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application research of substation management based on BIM technology 23

Application research of substation management based on BIM t...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Baitao Zhang Kaixin Gu Yi Yang Zhenzhe Zhang Haojie Ren State Grid Jiangsu Electric Power Engineering Consulting Co. Ltd. China East China Electric Power Design Institute Co. Ltd. Of China Power Engineering Consulting Group China

ISBN: (纸本)9798400709517

the development of BIM technology in the domestic engineering field, the most important thing at present is to pay close attention to the implementation of BIM landing application, closely in line with industrial changes and development trends. In the process of substation construction management, construction quality management and cost control are affected by many factors, and the importance of these factors is different, so the concept of prefabricated in the construction industry is introduced. In the past, WBS principle of work breakdown structure has been used for quality control. therefore, the construction and development of power transmission and transformation projects need to be integrated with the application progress of BIM technology and coding system to accelerate the application of three-dimensional design results. In the field of model coding system, this paper proposes a new algorithm for digital 3D model identification system, which has achieved initial results in practical engineering cases. Finally, the paper analyses the key difficulties and development direction of BIM technology application in substation construction in the future.

关键词： BIM application Construction management Model coding Substation three-dimensional building information model

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Enhanced Classification of Intel Images Using Refined EfficientNet and MobileNetV2 Frameworks

Enhanced Classification of Intel Images Using Refined Effici...

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Intelligent Technologies (CONIT), international conference on

作者： Raghav Jain Rishabh Sharma Devesh Tiwari Kireet Joshi Vishal Jain Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida U.P India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the proposed study describes the functioning of two modern deep learning models, namely EfficientNet and MobileNetV2, within the scope of image classification from a wide range of datasets. this study is designed to categorize images into six groups, namely: buildings, mountains, forests, sea/ocean, glaciers, and streets. the study utilizes a dataset from the Kaggle repository to assess the effectiveness of these models. the research methodology will include data preprocessing, modeling, and model evaluation. this paper will find out the balance between accuracy and efficiency which is provided by each model. EfficientNet is compared with MobileNetV2 because of its scalable structure and compromise between model depth, width, and resolution versus the latter which is known for its low weight and efficiency, especially in constrained circumstances. the research utilizes a framework of accuracy, precision, recall, F1-score metrics, and model efficiency analyses for comprehensive result evaluation and scrutiny. the analysis of this study indicates the unique advantages of separate models, for instance, EfficientNet is more accurate in most cases and MobileNetV2 is a good solution when you have to save computer resources. Comparing models highlights the importance of the selection that is made based on the specific application instead of performance metrics or computational demands.

关键词： Deep learning Training Analytical models Accuracy Forests Computational modeling Glaciers

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A Skin Type Classification Method Using Mobile Device-Based Deep learning Model

A Skin Type Classification Method Using Mobile Device-Based ...

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international conference on Applied System Innovation (ICASI)

作者： Hung-Tse Chan Yan-Wei Liao Sin-Ye Jhong Shih-Che Chien Kai-Lung Hua Yung-Yao Chen Dept. of Electronic and Computer Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC Department of Engineering Science National Cheng Kung University No.1 University Road Tainan City Taiwan ROC National Chung-Shan Institute of Science and Technology No.481 6th Neighborhood Sec. Jia’an Zhongzheng Rd. Longtan Dist. Taoyuan City Taiwan ROC Dept. of Computer Science and Information Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC

Skin care products should be tailored to suit different skin types. However, skin testing can be expensive and time-consuming, particularly for students or office workers who may need access to specialized equipment. In the present study, we developed a skin type detection system by using computer-vision and deep-learning techniques that can be easily accessed through a mobile phone application. Our system integrates with the TensorFlow Lite framework on the Android platform and therefore supports various hardware accelerations and easy model validation. TensorFlow Lite, an open-source library developed by Google, is a lightweight, cross-platform, machine-learning framework for mobile and Internet of things devices. It also supports various hardware accelerations. Our experimental results reveal that the proposed method has an accuracy of 96% and is easy to use on mobile devices. this system provides a convenient and cost-effective means of identifying the skin type and selecting appropriate skin care products.

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A Survey About the Decision-making of Self-driving Based on Deep Reinforcement learning

A Survey About the Decision-making of Self-driving Based on ...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Jin Zhang Nan Ma Yongqiang Yao Cheng Wang Zhixuan Wu Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Shuncheng High Technology Corporation Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Self-driving mainly includes four parts: perception, planning, decision-making and control. the decision system is used to output safe and reasonable driving behaviors according to vehicle's state and surrounding environment, so as to autonomously control the vehicle to driving, which is the key part and core of self-driving. With the development of machine learning and AI, the self-driving technology gets further improvement. Using the deep reinforcement learning (DRL) algorithms to solve the behavior decision-making problems of self-driving vehicles has become one of the most concerned research hot spot. Traditional rule-based method lacks the ability to adapt the dynamic environments, DRL can overcome this drawback very well, it has unique advantage for processing those problems. In this paper, we summarize recent years research on DRL for decision making in the field of self-driving and provide a classification of driving tasks using these methods. And analyze in detail the advantages and disadvantages of DRL methods proposed by researchers for solving these driving tasks, and finally summarize some of the challenges and difficulties in applying DRL to complete decision-making problems and look forward to future research works and directions. the study contributes to a deeper understanding of the theories and methods related to DRL and decision-making of self-driving, and provides a reference for future research.

关键词： Surveys machine learning algorithms Heuristic algorithms Decision making Big Data Deep reinforcement learning Planning

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the application of big data analysis in college english classroom vocabulary memory and learning efficiency teaching 2021

The application of big data analysis in college english clas...

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4th international conference on Information Systems and computer Aided Education, ICISCAE 2021

作者： Fang, Hua Caili, Liu Department of Basic Course Modern College of Northwest University Xi'an China

ISBN: (纸本)9781450390255

On the Internet platform, we can use big data mining technology to obtain rich teaching resources, assist classroom teaching, and change the previous relatively single word teaching content. When carrying out English word teaching, we should not only use textbooks to design courseware, but actively collect data resources such as pictures related to word teaching, and use these resources in courseware making to attract students' attention. In this paper, an intelligent recommendation system for English vocabulary learning is designed by using the intelligent recommendation model for English vocabulary learning. the function of uploading corpus is added to realize the automatic updating of the vocabulary. Integrate and update the learner ability model and learning style model, realize the accurate description of user groups, and improve the efficiency of system resource recommendation. through static acquisition and dynamic mining method to obtain the basic information and behavior interaction data of users, form the characteristics of learners, infer the ability and learning style of learners through machine learning algorithm, improve the effectiveness of teaching and mobilize students' interest in learning. © 2021 ACM.

关键词： Big data

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Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company 22

Sales Prediction Scheme Using RFM based Clustering and Regre...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Nithya Chalapathy Helen Josephine V.L. K Rajalakshmi Business Analytics School of Business and Management Christ University India Department of Computer Science Montfort College India

ISBN: (纸本)9781450399937

machine learning models are being used for better insights and decision making across many industries today. It shows to be quite useful for businesses in the ecommerce industry as well due to the vast amount of data generated and its potential. this research aimed to find insights on future sales of an ecommerce company [1]. the vast number of variables including both categorical and continuous variables under product data, customer information, transaction information, led us to implement a prediction model using regressors rather than just time series forecasting techniques. First an RFM (Recency, Frequency and Monetary) based clustering algorithm was used to get customer related information and then integrate those results into a regressor to achieve the desired goal of prediction of sales. Two schemes were tested one being predictions on individual clusters and the other where the clusters were one hot encoded back into the main data. Results show quite high accuracy of prediction. the high R-squared also indicated that our hypothesis of including the variables contributed significantly to the predicted sales values was correct in this case. this research fulfills an identified need to understand how machine learning algorithms can be implemented by multiple algorithms being integrated in sequential and logical orders thus helping derive business specific strategies rather than making it a mere technical process by providing empirical results about how the predicted sales values along with given inputs can contribute in business decision making relating to marketing, inventory management, dynamic pricing or many more such strategies.

关键词： machine learning algorithms

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SDPP: A tumor disease diagnosis application based on vibration spectroscopy and machine learning 23

SDPP: A tumor disease diagnosis application based on vibrati...

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proceedings of the 2023 4th international Symposium on Artificial Intelligence for Medicine Science

作者： Chenjie Chang Guifeng Zhao Xiaoyi Lv College of Software Xinjiang University China

ISBN: (纸本)9798400708138

Molecular vibrational spectroscopy is an essential technique for analyzing the biomolecular components of human tissues and body fluids with high sensitivity. there are many studies on tumor disease diagnosis that combine artificial intelligence with vibrational spectroscopy, and vibrational spectroscopy has shown good potential in non-invasive disease auxiliary diagnosis. However, the complexity of spectral data processing flow and the variety of algorithms make it difficult for non-professionals to use it efficiently, which limits the development of molecular vibrational spectroscopy-assisted diagnosis technology. this study developed the Spectral Data Processing Platform (SDPP) that normalizes the data processing flow. SDPP can quickly perform preprocessing, feature extraction, and model building on spectral data. As an application desktop program, SDPP has an executable file size of 232 M, an average response time of 3 s for each component, an average CPU utilization rate of 7.30%, and a throughput of 106 times/s. the experimental data selected for testing the platform were collected from Raman data and infrared data of patients and the control group. the results showed that SDPP could efficiently classify patients and the control group and also proved the stability and operability of SDPP.

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Keratoconus Classification Using Feature Selection and machine learning Approach 1

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5th international conference on Applied machine learning and Data Analytics, AMLDA 2022

作者： Smily Yeya Jothi, E. Anitha, J. Hemanth, Jude Department of Biomedical Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Department of Electronics and Communication Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

the development and design of new tools that can help identify the disease at an early stage may aid in preventing or delaying the progression of the disease. the preservation of young populations’ vision is crucial because Keratoconus (KC) typically affects people at puberty, with children being the most affected. the goal of this research is to identify the most pertinent variables in relation to the various keratoconus classifiers employed in the Harvard Dataverse keratoconus dataset. Out of 3162 observations, a total of 446 parameters are examined by 3 feature selection techniques, Sequential Forward, and Backward Selection (SFS and SBS), Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). the prediction is done through machine learning algorithms. the experimental results implied that Sequential forward selection and PSO provided the highest classification accuracy by the application of Negative Correlation based Deep Ensemble Network (NC-DEN), with an accuracy of 96.14%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Research on Missing Data Imputation Based on Conditional Variational Autoencoder 4

Research on Missing Data Imputation Based on Conditional Var...

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4th international conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Xu, Yebo Ni, Yingjie Information Engineering University Zhengzhou China Jiangnan Institute of Computing Technology Wuxi China

ISBN: (纸本)9781665406925

With the development of artificial intelligence technology, data has become an important resource with much attention being attracted. However, in the process of data collection, transmission and storage, data missing happens inevitably due to various reasons. In order to reduce the impact of missing data on data application, this paper proposes a multiple imputation model for missing data based on conditional variational autoencoder. the model is trained on an incomplete dataset, using multiple sampling from the latent space to generate multiple estimates. In the model accuracy of the estimates has been improved, constrained by classification label of the data. Experiments were carried out on open source datasets and evaluated with existing imputation methods. © 2021 IEEE.

关键词： learning systems

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Prediction of COVID-19 Epidemiology with Chest computerized Tomography Images Using machine learning Techniques

Prediction of COVID-19 Epidemiology with Chest Computerized ...

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international Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Khokhoni Innocentia Mpho Ramaphosa Tranos Zuva Temidayo Otunniyi Faculty of Applied and Computer Sciences Vaal University of Technology Vanderbijlpark South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

the COVID-19 pandemic placed immense pressure healthcare systems, emphasizing the need for efficient prediction methods. this research proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to classify chest CT scan images as either COVID-19 positive or negative. A dataset of 400 normal and 399 COVID-19 CT scan images was used to evaluate CNN models, including VGG19, DenseNet201, Xception, InceptionV3, and VGG16, over 25 and 50 epochs. At 25 epochs, DenseNet201 achieved the highest accuracy of 97%, followed by VGG16 at 96%, InceptionV3 at 93%, and VGG19 and Xception at 91%. At 50 epochs, accuracy improved, with VGG16 and DenseNet201 both reaching 98%, and VGG19 at 97%. Xception and Inception V3also saw improvements, reaching 96%. VGG16 consistently achieved 100% for both Precision and Recall across all epochs, while DenseNet201 maintained 100% Precision at 25 epochs but dropped to 95% at 50 epochs. Xception showed significant improvement in Recall, rising from 85% to 97%. Enhanced preprocessing, including noise reduction and contrast adjustment, further boosted model performance. A COVID-19 Prediction Web application (CPWA) was developed using the fine-tuned VGG19 model to offer a practical tool for healthcare professionals. this study demonstrates the potential of AI-driven diagnostics to streamline COVID-19 screening and improve early diagnosis, providing scalable solutions for clinical settings.

关键词： COVID-19 Accuracy Pandemics Computed tomography Noise reduction Medical services machine learning Streaming media Predictive models Convolutional neural networks

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