检索结果-内蒙古大学图书馆

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Zhang, Baozhong Yuan, Yin Xu, Jiang Sun, Hui Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (纸本)9798350383348

With the popularization of electric vehicles, the problem of electric vehicle charging scheduling has attracted more and more attention, but there is the problem of inaccurate result evaluation. Traditional charging scheduling methods often rely on manual experience and rules, which is difficult to meet the growing demand and the diversity of charging scenarios, and the evaluation is unreasonable. Therefore, this paper proposes a deep learning algorithm to evaluate and analyze the scheduling strategy of optimization and innovation. Firstly, the power system theory is used to analyze the information of the charging pile, and the index is divided according to the requirements of dispatching strategy evaluation to reduce the interference factors in the dispatching strategy evaluation. Then, the power system theory evaluates the charging dispatching strategy of electric vehicles, forms a dispatching strategy evaluation scheme, and comprehensively analyzes the evaluation results of dispatching strategy. The results show that the evaluation accuracy and charging efficiency of electric vehicle charging scheduling strategies by deep learning algorithms are better than those of traditional charging scheduling methods under certain evaluation criteria. © 2024 IEEE.

关键词： Charging scheduling deep learning algorithms Electric vehicle power system theory

来源：评论

学校读者我要写书评

暂无评论

Multi class Alzheimer disease detection using deep learning techniques

Multi class Alzheimer disease detection using deep learning ...

引用

International Conference on Decision Aid Sciences and Applications (DASA)

作者： Ganesh, C. H. S. C. A. Rama Nithin, G. Sri Akshay, S. Rao, T. Venkat Narayana Sreenidhi Inst Sci & Tech Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781665495011

Alzheimer's disease (AD) is a neurodegenerative disorder. Alzheimer's Disease is a predominant kind of memory disorder. Alzheimer's Disease is not reversible, and progressive brain disease shows a decrease in thinking with no validated disease-modifying treatment, implying no cure. The Disease's side effects are harmless initially, but later they become more extreme over the long run. Thus, a great effort is needed to develop methods for detecting early, especially at stages like very mild, mild, moderate, to slow or prevent disease progression because, on the off chance that the sickness is anticipated before, the Doctor can slow down the cell degeneration. To this end, the construction of a good prediction system for Alzheimer's Disease is the paper's aim, often reducing time to treatment, medical errors, and overall healthcare cost. This paper utilizes AI calculations to anticipate Alzheimer's illness utilizing Brain MRI Scans. In our study, we trained CNN models to detect from brain MRI images. The MRI image dataset has been collected from The OASIS dataset. Our study has three different deep CNN models: VGG - 16, Inception-V3, and Xception to classify Alzheimer's Disease. We trained and evaluated all the deep CNN models. After a relatively short amount of training (125 epochs), we achieved a 75% accuracy with the VGG-16 model, 70% accuracy with the Inception-V3 model, and 70% with the Xception model. Thus, we got very high classification accuracy with our research. In this paper, categorizing Alzheimer's disorder is done into multiple classes (no-dementia, very mild, mild, moderate) to enable the patient to undergo the best and most efficient treatment plan to the patient right away.

关键词： Alzheimer's Disease Dementia deep learning algorithms Psychological Parameters

来源：评论

学校读者我要写书评

暂无评论

Enriching Regional Economic Dynamics through a Knowledge-Driven Spatial Analysis Model: a deep learning Approach

引用

JOURNAL OF THE KNOWLEDGE ECONOMY 2023年第3期15卷 12293-12336页

作者： Cheung, Hin Yu Micah NYU Dept Econ New York NY 10012 USA

Based on spatial interaction theory and relevant economic paradigms, in conjunction with the principles of crafting an evaluation framework, this study meticulously selects socio-economic impact indicators that adeptly characterize the course of regional economic development. As a result, a comprehensive socio-economic impact indicator system is formulated to gauge the dynamics of regional economic progress. The research computes the holistic weightage of these indicators by harnessing the capabilities of deep learning algorithms to streamline the dimensionality of indicator data. By harnessing the power of the independent sub-analysis algorithm, the study constructs a spatial analysis model through data transformation and reclassification, fostering a harmonious interaction between the regional economic development system and the socio-economic factor system. The proposed methodology is applied to assess the economic development of a city, unveiling that the study area's overall economic trajectory falls within the mid-to-upper echelon. Notably, the region showcases elevated levels of urbanization and regional cultural vibrancy. This innovative approach aligns seamlessly with the spirit of advancing knowledge-based economic insights and regional evolution.

关键词： Spatial interaction theory Regional economic development deep learning algorithms Urbanization Regional cultural vibrancy

来源：评论

学校读者我要写书评

暂无评论

Emotion Classification of Songs Using deep learning

Emotion Classification of Songs Using Deep Learning

引用

International Conference on Green Energy, Computing and Sustainable Technology (GECOST)

作者： Mate, Nikita Akre, Durva Patil, Gaurav Sakarkar, Gopal Basuki, Thomas Anung GH Raisoni Coll Engn Dept Artificial Intelligence Nagpur Maharashtra India DY Patil Inst Master Comp Applicat & Management Pune Maharashtra India Univ Southampton Dept Comp Sci Iskandar Puteri Johor Malaysia

ISBN: (纸本)9781665486637

The sentiment prediction of songs will have a vast quantity of applications within the current era, like the Music recommendation system for request, choosing the music for public gatherings like procession or restaurants to enhance the emotional standing of a personal or cluster, seemingly folks and customers. A song is one way to say something indirectly and one of the most adventurous parts is that the one song could express more than one emotion. We can use songs to apologize, congratulate, express happiness or unhappiness, etc. Emotion is a vital part of daily life: it affects choice-making, conception, human interdependence, and human understanding. There are units of positive emotions and negative emotions;positive emotions are units a lot related to human health also as work potency, whereas negative emotions could cause health issues. In this research paper, we are going to use deep learning Frameworks, which are mostly used for classification techniques like Multilayer Perceptron (MLP), Gated Recurrent Units(GRU), Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). In this paper, the dataset operated is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)[1] for the enactment of audio categorization. As a result, we obtained graphs for each deep learning framework that is for training and validation loss, training and validation accuracy. After implementation, we got the highest accuracy for CNN that is 92.71% and the least accuracy for MLP that is 52.60% and for LSTM and GRU it lies between the range of 63% - 66%.

关键词： Emotion Classification deep learning algorithms Classification Algorithm

来源：评论

学校读者我要写书评

暂无评论

An Attention-Based deep learning Approach to Knee Injury Classification from MRI Images 26

An Attention-Based Deep Learning Approach to Knee Injury Cla...

引用

26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Nath, Kowshik Deb Rahman, A. F. M. Minhazur Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Knee injuries, prevalent in athletic and aging populations, pose significant challenges to healthcare professionals due to their complex nature and the critical function of the knee joint. Early and accurate diagnosis is paramount to ensure effective treatment and minimize long-term complications. Traditional diagnostic methods, including physical examinations and imaging techniques like MRI, require expert interpretation and can sometimes be inconclusive. This study introduces an approach to knee injury classification using deep learning techniques by leveraging convolutional neural networks (CNNs) with Attention Mechanism. This research work integrates powerful feature extraction capabilities of CNN and feature refinement of attention mechanism for the binary and multi-class classification of knee MRI images, with the aim of accurately identifying specific knee injury types. Based on our experiment on two comprehensive knee MRI datasets, our custom CNN model achieved 88% testing accuracy on Dataset-1 (Binary classification) and 77% accuracy on Dataset-2 (Multi-class classification). Meanwhile, the Attention-based CNN model achieved 100% accuracy on Dataset- 1 (Binary Classification) and 91% accuracy on Dataset-2 (MultiClass Classification). This approach not only holds promise for enhancing diagnostic accuracy but also for reducing the time to diagnosis. © 2023 IEEE.

关键词： Attention Mechanism Binary Classification Convolutional Neural Networks(CNNs) Data augmentation deep learning algorithms Knee Injuries MRI Knee Images Multi-class classification Radiologists

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of deep learning Algorithm in Face Expression Recognition

Research on the Application of Deep Learning Algorithm in Fa...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Luo, Yanling Huang, Longhui Wuhan Technology and Business University Wuhan430065 China

ISBN: (纸本)9798350308167

Recognition technology is a kind of extensive technology applied by AI technology, which mainly uses expressions and related data for face recognition. The key points in face recognition technology need intelligent algorithm ***, this paper proposes an intelligent technology of face recognition, which analyzes its expressions. First, collect expression data, then compare expressions according to expression formats and samples, and carry out the actual *** recognition can find the actual expressions of different people and make judgments based on them. The research results show that face recognition can better analyze actions and expressions, and provide technology for related *** recognition technology can be widely used. © 2023 IEEE.

关键词： deep learning algorithms facial expression recognition Image recognition technology

来源：评论

学校读者我要写书评

暂无评论

Structured Analysis of Traffic Video based on deep learning Algorithm

Structured Analysis of Traffic Video based on Deep Learning ...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Tang, Li Research Center for Wuhan University of Technology Intelligent Transportation Systems Wuhan430063 China

ISBN: (纸本)9798350308167

Peak video structure as an important basis in the process of traffic system, what important role for traffic violations, but in the process of teaching video structure analysis, there are some problems such as data fusion and poor feature recognition. Therefore, I propose an intelligent method to optimize the traffic structure, promote the fusion between different data and feature values, and make the video more easily recognized. The research results show that the algorithm proposed in this paper can make the blurred image clear, and realize the overall analysis of data, which can be supported by traffic violations and other operations, so this algorithm can meet the relevant requirements. © 2023 IEEE.

关键词： computer deep learning algorithms traffic video analysis Video structuring

来源：评论

学校读者我要写书评

暂无评论

A Critique on deep learning Methodologies Employed for the Identification of Diabetic Retinopathy Using Fundus Images

A Critique on Deep Learning Methodologies Employed for the I...

引用

2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Sivakamasundari, P. Niranjana, G. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350394580

In humans, diabetic retinopathy (DR) is a severe problem that can lead to prevent blindness. In many Computer-Aided Diagnosis (CAD) applications, early DR diagnosis is needed to assess the relevance of fundus image features for detection and classification. Based on the intricacy of the lesion characteristics, it becomes challenging to categorize the extent of DR. The test process requires an efficient identification strategy to find the retina's minor problems. Innovative algorithms for deep learning algorithm used for self-identification could potentially stop negative effects like eyesight loss. A very efficient and reliable system may be developed to help doctors autonomously diagnose DR at the beginning, despite the requirement for expert healthcare resources as a result of many advancements in AI methodologies. In order to categorize non-invasive DR, exudates, hemorrhaging, and microaneurysms, several contemporary structures constructed using DL networks are in-depthly examined in this study. This studyoffers a detailed overview of the use of DL algorithms at different phases of automated DR categorization using fundus pictures. On a number of datasets, effectiveness metrics are compared using DL techniques. This study concludes by summarizing the work that has already been completed to improve the accuracy of DL techniques. © 2023 IEEE.

关键词： Automated categorization Computer-Aided Diagnosis (CAD) deep learning algorithms Diabetic retinopathy (DR) Early diagnosis Fundus images

来源：评论

学校读者我要写书评

暂无评论

Improving Oil Spill Detection in Marine Environments Through deep learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,deepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： deep learning algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

来源：评论

学校读者我要写书评

暂无评论

Research and Application of deep learning Algorithm Based on Multi-Source Data Fusion of Power Grid and Communication Network 3

Research and Application of Deep Learning Algorithm Based on...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Li, Li Zhou, Wei Ma, Liang Jiang, Yingjie Sun, Chao Zhang, Pu Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China State Grid Shandong Electric Power Company 250001 China

ISBN: (纸本)9798350354171

Multi-source data fusion plays a very important role in improving the intelligent level of power grid operation, but there is a problem that multi-source data fusion is not ideal. The previous operation mode could not solve the problem of data fusion in the intelligent operation of the power grid, and the processing was inaccurate. Therefore, this paper proposes a deep learning algorithm for multi-source data fusion analysis. Firstly, the signal processing and estimation theory are used to integrate the power grid communication data, and the indicators are carried out according to the requirements of multi-source data fusion Partitioning to reduce interference factors in multi-source data fusion. Then, signal processing and estimation theory intelligently process power grid communication to form an intelligent processing scheme and fuse multi-source data The results were comprehensively analyzed. MATLAB simulation shows that under the condition of certain processing standards, deep learning algorithms intelligently process power grid communication accuracy and multi-source data fusion time are better than previous operating modes. © 2024 IEEE.

关键词： data fusion deep learning algorithms Improve the intelligent level of power grid operation multi-source data fusion signal processing and estimation theory

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：