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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

共 8820 条记录，以下是111-120 订阅

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ECG Signal Classification using Smoothed Pseudo Wigner-Ville Distribution 2

ECG Signal Classification using Smoothed Pseudo Wigner-Ville...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Desai, Rishikesh R. Gaikwad, Chandrakant J. Sangle, Sandeep B. Ramrao Adik Institute Of Technology D.Y. Patil Deemed To Be University Dept. Of Electronics And Telecommunication Engineering Navi Mumbai India

ISBN: (纸本)9798350381665

The electrocardiogram (ECG) contains the information required for identifying various cardiovascular diseases or abnormalities. Early identification of abnormalities will be immensely useful in the treatment of the abnormality. In this work, the ECG signals transforming into time-frequency images using the smoothed pseudo Wigner-Ville distribution (SPWVD). These images are split to train and test dataand fed to a Convolutional Neural Network (CNN) model, distinguishing between normal and abnormal heartbeats. Here, we use PTB database. The proposed method based on SPWVD images achieves an accuracy of 98.96%. The method discussed in this work is compared with the methods available in the literature. The effectiveness of the SPWVD-based method in accurately identifying abnormal heartbeats from ECG signals, suggesting its potential for clinical application. © 2024 ieee.

关键词： Electrocardiograms

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Emergency Management based Multimedia Aided Disaster Integration System 2

Emergency Management based Multimedia Aided Disaster Integra...

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2nd International conference on data science and information System, ICDSIS 2024

作者： Xu, Mingtong Changchun Institute Of Technology Changchun China

ISBN: (纸本)9798350381665

With the growth of smart device, responders of disaster and residents' community are captured as footage, pictures and disaster videos regions with wireless tablets and smart phones. The multiple media disaster situation data is difficult to assist the Emergency Management (EM) for efficiently respond in appropriate time. Recently, the information is not combined in incident command systems when reported the situation etc., are held. In this research, the Multimedia Aided Disaster Integration System (MADIS) that uses the advanced data mining methods for analyzing the situation reports, images and texts captured in region and automatically reported the links straightly for relevance content in multimedia. Particularly, the dynamic hierarchical image classification method is developed for classifying the disaster image to various subjects through integrating the text and image data. The performance of MADIS is evaluated with performance measures of accuracy, precision, recall and f1score. The average accuracy for 6 subjects is 85.4%, average precision of 84.6%, average recall of 83.6% and average f1score of 83.3% on visual-text based methods. © 2024 ieee.

关键词： Disasters

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Risk Assessment of Processing Cross Border Ecommerce Using Improved Radial Basis Function Kernel-Support Vector Machine in data Mining 2

Risk Assessment of Processing Cross Border Ecommerce Using I...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Chen, Xinyue Wang, Bing Guangdong University Of Science And Technology Dongguan China

ISBN: (纸本)9798350381665

data mining is a process of determining trends, patterns, and insights to extract significant data for decision-making which helps in better understanding and data utilization. Internet technology has been established and spreads primarily by utilizing Internet for financial management become a new kind of financial technology. Internet finance's profits and convenience, severe financial risks like loans and chaotic reputation management have appeared. To increase the cross-border e-commerce financial ability of enterprises to respond to over-financialization, the Improved Radial Basis Function kernel-Support Vector Machine (IRBF kernel-SVM) is proposed based on data mining in this research. Initially, data is obtained from a processing of cross border ecommerce enterprise with Chinese banks. The pre-processing using deleting and filling methods are utilized for missing values and outlier processing which improves the model performance. Then, the Chi-square is employed to select the relevant features and IRBF kernel-SVM is performed for risk assessment of processing cross border e-commerce. The proposed IRBF kernel-SVM achieves a better accuracy of 0.97 compared to the existing techniques like Decision Tree (DT), Random Forest (RF), and SVM respectively. © 2024 ieee.

关键词： Support vector machines

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RRCSNet:Recurrent Residual Convolutional Neural Network based Cloud Segmentation from Satellite Images 2

RRCSNet:Recurrent Residual Convolutional Neural Network base...

引用

2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Surya, S.R. Abdul Rahiman, M. University of Kerala Research scholar Lbs Centre for Science & Technology Kerala Thiruvananthapuram India Lbs Centre for Science & Technology Govt of Kerala Kerala Thiruvananthapuram India

ISBN: (纸本)9798350372892

A crucial role in remote sensing applications, particularly in satellite imagery analysis, is played by cloud segmentation. In this paper, RRCSNet, a novel Recurrent Residual Convolutional Neural Network designed for accurate and efficient cloud segmentation. The architecture combines the strengths of recurrent neural networks and residual convolutional neural networks and enhance feature representation. The model learns sequential patterns in satellite data through recurrent connections, and residual connections help train deeper networks by addressing the vanishing gradient problem. Experimental results on Landsat satellite datasets prove the higher performance of RRCSNet compared to the existing cloud segmentation methods. Compared to several other networks, this method establishes superior performance in cloud detection. © 2024 ieee.

关键词： Convolutional neural networks

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A Comprehensive Study on Advancements in Text Mining and Natural Language Processing 2

A Comprehensive Study on Advancements in Text Mining and Nat...

引用

2nd ieee International conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahire, Sarthak Balaji Saravanan, U.K. Murali, M. Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

Text mining and Natural Language Processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research. © 2024 ieee.

关键词： Adversarial machine learning

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Financial Analysis of Classifier Model Based on BP Neural Network 2

Financial Analysis of Classifier Model Based on BP Neural Ne...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Zhao, Yue Liaoning Vocational University Of Technology College Of Finance And Economics Liaoning Jinzhou China

ISBN: (纸本)9798350381665

Today's business environment is very complex, and it is important to accurately assess the company's financial situation and risks. There are some limitations to traditional financial analysis, but building a classifier model of the neural network can provide more accurate analysis results. In this paper, a classifier model based on BP neural network is used for financial analysis. By collecting a series of characteristic data related to the financial situation, the BP neural network is used for model training, and the network weights and deviations are continuously adjusted through the backpropagation algorithm to minimize prediction errors. Then use financial data and related information to predict the company's future financial situation and assess possible risks. The research results show that the accuracy rate of this model is as high as 97%. This classifier model shows high accuracy and predictive ability in financial analysis, and can provide enterprises and investors with accurate and timely financial assessment and decision-making support. © 2024 ieee.

关键词： data mining

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Interactive data science Hub with Chatbot 2

Interactive Data Science Hub with Chatbot

引用

2nd International conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Gayathri, S. Boopathy, S. Mounishvar, S. Nisanth, N. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode India

ISBN: (纸本)9798350328202

This study presents a data science Chat bot that uses Natural Language Processing techniques, namely the Natural Language Toolkit. As an intelligent conversational agent, the chat bot helps users navigate and derive insights from large, complex databases. By utilizing sophisticated natural language processing techniques, the chatbot can comprehend and analyze user inquiries, facilitating easy interaction between people and data. To understand the semantics of user inputs and provide accurate and context-aware responses, the system makes use of tokenization, part-of-speech tagging, and syntactic analysis. The chatbot also incorporates machine learning algorithms to improve its capacity to offer data-driven insights and recommendations. Using Natural Language Toolkit allows for continual development through ongoing model training in addition to robust language analysis. By making data analysis and access more approachable and engaging for users with different degrees of data science experience, our chatbot for data science seeks to democratize these processes. © 2024 ieee.

关键词： Syntactics

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Rule and Encryption based Secure Routing for Internet of Vehicle Networks 2

Rule and Encryption based Secure Routing for Internet of Veh...

引用

2nd ieee International conference on Networking and Communications, ICNWC 2024

作者： Anand, M. Muthurajkumar, S. Selvi, M. Thangaramya, K. Svn, Santhosh Kumar Kannan, A. Anna University Madras Institute of Technolog Department of Computer Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication devices and hence it helps in information exchange to vehicle drivers and the passengers. In IoV, Cybersecurity threats including Denial-of-Service (DoS) attacks are present and it compromises the security in communication among the vehicles and the passengers. Therefore, it is needed to develop a robust security mechanism that can protect the data communicated through routing in IoV. The inclusion of Encryption and Signature verification techniques in the routing algorithm will enhance the security. This paper proposes a Rule with Encryption-Oriented Secure Routing Protocol (REO-SRP) is proposed using clustering and RSA encryption with DSA Digital signature verification with rules for routing. Here, routing is performed after encryption of data and analysis of other vehicles using rules for reliable and secured data delivery. © 2024 ieee.

关键词： Vehicles

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A DNN Based Adaptive Filter for Speech Enhancement 2

A DNN Based Adaptive Filter for Speech Enhancement

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Pradeep Kumar, S. Sheela, Kancharla Anitha Kakatiya Institute Of Technology And Science Department Of Ece Warangal India Jawaharlal Nehru Technological University Department Of Ece Hyderabad India

ISBN: (纸本)9798350381665

The challenge of voice improvement in signal processing is enhancing the quality of speech signals recorded in noisy or unreliable environments. Digital communications, speech preparation for hearing aids, and recognition of speech all work better when speech augmentation is used. Enhancement of speech results in improving the transparency, ease of understanding and sound attributes of messages. Background noise is muted through speech enhancement. Numerous speech processing applications have emerged as a result of speech's practicality. In this paper, a DNN-based adaptive filter is used to improve the speech quality. DNN estimates filter coefficients. The proposed DNN-based algorithm works well. It accurately estimates filter coefficients from clamorous speech. DNNs can learn well. This enables the proposed algorithm to improve speech quality and intelligibility. © 2024 ieee.

关键词： Adaptive filters

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Research on Digital Training System of University Sports Based on SIFT Algorithm 2

Research on Digital Training System of University Sports Bas...

引用

2nd ieee International conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Jin, Jun University of Shanghai for Science and Technology Department of Physical Education Shanghai China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

In order to enhance the humanization and diversification of human-computer interaction, a digital training system for school sports was designed. Due to the large amount of data and high computational complexity of the content image classification retrieval method, an image classification method based on the SIFT algorithm is proposed and applied to sports image classification. In addition, we can also use the SIFT algorithm to find the image corresponding to the image to be tested, give full play to the advantages of the SIFT algorithm recognition technology, and use the output information to complete the control work of the practical application. After the method extracts the feature points from the image, it uses the DBS which can algorithm and the K-Mean algorithm to analyze the feature data, so as to obtain the data that best reflects the image characteristics, and then use these data to classify the image. © 2024 ieee.

关键词： Image classification

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