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Document Detection using Probabilistic Principal Component Analysis-Based Naïve Bayes Model 4

Document Detection using Probabilistic Principal Component A...

引用

4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Elavarasi, T. Nagarajan, R. Annamalai University Department of computer and information science India

ISBN: (纸本)9798331529635

Document detection is where algorithms are trained to detect and classify document types such that automating sorting and data extraction becomes more efficient in document management. Common limitations of existing methods include reliance on large volumes of labelled training data, sensitivity to differences in document formats, and problems with low-quality images. Therefore, this research performs the document detection using machine learning methodology. Initially, the data is manually collected from online sources in the form of documents together with their categories. These collected data are subjected to the pre-processing using the log-linear lemmatization approach. The features are extracted from these pre-processed data using the probabilistic Principal Component Analysis (PCA) approach. Finally, the document detection is accomplished using the Naive Bayes (NB) model that produces the final outcome. Simulation findings revealed that the proposed Probabilistic PCA-NB model returns better results than the other considered conventional methods respectively. The proposed Probabilistic PCA-NB for the document detection model is 0.62%, 0.72%, 0.71%, and 1.59% better than the other considered traditional methods in terms of accuracy, precision, sensitivity, and specificity respectively. © 2024 ieee.

关键词： data accuracy

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Research on intelligent data Mining and Knowledge Discovery Method Based on Software Information System

Research on Intelligent Data Mining and Knowledge Discovery ...

引用

2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Tang, Xiaojun Radio and Television Monitoring Center of Shandong Province Shandong Jinan China

ISBN: (纸本)9798350359244

data mining is an important branch of information technology, which is mainly used in search engine, knowledge management, decision analysis and so on. An important direction of modern information management is the transformation from the traditional 'carrier' to 'content' management mode. Today, with the increasing amount of information resources, people should not only be satisfied with the acquisition of ordinary raw materials, but also need to analyze the content of information in depth, because simple ordinary query and search can not let users obtain convincing knowledge, nor can they effectively use these information resources. In this paper, the intelligent data mining in software information system is explored, the steps of knowledge discovery are given, and the common data mining methods are compared. The data mining technology introduced in this paper is a typical and relatively novel technology, many of which have been successfully applied to data mining applications. K-nearest neighbor algorithm has an accuracy rate of 90% and a recall rate of 95%. The accuracy of SVM is 93%, and the recall rate is 94%. This paper provides a research direction for effectively mining information of interest to users in the field of data mining. © 2023 ieee.

关键词： data acquisition

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ChatAN: Interval Adaptive Normalization based on ChatGPT Knowledge Augmentation 8

ChatAN: Interval Adaptive Normalization based on ChatGPT Kno...

引用

8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： Yang, Hanyu Zhao, Xiaoyong Wang, Lei Zhang, Jingle Li, Jianhua Beijing Information Science & Technology University School of Information & Management Beijing China Beijing Information Science & Technology University Information Systems Institute Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China City Brain Zhongguancun Science City City Brain Co. Ltd. Beijing China

ISBN: (纸本)9798350302455

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method. © 2023 ieee.

关键词： Anomaly detection

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A Survey on data Security using Reversible Cellular Automata

A Survey on Data Security using Reversible Cellular Automata

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Degala, Divya Priya Athithan, Senthil Chinnasamy, P. KL University Department of Computer Science and Engineering Vijayawada India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350348217

Now a days, without network and advanced computer technologies we can't able to transfer data among users. Among this the security is the biggest issue with the CPS system. The major issue is figuring out how to increase these systems' security while still doing it effectively. In order to overcome the issues this survey proposes several techniques for providing security of cyber-physical systems using reversible computing and cellular automata. A Cyber-Physical System(CPS) is an intelligent system used for data storing, transferring and protecting. To protect data, many machine(ml) and deep learning(dl) algorithms have been designed, which require more computation, dissipating more *** solve this, we are offering an public key encryption using reversible cellular automata to enhance the security of data in Cyber-Physical systems © 2023 ieee.

关键词： Cyber Physical System

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Hybrid Deep Learning-Based Approach for Enhanced Multi-Label Emotion Classification 11

Hybrid Deep Learning-Based Approach for Enhanced Multi-Label...

引用

11th ieee international conference on intelligent computing and Information systems, ICICIS 2023

作者： Mohamed, Ebtesam Hussien Ismael, Osama El Kadi, Hatem Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Nowadays, social networking is controlling our lives. Every passing second, millions of tweets are shared by individuals through online social media platforms like Twitter as a means to express their feelings and thoughts. Exploring and examining the emotions conveyed within social media content, along with extracting valuable insights from extensive data, categorizing it into various groups, and foreseeing end-user behavior or emotions, holds potential advantages across multiple domains such as public health, social welfare, commerce, etc. This paper offered a deep learning-based solution for addressing multiple emotion classification challenges on Twitter. The novel proposed presents an advanced deep learning system with a hybrid approach that harnesses the strengths of combining Bidirectional Long-Short Term Memory and Recurrent Neural Network with an additional attention mechanism. This model has been trained using the "SemEval 2018 Task1 (Twitter data)"to evaluate the performance of the suggested model. The experimental outcomes demonstrated that, compared to individual SVM, RNN, or LSTM models, as well as hybrid models, it produces classification results that are more accurate and have greater F1 scores and recall. Our model surpasses the existing cutting-edge systems, achieving a superior accuracy score of 0.90. The proposed model provides an alternate resolution to the issue of long-term dependency within current models while also addressing the challenge of data loss with growing training data sizes. The presented model achieved improved accuracy, which was amplified further with larger training data sizes as well as an increased number of training epochs. © 2023 ieee.

关键词： Long short-term memory

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intelligent Multi-Agent System for Improving the Quality of Software by Taking into Account the Information of the Subject Area at All Stages of its Development 11

Intelligent Multi-Agent System for Improving the Quality of ...

引用

11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Lopatto, Ivan Hovorushchenko, Tetiana Popov, Peter Pavlova, Olga Khmelnytskyi Natl Univ Inst Str 1 Khmelnytskyi Ukraine City Univ London Northhampton Sq London England

ISBN: (纸本)9781665426053

For improving the quality of software, it is necessary to take into account the information of the subject area at all stages of its development. The paper first time proposes the structure of an intelligent multi-agent system for improving the quality of software by taking into account the information of the subject area at all stages of its development, which: performs semantic parsing of real software documents to find the necessary elements of information of the subject area;forms appropriate real ontologies for each document;compares ideal and real ontologies;estimates the magnitudes of the subject area information losses for each software document.

关键词： software quality information of the subject area intelligent multi-agent system ontology-based intelligent agent

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Load balancing promotes effective IoT-based data transfer in Snowflake Storage systems 3

Load balancing promotes effective IoT-based data transfer in...

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Jesupriya, J. Kavitha, M. Deeksha Presidency University India Presidency College Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In networked devices, efficient data transportation is important for guaranteeing dependable and scalable Internet of Things (IoT) infrastructures. For this purpose, in the current work, a dynamic load-balancing mechanism is proposed to optimize the data transfer between Internet of Things nodes and Snowflake data storage. It may be fairly distributing the distributed data between IoT nodes, but traditional round-robin load balancing often overlooks the difference in node performance, which may lead to inefficient data transfer. A solution to this problem is a dynamic load-balancing mechanism that depends on nodes in real-time performing, hence its variation with transfer speed and latency. This system would dynamically select the most effective nodes that are to transmit the data and would ensure quicker and more dependable delivery of information. In the following simulation, round-robin and dynamic load-balancing strategies are compared using ten IoT nodes that are sending segmented data to a Snowflake server. Outcomes: These show that although the round-robin accomplishes reduced latency and faster transfer rates, the dynamic approach presents a fairer distribution of the load as well as reduces the possibility of node overloading while enhancing overall system robustness in the long term. This work adds to the future prospects of IoT load-balancing solutions, thus vividly showing how round-robin and dynamic approaches could present trade-offs between one another. © 2025 ieee.

关键词： Snow melting systems

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Research on Day-ahead Optimization Operation Strategies for data Centers in Scenarios of Orderly Electricity Use 4

Research on Day-ahead Optimization Operation Strategies for ...

引用

4th international conference on intelligent Power and systems, ICIPS 2024

作者： Tong, Yongjie Liu, Yang Yin, Hongyan Fu, Yifeng Zhang, Qianru Jinzhou Power Supply Branch State Grid Liaoning Electric Power Supply Co. Ltd Jinzhou China

ISBN: (纸本)9798331532765

With the increasing energy consumption of data centers, its impact on the power system is becoming more and more significant. data centers are not only energy-intensive loads, but also important waste heat producers, with supply and demand bidirectional. At present, the operation of data center micro-energy network is complicated, and there are problems such as the difficulty of fine load modeling of data center and the difficulty of integrating demand response resources to participate in the power market. It makes it difficult for data centers to provide reliable regulation capabilities, resulting in orderly power consumption difficulties. In this paper, a data center load model is established to calculate the time-space transfer characteristics of the load in the day-ahead market, including the IT equipment energy consumption model and the cooling system energy consumption model, as well as the calculation load and server operation model. Then, based on the power market price signal, the optimal scheduling model of multi-location data center is established to minimize the total cost of multi-location data center, and the optimal distribution of computing load is realized. © 2024 ieee.

关键词： Demand response

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Xabcd: A Better Model for Named Entity Recognition in Cyber Threat Intelligence 9

Xabcd: A Better Model for Named Entity Recognition in Cyber ...

引用

9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Wang, Ruiqi Zheng, Zhiqiang Ao, Shengqin Fu, Yuxia Wang, Xuren Jiang, Zhengwei Xie, Gang School of Big Data and Computer Science Guizhou Normal University Guiyang China Information Engineering College Capital Normal University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376548

Due to the ambiguity between naming entity nouns and daily usage nouns in cyber threat intelligence, such as hacker organizations, attack tools, and attack techniques, the understanding of their content heavily relies on cyber security knowledge. How to quickly and accurately extract named entities related to network security from multi-source texts has become a hot and difficult research topic in the current threat intelligence field. In this paper, a named entity recognition framework from cyber threat intelligence unstructured text is designed. For the business requirements in the field of cyber threat intelligence, this framework completely covers data acquisition, text classification, entity and relationship definition, entity and relationship analysis and modeling and ability application. The framework is available for experimental exploration of entity recognition technology for threat intelligence. In addition, this paper proposes an entity recognition model for cyber threat intelligence, Xabcd, based on data and feature knowledge. Based on open network security text corpus, Xabcd model obtained the key indicators such as precision, recall, and F1-score as 97.35%, 97.15% and 97.23% respectively, which are all higher than the baseline model levels, so the experimental results demonstrate the feasibility and effectiveness of the model. © 2024 ieee.

关键词： Cyber attacks

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Non-Uniform Tiling of Point Cloud Layer to Optimize Updates of HD Map data 18

Non-Uniform Tiling of Point Cloud Layer to Optimize Updates ...

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18th ieee international conference on advanced Networks and Telecommunications systems, ANTS 2024

作者： Deep, Sayon Annavazzala, Madhuri Antony Franklin, A. Indian Institute of Technology Hyderabad India

ISBN: (纸本)9798350391725

This paper introduces a novel algorithm for the efficient handling of crowd-sourced point cloud data in high-definition (HD) map updates, a critical component for enabling autonomous vehicles. The algorithm employs a non-uniform tiling technique that divides the point cloud layer of the HD map based on detected changes, optimizing data processing while preserving necessary details for accurate map updates. By incorporating caching techniques, the proposed method tackles the limitations and difficulties faced by existing tiling approaches, resulting in improved performance. Specifically, it addresses challenges such as large data volume, transmission overhead, and latency associated with crowd-sourced map updates. Experimental results demonstrate the effectiveness of the proposed approach in overcoming these challenges, reducing computation costs, and enhancing the efficiency of HD map updates. Simulation results show that there is 37.5% improvement in caching performance and 27.77% faster data processing in case of non-uniform tiling as compared to uniform tiling scheme. © 2024 ieee.

关键词： data handling

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