检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,137 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,181 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,738 篇 工学
- 2,465 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 587 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 216 篇 电气工程
- 211 篇 光学工程
- 148 篇 安全科学与工程
- 136 篇 机械工程
- 124 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 107 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,369 篇 理学
- 675 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 307 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
157 篇 教育学
- 155 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

526 篇 machine learning
243 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
75 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
58 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 neural networks

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

3,930 篇 英文
250 篇 其他
34 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4181 条记录，以下是1241-1250 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Deep learning System in Curvelet Domain for Skin Cancer Diagnosis 4

Deep Learning System in Curvelet Domain for Skin Cancer Diag...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sudha, G. Birunda, M. Gnanasoundharam, J. Alphas Jeba Singh, J. Muthayammal Engineering College Department of Biomedical Engineering Tamil Nadu Namakkal India St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Thamirabharani Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350300093

Early identification and diagnosis are essential in order to provide appropriate treatment and reduce the mortality rate due to skin cancer. However, Skin Cancer Diagnosis (SCD) may be difficult, even for experienced doctors, since it needs visual inspection of skin lesions and a high degree of knowledge to differentiate between anomalies. this makes it difficult to detect skin cancer. computer Aided Diagnosis (CAD) systems have the potential to improve the accuracy of SCD. this study proposes a Curvelet based Deep learning Approach (C-DLA) for SCD. At first, the dermoscopic images are decomposed using Curvelet transform. then the low frequency components are fed to the Convolutional Neural Network (CNN) for the classification. Results of the proposed C-DLA approach on PH2 database images shows promising results of 99% accuracy for the classification. the proposed CDLA can potentially provide more accurate diagnoses of skin lesions, as it can analyze deep features from the convolutional layers and detect patterns by dense layer that may be difficult for human observers to discern. © 2023 IEEE.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Predicting Jet Count in Proton-Proton Collisions using machine learning and Deep learning Models 8

Predicting Jet Count in Proton-Proton Collisions using Machi...

引用

8th international conference on computer Science and Engineering, UBMK 2023

作者： Sag, Canda Deniz Sahin, Onur Izmir Turk College Science İzmir Turkey

ISBN: (纸本)9798350340815

In the field of high-energy physics, analyzing and elucidating data from particle collisions at the CERN Large Hadron Collider (LHC) poses significant challenges due t o the complexity and high dimensionality of the data. Proton-proton collisions at the LHC primarily involve strong interactions in which quark degrees of freedom are involved, with electromagnetic processes characterized by small energy loss of incident protons resulting in small multiplicity of the final state. A correlation can be established between the impact parameter of the collision and the probability of strong or electromagnetic interaction. In this study, we apply various machine learning and deep learning models to predict the number of jets produced in these collisions, a key observable in many physics analyses at the LHC. Our models, which include K-Nearest Neighbors (KNN), Weighted K-Nearest Neighbors (W-KNN), Random Forest Classifier (RFC), Multi Layer Perceptron (MLP), and a custom Deep learning model (DL), are trained and evaluated on the 'CERN Proton Collision Prediction' dataset from Kaggle. Our results demon-strate the effectiveness of these models, with accuracies ranging from 81.00% to 93.92%. the RFC model outperformed all others, demonstrating the effectiveness of ensemble methods in handling high-dimensional data and reducing the risk of overfitting. these results underscore the potential of machine learning and deep learning techniques in the analysis and interpretation of complex data from particle collisions, and their ability to enhance our understanding of particle physics. Future work will focus on further optimization of the models, exploration of other models and feature engineering techniques, and application of these techniques to other problems in particle physics and beyond. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Outperforming Cryptocurrency Price Prediction Using Deep Reinforcement learning Approach 4

Outperforming Cryptocurrency Price Prediction Using Deep Rei...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： R Subba Rao, P.V. Natarajan, B. Bhuvaneswari, R. Dakshinamurthi, Vidyabharathi Oviya, I.R. Husain, Syed Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Sona College of Technologv Department of Computer Science and Engineering Salem India K. Ramakrishnan College of Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350306118

Cryptocurrency price prediction is a complex and dynamic task. Cryptocurrencies are highly volatile digital assets impacted by a wide range of variables, including investor mood, legislative changes, market demand, and technical breakthroughs. this research presents a novel approach to cryptocurrency price prediction using Deep Reinforcement learning (DRL). the proposed model incorporates proximal policy optimization (PPO) and a Hybrid Convolutional Neural Network Long Short-Term Memory(CNN-LSTM)model. the proposed model was evaluated for three benchmark datasets such as Bitcoin (BTC), Litecoin (LTC), and Ethereum (Eth). this novel approach significantly improves the prediction rate by investigating price movements and optimizing investment returns. the experimental results show the anticipated daily and weekly returns and the actual and forecasted prices using a sentiment analysis approach. the proposed model outperforms existing approaches by obtaining profits scores such as 1.04 for the LTC dataset, 0.98 Eth dataset, and 0.97 BTC dataset. these scores justify the effectiveness of the proposed model by generating favorable returns in cryptocurrency investments. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Emotion Recognition Based on Baseline-corrected Differential Entropy Features and Sparse Graph Regularized Extreme learning machine 4

Emotion Recognition Based on Baseline-corrected Differential...

引用

4th international conference on computers and Artificial Intelligence Technology, CAIT 2023

作者： Liu, Haoying Yan, Xin Guo, Yuzhu Beijing University of Technology Faculty of Information Techonology Beijing China Beihang University Sino-French Engineer School Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350326710

In recent years, with the rapid development of brain science and information science, affective Brain computer Interfaces (aBCI) have become a hot topic. EEG signals which record the electrical activity of neurons in the brain can provide direct evidence for emotional states. Compared with end-to-end deep learning methods, expert knowledge based emotion recognition methods, using hand-crafted features, are computationally cheaper and can have better generalization ability and interpretability. Due to the effort of balancing the energy spectrum of EEG signals, differential entropy features have been proved a powerful tool for emotion recognition. However, differential entropy of EEG signals still exhibits a decrease trend with increasing frequency, resulting in a low representation of the effect of high-frequency components in emotion recognition and do not perfectly solve the problem caused by the 1/f power law of EEG signals. In this paper, a decibel (dB) feature is extracted by introducing baseline normalization of EEG power spectral density (PSD) , which balances the effect of different rhythmic components better. On this basis, for the features are smoothed and a sparse graph regularized extreme learning machine (SGELM) is used to compare the dB feature with DE in emotion recognition. the proposed method is validated on DEAP data set, and the average classification accuracy increased by 11.33% over those of DE features with wild feature smoothing, and by 2.96% with high feature smoothness. the baseline normalized DE feature not only provide a new option for low-cost affective brain-computer interfaces can but also be used as an alternative input feature for deep learning models. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

18th international conference on Artificial Intelligence and Natural Language Processing and international conference on Artificial Intelligence and Internet of things, iSAI-NLP 2023

18th International Conference on Artificial Intelligence and...

引用

18th international Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2023 and 4th international conference on Artificial Intelligence and Internet of things, AIoT 2023

ISBN: (纸本)9798350371215

the proceedings contain 43 papers. the topics discussed include: solar-powered IoT charging system for ESP32 in organic rice field;STAT intelligence: data management and monitoring platform for BCG economy model;comment usefulness classification on YouTube using artificial neural networks;vehicle telematics system design for real-time applications using mobile networks;using knowledge graphs to enhance keyword search efficiency of the online encyclopedia and taxonomy system of the office of the royal society;decode brain signal into thai word using EEG and L-SVM;cloud stateless server failover prediction using machine learning on proactive system metrics;generative AI for self-healing systems;machine learning to examine the foraging periods of bees;spoof detection using voice contribution on LFCC features and ResNet-34;and Simple2In1: a simple method for fusing two sequences from different captioning systems into one sequence for a small-scale thai dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Broad Survey on Detection of Depression in Societal Platforms using machine learning Model for the Public Health Care System 4

A Broad Survey on Detection of Depression in Societal Platfo...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Lakshmi Priya, P. Vijaya Prakash, R. Cs&ai Sr University Warangal506371 India Cse Vjit Hyderabad500082 India

ISBN: (纸本)9798350300093

Anxiety and depression are on the rise, particularly since the COVID-19 epidemic, yet detection rates have not kept pace. there has been a lot of talk about people showing signs of mental health problems on social media sites like Facebook, Twitter etc. the social media anxiety and sadness detected using machine learning algorithms is considered and reviewed in this research. Soon after depression was recognized as a major public health problem around the world, efforts were made to improve its detection. the speed with which technology is developing is changing the way people talk to one another. Standardized scales that rely on patients' subjective reactions or clinical diagnoses from attending clinicians are typically used to detect depression, despite their limitations. First, the replies patients give on conventional standardized measures may be influenced by factors such as the patient's current mental state, the nature of the clinician-patient relationship, the patient's current mood, and the patient's previous experiences and memory bias. Social media platforms like Twitter, Facebook, Telegram, and Instagram have exploded in popularity as places for people to open up about their innermost thoughts, psyche, and feelings with the proliferation of the Internet. Text is analyzed using psychological analysis to pull out relevant aspects, characteristics, and information from the perspectives of users. Psychological analysts rely on social media for the early identification of depressive symptoms and patterns of behavior. A person's social network may tell us a lot about the thoughts and actions that precede the start of depression, such as the person's isolation, the importance they place on themselves, and the hours they spend awake. this research presents a brief review that attempts to synthesize the literature on the use of machine learning (ML) techniques on social media text data for the purpose of detecting depressive symptoms and to point the way toward f

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

4th International Conference on Advances in Computational Sc...

引用

4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

ISBN: (纸本)9789819729760

the proceedings contain 52 papers. the special focus in this conference is on Advances in Computational Science and Engineering. the topics include: Sensitivity Analysis on Absorber Column as Hydrogen Gas Purification Unit Using Aspen Plus;use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer;Accelerating Density-Based Spatial Clustering of applications with Noise (DBSCAN) Using Vincenty’s Inverse Method on CUDA;procedural Modeling for Sustainable Urban Development and Planning: A Blender Plugin for 3D Modeling of Philippine Cities;estimating Orangutans Population Size in Sabah Rainforest in Malaysia Using Spatial Modelling;multilayer Gaussian Feature Extraction Algorithm for Sky Image Classification;face Expression Recognition: A Survey on Hyperparameter Optimization;Enhancing Low Light Image Classification Using MADPIP Approach;a Robust Multiple Adaptive Derivative Face Recognition System on Pose and Illumination;performance Comparison of Convolutional Neural Network Deep learning Architectures for Remote Sensing Image Segmentation;revolutionizing Human–computer Interaction: Unraveling the Power of Deep learning Convolutional Neural Networks in Face Recognition;Multiple Adaptive Derivative Passive Image Processing Approach to Unsharp Masking Restoration for CNN on Low Light Images;data-Driven Insights for Strengthening Information Security Awareness in Higher Education Institutions;Determining Motivational Factors for Retention and Course Completion Among Filipino MOOC Learners: A thematic Analysis;development of Kansei-Based Visualization Pattern for E-learning Website;augmented Reality in Education: Transformative Innovations and Immersive learning Experiences;classification of Game Mechanics: A Brief Review;challenges and Issues in Team Gamer Loyalty for Massively Multiplayer Online Game;optimizing Cloud-Based Educational Services for Enhanced learning in Higher Education Institutions;biomarker Identification for Lung Canc

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reviewing the Landscape of Deep learning Approaches for Breast Cancer Detection 4

Reviewing the Landscape of Deep Learning Approaches for Brea...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Maiti, Souvik Juneja, Sonam Goyal, Reema Chaudhry, Navneet Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350306118

Rapid and precise identification of breast cancer is crucial in order to improve patient outcomes for this prevalent and life-threatening disease. the emergence of AI and deep learning techniques has opened new avenues for enhancing breast cancer screening and diagnosis. CNNs and ensemble models, among other deep learning architectures, have demonstrated promising results in the analysis of medical pictures and the extraction of useful characteristics for precise diagnosis. Additionally, the use of transfer learning and pretrained CNN models has demonstrated superior performance in classifying breast tumor images. Studies investigating the usage of one-dimensional data conversion into pictures and utilizing stacked ensemble models for increased classification accuracy show that the use of deep learning extends beyond image-based research. these techniques offer potential solutions for addressing the challenges in breast cancer detection, such as distinguishing between benign and malignant tumors and predicting prognosis. By automating the diagnosis process, deep learning algorithms can assist medical professionals in making informed decisions and providing personalized treatment plans. Enhancing the accuracy and reliability of breast cancer prediction models necessitates further research into the incorporation of larger datasets, processing multi-modal data, and advancements in image generation methods. Overall, AI and deep learning hold great promise in revolutionizing breast cancer screening and diagnosis, ultimately leading to improved survival rates and better patient care. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

proceedings of the 2023 4th international conference on Artificial Intelligence in Electronics Engineering, AIEE 2023

Proceedings of the 2023 4th International Conference on Arti...

引用

4th international conference on Artificial Intelligence in Electronics Engineering, AIEE 2023

ISBN: (纸本)9781450399517

the proceedings contain 18 papers. the topics discussed include: an improved ant colony optimization for UAV minimum time search path planning;learning visual navigation system in simulation for autonomous ground vehicles in real world;a study on the control system of double-volume tank based on Simulink emulation;a robust optimal problem for integrated fleet assignment and aircraft routing with cruise time control;intelligent operation and maintenance system of marine equipment based on PHM;research on the portable comprehensive diagnosis device applied in 20kV distribution line based on touch screen design;analysis and treatment of a 220kV transformer winding deformation due to a single-phase short-circuit fault;characteristic analysis of arc grounding fault in distribution network;insulation diagnosis technology of capacitive voltage transformer based on secondary voltage change;research on realization path of electrical automation system based on computer artificial intelligence;and risk assessment of highway safety based on artificial intelligence: a new method of highway risk assessment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Deep learning-Based Framework for Opinion Mining of Patient Reviews on Web Forums 4

A Deep Learning-Based Framework for Opinion Mining of Patien...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, Anuj Mangal, Anuj Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350300888

the number of users and daily tweets on Twitter is among the highest of all social media outlets. New forms of communication like social media greatly contribute to the rapid treatment of panic amid global health crises like pandemics. the purpose of this research is to examine and evaluate the tone of the Tweets that were posted during the MONKEYPOX disease, for that purpose Long Short-Term Memory (LSTM), CNN, and a hybrid model of CNN-LSTM is used, in this the negative and positive sentiment classes of tweets are found. the findings indicate that there is only a tiny difference between positive and negative tweets, which highlights the necessity for attention to be paid to increase awareness and, as a consequence, decrease negativity. In addition, the LSTM was able to accurately predict the sentiment classes (Negative and Positive), with an accuracy rate of 72.15%. CNN with an accuracy of 82% and hybrid model CNN-LSTM with an accuracy of 91% the great accuracy leads one to the conclusion that Hybrid model CNN-LSTM is capable of adapting to new situations and predicting the emotion of a text. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共419页 << < 121 122 123 124 125 126 127 128 129 130 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：