

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Demand Forecasting in Planned Production Orders Using a Dual-Path Time Series Decomposition and Fusion Multi-level Ensemble Model 8th

Demand Forecasting in Planned Production Orders Using a Dual...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Cheng, Sheng-Tzong Li, Chang-Ching Lyu, Ya-Jin Natl Cheng Kung Univ Tainan Taiwan

ISBN: (纸本)9783031856273;9783031856280

Our study proposes a Dual-Path Time Series Decomposition and Fusion Multi-Level Ensemble Model (DP-TDFM) architecture divided into two paths. The first path handles non-stationary time series using STL decomposition to separate the sequence data into trend, seasonality, and residual components. These components are then processed by our Multi-Level Ensemble Model (MLEM), which incorporates algorithms such as Random Forest, Support Vector Regression (SVR), and Decision Tree, with a neural network in the hidden layer serving as the final prediction model. The second path employs the GatedTabTransformer, integrating trend and seasonality features alongside external environmental factors as augmented features (AF). Experimental results indicate that, even when individual models exhibit overfitting, our DP-TDFM architecture maintains stable overall performance and achieves the highest prediction accuracy among all models across five-time points, demonstrating more stable and smoother prediction results. This model effectively addresses several challenges in prediction tasks, including overfitting, sparse data, and long-distance dependencies.

关键词： Time series prediction Temporal decomposition Deep Learning Hybrid models Order forecasting

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Assessment of Dance Movement Therapy Outcomes: A Preliminary Proposal 8th

Assessment of Dance Movement Therapy Outcomes: A Preliminary...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Daoudagh, Said Ignesti, Giacomo Moroni, Davide Sebastiani, Laura Paradisi, Paolo ISTI CNR Via G Moruzzi 1 I-56124 Pisa Italy Univ Pisa Dept Translat Res & New Surg & Med Technol Pisa Italy BCAM Basque Ctr Appl Math Bilbao Basque Spain Univ Pisa Pisa Italy

ISBN: (纸本)9783031838446;9783031838453

Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional. Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations. Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants' poses during the DMT sessions for more reliable movement analysis. Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants' poses. Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants' movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.

关键词： Dance Movement Therapy (DMT) DMT Assessment Tool Human Pose Estimation Movement Analysis Physiological Signal Processing Social Well-Being

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Development of Blockchain Integrated Agricultural Land Lease Framework with a Voting-Based Consensus Protocol Using Event-B 4th

Development of Blockchain Integrated Agricultural Land Lease...

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

作者： Verma, Sudhani Yadav, Divakar Chandra, Girish AKTU Jankipuram Dept Comp Sci & Engn Inst Engn & Technol Lucknow 226021 Uttar Pradesh India

ISBN: (纸本)9783031837821;9783031837838

India's agriculture sector, contributes 18% to the national GDP, remains largely unorganized. This study proposes a blockchain-based solution to digitize land lease contracts, aiming to reduce risks of document forgery and fraud. The proposed system has transactions such as stamp procurement, registration of land and users etc. which are validated and stored using blockchain. To ensure fairness in execution of transactions, a voting-based consensus algorithm is also proposed. Compared to conventional consensus methods, the proposed algorithm achieves a 39% increase in throughput by reducing message exchange overhead. The proposed algorithm is formally verified using Event-B.

关键词： Blockchain Consensus Protocols Event-B Formal Methods Distributed Ledger technology (DLT)

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Smart Farming: Leveraging a Deep Learning Model for Plant Leaf Classification 8th

Smart Farming: Leveraging a Deep Learning Model for Plant Le...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： ALomani, Shahd Almdahrsh, Mai Alessa, Shareefah Bushnag, Anas Oyouni, Atif Univ Tabuk Tabuk Saudi Arabia

ISBN: (纸本)9783031856273;9783031856280

The intelligent systems specialized in monitoring plant growth have entered into precision agriculture, and integrated systems have been built that contain built-in cameras within these systems. This monitoring takes place over varying periods and results in many captured images. The monitored plants may be unhealthy and infected with bacteria. However, some are healthy, leading to significant damage as these bacteria move around and spread to healthy plants. From this perspective, a proposed system based on deep learning is developed to classify plant images into healthy and unhealthy categories. The images from the observation are usually random and need to be more organized. Hence, using classification algorithms helps extract organized and arranged image data for easier access according to labels. This process enables us to save time and effort by monitoring a plant's leaves. The proposed model achieved 87% accuracy on the plantvillage tomato leaf dataset, consisting of one healthy class and nine different unhealthy classes.

关键词： Artificial Intelligence Deep Learning Image Classification Smart Farming

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1-Line Minimum λ-Steiner Tree Problem 42nd

1-Line Minimum λ-Steiner Tree Problem

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42nd National Conference on Theoretical computer science

作者： Chen, Yinhua Li, Jianglin Wang, Wencheng Zhang, Tongquan Yunnan Minzu Univ Sch Math & Comp Sci Yuehua St 2929 Kunming 650504 Yunnan Peoples R China Yunnan Minzu Univ Sch Appl Technol Yuehua St 2929 Kunming 650504 Yunnan Peoples R China

ISBN: (纸本)9789819614899;9789819614905

In this paper, we consider the problem of 1-line minimum lambda-Steiner tree problem, denoted as the 1L-M(lambda)StT problem. Given a set X = {r(1), r(2),..., r(n)} of n points in the lambda-plane and a straight line l in R-2, we need to construct a Steiner tree T-l connecting the n points in X and the straight line l. The objective is to minimize the total cost of such a Steiner tree T-l, i.e.,min{Sigma(epsilon is an element of Tl) w(e) | T-l is the mentioned Steiner tree}, and if an edge e = uv is an element of T-l has both endpoints u and v lying on the line l, the weight is defined as w(e) = 0;Otherwise, it is defined as the distance of the two endpoints u and v in the lambda-plane. In particular, if all Steiner points in T-l are constrained to lie on the line l, the problem is denoted as the 1-line minimum lambda-spanning tree (1L-M lambda ST) problem. We present two main results. (1) By using the strategies of the sweep-line algorithm, we can design an exact algorithm in time O(lambda n log n) to solve the 1L-M lambda ST problem;(2) Using some properties of lambda-plane, we showed that our algorithm is a rho(lambda)-approximation algorithm for the 1L-M(lambda)StT problem, where rho(lambda) is inverse of the Steiner ratio in the lambda-plane.

关键词： Sweep-line algorithm Approximation algorithm 1-line lambda-spanning tree 1-line lambda-Steiner tree

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Short-Term Load Forecasting of Secondary CEEMDAN-SE-Transformer-BiLSTM Combined with Error Correction 5th

Short-Term Load Forecasting of Secondary CEEMDAN-SE-Transfor...

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

作者： Wang, Lin Wang, Linxian Li, Weishuo Wang, Tao Zhu, Jiajun Luo, Zhiheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Shandong Jicheng Zhitong New Energy Corp Jinan 250101 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

The power load series is characterized by volatility, complexity, and fuzziness, which brings challenges to the forecast. To fully mine the important information in the sequence and reduce the complexity and volatility of the sequence, a two-stage prediction model and method of quadratic CEEMDAN-SE-Transformer-BiLSTM combined with error correction is proposed in this paper. In the first stage, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to decompose the power load data. To reduce sequence complexity and volatility. The component is reconstructed according to the sample entropy (SE) value. The high-frequency components after reconstruction are decomposed and reconstructed again to fully reduce the sequence complexity. The Transformer model is used to forecast each component, and the predicted values are combined to obtain the final forecast data. In the second stage, error distribution data is obtained based on the error between the predicted value and the actual value, and the Bidirectional Long Short Term Memory (BiLSTM) network model is used to train and predict the error distribution data. The error-corrected load value is obtained by combining the error-predicted value with the load-predicted value. Compared with several traditional models, the results show that the prediction index R2 of the secondary CEEMDAN-SE-Transformer-BiLSTM load forecasting model established in this paper is 85.91%, MAPE is 0.19%, and RMSE is 42.1861 kW, which is more accurate and effective than other traditional forecasting models.

关键词： Load forecasting CEEMDAN algorithm Sample entropy Transformer BiLSTM model Error correction

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Modeling the Traffic Scene in Intelligent Transport Systems for Cooperative Connected Automated Mobility 12th

Modeling the Traffic Scene in Intelligent Transport Systems ...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Yague-Cuevas, David Marin-Plaza, Pablo Sesmero, Maria-Paz Sanchis, Araceli Univ Carlos III Madrid Intelligent Syst Lab Leganes 28911 Spain Univ Carlos III Madrid Control Learning & Syst Optimizat Grp Leganes 28911 Spain

ISBN: (纸本)9783031709654;9783031709661

Cooperative, connected and automated mobility (CCAM) is a step further compared to the driving solutions of the past decade. The possibility of sharing up-to-date information about the state of the traffic infrastructure, or about any agent on the road, makes it possible to generate more complex decision systems in connected and automated vehicles. In that sense, the notion of what is happening around the vehicle, as well as the representation of relevant traffic events, becomes of vital importance for decision making processes in complex traffic situations. Before starting its journey, the vehicle systems need to have a robust and clear notion of the traffic scene, i.e. all those static and dynamic elements the vehicle can come up with while driving. Thus, this information, when processed, would provide sufficient semantics of the surroundings to find out what needs to be done next depending on the context. This chapter proposes the development and design of an information system that aims to generate the necessary data structures to represent the traffic scene information. This representation will serve, once integrated in an automated software ecosystem, to understand the traffic context and act accordingly to the semantics of the scene.

关键词： Automated driving Cooperative Connected Automated Mobility (CCAM) Intelligent Transport Systems (ITS) information system Data driven software

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Performance Analysis of Energy Efficient Routing Protocols in Wireless Sensor Networks 5th

Performance Analysis of Energy Efficient Routing Protocols i...

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5th International Conference on Computing science, Communication and Security

作者： Patel, Priyanka Patel, Amrut Patel, Manish Ganpat Univ UV Patel Coll Engn Mehsana Gujarat India Sankalchand Patel Univ Sankalchand Patel Coll Engn Visnagar Gujarat India

ISBN: (纸本)9783031751691;9783031751707

The field of wireless sensor network is a booming research area and its use is expanding quickly. It consists of many small sensor nodes. These nodes are operated through battery. Network lifetime depends on energy consumption. Nodes are deployed in unattended area. Therefore, replacing the battery or power source is often impossible. Data transmission consumes more amount of energy and sensor nodes' battery life is constrained. Consumption of energy is the most fundamental parameter to be considered in wireless sensor networks. Research is going on in designing less energy consuming protocol to enhance network lifespan. In this paper, we have shown the simulation results of several types of energy efficient routing protocols for analysis. Experimental results of LEACH, TEEN, DEEC, SEP, improved LEACH - iLEACH, improved TEEN - iTEEN, improved DEEC - iDEEC and improved SEP - iSEP protocols are conducted under the MATLAB Simulator. We have also discussed a few unresolved research questions in this field for future investigation.

关键词： Network lifetime Data transmission Battery power Energy

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Teaching LLMs the Nuances of Hospital Funding Instruments 8th

Teaching LLMs the Nuances of Hospital Funding Instruments

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Pitkaranta, Tapio Aalto Univ Dept Comp Sci & Engn Espoo Finland

ISBN: (纸本)9783031838446;9783031838453

Recent advancements in large language models (LLMs) have sparked significant interest across a broad spectrum of use cases and business domains [2,5,12]. The objective of this paper is to investigate whether large language models (LLMs) can effectively understand and interpret the complex logic behind hospital funding instruments, such as the NordDRG system [3]. The integration of Large Language Models (LLMs) with the NordDRG system enhances AI-driven healthcare management, particularly in CaseMix systems. Traditionally developed by a small group of experts, the complex logic of NordDRG could be made more accessible to a wider range of stakeholders through LLMs, enabling natural language interaction and broader engagement. This paper evaluates the effectiveness of three distinct AI design patterns - Custom GPT, Retrieval-Augmented Generation (RAG), and Multi-Agent Systems (MAS) - in developing an AI architecture capable of understanding the NordDRG specification. Our findings suggest that most current general LLMs, such as GPT4, struggle to effectively handle the NordDRG logic. However, when LLMs are enhanced with Retrieval Augmented Generation (RAG) [6], the enhanced NordDRG AI agent is capable of engaging in natural language conversations with various stakeholders. Also, the introduction of the most advanced models, such as GPT-4o, reaches the same level of results as previous generation models with RAG. This paper provides a comparative analysis of three AI design patterns. Our research contributes by establishing a CaseMix benchmark dataset for evaluating large language model (LLM) capabilities using the NordDRG system. To the best of our knowledge, this is the first benchmark in the hospital funding sector. Our findings highlight the potential of LLMs to transform CaseMix system development by providing robust, scalable, and context-aware AI solutions.

关键词： NordDRG CaseMix systems Clinical classifications Retrieval augmented generation (RAG) Large language models (LLM) IR information retrieval Multi-agent systems (MAS)

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Navigating the International Data Space To Build Edge-Driven Cross-Domain Dataspace Ecosystem 1st

Navigating the International Data Space To Build Edge-Driven...

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1st Global Internet of Things and Edge Computing Summit

作者： Singh, Parwinder Meratnia, Nirvana Beliatis, Michail J. Presser, Mirko Aarhus Univ Dept Business Dev & Technol DK-7400 Herning Denmark Eindhoven Univ Technol NL-5612 AZ Eindhoven Netherlands

ISBN: (纸本)9783031785719;9783031785726

Data is an asset for the modern industrial era that plays a crucial role in driving intelligence through machine learning techniques and methods for business, technical, economic, and social decision making. Generally, data generated in a specific domain is often used in other domains, which requires cross-domain data integration (CDDI) and sharing across edges. Here, an edge represents an organization, system, or entity with computing data processing capabilities. This CDDI and sharing across edges need data sovereignty to govern the relevant usage context, wherein the sharing edge domain's data context must be respected by the shared edge domain. This edge domain's data context can be seen as a medium to define rules for associated data usage, access, and identity management. This can be achieved by the common dataspace vision of the European Union for CDDI to turn the European market into a unified data-driven European market. International Data Space (IDS) is such an effort backed up by the European Commission to develop relevant standards and specifications for CDDI. However, even though a vast amount of IDS ecosystem information is available online, it is scattered and hard to navigate. This obstructs the use and adoption of the IDS at a desired pace and in a simplified manner. This study contributes to this need by converging the information in one place and leveraging theoretical and pragmatic insights on building IDS-based edge-driven dataspace in real-world scenarios. We dive into a wind industry supply-chain-specific use case realized through a locally developed IDS platform to showcase and validate the use of CDDI.

关键词： Dataspace International Data Space (IDS) Cross-domain Data Integration Data Sovereignty

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