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检索条件"任意字段=2nd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2022"

共 201 条记录，以下是111-120 订阅

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Comparative analysis using Transfer Learning Models VGG16, Resnet 50 and Xception to Predict Pneumonia

Comparative Analysis using Transfer Learning Models VGG16, R...

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international conference on Innovative Sustainable computational Technologies (CISCT)

作者： Anil B. Desai D. R. Gangodkar Bhaskar Pant Kumud Pant Department of Computer Science & Engineering Graphic Era Deemed to be University & Graphic Era Hill University Dehradun U.K India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun U.K India Department of Biotechnology Graphic Era Deemed to be University Dehradun U.K India

ISBN: (纸本)9781665474177

Pneumonia is common illness affecting 450 million persons every year and approximately 7% of the world population getting affected among all age groups. The existence of X-ray images is an age-old method in diagnosis of different diseases. Pneumonia is one such pulmonary disease, which can be predicted by manually examining the chest X-ray (CXR) images, but the manual inspection may have its own limitations. Developments in Computer Vision Techniques using image processing lack in predicting the disease accurately. Improvements based on deep learning Neural Network models also have challenges diagnosing disease to the nearest accuracy. Recent developments based on transfer learning techniques have shown promising results. However, many researchers have published their results after experimenting with the individual models and also using ensemble techniques with varying degree of accuracy. In this paper we try to make comparative analysis using deep learning models Visual Geometry Group (VGG16), Residual Networks (Resnet50) and Extreme Inception (Xception) based on transfer learning techniques for different parameter setup on different datasets and by applying data augmentation techniques for the same cycle of epochs. The analysis carried out would help to understand the models under consideration for their capability to learn and predict new image sets.

关键词： Deep learning Analytical models Visualization Pulmonary diseases computational modeling Transfer learning Predictive models

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An Evolutionary EMD-FA-ELM approach for Short Term Wind Power Prediction using Wind Speed as Input

An Evolutionary EMD-FA-ELM approach for Short Term Wind Powe...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Prachitara Satapathy Sabita Chaine Shaktinarayana Mishra Lokanath Tripathy P.K Dash Sumant Kumar Dalai Department of EE College of Engg. & Technology Bhubaneswar India Department of ECE National Institute of Technology Meghalaya India Department of EE Siksha ‘O’ Anusandhan Bhubaneswar India Department of EE National Institute of Technology Meghalaya India

ISBN: (纸本)9781665478403

A hybrid method combining Efficient Firefly Algorithm (EFA), Empirical Mode Decomposition (EMD) and Special Extreme Learning approach (SELM) is presented in this paper for wind power prediction. The time series wind speed data is decomposed in different Intrinsic Mode Functions (IMFs) by the help of EMD. The optimal IMFs can be selected by the help of the IFA to be given as input to the SELM. The absence of randomness in SELM makes it different from the conventional ELM approach. The optimal IMFs are considered as the input to the SELM to predict the wind speed. Here the wind speed data is considered for 10 minute prediction interval. The predicted wind power results from the proposed optimized EMD-SELM are compared with EMD-SELM and EMD-ELM through various performance metrics. The performance of the IF A is also assessed against other evolutionary approach like differential evolution (DE) and Genetic Algorithm (GA).

关键词： Measurement Power engineering Wind speed computational modeling Time series analysis Wind power generation Predictive models

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Development and Comparison of Scoring Functions in Curriculum Learning

Development and Comparison of Scoring Functions in Curriculu...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Himmet Toprak Kesgin Mehmet Fatih Amasyali Department of Computer Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

Curriculum Learning is the presentation of samples to the machine learning model in a meaningful order instead of a random order. The main challenge of Curriculum Learning is determining how to rank these samples. The ranking of the samples is expressed by the scoring function. In this study, scoring functions were compared using data set features, using the model to be trained, and using another model and their ensemble versions. Experiments were performed for 4 images and 4 text datasets. No significant differences were found between scoring functions for text datasets, but significant improvements were obtained in scoring functions created using transfer learning compared to classical model training and other scoring functions for image datasets. It shows that different new scoring functions are waiting to be found for text classification tasks.

关键词： Training computational modeling Transfer learning Text categorization data models Task analysis Machine intelligence

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A study on the adoption of Wireless Communication in Big data Analytics Using Neural Networks and Deep Learning

A study on the adoption of Wireless Communication in Big Dat...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： P. Rohini Sachin Tripathi C M Preeti A. Renuka José Luis Arias Gonzales Durgaprasad Gangodkar Computer Science and Engineering Faculty of Science and Technology (ICFAI Foundation for Higher Education)HyderabadTelangana State Symbiosis International (Deemed) University Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India JNTUH Department of CSE Department of Business University of British Columbia Department of Computer Science &#x0026 Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The study explores about the Wireless communication which is one of the most rapidly evolving and active technology fields in the communication world. Wireless communication is a means of transmitting data from one point to another without the use of wires, cables, or another physical medium. As a result, commercial network operators have risen at an accelerating rate, bringing in the era of big data. Machine learning has been applied in a range of corporate and academic research contexts as one of the most promising artificial intelligences (AI) methods for deciphering this deluge of knowledge. This study presents a high-level introduction of big data handling and technological advances, as well as their potential applications in next-generation wireless networks (NG). Following that, we employ advanced analytics to estimate mobile users' demands and then use that knowledge to improve the efficacy of “community wireless communication channels.” In specifically, a unified, huge data-aided computer learning framework comprised of feature extraction, data modelling, and prediction/online refinement is provided. The primary benefits of the proposed framework are that we could create the logic, problem formulations, and method of powerful computational models inside the frame of wireless networks by depending on vast data that reflects the both spectrum and other hard demands of users. Via general, information is conveyed from sender to receiver across a certain distance in a communication system. The transmitter may be located anywhere within a few meters using Wireless Communication… We examined currently known approaches and explored their benefits and drawbacks in order to identify new research avenues for future advancements in underwater sensor networks.

关键词： Underwater cables Wireless sensor networks data analysis Transmitters Wireless networks computational modeling Wires

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2nd international conference on Computer modeling, simulation and Algorithm - Applied Mathematics, data analysis and data Mining

2nd International Conference on Computer Modeling, Simulatio...

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2020 2nd international conference on Computer modeling, simulation and Algorithm, CMSA 2020

The proceedings contain 261 papers. The topics discussed include: MBSE-based modeling technology for aircraft assembly tooling design demand;modeling and implementation of distributed rain water storage and utilization;modeling free surface elevation around tandem piers of the longitudinal bridge by computational fluid dynamics;L1-finite difference method for inverse source problem of fractional diffusion equation;EMD and singular value difference spectrum based bearing fault characteristics extraction;research on a distribution-outlier detection algorithm based on logistics distribution data;activation detection algorithms for pattern division multiple access uplink grant-free transmission;application of enhanced whale adaptive threshold noise reduction method in transformer ultrasonic 3D imaging detection;human body shape reconstruction from binary image using convolutional neural network;a pricing mechanism which implements allocation in Shannon formula of home cellular wireless communication;and research on power consumption information acquisition system based on broadband power line carrier technology.

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Artificial Intelligence Approach for modeling House Price Prediction

Artificial Intelligence Approach for Modeling House Price Pr...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Melihsah Cekic Kübra Nur Korkmaz Habib Müküs Alaa Ali Hameed Akhtar Jamil Faezeh Soleimani Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Mathematical Sciences Ball State University Muncie Indiana USA

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

Real estate has a vast market volume across the globe. This domain has been growing significantly in the past few decades. An accurate prediction can help buyers, and other decision-makers make better decisions. However, developing a model that can effectively predict house prices in complex environments is still a challenging task. This paper proposes machine learning models for the accurate prediction of real estate house prices. Furthermore, we investigated the feature importance and various data analysis methods to improve the prediction accuracy. Linear Regression, Decision Tree, XGBoost, Extra Trees, and Random Forest were used in this study. For all models, hyperparameters were first calculated using k-fold cross-validation, and then they were trained to apply to test data. The models were tested on the Boston housing dataset. The proposed method was evaluated using Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) metrics.

关键词： Measurement computational modeling Urban areas Linear regression Predictive models Eigenvalues and eigenfunctions data models

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A Digital Twin City Model for Capturing Walkability Distress of Elderly Adults

A Digital Twin City Model for Capturing Walkability Distress...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Theodora Chaspari Jinwoo Kim Chanam Lee Youngjib Ham SangHyun Lee Changbum R. Ahn Computer Science & Engineering Texas A&M University Landscape Architecture & Urban Planning Texas A&M University Construction Science Texas A&M University Civil & Environmental Engineering University of Michigan Architecture/Architectural Engineering Seoul National University

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

This paper overviews the design of a digital twin city (DTC) model that captures and quantifies walkability distress of older adults via harnessing experiential and physiological data collected from mobile phones and wearable sensor devices. Based on a transdisciplinary endeavor among construction science, urban planning & landscape architecture, civil engineering, computer science, and public health, the goals of this work are to: (1) create a DTC model that quantifies older adults' collective distress and associated environmental conditions; and (2) leverage the DTC model to develop and evaluate technological and environmental interventions to alleviate older adults' distress while walking, which can lead to promoting their independent mobility and health. This research is conducted in collaboration with local stakeholders and older adults in an underserved neighborhood in Houston, TX, Magnolia Park-Manchester. This paper will discuss the experimental design, community engagement activities, and tentative design of interventions driven by convergent insights obtained from data analysis and community feedback to improve mobility of older adults in the urban environment.

关键词： data analysis Smart cities computational modeling Urban planning data models Digital twins Stakeholders

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The Application of Computer Technology in English Teaching

The Application of Computer Technology in English Teaching

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Information Science and Education (ICISE-IE), international conference on

作者： Yilin Wu Pingxiang University Pingxiang China

ISBN: (纸本)9781665438308

For the database of computer English teaching platform, the specific application of computer technology in English teaching is an important way to improve its English teaching ability. Secondly, based on the construction of computer algorithm application system platform, this paper puts forward the basic mode of formulating English teaching objectives. Finally, through data collection, storage, mining and analysis, it provides strong computer data model support for modern English teaching reform, This paper discusses the importance and necessity of improving the quality of innovation and entrepreneurship education by using the computer mathematical analysis and experiment platform, introduces the general modeling theory, comprehensive simulation theory and method, planning theory and method, safety theory and method, and systematically summarizes the operation optimization and control, benefit evaluation and operation mechanism The existing problems are summarized and analyzed.

关键词： Analytical models Technological innovation computational modeling Education Mathematical analysis data models Safety

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Research on the application of virtual simulation technology in law experiment teaching under the background of educational informatization

Research on the application of virtual simulation technology...

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Information Science and Education (ICISE-IE), international conference on

作者： Guohui Cai Science and Technology College Gannan Normal University Ganzhou China

ISBN: (纸本)9781665438308

Virtual simulation system law teaching system usually adopts multi-level technology and modular technology, which makes the teaching system have good scalability, allows users to carry out practical a operation in actual law teaching, and adds new experimental teaching modules at the same time. The goal of establishing law laboratory is to create a diversified and systematic teaching environment. Based on computer technology, make full use of virtual simulation technology to establish three- dimensional legal model and construct virtual simulation legal experiment system. It can further realize the data analysis of virtual simulation technology and law teaching. The laboratory system has a complete data protection mechanism. All data confidence in the system will be backed up by the system, using incremental backup and stage overall backup model. It can better preserve the data information and operation process generated in the process of law experiment teaching, and Hitler can quickly restore even if data loss occurs.

关键词： Solid modeling Technological innovation Information science Systematics Law computational modeling Scalability

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2nd international conference on Computer modeling, simulation and Algorithm - Advanced Algorithms, analysis Model, Optimization Design and Its Application

2nd International Conference on Computer Modeling, Simulatio...

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2020 2nd international conference on Computer modeling, simulation and Algorithm, CMSA 2020

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