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共 200 条记录，以下是81-90 订阅

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Research on Fault Detection and Prediction Model in Information System Operation and Maintenance

Research on Fault Detection and Prediction Model in Informat...

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

作者： Wang Lin Sun Yuchen Fu Haixiang Xu Tianming Liu Hao Wang Xiaogang Information System Integration Branch Company Nari Technology Co.Ltd Nanjing China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study explores the effectiveness of applying big data-driven fault detection and prediction models in information system operation and maintenance. It mainly focuses on building accurate fault prediction models through machine learning algorithms to detect potential system failures in advance and improve operation and maintenance efficiency. The study first collects and processes a large amount of operation and maintenance data in a big data environment, and combines a variety of machine learning algorithms such as decision trees, support vector machines, and neural networks to design a comprehensive fault detection and prediction model. The model cannot only classify and identify existing faults, but also predict possible future fault points based on historical data. Through system simulation, this paper verifies the performance of the model in multiple scenarios. The simulation results show that the model has achieved significant improvements in key indicators such as accuracy, recall rate, and F1 value, especially in complex and changeable system environments. It shows strong robustness. Accurate data analysis further shows that the designed model can effectively reduce system downtime and improve overall operation and maintenance efficiency.

关键词： Support vector machines Accuracy Machine learning algorithms Fault detection computational modeling Predictive models data models Maintenance Systems simulation Information systems

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Technologies Integration With Semantic Web Techniques For Business Valuation: Identifying Equities' Intrinsic Value Without Market Bias

Technologies Integration With Semantic Web Techniques For Bu...

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Cyber Resilience (ICCR), international conference on

作者： Alessio Faccia Rania Itani Ahmed Ae Eltweri Leena Wanganoo Leonardo Luigi Pio University of Birmingham Dubai UAE Murdoch University Dubai UAE Applied Science Research Center Applied Science Private University Amman Jordan Liverpool John Moores University UK Universita di Foggia Italy

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This text sets out to present an innovative approach to intrinsic equity value estimation, deploying state-of-the-art mechanisms and new technologies, but in particular, focusing on the integration of Semantic Web techniques. Its focus is on the problem of market sentiment, which commonly causes the value of equities to be far from their real worth. The proposed solution is to use in a mixed way sophisticated computational techniques and the capability of the Semantic Web's structured data handling to keep the valuations free of bias. The methodology employs advanced analytics, machine learning models, and Natural Language Processing (NLP) for the discovery of patterns and insights that may not easily be visible from traditional valuation methodologies. The holistic approach also involves qualitative analysis to enhance the understanding of the company's prospects. This approach integrates these technologies into the framework of the Semantic Web to provide a transparent and replicable way of determining the intrinsic value of equities, with minimum distortion by market sentiment. The paper argues that this method is quite opposite to traditional business valuation models that use a framework that emphasizes upon empirical evidence and methodological rigor. It postulates that, in the future, if these Semantic Web tools can be put into use for the systematic gathering of varied datasets for contextual analysis, the way valuation functions are handled will undergo a revolution. It would mean a shift in business valuation towards one that is more empirical and technology-enabled towards potentially changing investment analysis by methodological stringency and empirical data. CCS CONCEPTS. Computing methodologies ~ modeling and simulation ~ Model development and analysis ~ modeling methodologies

关键词： Semantic Web Analytical models Systematics computational modeling Machine learning Natural language processing Cost accounting

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Research and Application of Cloud Computing Platform Monitoring System Based on Virtual Cluster

Research and Application of Cloud Computing Platform Monitor...

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

作者： Yuhan Liu Tingting Yu Jiayi Chen Xinyue Luo Jiajia Jiang Huiling Xue Jingxi Liu Department of Business and Economy University of Hong Kong Hong Kong China Guangdong Baiyun University Guangzhou China Hebei University of Engineering Handan China Liaoning University Shenyang China Jiangxi University of Finance and Economics Nanchang China Jiangxi Normal University Nanchang China Yunnan Normal University School of Economics Kunming China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a sentiment recognition algorithm for digital market text data based on natural language processing (NLP) technology. The algorithm uses the global vector (GloVe) model to represent words, which can better capture the semantic relationship between words. In the algorithm design, the paper first preprocess the text data, including noise removal, word segmentation and word frequency analysis. Then, the word vector generated by GloVe is used as input to perform sentiment classification through a deep learning model. The simulation results of the model show that the accuracy of the algorithm reaches 87.6% and the F1 value is 85.3% in the user comments and product evaluation data related to the digital market. Compared with traditional sentiment analysis methods, it has higher accuracy and stability. In addition, this paper also conducts detailed data analysis and discussion on the model performance under different parameter settings, further verifying the effectiveness and adaptability of the algorithm. Future research will further optimize the model structure to improve the recognition ability of complex emotional expressions.

关键词： Deep learning Adaptation models Analytical models Accuracy Text recognition computational modeling simulation Vectors data models Classification algorithms

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A new computational approach for scoliosis assessment from a non-invasive body scanner 2

A new computational approach for scoliosis assessment from a...

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2nd international Workshop on Mathematical modeling and Scientific Computing: Focus on Complex Processes and Systems, MMSC 2022

作者： Roy, S. Grünwald, A.T.D. Lampe, R. Technical University of Munich School of Medicine Klinikum Rechts der Isar Orthopaedic Department Research Unit The Buhl-Strohmaier Foundation for Cerebral Palsy and Paediatric Neuroorthopaedics Ismaningerstrasse 22 Munich81675 Germany Markus Würth Professorship Technical University of Munich Germany

Idiopathic scoliosis is a three-dimensional deformity of the vertebral column and one of the common pediatric spinal diseases, while neuromuscular scoliosis is often associated with cerebral palsy. Especially during periods of growth, there is a high risk of progression of scoliosis. Thus, frequent clinical monitoring is important for proper treatment. X-rays are the Gold standard and the most commonly used method for follow-up of scoliosis. The spinal curvatures are measured by the Cobb angles from x-rays. Patients with scoliosis therefore accumulate higher ionizing radiation doses from repetitive x-rays over their life span. Hence, reducing exposure to ionizing radiation is an important consideration in the follow-up of adolescent scoliosis. For that purpose a body scanner system is proposed, in particular, for complementary diagnostics in children and adolescents with cerebral palsy. The system is non-invasive, free of ionizing radiation and provides three-dimensional scan images of the human torso. In this study, two intermediate steps are described that are useful for the development of analysis methods of the scan images. Two-dimensional transverse cross sections of the torso can be extracted from a scan image along the vertical body axis. Here, methods are described that will be useful for the shape analysis of the two-dimensional transverse body contours. Based on mathematical calculations in polar coordinates, the asymmetry between left and right side of the two-dimensional transverse cross sections is captured by a linear fitting of the course of the contour on either side. Further, the two-dimensional transverse contour data are fitted with an ellipse. © Published under licence by IOP Publishing Ltd.

关键词： Ionizing radiation

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems

Machine Learning-driven Antenna Selection Strategies for Fad...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), international conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice.

关键词： Measurement Hands Adaptation models Machine learning algorithms computational modeling Neural networks Reinforcement learning Throughput Real-time systems Signal to noise ratio

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An Empirical Investigation into Machine Learning Approaches for Breast Cancer Classification

An Empirical Investigation into Machine Learning Approaches ...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), international conference on

作者： Anoushka Duggal Samya Muhuri Department of Computer Science & Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Breast cancer was diagnosed in 2.3 million individuals worldwide in 2022, and was the cause of 670,000 deaths. It ranks as the second most common cancer overall and the most frequently diagnosed cancer in women. Since survival is highly dependent on early detection, the development of computational models for diagnosis is crucial. Among the various models available, we have chosen to focus on three supervised machine learning models in this study: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Feed-forward Neural Networks (FFNN). Their roles in breast cancer detection are investigated. We have pre-processed the data using normalization and feature selection to enhance these models’ performances. They are trained on real-life documented patient data to predict breast cancer. It is determined that the FFNN is more accurate than both SVM and KNN. This paper provides a comprehensive analysis of the various machine learning models, highlighting their positive and negative aspects in diagnosing breast cancer. The results underscore the potential of advanced neural networks in medical diagnostics and pave the way for further research and development in this domain.

关键词： Support vector machines computational modeling Machine learning Nearest neighbor methods Breast cancer Vectors Medical diagnosis Sustainable development Information technology Research and development

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Research on the Prediction Model of Automobile Brand Preference Based on Big data analysis and Cloud Computing

Research on the Prediction Model of Automobile Brand Prefere...

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

作者： Yang Jing Chen Ziyi Wan Tiantian Zhang Fan China Automotive Technology and Research Center Co. Ltd. Tianjin China China Auto Information Technology (Tianjin) Co. Ltd. Tianjin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This project intends to build a set of automobile brand preference prediction methods that integrate cloud computing and big data to achieve effective processing of a large amount of user behavior data and accurate prediction of brand preferences. We need to use big data to deeply mine users' shopping habits and preference characteristics to achieve efficient processing of massive data. This method is based on reinforcement learning, and gradually improves the accuracy and robustness of predictions in repeated iterations. Then this paper proposes a method based on reinforcement learning, which can dynamically adjust decisions through interaction with the outside world, thereby improving the accuracy of predicting customers' brand preferences. In addition, this project will also use massive historical data to analyze the brand selection behavior and development trends of various types of consumer groups from multiple perspectives. Finally, a simulation test is conducted on the constructed model to verify its correctness. The experiment proves that this method can effectively predict brand preferences in different scenarios, with an accuracy of more than 85%. This study shows that the model of this study can effectively and robustly analyze consumer behavior data to predict brand preferences.

关键词： Cloud computing Accuracy Consumer behavior computational modeling Reinforcement learning Big data Predictive models data processing Real-time systems data models

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PharmaLab: A Tool to Study the Drug Action on the Mouse Ventricular Myocyte 2

PharmaLab: A Tool to Study the Drug Action on the Mouse Vent...

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2021 2nd international Symposium on Automation, Information and Computing, ISAIC 2021

作者： Camargo, M.L.A. Goroso, D.G. Bissaco, M.A.S. Silva, R.R. Research and Technology Center University of Mogi das Cruzes SP Mogi das Cruzes Brazil Human Motor Skill Analysis Laboratory FACDEF/National University of Tucumán Tucumán Argentina LabNECC Center for Biomedical Engineering University of Campinas SP Campinas Brazil

Mathematical and computational modeling has been a great ally in the development of new drugs. It helps by providing preliminary results that ultimately guide the path that the tests should take. Thus, the objective of this work was to develop the PharmaLab computational tool, which simulates the pharmacological action in the mouse ventricular myocyte using the mathematical model of Mullins & Bondarenko (2013). Having as one of the main differentials the integration with a model for the force of contraction, at PharmaLab it is possible to perform pharmacological tests by changing parameters of the mathematical model and registering drugs to be used in the simulations. The computational tool has its own interface for plotting results and support material for educational use. As a demonstration of the tool, a validation was performed, comparing simulations with experimental data for the drug Niferidil and two "in silico"tests: SS-68 correcting the "Catecholaminergic Polymorphic Ventricular Tachycardia"(CPVT) and Niferidil correcting the Short QT Syndrome (SQTS). The drugs showed good results in the correction of arrhythmias. Finally, it can be concluded that the PharmaLab computational tool has resources for use in both research and teaching. © Published under licence by IOP Publishing Ltd.

关键词： computational methods

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Financial data Abnormality Alerting and Empirical Research on Chinese Real Estate Enterprises - based on Information Entropy and Logistic model

Financial Data Abnormality Alerting and Empirical Research o...

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Computer Network Technology and Electronic and Information Engineering (CNTEIE), international conference on

作者： Lijun Liang Guoyu Chen Mingyi Chen School of Business Beijing University of Information Science and Technology Beijing China School of Management Science and Engineering Beijing University of Information Science and Technology Beijing China School of Management University of Southampton Southampton UK

ISBN: (数字)9798331524432

ISBN: (纸本)9798331524449

This paper summarizes the abnormal warning signal indicators of listed real estate enterprises' financial data and their behavioral characteristics. The study is based on 708 valid financial data of real estate enterprises from 2010 to 2022 in the Cathay Pacific database. The data was classified into normal (73) and abnormal real estate enterprises (121). The study screened and constructed the warning indicator system using information entropy and modeled it through Logistic regression analysis. The model passed the statistical test. The study shows that: (1)the warning model constructed by fusing information entropy and logistic regression achieves a warning accuracy of 88.70%. (2)Financial indicators such as accounts receivable turnover ratio, inventory turnover ratio, net profit margin, and return on assets play a significant role in identifying abnormal financial data effectively. Real estate enterprises should prioritize monitoring these indicators. (3)Compared with traditional financial analysis models, logistic regression analysis demonstrates higher accuracy and practical value in identifying financial anomalies of real estate enterprises. The study aims to offer a valuable reference for real estate companies and auditors to promptly identify and uncover potential financial anomaly risks.

关键词： Logistic regression Analytical models Electric potential Accuracy computational modeling Companies data models Information entropy Monitoring Logistics

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The Method of Processing Abnormal Values for Flight Test data Based on Wavelet Transform

The Method of Processing Abnormal Values for Flight Test Dat...

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Electronic Information Engineering and Computer Technology (EIECT), international conference on

作者： Jingxiang Xu Lei Zuo Zheng Zhao National Laboratory of Radar Signal Processing Xidian University Xi’an China State Key Laboratory of Experimental Physics and Computational Mathematics Beijing China

In the flight test data, there usually are abnormal values due to uncertainties in acquisition, storage and transmission. Processing the abnormal values is essential to ensure the accuracy of subsequent test data analysis. In this paper, we propose an abnormal value processing method based on wavelet transform for flight test data. We use the summation of high- frequency wavelet details as the characteristic quantity for abnormal value detection. Moreover, the detection threshold is constructed based on the idea of constant false alarm detection to locate and process the abnormal values. simulation results show that the method can detect and process the abnormal values in the time-series flight test data fast and accurately.

关键词： Wavelet transforms Uncertainty data analysis simulation Feature extraction Robustness High frequency

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