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

共 6952 条记录，以下是231-240 订阅

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Hybrid Model for Sentiment Analysis of Whatsapp data 3

Hybrid Model for Sentiment Analysis of Whatsapp Data

引用

3rd international conference on Advances in computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Kaushal, Royal Chadha, Raman Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350346169

Sentiment analysis, also called opinion mining (OM), is an approach to analyzing the sentiments expressed in data. This approach uses Natural Language processing to classify the data based on emotions in different classes. Various Social media data are analyzed to determine sentiment, and Machine Learning (ML) techniques classify the data. This study utilizes ML models to analyze sentiment in WhatsApp data. The sentiment analysis process includes some steps, such as to pre-process the data, extract the features, and classify the data. The initial stage contributes to the clean-up of raw data and transforms it to make it suitable for analysis. Feature extraction is a stage to retrieve a relevant feature from the pre-processed data that contribute to determining sentiment. Finally, machine learning algorithms classify data to determine the sentiments expressed in the text. This work proposes a voting classifier which is hybrid architecture comprising SVM, KNN, and a Decision tree. Python is executed to simulate the suggested algorithm, and its performance is evaluated based on accuracy, precision, and recall metrics. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data. © 2023 IEEE.

关键词： Sentiment analysis

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Cloud-Edge Collaboration-Based data processing Capability Reconfiguration Optimization in Low Voltage Power Distribution Grid

Cloud-Edge Collaboration-Based Data Processing Capability Re...

引用

2023 international conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2023

作者： Liu, Rirong Zhang, Jie Lu, Tao Li, Qian Jiang, Zewei Guangdong Power Grid Co. Ltd. Metrology Center Guangdong China

ISBN: (纸本)9781510666474

In order to meet the requirements of lightweight power distribution data processing, raise the utilization rate of new energy resources, improve the processing efficiency of refined services, and minimize the delay. In this paper, the cloud-edge collaboration-based data processing capability reconfiguration architecture is proposed to meet the multi-service requirements of low-voltage power distribution. Secondly, the resource block matching-embedding cloud-edge collaboration data processing capacity reconfiguration algorithm is proposed, achieving embedding of resource blocks to edge servers to minimizes data transmission delay and data computing delay. Simulation results show that compared with traditional matching-based data processing reconfiguration algorithm (TMPR), the proposed algorithm can better guarantee the delay requirement of high priority service. © 2023 SPIE.

关键词： Energy resources

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Research on the Optimization of Apriori Algorithm Based On Cloud computing and Medical Information data 11th

Research on the Optimization of Apriori Algorithm Based On C...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Zhuang, Wenbin Fan, Lihong Medical College Nantong University Jiangsu Nantong226000 China

This study proposes an Hadoop-based improved Apriori algorithm (IM Apriori) that optimizes the efficiency of data processing by reducing the set of candidate items and the time to calculate support. The improved algorithm solves two major bottlenecks in traditional Apriori algorithms: scanning databases frequently and generating a large number of candidate sets. Specific improvements include adopting new database mapping methods, avoiding repeatedly scanning the database, further pruning frequent and candidate sets, and calculating support using overlapping strategies to achieve efficient data processing. To verify the performance of the improved algorithm, simulation experiments were conducted on the Hadoop platform and compared with the traditional Apriori algorithm, BITXOR algorithm and several other association analysis algorithms. The experimental results show that the improved IM Apriori algorithm is better than the traditional and other improved algorithms in terms of efficiency and performance, which significantly reduces the number of database scans and alleviates the input / output bottleneck problem. Through this study, we prove the application potential of cloud computing based and improved data mining algorithm in medical data analysis, and improve the efficiency and accuracy of medical data mining. © 2024 The Authors. Published by Elsevier B.V.

关键词： data accuracy

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Research into data Mining algorithms Based on Cloud computing Environments

Research into Data Mining Algorithms Based on Cloud Computin...

引用

2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Song, Yu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

Focusing on the Hadoop architecture in a cloud computing environment and based on the distributed Hadoop system, the MapReduce model and the HBase database, an improved K-means clustering algorithm based on the Hadoop framework was developed, the original cluster analysis designer was improved, data elements were extracted and an analysis model for hot topics in the network was created, and simulation experiments with different databases were completed. The simulation experiments on different datasets and the data mining results in terms of the sampling interval and system accuracy with the improved K-means clustering algorithm suggest that the algorithm is more suitable for data mining in cloud computing and that it has some advantages. © 2023 IEEE.

关键词： cloud computing environment data mining K-means clustering algorithm

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An Exploration of An Operational Multi-data-Set Kernel Culture Scheme for Practice in data Mining 1

An Exploration of An Operational Multi-Data-Set Kernel Cultu...

引用

1st IEEE international conference on Innovations in High Speed Communication and Signal processing, IHCSP 2023

作者： Aneja, Aseem Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350345957

data-centric solutions are vital to all businesses, media, and research. Since data size matters, knowledge mining from massive data requires automation. The world awaits machine learning with AI. Industrial productions to purchase decisions rely on knowledge-mined solutions. Machine learning benefits medical research. This study examines the elements that influence the effectiveness of machine learning algorithms and suggests approaches for improving the knowledge-gathering process. In this study, we experiment with a solution based on support vector machines. Oversampling and under-sampling are both managed by carefully keeping an eye on the sampling methods utilised. An improved SVM is conducted after many time series data have been combined, validated, and analysed. When working with data from several sources, researchers have found that SVM kernel learning has its limits;hence researchers have turned to the dual SVM notion as a workaround. Multiple kernels were tried out for use in classification difficulties, and a co-variance based computing approach was put into practise for processing data from various sources. Dual SVM is shown to be more efficient than single SVM in terms of both processing time and classification accuracy, according to independent validation on many datasets. In this study, we provide the WMKL (Weighted Multi Kernel Learning) technique, which may be used to effectively learn from multidimensional, massive data sets. Computational workloads that involve many algorithms are typically labor-intensive and need for human intervention throughout the kernel selection process. We compare our new method to the standard method and find that it performs better and has less of an impact on the available time. © 2023 IEEE.

关键词： data mining

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data-centric Edge-AI: A Symbolic Representation Use Case 7

Data-centric Edge-AI: A Symbolic Representation Use Case

引用

7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Ilager, Shashikant De Maio, Vincenzo Lujic, Ivan Brandic, Ivona Vienna Univ Technol TU Wien Vienna Austria Ericsson Nikola Tesla Zagreb Croatia

ISBN: (纸本)9798350304831

Today's machine learning pipelines are primarily executed in the cloud, from data storage to data processing, model training, and deployment. However, machine learning is moving to edge devices, creating the demand for AI applications at the edge, known as Edge-AI. Traditional data management practices applied in the cloud are proving to be inefficient for Edge-AI, due to resource and energy constraints of edge devices and real-time requirements of applications. This paper identifies the challenges associated with data processing for Edge-AI. We then discuss methods for efficient data processing at the edge, leading to data-centric Edge-AI. As a use case scenario, we discuss the symbolic representation of time series data and explain how it could help save the cost of data storage and processing in developing Edge-AI applications.

关键词： Edge-AI data-centric Edge IoT data Symbolic representation of data Big data

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IoT-enabled Low Communication Cost Sentiment Analysis Architecture for Intelligent Healthcare

IoT-enabled Low Communication Cost Sentiment Analysis Archit...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Yao, Yinan Qiu, Decheng Li, Bolun Dong, Chen Xu, Li Guo, Xiaodong Lai, Changping College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (纸本)9798350304602

The rapid development of the Internet of Things makes it possible to analyze and make decisions on complex tasks in human society based on big data and artificial intelligence technologies. Intelligent healthcare is a part of it that is closely related to human beings. Traditional intelligent healthcare lacks mental health diagnosis and monitoring. The mental health diagnosis method relies on subjective decision-making and cannot achieve mental health diagnosis and early warning on a large scale. This paper combines artificial intelligence and low communication cost edge cloud solutions with intelligent healthcare. Also, the paper proposed an IoT-based mental health diagnosis solution with the help of sentiment analysis technology. The scheme targets current speech in various scenarios and relies on sentiment analysis technology to quantify emotions. Then, it cooperates with algorithms to achieve real-time monitoring and early warning of mental health conditions. The experimental part uses self-collected data as well as publicly available data. The experimental results show that the framework proposed in this paper accomplishes the tasks on multiple data sets. © 2023 IEEE.

关键词： Internet of things

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Design of Computer Network Security Storage System Based on Digital Cloud computing Technology

Design of Computer Network Security Storage System Based on ...

引用

2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Guo, Sheng Wang, Zhi Yang, Lei Wenhua College Faculty of Information Science and Technology Wuhan China

ISBN: (纸本)9798350313413

The design of a computer network security (CNS) storage system based on digital cloud computing technology (CCT) is a solution proposed to address the increasing network security threats. With the widespread application of computer networks in various industries, data security has become an important concern. Traditional storage systems often cannot meet the requirements for data security and reliability, so a new design is needed to protect network data. This storage system based on digital CCT has multiple functions. Firstly, it can encrypt, backup, and restore data to ensure that it is not accessed, tampered with, or lost without authorization. Secondly, the system ensures that only authorized users can access and modify data through authentication and access rights management, thereby preventing unauthorized access. In addition, the system can also record the access and operation of data, making it easy to trace and detect whether there are security vulnerabilities or violations in the later stage. Storage systems based on CCT also have the characteristics of elasticity and scalability. It can flexibly expand storage space according to user needs and provide high availability services. Through experiments, it can be seen that the CNS storage system using digital CCT has only one successful intrusion, data loss, and running error within one minute, which is a significant improvement compared to traditional network security storage systems. Finally, the design of a CNS storage system based on digital CCT can also optimize the use of network and storage resources, improve the efficiency of data transmission and processing, and provide a better user experience. In short, this system can effectively protect the security and confidentiality of data, provide reliable storage and access services, and provide better data protection and security defense capabilities for network users. © 2023 IEEE.

关键词： cloud computing technology computer networks data transmission network security savings systems

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Evaluation Methods of Machine Translation Based on Big data algorithms 3

Evaluation Methods of Machine Translation Based on Big Data ...

引用

3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Zhang, Bing School Of Foreign Languages Guangdong University Of Science And Technology Dongguan China

ISBN: (纸本)9798350318609

In order to solve the challenges of machine translation evaluation methods, this study proposes an innovative machine translation evaluation method based on big data algorithms in view of the shortcomings of existing dynamic programming algorithms. This new scheme uses the principle of overlapping sub-problem theory to accurately identify and locate the key influencing factors, and accordingly carries out a wise classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of big data algorithms, this scheme cleverly constructs the design strategy of translation evaluation methods. The empirical results show that the proposed scheme shows a significant improvement over the traditional dynamic programming algorithm in terms of the accuracy of the machine translation evaluation method and the processing efficiency of key factors, showing its obvious strong advantages. Machine translation evaluation methods play a crucial role in translation systems, which can accurately predict and optimize the growth trend and output results of machine translation evaluation methods. However, in the face of complex simulation tasks, traditional dynamic programming algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of machine translation evaluation method optimized by big data algorithms, and accurately controls the influencing parameters through the overlapping sub-problem theory, and uses this as a road map for index allocation, and then uses big data algorithms to innovate and construct a system scheme. © 2024 IEEE.

关键词： Big data

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Exploration of Hybrid Recommendation algorithms for Learning Nature Software 2

Exploration of Hybrid Recommendation Algorithms for Learning...

引用

2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Zhang, Zixiang Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China

ISBN: (纸本)9798350339765

Recommendation algorithm is a type of algorithm in the computer profession which includes methods such as content-based recommendation, collaborative filtering, model-based recommendation, and social relationship-based recommendation. It uses some mathematical algorithms to speculate what a user might like. The main place where recommendation algorithms are better applied is the web, where it finds out what a user might like by his or her behavior. Recommendation algorithms have been applied to websites in various fields, including books, music, videos, news, movies, maps, and so on. The application of e-commerce has also become popular in recent years. Amazon, ebay, staples, ***, Douban Books, Taobao, etc. all use e-commerce recommendation systems, which not only bring huge additional benefits to these Internet merchants, but also improve user satisfaction and increase user stickiness The goal of this research is to provide Learning users (e.g. middle school students and high school students) to explore a personalized recommendation algorithm that is efficient and convenient for search recommendations, helps to understand users' needs, and is based on user preferences. The goal is to bring a better experience and sense of use to users. © 2023 IEEE.

关键词： Collaborative filtering

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