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

6,378 篇 会议
161 册 图书
46 篇 期刊文献

馆藏范围

6,585 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,749 篇 工学
- 4,176 篇 计算机科学与技术...
- 3,369 篇 软件工程
- 1,157 篇 信息与通信工程
- 762 篇 生物工程
- 563 篇 生物医学工程（可授...
- 497 篇 控制科学与工程
- 420 篇 电气工程
- 326 篇 动力工程及工程热...
- 267 篇 机械工程
- 254 篇 光学工程
- 251 篇 电子科学与技术（可...
- 240 篇 安全科学与工程
- 219 篇 化学工程与技术
- 214 篇 环境科学与工程（可...
2,076 篇 理学
- 942 篇 数学
- 830 篇 生物学
- 413 篇 物理学
- 399 篇 统计学（可授理学、...
- 278 篇 系统科学
- 222 篇 化学
1,415 篇 管理学
- 784 篇 管理科学与工程(可...
- 774 篇 图书情报与档案管...
- 514 篇 工商管理
615 篇 医学
- 580 篇 临床医学
- 555 篇 基础医学(可授医学...
- 331 篇 药学(可授医学、理...
- 302 篇 公共卫生与预防医...
359 篇 法学
- 266 篇 社会学
208 篇 农学
- 199 篇 作物学
197 篇 经济学
- 196 篇 应用经济学
132 篇 教育学
23 篇 军事学
13 篇 文学

主题

278 篇 internet of thin...
262 篇 machine learning
253 篇 cloud computing
230 篇 big data
219 篇 computational mo...
207 篇 deep learning
182 篇 data mining
157 篇 data models
137 篇 predictive model...
135 篇 artificial intel...
133 篇 training
130 篇 feature extracti...
122 篇 real-time system...
119 篇 blockchain
108 篇 support vector m...
107 篇 accuracy
107 篇 machine learning...
102 篇 convolutional ne...
100 篇 forecasting
96 篇 medical services

机构

54 篇 graphic era deem...
44 篇 chitkara univers...
24 篇 department of co...
24 篇 department of co...
21 篇 department of en...
19 篇 school of comput...
19 篇 teerthanker maha...
18 篇 school of inform...
17 篇 jaipur national ...
16 篇 university of ch...
16 篇 uttaranchal inst...
15 篇 fujian provincia...
14 篇 srm institute of...
14 篇 department of co...
14 篇 uttaranchal inst...
13 篇 computer science...
13 篇 school of comput...
11 篇 uttaranchal inst...
10 篇 college of compu...
10 篇 school of manage...

作者

21 篇 pant bhasker
20 篇 chang kuo-chi
13 篇 tripathi vikas
12 篇 kukreja vinay
12 篇 vinay kukreja
11 篇 jacob lija
11 篇 joshi kapil
11 篇 gangodkar durgap...
10 篇 iwendi celestine
10 篇 lourens melanie
10 篇 chu kai-chun
10 篇 sujithra m.
10 篇 alanya-beltran j...
9 篇 rajeswari d.
9 篇 chang fu-hsiang
9 篇 singh rajesh
8 篇 kapil joshi
8 篇 kapila dhiraj
8 篇 pavithra g.
8 篇 sanjay misra

语言

6,395 篇 英文
184 篇 其他
15 篇 中文

检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6585 条记录，以下是181-190 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：

Improving data Encoding for Enhanced AI Performance in Complex datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

引用

Pervasive Computational technologies (ICPCT), international conference on

作者： Gurpreet Singh Walia Sathiya Priya S KV Karthikeya D. Suresh P Sudheer V. Syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (AI) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling Space mapping Quantum state Encoding Hardware Quantum circuit

来源：评论

学校读者我要写书评

暂无评论

Integrating Artificial Intelligence in Supply Chain: Enhancements and Challenges

Integrating Artificial Intelligence in Supply Chain: Enhance...

引用

Innovative Research in Applied science, Engineering and Technology (IRASET),conference on

作者： Oussama Zabraoui Anas Chafi Salaheddine Kammouri Alami Department of Industrial Engineering Sidi Mohammed Ben Abdellah University Faculty of Science and Technology Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The integration of Artificial Intelligence (AI) into supply chain enhances processes like logistics, procurement, and decision-making. AI technologies such as machine learning and predictive analytics improve demand forecasting, inventory management, and supply chain optimization. Benefits include real-time decision-making, automation, cost reduction, and risk mitigation. However, challenges like data silos and workface readiness persist. advances in cloud computing and loT are accelerating AI adoption, leading to more agile and resilient supply chains. Continuous investment in AI adoption, leading to more agile and resilient supply chains. Continuous investment in AI and skill development is essential for maintaining a competitive edge.

关键词： Procurement Costs Automation Supply chains Decision making Machine learning Real-time systems Predictive analytics Investment Risk mitigation

来源：评论

学校读者我要写书评

暂无评论

Telemedicine and Remote Patient Monitoring: Enhancing Access to Healthcare Through IoT and AI technologies

Telemedicine and Remote Patient Monitoring: Enhancing Access...

引用

advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Sachin Ramchandra Misal Lavanya Mohan Suresh Kumar Sundararajan Balakrishna Bathala Saheli Majumdar C. Sivakumar International Institute of Management Science Pune Maharashtra India Department of Psychiatry Azeezia Medical College Hospital Kollam Kerala India Department of Medical Oncology Kalaignar Centenary Super Speciality Hospital Institution Chennai Tamil Nadu India Cardiovascular and Pulmonary Department of physiotherapy School of Paramedical Allied and Healthcare Sciences Mohan Babu University Tirupati Andhra Pradesh India Department of Hospital and Health Management Institute of Management study Kolkata West Bengal India Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

A significant shift in contemporary medical treatment is represented by combining Artificial Intelligence (AI) and the Internet of Things (IoT) into telemedicine, virtual medical treatments, and remote patient monitoring. An increasing elderly demographic, the rise of chronic illnesses, and unequal access to healthcare treatment are driving up the need for worldwide medical structures, and AI is emerging as a key instrument in tackling these urgent problems. This investigation utilized a smart monitoring gadget with an IoT emphasis to monitor the heart rate of elderly people. The device tracks patients' GPS statistics in real-time and informs healthcare professionals of any confinement violations for any emergency. The smart sensor connects to a networking interface in the IoT cloud, which processes and analyzes data to determine bodily functioning. The suggested architecture has 3 levels of functionality: a cloud tier with Application Peripherals Interfaces for mobile gadgets, smart IoT detectors, and smartphone applications. Each layer has a particular role. Statistics from the IoT perception tier are initially gathered to detect heart attacks. This layer stores data in a cloud server for preventative measures, alerts, and speedy actions. The smartphone program alerts the patient and the family during emergencies. This study proposes CNN-UUGRU, a new deep neural network architecture that combines convolutional and upgraded gated recurrent segments for identifying human actions. This system outperformed other deep neural networks on the Kaggle database, accomplishing 97.8% accuracy, 96.9% precision, and an F-value of 97.85%.

关键词： Temperature measurement Temperature sensors Heart rate Patient monitoring Cloud computing Telemedicine Medical treatment Internet of Things Artificial intelligence Older adults

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge computing and Real-Time Decision-Making in Uncertain IoT Environments

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

引用

advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： B.P. Joshi Akhilesh Singh Nagendra Kumar Siddharth Rautela Department of Mathematics Department of Computer Science & Engineering Graphic Era Hill University India Department of Electrical Engineering VMSB UTU Campus Pithoragarh India Department of Electrical & Electronics Engineering GL Bajaj Institute of Technology & Management Greater Noida India Department of Mathematics Central Board of Secondary Education Bhimtal Uttarakhand India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff.

关键词： Schedules Uncertainty Q-learning Decision making Deep reinforcement learning Real-time systems Delays Automobiles Standards Edge computing

来源：评论

学校读者我要写书评

暂无评论

Smart Sensors and IoT in Mechanical Engineering: Enhancing Monitoring and Control of Industrial Processes

Smart Sensors and IoT in Mechanical Engineering: Enhancing M...

引用

advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Babu J Annavarapu N G Hareesha Goutham Kacheru Ashif Mohammad Jacky Chin Gauri Ghule Department of Management KLEF Deemed to be University Guntur Andhra Pradesh India Department of Aeronautical Engineering Dayananda Sagar College of Engineering Bengaluru India Department of IST Wilmington University United States Department of Institute of Energy University of Dhaka Bangladesh Department of Industrial Engineering Mercu Buana University West Jakarta Indonesia Department of E and TC VIIT Pune India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Unmanned aerial vehicles (UAVs) are currently regarded as among the most effective remote sensing methods for collecting data across wide regions. Along with measuring output and aiding in decision-making, they are now employed in the industrial domain as sensing equipment for proactively resolving or avoiding numerous problems. UAVs offer an economical, functional, and reliable technical system for event monitoring and data collecting. Cloud computation has proven to be an essential instrument for addressing processing needs, and the Internet of Things (IoT) transmits industrial data from platforms that monitor and manage surroundings to data processing platforms. The IoTs port connects many items to the internet in fog computation. It supports many communication standards and can function as a joint portal for various systems. Much work has gone into creating UAVs and platforms with several UAVs. This work presents a smart IoT monitoring and control platform depending on a UAV. It utilizes fog computation as an interface between IoT tiers and cloud computation activities. Its ability to seamlessly connect UAVs to industrial control platforms via IoT port platforms and process and analyze UAV shots in real-time and in a structured manner in the cloud is what makes it distinctive. The outcomes show unequivocally that the suggested structure is feasible in offering a dependable and effective UAV distant control platform to enhance product grade and minimize waste. Hence this study investigated the communication delay in several IoT ports between the various IoT levels.

关键词： Cloud computing Process control Autonomous aerial vehicles Real-time systems Delays Internet of Things Monitoring Portals Intelligent sensors Drones

来源：评论

学校读者我要写书评

暂无评论

Hybrid CNN-LSTM Approach for Predicting and Analyzing Customer Churn in the Banking Sector

Hybrid CNN-LSTM Approach for Predicting and Analyzing Custom...

引用

advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Amit Kaushik Rahul Gupta Ajay Singh Hassan Safi Ahmed Vuda Sreenivasa Rao B. Anitha Vijayalakshmi Amity University (Working In Amity School of Insurance Banking & Actuarial Science) Noida India Amity University Noida India ABES Business School Ghaziabad India Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Customer attrition therefore becomes a major issue in the context of highly competitively, provider specific analysis in banking sector that affects the organizational profitability and its sustainable growth. Identifying those customers most likely to churn is strategic important for the establishment of timely customer retention strategies among banks. This research seeks to reduce customer attrition in the banking framework using a CNN-LSTM composite model. It uses CNN which, for its part, helps extract or learn features while using the LSTM networks for learning the temporal patterns because it also incorporates time and behavioral patterns in the customer data. Further, customer segmentation is applied in order to increase the precision of churn predictions made based on the different levels of tension found in specific customer segments. This hybrid architecture allows for a more comprehensive analytical approach to the customer data with the ability to identify various types of behavioral patterns and temporal changes that may impact churn. For churn prediction, the proposed framework incorporates a CNN-LSTM model. CNN layers work on temporal customer transaction data extracting crucial features; LSTM layers maintain long-term dependencies. The framework also applies segmentation analysis based on cluster analysis to categorize customers with regard to behaviour, age, and product consumption patterns. The model is developed in Python and tested using a real-word data involving customer data. Using churn-forecasting metric, the hybrid CNN-LSTM model achieved accuracy and robustness with an accuracy of 98.5%, precision of 97%, recall score of 95% and an F1-score of 97%. This performance enhanced the capability to capture spatial patterns and temporal dependencies of customer behaviour beyond the traditional models.

关键词： Accuracy Profitability Computational modeling Banking Predictive models Feature extraction Robustness data mining Churn Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Motion Planning in Automated Vehicles Based on Hierarchical MPC

Motion Planning in Automated Vehicles Based on Hierarchical ...

引用

advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Pooja Kapila Pankaj Kumar Rahul Negi Bhupender Kumar Bharti Akash Punhani Suraj Singh Bisht Department of Artifical Intelligence & Data Science IIMT College of Engineering Department of Statistics Amity University Kolkata India Department of ECE Tula's Institute Dehradun India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India Department of Computer Science KIET Group of Institutions Noida India Department of ECE Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors.

关键词： Analytical models Autonomous systems Computational modeling Decision making Turning Planning Trajectory Safety Vehicle dynamics Robots

来源：评论

学校读者我要写书评

暂无评论

Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network

Development of an Arabic Sign Language Recognition System Ut...

引用

advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Meenal R Kale Chamandeep Kaur Indrajeet Kumar Haider Sharif Mahdi Vijay Uprikar Sankari V Department of Humanities Yeshwantrao Chavan College of Engineering Nagpur India Department of Computer Science & Information Technology Jazan University Jizan Kingdom of Saudi Arabia Department of CSE Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Datta Meghe Institute of Management Studies Nagpur India Department of AI&DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. This study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. The suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. The results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. The findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python.

关键词： Training Representation learning Sign language Accuracy Computer architecture Oral communication Feature extraction Real-time systems data models Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Tax Revenue Forecasting Using CapsNet and CNN In Cryptocurrency-Driven Economics

Enhancing Tax Revenue Forecasting Using CapsNet and CNN In C...

引用

advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Richa Mehta Raghuveer Katragadda Atluri Sree Ram P. Pirakatheeswari Bakkala Santha Kumar Bhavani P Department of Commerce St. Francis College for Women Hyderabad India School of Management V.R. Siddhartha Engineering College (Deemed to be University) Kanuru A.P India Department of Commerce Sri Ramakrishna College of Arts & Science (Autonomous) Coimbatore India Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Dept of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The modern concept of taxation has been accelerated by the emergence of internet-based economy and the use of cryptocurrencies. This shift raises various difficulties for tax authorities in terms of revenue estimates; it requires sophisticated methods for quantitative analysis of intricate economic trends. Previous works have mainly employed conventional ML methods which are weak in their ability to recognize dependencies in the features for data with high dimensionality, hence poor forecast precision. To counter these drawbacks, the use of CapsNets with CNNs, which forms a combined model contributing to better predictive capability is introduced. CapsNets ability to keep ownership of spatial hierarchies and sophisticated features of the input makes the method to accurately extract and analyze features from digital transaction data. Working in Python, the suggested model was compared to several traditional algorithms with which it demonstrated the highest accuracy rate at 99.1%. As concluded from the evaluation, the CapsNet-CNN model is not only resistant to the formulation of new structures, but also flexible to the fluidity of the digital economy environment. This work demonstates the promise of using modern DL methodologies for improving the accuracy of the tax revenues and provides insights for the policy makers who are keen on evoking sensitive and dynamic response strategies of the taxation authorities in a world where more and more of the economic operations are being carried out in the cyberspace. Future research will quantify the components in the model and expand the investigation to other industries and locations.

关键词： Resistance Industries Accuracy Statistical analysis Finance Predictive models Feature extraction Prediction algorithms Market research Information age

来源：评论

学校读者我要写书评

暂无评论

Real-Time Anomaly Detection in Industrial Automation Using Generative Adversarial Networks

Real-Time Anomaly Detection in Industrial Automation Using G...

引用

advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Ravindra Changala Shamim Ahmad Khan Vijay Kumar V. Kanpur Rani K. Dhilli I Infant Raj Department of CSE Guru Nanak Institutions Technical Campus Hyderabad India Department of ECE Glocal School of Science & Technology Glocal University Uttar Pradesh India Department of Physics Graphic Era Hill University Dehardun Graphic Era Deemed to be University Dehradun India Department of EEE Hindustan Institute of Technology and Science Chennai India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam India Department of CSE K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The study presents the use of GANs in anomaly detection for real-time industrial automation. A new model is proposed and implemented in Python, taking into consideration the dual nature of the algorithm known as GANs, which work on a dual framework including a generator and a discriminator. The normal behavior of industrial automation must be generated by a generator; meanwhile, the discriminator distinguishes between normal and anomalous data. It would facilitate the effective identification of anomalies, through adversarial training, based on learning normal operational patterns in real time, with the advantage of being adaptable in very dynamic and complex industrial environments. The ability to be timely about intervention, given its real-time capability, significantly enhances such attributes as predictive maintenance, improved quality control, and safe and efficient operations. This has been found in the study that the right identification of anomalies will prevent expensive downtime and will be a sign of smooth industrial process. In the proposed GAN model, 99.12% accuracy is achieved compared with other approaches, such as DBN, AnoGAN, and BiGAN, with an impressive gap of 4.05%. Thus, superior performance indicates that GANs are robust and reliable for dealing with real-time industrial automation challenges. A critical characteristic of this Python implementation model is its adaptability, flexibility, and amenability to a practitioner in the industry as much as a researcher. Overall, this study further provides significant additions to the overall understanding of anomaly detection in an industrial setting with its demonstration capabilities toward the resiliency enhancement and efficiency potential in modern systems of industry through GAN applications.

关键词： Industries Training Adaptation models Automation Generative adversarial networks Real-time systems Generators Anomaly detection Predictive maintenance Resilience

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500页 << < 15 16 17 18 19 20 21 22 23 24 > >>

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：