检索结果-内蒙古大学图书馆

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Intracranial Hemorrhage Diagnosis through Deep Learning Models 2

Enhancing Intracranial Hemorrhage Diagnosis through Deep Lea...

引用

2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Malik, Payal Dureja, Ajay Dureja, Aman Rathore, Rajkumar Singh Malhotra, Nisha New Delhi India Rohini New Delhi India Department of Computer Science Cardiff School of Technologies United Kingdom

Timely diagnosis is crucial for the successful treatment of a serious medical condition like brain hemorrhage. Deep learning algorithms have shown great promise in applications for medical image analysis, like the identification of brain hemorrhages. The goal of this study is to assess how well various deep learning algorithms can detect brain hemorrhages. Using a suitable dataset, the study evaluates the computational efficiency, accuracy, sensitivity, and specificity of the selected algorithms. The results demonstrate the potential of deep learning models to assist physicians in identifying this potentially fatal condition and demonstrate how well they can identify brain hemorrhages. The study's findings improve automated brain hemorrhage detection technology, improving patient outcomes and the efficiency of healthcare delivery. EfficientNetB3 typically achieves higher accuracy due to its increased model complexity. Despite its increased complexity, EfficientNetB3 is still more parameter-efficient and computationally efficient than many alternative architectures. EfficientNetB3's strong performance and feature extraction capabilities make it a good choice for transfer learning tasks. Out of all the models implemented in this paper, the proposed model with EfficientNetB3 gave the best accuracy for training as well as validation i.e. 99.95% and 93.29% respectively followed by EfficientNetB2, ResNet, SEResNext and ResNext. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Prediction of Passenger Flow During Peak Hours Based on Deep Learning 7th

Prediction of Passenger Flow During Peak Hours Based on Deep...

引用

7th International Conference on Information science, Communication and Computing, ISCC 2023

作者： Li, Yajing Cheng, Jieren Kou, Yuqing Xia, Dongwan Sheng, Victor S. School of Computer Science and Technology Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China Department of Computer Science Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9789819971602

In many deep learning tasks, feature extraction and fusion in multivariate time series is an indispensable part of passenger flow prediction. Because there is also a certain correlation between passenger flow time series, it is very meaningful to successfully capture the time characteristics of the series and the dependencies between each series. Therefore, this study combined Residual Network (ResNet) and Attention Gated Recurrent Unit (Attention GRU) to build a ResGRU model for predicting subway passenger flow data during peak hours. This study has improved ResNet and attention GRU, then designed the ResGRU model architecture. Among them, the subway network topology is constructed by graphs, ResNet is used to capture the hidden spatial features between data, attention GRU captures the deep temporal features between data. This study not only considered passenger flow data, but also added subway network topology data, even weather and air pollution index data. Finally, three time solts of 10, 15 and 30 min were used to forecast the peak passenger flow on the public dataset. The ResGRU model was compared with the single model, the combined model and an ablation experiment. The experimental results demonstrate the high robustness and superiority of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

引用

Journal of computer science & Technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

来源：评论

学校读者我要写书评

暂无评论

Personalized English Lexical Simplification for Chinese 23

Personalized English Lexical Simplification for Chinese

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yin, Yujie Zhang, Feng Li, Yun Zhang, Jing Qiang, Jipeng Yangzhou University Department of Computer and Science Yangzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Current English lexical simplification (LS) methods adopt one simplification scheme for all users, regardless of individual users' language proficiency. To solve the problem, we propose a personalized English LS method for Chinese based on their English certification. Specifically, our method only simplifies these words that need to be simplified and utilizes both pertained language modeling BERT and Thesaurus to generate the best substitutes. To our knowledge, this is the first work that focuses on personalized English LS for Chinese. Experimental results show that our method obtains the most suitable simplification compared with the baselines. © 2021 IEEE.

关键词： Dictionaries Bit error rate Libraries Thesauri Certification

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

引用

Annals of data science 2023年第1期10卷 183-208页

作者： Kurani, Akshit Doshi, Pavan Vakharia, Aarya Shah, Manan Department of Computer Engineering Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering Indus University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Petroleum University Gujarat Gandhinagar382007 India

From exchanging budgetary instruments to tracking individual spending plans to detail a business's profit, money-related organisations utilise computational innovation day by day. Here in this paper, we focus on the significance of innovation in accounts such as financial risk management and stock prediction. We discuss two significant algorithms that have a notable role in stock forecasting. Artificial Neural Networks (ANN), as absenteeism of some data points, does not hamper the network functioning. Secondly, Support Vector Machines (SVM) has several features, and due to simple decision boundaries, it avoids over-fitting. The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios. The paper covers the recent past studies to explore the concepts and methodologies through which ANN's and SVM's have been used. Additionally, the paper incorporates significant aspects of novel methods and technologies in which ANN as a hybrid model like ANN-MLP, GARCH-MLP, a combination of the Backpropagation algorithm and Multilayer Feed-forward network, yields better results. Simultaneously, SVM's have been successfully applied in stock prediction, giving an accuracy of about 60%–70% for simple SVM, which is further improved by combining methods like Random Forest, Genetic Algorithm more accurate outcomes. Further, we present our thoughts on where SVM's and ANN's stand as prediction algorithms and challenges like the time constraint, current scenarios, data limitation, and cold start problems were raised. Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can further be enhanced with their integration with other novel techniques resulting in hybrid methodologies. It will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting. © 20

关键词： Machine learning ANN SVM Artificial neural network Support-vector machine Decision tree Backpropagation Stock market Autoregressive conditional heteroskedasticity Neural network data mining

来源：评论

学校读者我要写书评

暂无评论

FREEnet: a dynamic deep-learning model for freehand diffuse optical tomography

FREEnet: a dynamic deep-learning model for freehand diffuse ...

引用

Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Dale, Robin O'Sullivan, Thomas Dehghani, Hamid University of Birmingham School of Computer Science United Kingdom University of Notre Dame Department of Electrical Engineering United States

A deep-learning (DL) model for handheld diffuse optical tomography is presented. The fully convolutional network can reconstruct 3D absorption and scattering from arbitrarily undersampled scan data at a rate of 18.5Hz... 详细信息

ISBN: (纸本)9781957171340

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Framework for a computer-Aided Treatment Prediction (CATP) System for Breast Cancer

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3007-3028页

作者： Emad Abd Al Rahman Nur Intan Raihana Ruhaiyem Majed Bouchahma Kamarul Imran Musa School of Computer Sciences Universiti Sains MalaysiaPenang11800Malaysia School of Computer Science Higher Colleges of TechnologyRAKUAE Department of Community Medicine School of Medical SciencesUniversiti Sains MalaysiaKubang KerianKelantan16150Malaysia

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)*** rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent *** has been the most utilized breast ima-ging technique to *** have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human *** the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely *** such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of *** develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)*** Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction *** best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

关键词： Breastcancer machinelearning featureimportance featureselection treatment prediction SEER dataset computer-aided treatment prediction(CATP) clinical decision support system

来源：评论

学校读者我要写书评

暂无评论

Cloud-Enabled Neural Networks for Intelligent Vehicle Emissions Tracking and Analysis

Cloud-Enabled Neural Networks for Intelligent Vehicle Emissi...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Anusha, N. Jeslin, J Gnana Srividhya, V. Gupta, Nukala Sujata Meenakshi, R. Srinivasan, C. Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Hyderabad Gandipet India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is crucial. Scalability and real-time data processing are issues for traditional approaches. Thus, cloud-enabled neural network architecture for scalable and efficient vehicle emissions monitoring using distributed computing is developed. A neural network model trained on various vehicle emission datasets, allowing for precise pattern analysis and prediction of emissions. The scalability of the system is guaranteed by using cloud resources, which can accommodate the rising amount of data created by an increasing number of vehicles. By incorporating cloud-based data processing seamlessly, real-time monitoring is accomplished, enabling immediate analysis and prompt emission alerts. The suggested emissions tracking platform is robust and scalable to intelligent transportation systems by revealing vehicle emissions' environmental effect. Future smart city and sustainable urban transportation developments are built on cloud computing and neural networks. The proposed system shows how cloud-enabled neural networks may solve complicated vehicle emissions problems. © 2024 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach 4

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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：