检索结果-内蒙古大学图书馆

2nd International Conference on Intelligent Engineering and Management, ICIEM 2021

作者： Ramya, A. Rohini, K. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India

ISBN: (纸本)9781665414500

The Data mining is a goal part of different areas looking like financing, quantifiable exploration, instructing, retail, e-business, advertising, human services and so forth. Numerous analysts have been deliberately been inspected and reviewed in social insurance, which is a functioning interdisciplinary region that is the degree of information mining. The assignment of understanding evacuation in the social insurance records is a requesting undertaking and complicated as well. The interoperability of heterogeneous clinical gadgets, security of the patient data, and customized representation of the handled information is by all accounts exceptionally fundamental. In this paper analyzed Data mining techniques with machine learning algorithms and tools. © 2021 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Studying the Fundamentals of Internet of Things (IoT) in the Healthcare Applications 1

Studying the Fundamentals of Internet of Things (IoT) in the...

引用

1st International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Kadam Rangrao, Shrikant Patil Abhijit, Seema Sharma, Lakshay Rani, D Shobha Ukirade, Chaitali Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Indraprastha Institute of Information Technology Delhi India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Obstetrics and Gynecology Karad India

ISBN: (纸本)9798350360707

The Internet of Things (IoT) has brought smart healthcare systems to the medical industry. These systems are typically made up of a network, a remote server, and sensors with smart capabilities. The primary goals of this system are to offer multidimensional feature monitoring and fundamental therapy recommendations. The primary goals of Internet of Things (IoT)-based healthcare services are to enhance quality of life and offer a great user experience at an affordable price. The primary purpose of IoT is to give elderly patients with chronic illnesses access to trustworthy, efficient, and intelligent healthcare services as well as connectivity to the medical resources that are readily available. Thus, many studies are developed on the different applications which worked on the IoT for the healthcare which includes wearable as well as non-wearable devices or machines. Thus, it makes the users to obtain the data from anywhere and wherever required for the study or analysis of the patients. In the meantime, the Internet of Things is expanding to include even the tiniest sensors and the fundamentals of automation and machine-to-machine communication. © 2024 IEEE.

关键词： healthcare internet of things (IoT) network service

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Determination of Colon Cancer Through Deep Neural Network

An Early Stage Determination of Colon Cancer Through Deep Ne...

引用

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： M. Kalaivani K. Abirami K. Dharmarajan Department of Computer Science Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai TamilNadu Department of Information Technology Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai TamilNadu

Identification and prediction of Colon cancer have always been difficult and essential tasks for medical professionals. Hospitals are providing costly therapies and operations to treat the illness. So, diagnosing colon cancer at an early stage will be helpful to patients around the world. Currently, the recent improvements in medical technologies, Deep Learning techniques exhibit a major role in predicting the development of colon cancer. In this research work, the risk factors that produce colon cancer-causing genes are selected using feature selection methods such as Fisher Score Univariate Filter (FSUF) and ReliefF Multiclass Filter (RFMF), which are applied separately and integrate the selected prominent genes to minimize the dimensionality of the data. To predict colon cancer, Deep Neural Network (DNN) classifier is utilized on the reduced data and computes the classifier's performance using evaluation metrics. Finally, the outcomes of experiments are compared with two machine learning algorithms, like Independence Bayes(IB) classification model and the k-nearest neighbors(k-NN) classifier. The experimental outcomes demonstrate that the research model significantly reduces the dimension of data space and produced an accurate prediction of colon cancer when compared with other classifiers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction performance of crime against women using rule based decision tree J48 classification algorithms in various states of India 2

Prediction performance of crime against women using rule bas...

引用

2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Lavanya, S. Akila, D. Vels Institute of Science Technology Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9781728194912

Crimes against women be situated collective societal difficult distressing eminence of lifecycle in addition monetary development of a civilization. Through expanding violations, rule implementations organizations remain proceeding towards request propelled frameworks and new ways to deal with improving wrongdoing investigation and better ensure their networks. Rule Based Decision tree (RBDT J48) and Naive Bayesian algorithm functional happening framework of commandment implementation then cleverness investigation embraces capacity of improving problems. Information Mining stands method in the direction of citation information available, frequently huge record sets;the widest collection of machine learning solicitations consumes prepared this one significant field of exploration. Crime type stands progression intentions towards classify law-breaking individualities. Exploration measured improvement in wrongdoing expectation model utilizing Rule Based Decision Tree (RBDT J48) calculation and Naive bayes since it has been considered as the most effective AI calculation for a forecast of wrongdoing information. And furthermore think about the two distinctive grouping calculations to be specific, Naive bayes and Rule Based Decision Tree J48 for anticipating wrongdoing type for various states in India. © 2021 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Evaluating importance of AI in Robotics and Automation in Healthcare for Different age Groups 1

Evaluating importance of AI in Robotics and Automation in He...

引用

1st International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Pethkar, Rajendra Huda, Bintul Patil, Jaykar Bhaskar Gatla, Ranjith Kumar Kumbhar, Ramchandra Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Mechanical Engineering Karad India Sharda University Department of Computer Science & Technology Srinagar India Jaywant Institute of Management Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Radio-Diagnosis Karad India

ISBN: (纸本)9798350360707

Nowadays robotics is mostly used in the world. There are many industries which used robots for reducing time and to have high precision of work. The use of artificial intelligence is now increasing which helps in automations and processing of machines. Thus, the focus of study is to find the different applications of automation of robotics (i.e. robotic process automation) in the field of healthcare and its impact on the employment of medical field. Four major categories can be used to group AI applications in healthcare: expressive, analytical, prognostic, and prescriptive. The only way to close the skills gap in the healthcare industry is to increase the application of artificial intelligence. Hospitals are developing mental commit robots to treat their patients. Through physical contact, these robots can have an impact on humans' psyche, physiology, and social interactions. Thus, in coming time most of the healthcare industry will be with the robots which will reduces the human interventions with the patients through one of the host servers. © 2024 IEEE.

关键词： Medical robotics

来源：评论

学校读者我要写书评

暂无评论

User Behaviour-Based Accurate Detection of Credit Card Frauds 1st

User Behaviour-Based Accurate Detection of Credit Card Fraud...

引用

1st International Conference on Mathematical Modeling and Computational science, ICMMCS 2020

作者： Sudha, C. Akila, D. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9789813343887

The arrival of communication methods, along with online imbursement dealings, is growing every day. Together with this, fiscal scams linked with these dealings are also escalating. Amid several fiscal scams, credit card scam is the utmost common and hazardous one owing to its extensive practice. To perceive these illegal bases of communications, a credit card scam recognition structure is obligatory. In this paper, we propose to design a user behaviour-based accurate detection for credit card frauds. In this technique, user behaviours over a banking website are collected and analysed. The main features related to user behaviour are frequently visited pages, time spent on each page, clicking, etc. From these details, the page reachability and page utility are determined. From this, the normal and abnormal behaviours of users are classified using web Markov skeleton process (WMSP) model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavioral research

来源：评论

学校读者我要写书评

暂无评论

Robust and Rapid Fabric Defect Detection Using EGNet 4

Robust and Rapid Fabric Defect Detection Using EGNet

引用

4th International Conference on computing and Communications Technologies, ICCCT 2021

作者： Sudha, K.K. Sujatha, P. VELS Institute of Science Technology Advanced Studies Department of Computer Science Pallavaram India VELS Institute of Science Technology Advanced Studies Department of Information Technology Pallavaram India

ISBN: (纸本)9781665414470

The quality of the fabric item is considered as one of the most significant concentrations in the textile industry. Convolutional Neural Network has shown commendable execution of fabric defect identification with computer vision and image handling. In this paper, we have implemented a Reformed Convolution Neural Network architecture known as 'EGNet' for fabric defect detection. The model has trained on the Cotton Incorporated dataset with 70% data as training and 30% as validation dataset. The model consists of 22 layers of Convolutional layer and Pooling Layer one after the other. The recognition of fabric faults using EGNet is executed utilizing load image dataset, load EGNet, replace final layers, network training, classify validation images. The EGNet is optimized using stochastic gradient descent with momentum. Data augmentation and max-pooling techniques are used to reduce the network's overfitting issue. To infer the significance of EGNet, comparative analysis is done with AlexNet and the result shows that EGNet architecture exposes the fabric defects in 23 seconds of elapsed time and with 100 percent of accuracy. © 2021 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science

Quantum Machine Learning Techniques based on Nurturing Agri-...

引用

Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： R Deepa V Jayalakshmi P Thilakavathy A Bennet Prabhu R Surendran Department of Computing Technologies School of Computing SRM institute of science and technology Chennai India Department of Computer Applications DRBCCC Hindu College Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India KONE Elevator India Technology & Engineering Centre Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards.

关键词： Technological innovation Systematics Standards organizations Machine learning Organizations Ontologies Stakeholders

来源：评论

学校读者我要写书评

暂无评论

Fidelity and diversity metrics for validating hierarchical synthetic data: Application to residential energy demand

Fidelity and diversity metrics for validating hierarchical s...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Thorve, Swapna Vullikanti, Anil Mortveit, Henning S Swarup, Samarth Marathe, Madhav V. Department of Computer Science Biocomplexity Institute University of Virginia United States Department of Engineering Systems and Environment Biocomplexity Institute University of Virginia United States Network Systems Science and Advanced Computing Biocomplexity Institute University of Virginia United States

ISBN: (纸本)9781665480451

Synthetic data is gaining rapid importance in many application domains due to privacy issues, bias, lack, or simply unavailability of real data. It is important that the synthetic data be a good representation of real data for successfully completing the task at hand. Thus, devising characteristic validation metrics is crucial and remains an open problem in many domains (e.g., image generation). Good validation metrics must be able to disentangle the differences between the quality and the variability coverage of the synthetic data. We propose to use a 3-dimensional metric (precision α, recall β, coverage γ) to describe the fidelity and diversity of the synthetic data. In this paper, we improve on existing definitions of precision, recall, and coverage to extend to large scale time series data. Traditional nearest neighbor manifolds from the literature are replaced by unsupervised learning techniques such as clustering to deal with large scale fine resolution time series while computing the validation metrics. The proposed metrics are employed to validate synthetic data in the domain of residential energy demand. In addition, we extend these definitions to datasets that have a natural hierarchical structure. We propose a hierarchical data-tree model in which precision, recall, and coverage can be computed at multiple inherent (and/or custom) levels of groupings of the data. © 2022 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Granger causality for compressively sensed sparse signals

引用

Physical Review E 2023年第3期107卷 034308-034308页

作者： Aditi Kathpalia Nithin Nagaraj Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague 18200 Czech Republic Consciousness Studies Programme National Institute of Advanced Studies Bengaluru 560012 India

Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted, and stored using far fewer measurements than done by conventional means employing the Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a number of applied physics and engineering applications, particularly in designing signal and image acquisition strategies, e.g., magnetic resonance imaging, quantum state tomography, scanning tunneling microscopy, and analog to digital conversion technologies. Contemporaneously, causal inference has become an important tool for the analysis and understanding of processes and their interactions in many disciplines of science, especially those dealing with complex systems. Direct causal analysis for compressively sensed data is required to avoid the task of reconstructing the compressed data. Also, for some sparse signals, such as for sparse temporal data, it may be difficult to discover causal relations directly using available data-driven or model-free causality estimation techniques. In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger causality (GC). We then verify this theorem on a number of bivariate and multivariate coupled sparse signal simulations which are compressed using these matrices. We also demonstrate a real world application of network causal connectivity estimation from sparse neural spike train recordings from rat prefrontal cortex. In addition to demonstrating the effectiveness of structured matrices for GC estimation from sparse signals, we also show a computational time advantage of the proposed strategy for causal inference from compressed signals of both sparse and regular autoregressive processes as compared to standard GC estimation from original signals.

关键词： Dynamics of networks Neuronal dynamics Directed networks Time series analysis

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：