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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6957 条记录，以下是961-970 订阅

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ViT-based Classification of Oracle Bones for Periodization of the Shang Dynasty

ViT-based Classification of Oracle Bones for Periodization o...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Bang Ding, Zengmao Zheng, Rongxin Han, Shengwei Zhang, Han Zhang, Zhan Guo, An Wang, Nan Gao, Feng Liu, Yongge School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Henan Anyang455000 China International Joint Research Laboratory for Perception Data Intelligent Processing of Henan Anyang Normal University Henan Anyang455000 China

ISBN: (纸本)9781510671881

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks. © 2023 SPIE. All rights reserved.

关键词： Domain Knowledge

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Enterprise Financial Information Management System Based on data Analysis Algorithm 2

Enterprise Financial Information Management System Based on ...

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2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhou, Xiaohua Gingko College of Hospitality Management Department of Financial Management Sichuan Chengdu China

ISBN: (纸本)9798350347456

How financial management and accounting can be more closely integrated with all aspects of enterprise production and operation, and how to use modern information management tools to make financial information more accurate, fast and smoothly delivered to the relevant decision-making level and to avoid financial risks, have become new issues that need to be addressed. The purpose of this paper is to study the enterprise financial information management system based on data analysis algorithms. SQL server 2008 and efficient and simple JSP technology were used to implement the design of the functional modules. During the design of this paper, the BP neural network algorithm is analysed to evaluate and analyse the asset management in the financial management process, which effectively realises the intelligence of the financial management system. © 2023 IEEE.

关键词： Informatization

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Probabilistic data Association Performance during Launch Vehicle Tracking 3

Probabilistic Data Association Performance during Launch Veh...

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3rd international conference on Range Technology, ICORT 2023

作者： Ravindra, Challa Rajkumar, S. Vaidhyanathan, A. SDSC-SHAR ISRO Sriharikota India

ISBN: (纸本)9781665493314

Probabilistic data Association (PDA) is Bayesian approach of deriving likelihood function for all measurements reported by radar. Tracking of Launch Vehicle with skin mode radar involves processing of multiple returns from rocket, separated stages and rocket plumes, which makes association of track measurements more uncertain. Using PDA we bring down the uncertainty of measurements by computing posterior probability for all measurements reported by radar during real time. Measurement with highest posterior probability is selected and given as input to processing filter. In this paper we analyze Probabilistic data Association (PDA) performance during tracking of Launch Vehicle by Multi Object Tracking Radar. © 2023 IEEE.

关键词： Launch vehicles

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The Development of Machine Learning innovation technology for data Mining In Smart Healthcare 2

The Development of Machine Learning innovation technology fo...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Manikandan, V. Sharma, Sonal Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The field of Smart Healthcare has undergone a revolution due to the development of machine learning technologies. Artificial intelligence (AI) has a subfield called machine learning that allows computers to learn from data and develop over time without explicit programming. Healthcare workers can now discover trends, diagnose diseases, and make decisions based on more precise and timely data thanks to machine learning's capacity to comprehend and analyze enormous amounts of data. Finding patterns and links in massive datasets is known as data mining. data mining is used in Smart Healthcare to locate patterns in patient data that can be utilized to comprehend a patient's health status better, diagnose specific disorders, and forecast the most effective course of therapy. data mining can create novel cures and treatments customized to a person's genetic profile. data mining in Smart Healthcare is now considerably more effective and efficient, thanks to machine learning. Using complex algorithms, machine learning models can analyze vast amounts of data markedly faster and more correctly than conventional methods. Healthcare experts may not immediately notice hidden patterns and connections in data that machine learning algorithms can find. As a result, machine learning can assist healthcare practitioners in making decisions that are better informed, improving patient outcomes. © 2023 IEEE.

关键词： Diagnosis

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Web User data Analysis in Behavioural Artifacts Detection Using Ensemble Attribute Ratio Rule Algorithm with Machine Learning Architectures

Web User Data Analysis in Behavioural Artifacts Detection Us...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Gowri, M. Sasikala, T. Sathyabama Institute of Science and Technology India

ISBN: (纸本)9798350333244

Malware is intended to harm computers or networks, and it frequently entails engaging in unlawful or forbidden activity that can be utilised for espionage or financial gain. Malware assaults are now beginning to affect embedded computational platforms, including Internet of Things (IoT) devices, medical equipment, and environmental and industrial control systems. This research propose novel technique in web user data analysis for behavioural artifacts detection using machine learning (ML) architectures. Here web user access data has been collected and processed for noise removal and smoothening. Then this data feature is trained and selected for detection of malware activities using attribute ratio rule based auto encoder training. Then the selected data is classified using ensemble of spatio temporal Q-learning architectures. the experimental analysis is done for various dataset in terms of accuracy, F-measure, detection time, mean average precision, processing time, specificity. Proposed technique attained accuracy of 97%, F-measure of 86%, detection time of 75%, mean average precision of 63%, processing time of 77%, specificity of 85%. © 2023 IEEE.

关键词： Internet of things

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Measuring the Performance of Clustered Image data Sets using Image Proprietary 7

Measuring the Performance of Clustered Image Data Sets using...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Saravanan, D. Hyderabad India Hyderabad India

ISBN: (纸本)9781665464086

Image extraction from complicated sources of information is currently a speedily developing domain. Storing and extracting the image data is a puzzling assignment for several investigators because of the complication of the static picture information groups. The related content in this image data set can be extracted using various existing techniques. In the proposed method, image data sets are grouped and create ranking among the group. Based on the user input, high-ranking groups are retrievedand sent to the user. Here, image retrieval is done based on the image's optical structures. The resulting technology can be deployed in information systems and many other areas depending on the needs of the applications. The proposed method works fine with all kinds of response and retrieval processing time compared to the current methods. © 2023 IEEE.

关键词： data mining

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Imbalanced data classification method based on an improved fruit fly optimization algorithm and Adaboost 9

Imbalanced data classification method based on an improved f...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Li, JinLong Bao, LiQun Zhang, WeiZhao College of Electronic Information Engineering Lanzhou Institute of Technology Lanzhou China College of Physics and Electronic Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9798350385991

The application of conventional classification algorithm in imbalanced data classification processing has the problem of low classification accuracy for minority samples. Therefore, this study proposes an imbalanced data classification method based on the fruit fly optimization algorithm (FOA) and the AdaBoost algorithm. Firstly, an improved FOA based on dynamically adjusting the optimization step was proposed. Then, utilizing the excellent search performance of the improved FOA, the misclassification cost of samples in an imbalanced dataset is optimized. Finally, the obtained misclassification cost of samples will be combined with the AdaBoost algorithm to improve the classification performance of the model. The experimental results based on the KEEL dataset show that the proposed method can achieve good classification performance in imbalanced data classification problem. © 2024 IEEE.

关键词： data accuracy

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Dynamic Representative data Management of Multiple Time Series for Edge computing

Dynamic Representative Data Management of Multiple Time Seri...

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IEEE international conference on Communications (IEEE ICC)

作者： Oikawa, Hiroki Ge, Hangli Koshizuka, Noboru Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo Japan

ISBN: (纸本)9781538674628

With applications demanding increased amount of data, edge computing, where data are processed on the edge, is gaining popularity. Edge devices can store a limited amount of data and need to extract important data from the collected data. On-line interpolation-based data management (O-IDM) is an efficient method for managing time series data on an edge. O-IDM extracts and stores data to represent its overall shape. However, using this method becomes computationally expensive when the amount of the retained data is large. In this paper, we propose fast-O-IDM (FO-IDM), which can perform the same data extraction as O-IDM at a lower computational cost. Furthermore, we propose dynamic representative data management (D-RDM), which is a multiple time series management method that applies FO-IDM. D-RDM can efficiently hold the characteristics of multiple time series by dynamically controlling the capacity based on the data features. The extraction accuracy and computational costs of D-RDM are evaluated via comparison with other management methods. The experimental results confirmed that D-RDM could extract the data more accurately than the other methods, reducing the data amount required to convey the feature of the data by up to 25.0 percentage points compared to naive O-IDM. In addition, when the number of time series and the amount of the retained data were 3 and 30,000 respectively, the processing time of D-RDM was reduced by 91.2 percentage point compared to O-IDM, indicating that the computational cost of D-RDM is sufficiently small for use in edge devices.

关键词： edge computing data management

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Health Insurance Recommendation using Probabilistic data Structures 14

Health Insurance Recommendation using Probabilistic Data Str...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Chauhan, Arjit Singh, Amritpal Dr. B.R. Ambedkar National Institute of Technology Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9798350335095

Recommender systems are constantly evolving in the health insurance industry, with a focus on speed and efficiency. To meet the demands of clients, insurance providers need to generate personalized policy recommendations that are quick and accurate. This paper suggests a health insurance recommendation system that combines advanced algorithms to offer efficient and personalized recommendations based on user preferences and similarities. The proposed system leverages the MinHash and LSHForest algorithms, as well as collaborative filtering, to improve the recommendation process. MinHash approximates the Jaccard similarity between policies, while LSHForest enables faster search for similar policies. Collaborative filtering considers user preferences and similarities among policies and users. To ensure user input is secure and legitimate, the system employs a Bloom filter. This filters out unauthorized access to sensitive healthcare data and validates user input. The architecture of the system addresses various challenges related to personalization and privacy in health insurance recommendation systems. Based on the results, the proposed system surpasses existing approaches in accuracy, efficiency, and privacy preservation. This has the potential to be advantageous for both insurance providers and users as it helps streamline the process of choosing appropriate health insurance plans that align with individual requirements and preferences. © 2023 IEEE.

关键词： data structures

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Lung Cancer Detection using Machine Learning Approach 22

Lung Cancer Detection using Machine Learning Approach

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22nd IEEE international conference on Trust, Security and Privacy in computing and Communications, TrustCom 2023

作者： Hamim, Md Abrar Tanmoy, F.M. Tuj Asha, Umme Fatema Nazmul Haq, Md Alam, Maruf Ghosh, Bijoy Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of South Wales Department of Computer Science and Engineering United Kingdom

ISBN: (纸本)9798350381993

The detection of lung cancer is a critical factor in enhancing patient survival rates. The integration of intelligent computer-aided systems can significantly aid radiologists in this endeavour. The present study centres on the development of a machine learning-oriented methodology aimed at detecting lung cancer through the analysis of text-based medical data extracted from authentic medical reports. The present dataset encompasses a range of machine learning algorithms that have been utilised for binary classification purposes. These algorithms include Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, and Catboost. The objective of this study is to determine the optimal algorithm for the detection of lung cancer with the highest degree of accuracy. By means of comprehensive experimentation, the SVM and Logistic Regression models yielded the highest accuracy rates of 95% and 94%, respectively. The findings of this study indicate the capability of machine learning algorithms in the prompt identification of lung cancer, thereby facilitating enhanced diagnosis and timely intervention. © 2023 IEEE.

关键词： Support vector machines

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