检索结果-内蒙古大学图书馆

23rd IEEE international conference on Mobile data Management (IEEE MDM)

作者： Cai, Yuanqin Wei, Tongquan East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665451765

ISBN: (纸本)9781665451765

Distributed machine learning such as split learning can train a model using data on mobile devices while protecting privacy. However, its training time is impractical when the number of devices is large, and the model performance will decrease greatly if data are non-iid. To solve these problems, an efficient parallel split learning algorithm is proposed. Specifically, the parallel algorithm with a distillation loss function instead of parameter synchronization reduces the training time without losing the accuracy. And an incentive mechanism based on Stackelberg Game is designed to adapt to the training environment with non-iid mobile data. The experiments on the CIFAR-10 dataset demonstrate the superior performance of the proposed algorithm in terms of training time and model accuracy.

关键词： Distributed machine learning Split learning Model Distillation Incentive Mechanism

来源：评论

学校读者我要写书评

暂无评论

Study on Software Defects Prediction Model based on machine learning

Study on Software Defects Prediction Model based on Machine ...

引用

2022 international conference on Mathematics, Modeling, and Computer Science, MMCS 2022

作者： Ren, Wenqing Chengdu Neusoft University Chengdu China

ISBN: (纸本)9781510664593

In the rapid development of network science and technology, the software, as the basic part of the network system operation, the practical application quality directly determines the realization of the function, so the users put forward higher requirements for the software quality. According to the application situation of network system software in recent years, software defects are the main factor affecting the application quality, and the relevant detection technology is the only way before the formal promotion of software. Therefore, researchers have put forward a defect prediction scheme based on the software code, which can not only reduce the cost, but also improve the practical efficiency. This paper focuses on the understanding of the machine learning algorithm and constructing automatic and comprehensive learning models according to the software defect prediction technology, thus discovering the defects in the software. The final experimental results prove that different algorithms have different advantages in different evaluation indicators. By using these advantages and the stacking integrated learning methods in machine learning, building a prediction model with combined machine learning algorithms as the core can find defects more accurately and perfectly. © 2023 SPIE.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Enhanced Channel Modeling and Generation with Reconstructive Generative Adversarial Network 3

Enhanced Channel Modeling and Generation with Reconstructive...

引用

3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Huo, Yuqi Deng, Zhongliang Gao, Yanbiao Pan, Tianbao Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350359961

Advancements in deep learning (DL)-based wireless communications have been hindered by the increasing complexity of channel modeling and the challenge of collecting high-quality wireless channel data. This study proposes a novel machine learning approach, the Reconstructive Generative Adversarial Network (ReGAN), which leverages an AutoEncoder (AE) architecture to enhance data generation efficiency. The method was assessed using a comprehensive dataset of various Clustered Delay Line channel realizations, created using MATLAB. The ReGAN model demonstrated high consistency in channel similarity, setting a new benchmark in channel modeling and generation. This approach has the potential to expedite the development of wireless communication systems by offering more efficient and accurate channel models. © 2023 IEEE.

关键词： 5G Channel Modeling Generative Adversarial Network machine learning Wireless Communications

来源：评论

学校读者我要写书评

暂无评论

How can machine learning improve waste-to-energy plant operation 28

How can machine learning improve waste-to-energy plant opera...

引用

Joint conference of the IEEE 28th international conference on engineering, Technology and Innovation (ICE/ITMC) / 31st conference of the international-Association-for-Management-of-Technology (IAMOT)

作者： Pehlken, Alexandra Garmatter, Henriette Dawel, Lisa Cyris, Fabian Beck, Hendrik Schwark, Fenja Scharf, Roland Niesse, Astrid OFFIS Inst Informat Technol R&D Div Mfg Oldenburg Germany Leibniz Univ Hannover Inst Kraftwerkstechn & Warmeubertragung Hannover Germany Energy Waste GmbH Abt Techn EEW Helmstedt Germany

ISBN: (纸本)9781665488174

This paper deals with the integration and the role of waste-to-energy plants. The hygienic and safe disposal of waste is a central aspect of human infrastructure. It is a prerequisite for preventing the spread of disease in society and is a very important issue today as it has been in the past. While serving as waste disposal in the past, today, waste incineration has changed to waste-to-energy plants, in addition to keeping the first goal of waste reducing and sanitation. Therefore, the highest energy harvesting is not the primary goal of the plant. The plant operation is difficult to control related to the stochastical variation of the properties of municipal waste due to its heterogeneous nature. Hence, it is expected that the optimization of waste-to-energy plants will benefit significantly if any applied method may handle the stochastic properties well. The work presented here aims to provide insights into a novel approach to develop a new method for enhancing the performance of the waste-to-energy plant related to blast cleanings to prevent build-up of particles. This also ensures that the overall performance of the plant improves. Artificial Intelligence was applied to sensor emission data directly from an operating real incinerator and with machine learning the data shows the effects of the blast cleanings related to typical plant properties. The results of this paper give first indications that forecasting of the incineration process is possible. With these results, the plant operator could handle the plant efficiently with the least performance reduction.

关键词： waste-to-energy machine learning artificial intelligence regression XGBoost

来源：评论

学校读者我要写书评

暂无评论

Prediction of Peak Shear Strength of Joints Based on machine learning Algorithms 3

Prediction of Peak Shear Strength of Joints Based on Machine...

引用

3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Shen, Hui Liu, YaQun Li, HaiBo Xia, Xiang Liu, Bo Yu, Chong Institute of Rock and Soil Mechanics Chinese Academy of Sciences State Key Laboratory of Geomechanics and Geotechnical Engineering Hubei Wuhan China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665459112

The peak shear strength of rock joints is closely related to the normal stress, the roughness of the joint surface, and the mechanical properties of the material. Based on the published data, three machine learning (ML) algorithms, including Support Vector machine (SVM), BP Neural Network (BPNN), and Random Forest (RF) are used to established regression models for predicting the peak shear strength of joints. 10-fold cross-validation is adopted to evaluate the performance of three ML models. In addition, three empirical peak shear strength models are selected for comparison with the ML models. Compared to the empirical models, we find that the ML models predict the peak shear strength with higher accuracy and lower error, indicating that the ML models perform better. Finally, the parameter analysis is performed to evaluate the influence of input parameters and the robustness of the ML models. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Phase Synchronization-based Topological Features for Autism and ADHD Classification 24

Phase Synchronization-based Topological Features for Autism ...

引用

15th Indian conference on Computer Vision Graphics and Image Processing

作者： Kaur, Karamjot Chawla, Vaishali Rana, Bharti Univ Delhi Dept Comp Sci Delhi India

ISBN: (纸本)9798400710759

Neurodevelopmental disorders like Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) impact around 10% of the global pediatric population. Their diagnosis involves interview-based tests that may invite subjectivity, in addition to a delayed diagnosis. Neuroimaging, particularly resting-state functional MRI (rs-fMRI), provides a more objective diagnosis and may help to develop a machine learning (ML) based automated diagnostic system. Our work proposes an end-to-end ML-based diagnostic method to classify ADHD and ASD subjects using rsfMRI data. We also attempt to classify them from healthy controls (HC). Two public repositories - ADHD-200, and ABIDE1 are used to collect rs-fMRI data. This study involves (a) utilization of the Phase Synchronisation Index (PSI) to design connectivity maps among brain regions, (b) considering the brain as a sparse network represented with PSI values and extracting topological features from the graph, (c) successfully distinguishing between ADHD and ASD subjects, in addition to segregating them from HCs. We use Random Forest, and Support Vector machine classifiers. The proposed model achieves 66.8% accuracy in distinguishing between ADHD and ASD. Potentially atrophic brain regions are identified in addition to analyzing the importance of topological features. We hope that the proposed work gives direction to future work for automatic diagnosis of psychiatric disorders.

关键词： ADHD Autism rs-fMRI Phase Synchronization Index graph-based features machine learning Classification

来源：评论

学校读者我要写书评

暂无评论

An Empirical Analysis of Python Programming for Advance Computing 2

An Empirical Analysis of Python Programming for Advance Comp...

引用

2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Baliyan, Anupam Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Chitkara University Chitkara University Institute of Engineering & Technology Punjab India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram522002 India

ISBN: (纸本)9781665437899

Python is an object-oriented, scripting, and interpretive programming language that may be used for mentoring and real-world applications. This paper focusses primarily on Python software packages used in data science, pattern recognition, and IoT. This paper will first explain Python as a language, then introduce data Science, machine learning, and IOT, describing prominent packages in the data Science and machine learning community, such as NumPy, SciPy, TensorFlow, Keras, Matplotlib. This paper will also demonstrate the significance of Python in the development of the industry. Throughout, we shall utilize many code samples. We review so many research papers to analyze the usage of python in different fields and easily import packages in the programming software. © 2022 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

JEPX spot prices forecasting system using GIS

引用

ENERGY REPORTS 2023年 9卷 329-336页

作者： Nakayama, Shuntaro Shiota, Atsushi Mitani, Yasunori Watanabe, Masayuki Kyushu Inst Technol Tobata Ku 1-1 Sensui Cho Kitakyushu Fukuoka 8048550 Japan Kitakyushu City Off Kokurakita Ku 1-1 Jonai Kitakyushu Fukuoka 8030813 Japan

Japan Electric Power Exchange (JEPX) is the only electricity market in Japan that allows transactions for electric power established in 2005. Several commodities are traded there, such as the spot market and the forward market. In particular, the spot market is a major trading market. The price of the spot market changes depending on the relationship between supply and demand. Therefore, it is important to forecast spot market prices to make a supply and demand plan for the next day. This research focused on the factors that determine supply and demand are related to geospatial information-first, deriving the explanatory variables for JEPX spot prices using Geographic Information System (GIS). Then, constructing the spot price forecasting system by machine learning using the derived explanatory variables. By using this system, it is possible to forecast electricity prices with higher accuracy than existing methods. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Japan electric power exchange Geographic information system Spot price Meteorological data

来源：评论

学校读者我要写书评

暂无评论

data analytics system for product management in retail business 1

Data analytics system for product management in retail busin...

引用

1st international conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Rukkamate, Nuttanard Rodamporn, Somphop Kanuengkid, Anapat Kamonlakorn, Kittiphum Srinakharinwirot University Computer Engineering Nakhon Nayok Thailand Srinakharinwirot University Computer Engineering Department Nakhon Nayok Thailand

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

This paper aims to develop a data analytics system for product management in retail businesses based on the Winters' Method and tests the milk sales forecasting model with machine learning using linear regression, random forest, and eXtreme Gradient Boosting (XGBoost). Using Minitab, apply machine learning test data to separate milk statistical analysis with five forecasting methods. Actual sales figures were compared to the difference using the results of the machine learning model and statistical analysis. The results of machine learning show that random forests have more minor deviations than a statistical analysis performed with Minitab that showed Winters' Method (Additive Method) with UHT of less than 7.9 percent. Pasteurized was less than 9.54 percent. Soymilk was less than 12.57 percent. Fermented milk was less than 10.47 percent. The result is a desirable user satisfaction rating of 4.47 out of 5. © 2022 IEEE.

关键词： data Analytics

来源：评论

学校读者我要写书评

暂无评论

Lightweight Object Detection-Tracking using Deep Feature Distillation 24

Lightweight Object Detection-Tracking using Deep Feature Dis...

引用

16th international conference on machine learning and Computing (ICMLC)

作者： Xue, B. Zheng, Q. Li, Z. Zhao, W. Wang, H. Feng, X. Tsinghua Univ Xian Jiaotong Univ Natl Univ Def Technol Beijing Peoples R China Tongji Univ Xian Jiaotong Univ Shanghai Peoples R China Natl Univ Def Technol Changsha Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400709234

Object detection and tracking are critical and fundamental problems in machine vision task. In this paper, an object detection and tracking method is proposed based on deep feature distillation. Particularly, an adaptive unsupervised Teacher-Student unified framework is developed. The Teacher module is performed by an expandable generative adversarial network mixture model. And knowledge discrepancy ranking (KDR) is designed to optimize Teacher resource allocation with the historical underlying knowledge. The Student module is developed based on a lightweight probabilistic generative model. And an unsupervised learning scheme is presented based on Gumbel-Soft sampling optimization to train jointly. A series of experiments are performed on authoritative dataset, demonstrating that the proposed method outperforms the state-of-the-art comparison methods.

关键词： Object detection object tracking deep learning knowledge distillation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：