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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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predictive Modeling of Earthquake Impact on Population Centers Using Random Forest Algorithm 6

Predictive Modeling of Earthquake Impact on Population Cente...

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6th international conference on Computational Intelligence and Networks, CINE 2024

作者： Vennam, tharun Reddy Nallamalli, Saketh Dyavathi, Adarsh Regatti, Rohith Kumar, S. Satheesh Regulwar, Ganesh.B. Vardhaman College of Engineering Shamshabad Dept. Information Technology Hyderabad India

ISBN: (纸本)9798331516796

Natural disasters like earthquakes have a profound and a wide-ranging effect on infrastructure, economic systems, and communities across the world. Accurately estimating an earthquake's 'level of impact' is essential for risk reduction, effective disaster management, and long-term planning in seismically active areas. the analysis of the effects of earthquakes has been facilitated by recent developments in information technology, including increased storage capacity and novel techniques for organizing and gathering data. this study is about using a comprehensive dataset, made up of earthquake characteristics, geographical parameters, and the degree of impact of the earthquake, to construct a model that can effectively predict the level of impact of earthquakes using machine learning regression methods. Pre-processing of the raw data, feature selection, model validation, and optimization are necessary to guarantee the robustness and generalizability of the final product. the models produced will be assessed for accuracy and performance. A variety of machine learning algorithms including linear and polynomial regression, support vector regression, decision trees, and ensemble methods like random forest regression, will be used. Urban planners, emergency response teams, and legislators can all benefit by using these models to develop and carry out leaner, more effective disaster response plans. © 2024 IEEE.

关键词： Risk management

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Short-term power load forecasting based on multi-step probability distribution deep learning network 6

Short-term power load forecasting based on multi-step probab...

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6th international conference on Computer Information Science and Application Technology, CISAT 2023

作者： Chen, Ming Gao, Huaijin Chen, Kun Du, Chong Yang, Zhijie Computer Technology and Software Engineering Wuhan Polytechnic Wuhan430074 China Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan430077 China Huanggang Power Supply Company State Grid Hubei Electric Power Co. Ltd Huanggang438021 China Wuhan Marine College Wuhan430062 China

ISBN: (纸本)9781510668546

With the improvement of renewable energy utilization rate, its proportion in power generation continues to increase. However, the uncertainty of renewable energy power generation has always been an urgent problem. Accurate forecast of short-term power load can assist modern power systems in accurately allocating the proportion of traditional and renewable energy generation. this is crucial for the safety and economic operation of the power grid. Power load forecasting can be seen as a multivariate time series forecasting problem. Most of existing studies have focused on predicting the next moment power load. However, a great number of decisions in power planning scenarios require predictive models that can provide a complete conditional distribution with richer information, namely probability distribution prediction. To address this limitation, we applied a multi-step probability prediction model based on recurrent neural networks on short-term power load forecasting. Experiments conducted on the annual power load of multiple regions in the United States and comparison with some classic models verify the superiority of the applied model. the results indicate that the model applied in the article has a promising predictive performance. © 2023 SPIE.

关键词： Forecasting

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What Makes a High-Quality Training Dataset for Large Language models: A Practitioners' Perspective 24

What Makes a High-Quality Training Dataset for Large Languag...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Yu, Xiao Zhang, Zexian Niu, Feifei Hu, Xing Xia, Xin Grundy, John Huawei Hangzhou Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China Monash Univ Fac Informat Technol Melbourne Vic Australia Wuhan Univ Technol Chongqing Res Inst Chongqing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Large Language models (LLMs) have demonstrated remarkable performance in various application domains, largely due to their self-supervised pre-training on extensive high-quality text datasets. However, despite the importance of constructing such datasets, many leading LLMs lack documentation of their dataset construction and training procedures, leaving LLM practitioners with a limited understanding of what makes a high-quality training dataset for LLMs. To fill this gap, we initially identified 18 characteristics of high-quality LLM training datasets, as well as 10 potential data pre-processing methods and 6 data quality assessment methods, through detailed interviews with 13 experienced LLM professionals. We then surveyed 219 LLM practitioners from 23 countries across 5 continents. We asked our survey respondents to rate the importance of these characteristics, provide a rationale for their ratings, specify the key data pre-processing and data quality assessment methods they used, and highlight the challenges encountered during these processes. From our analysis, we identified 13 crucial characteristics of high-quality LLM datasets that receive a high rating, accompanied by key rationale provided by respondents. We also identified some widely-used data pre-processing and data quality assessment methods, along with 7 challenges encountered during these processes. Based on our findings, we discuss the implications for researchers and practitioners aiming to construct high-quality training datasets for optimizing LLMs.

关键词： Large Language models High-Quality Data Practitioners' Perspective Empirical Study

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Digital Twin System for River Network Based on Graph Neural Network 36

Digital Twin System for River Network Based on Graph Neural ...

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36th international conference on software engineering and Knowledge engineering, SEKE 2024

作者： Jin, Zishuo Ye, Feng Zhang, Peng Hohai University NanJing China Jiangsu water Conservancy Engineering Planning Office NanJing China

ISBN: (纸本)1891706594

River network management presents a significant challenge in the field of water conservancy, and the subsequent advancements in digital technology offer a promising avenue for addressing this challenge. However, the utilization of cutting-edge technologies like digital twin and deep learning within the water conservancy domain remains largely experimental, hampered by the absence of applicable models, authentic scenarios, and viable solutions. To address this, we propose a river network prediction and response system based on digital twin. this system leverages an improved graph attention network to discern spatial correlations within the detection data of individual sites, integrating bidirectional long short-term memory neural network for predictive analysis. Concurrently, we have developed a suite of dynamic visualization tools designed to streamline real-time situation analysis for users. through the establishment of an interactive digital twin river network system, we furnish a fresh strategic framework for addressing water conservancy challenges. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Rivers

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Real-Time Anomaly Detection in Time Series Using Transformer-Like Architecture 6

Real-Time Anomaly Detection in Time Series Using Transformer...

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6th IEEE international conference on Artificial Intelligence Testing (AITest) as a part of the IEEE international Congress on Intelligence and Service-Oriented Systems engineering (CISOSE)

作者： Zhang, Meixian Shi, Xue Huang, Jiaxin Baidu Grp Shanghai Peoples R China

ISBN: (纸本)9798350365054

Real-time monitoring plays a vital role in software testing by facilitating the identification and diagnosis of issues during canary deployments or limited-scale testing phases. this approach serves as a method for testing and issue identification in the deployment phase. Furthermore, efficiently detecting anomalies in software instrumentation data is a crucial aspect of real-time monitoring. However, due to the volatility of software telemetry data, achieving accurate anomaly detection is quite challenging. In this paper, we present an efficient transformer-based framework designed for real-time anomaly detection. this approach called TEAD deeply explores the temporal correlations in time series data to uncover anomalous patterns, ensuring stability in predictions amidst fluctuating data. Empirical validation conducted at a prominent Internet company highlights the effectiveness of TEAD, emphasizing its ability to improve both efficiency and accuracy in real- time monitoring. Despite its significance, the method is constrained by its business-centric nature, warranting further cross-industry dissemination research. Overall, this study provides a novel solution for the real-time monitoring phase of software testing, validating the applicability and effectiveness of transformer- like models in this field.

关键词： software testing real-time monitoring anomaly detection transformer

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Analytical Approach of Machine Learning for Prediction models 6

Analytical Approach of Machine Learning for Prediction Model...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Mohit Dubey, Sanjay Kumar Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

With the increasing availability of data, machine learning (ML) predictive models have become a popular tool for making informed decisions in various fields. However, choosing suitable algorithms and techniques to develop accurate forecasting models is a challenging task. In this study, we aim to compare different ML algorithms and methods to develop accurate prediction models. We analyzed the results to determine the most effective approach to developing accurate forecasting models. In addition, we investigated the effect of hyperparameter tuning on the performance of this model. the results of this study can provide valuable insights for practitioners and researchers in choosing the most appropriate machine learning algorithms and methods to develop accurate prediction models. Our study shows the impact of choosing the right features and choosing the method of data pre-processing and hyperparameter tuning on model performance. Finally, our results can inform the design of more accurate prediction models and contribute to the development of the field of machine learning. © 2023 IEEE.

关键词： decision-tree machine learning neural network regression

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Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions 6

Automatic classification and detection of 12-lead electrocar...

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6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Li, Siyuan Su, Xuesong Yao, Qingyu Chen, Gongwen School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Technology Inspection Center of Shengli Oilfield SINOPEC Dongying Shandong257000 China

ISBN: (纸本)9781510668249

In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. the model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. the experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. this innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Electrocardiograms

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Enhancing Supply Chain Risk Management with Large Language models: software Prototyping and Interactive Visualization 20

Enhancing Supply Chain Risk Management with Large Language M...

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20th IEEE international conference on e-Business engineering, ICEBE 2024

作者： Zhao, Ming Hussain, Omar Zhang, Yu Saberi, Morteza Leshob, Abderrahmane School of Business Unsw Canberra Canberra Australia School of Computer Science University of Technology Sydney Sydney Australia University of Quebec in Montreal Department of Management Science Montreal Canada

ISBN: (纸本)9798350365856

the rapid evolution of risk landscapes in supply chains necessitates innovative management tools that enhance decision-making and process efficiency. Building on our previously proposed framework, this paper introduces a software prototype named LARD-SC (LLMs for Automated Risk Detection in Supply Chains). It utilizes Large Language models (LLMs) for automated risk identification and assessment of risks impacting on global supply chain and integrates the Cambridge Taxonomy of Business Risks (CTBR) to categorize risk events. Additionally, it employs Neo4j, a graph database, for dynamic risk visualization. this integrated approach aims to upgrade traditional Supply Chain Risk Management (SCRM) processes by providing supply chain professionals with an intuitive, interactive tool to manage risks more effectively, thereby increasing resilience and adaptability in complex supply environments. A case study testing the functionality and impact of the prototype in a real-world scenario with Apple Inc. demonstrates its efficacy, showing a significant improvement in risk detection and management efficiency. © 2024 IEEE.

关键词： Data Visualization Large Language models predictive Analytics in Supply Chain Supply Chain Risk Management

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Optimizing the classroom Space Design for Intellectually Disabled Students Based on the Analysis of the Acoustic Environment 6

Optimizing the classroom Space Design for Intellectually Dis...

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6th international conference on Communication engineering and Technology, ICCET 2023

作者： Jebril, Tawfiq Chen, Yang Shirahatti, Heena M Hossain, Syed Far Abid Xi'an Jiaotong University School of Human Settlements and Civil Engineering Xi'an China Kle Technological University Karnataka Hubli India Brac University Brac Business School Dhaka Bangladesh

ISBN: (纸本)9798350336153

the present study focuses primarily on the effects of designing a rounded corner classroom for intellectually disabled students in primary schools based on the reverberation time and noise level. Usually, straight 90-degree edges are used for classroom corners, but rounded edges for walls and furniture are more comfortable and help to ensure students safety. therefore, this study examines how a rounded-edge classroom design for walls and furniture affects additive babble and environmental noise level conditions in primary school classrooms for intellectually disabled students. the study compares the two classroom designs (rounded and right-angle corners) covering the same area with different variable factors to improve students' performance. the computer software program ODEON and the MATLAB programming language were primarily used to investigate the two models. these models were used to predict five design scenarios' impact to assess acoustic behavior with and without furniture on intellectually disabled students learning. the findings reveal that the proposed model with rounded corner walls and furniture and a high and sloped ceiling performs better than the regular classroom model. the study also confirms that a better study environment can be created for intellectually disabled students to study alongside regular students. © 2023 IEEE.

关键词： MATLAB

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Design And Analysis of Pump Simulation Approach through Reverse engineering 6

Design And Analysis of Pump Simulation Approach Through Reve...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Gupta, Divyansh Trivedi, Snehal Pandya, Jignesh Faculty of Engineering & Technology Parul University Vadodara India Variational Technology Pvt. Ltd. Vadodara India

ISBN: (纸本)9798350369748

When designing a product without original documentation, reverse engineering is essential. It entails copying essential characteristics from real-world things in order to produce precise digital or tangible replicas. this covers jobs like creating accurate CAD models, describing geometric models, and segmenting structures. It helps with the shift from physical to digital designs and finds applications in a variety of industries. In order to collect 3D data, reverse engineering frequently uses digitization techniques like CMMs and laser scanning. A recent study examined reverse engineering techniques, particularly with regard to obtaining 3D data through the use of instruments such as CMMs and laser scanning. the study also covered the use of CFD to evaluate the performance of a centrifugal pump and calculate variables like fluid speed, pressure, and flow rate. Solid-body FEM simulations were performed using CAD/CAM software simulating the geometry of the pump. the study emphasized the advantages of numerical modelling and simulation for product analysis and design enhancement, especially in CFD and FEM. © 2024 IEEE.

关键词： Centrifugal pumps

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