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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471-480 订阅

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Earthquake big data processing and visualization based on PyEcharts 3

Earthquake big data processing and visualization based on Py...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Wu, Jiandong Wang, Qiang Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang China Software School of East China University of Technology Nanchang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper mainly uses the Python language and the pyecharts library to mine the earthquake data that occurred around the world in 2020, and finally displays it with pie charts, bar charts and other graphics. It lays a foundation for better use and processing of earthquake big data in the future, and also demonstrates the application of big data in other industries. © 2023 SPIE.

关键词： Visualization

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The effect of data Augmentation Using SMOTE: Diabetes Prediction by Machine Learning Techniques 6

The effect of Data Augmentation Using SMOTE: Diabetes Predic...

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6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th Asia Digital Image processing conference

作者： Al-Qerem, A. Ali, A. M. Alauthman, M. Al Khaldy, M. Aldweesh, A. Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Al Ahliyya Amman Univ Fac Engn Commun & Comp Engn Dept Amman 19328 Jordan Univ Petra Fac Informat Technol Dept Informat Secur Amman Jordan Univ Petra Business Inteligence & Data Analyt Amman Jordan Shaqra Univ Coll Comp & Informat Technol Shaqra 11911 Saudi Arabia

ISBN: (纸本)9798400716225

Diabetes mellitus, a severe and enduring condition characterized by impaired glucose metabolism, poses a substantial threat to public health. Its pervasive impact continues to escalate globally, with a rising incidence that challenges preventive measures. Despite earnest efforts, individuals struggle to evade the clutches of diabetes, necessitating innovative approaches for disease management. Traditional methodologies in diabetes health monitoring exhibit limitations, prompting the exploration of advanced techniques. This study employs machine learning (ML) methods to delve into diabetes, aiming to enhance diagnostic accuracy. The primary objective is to develop a method capable of precise diabetes diagnoses with a heightened level of precision. The investigation incorporates machine learning algorithms, specifically Random Forest (RF), K Nearest Neighbor (KNN), and Logistic Regression. The inclusion of these algorithms seeks to streamline data processing ***, this study incorporates the Synthetic Minority Over-sampling Technique (SMOTE) as a data augmentation strategy. SMOTE addresses imbalances in the dataset, contributing to a more robust and representative sample. The research evaluates the effectiveness and accuracy of diabetes prediction using these algorithms both before and after SMOTE implementation. By considering the impact of SMOTE, the study aims to determine the optimal algorithm for assessing diabetes development. The comparative analysis sheds light on how SMOTE enhances the overall performance of machine learning models. This nuanced approach not only refines diabetes diagnostic protocols but also underscores the significance of addressing data imbalances in predictive modeling for enhanced precision in disease prediction.

关键词： Classification data augmentation Diabetes

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2023 international conference on Control, Communication and computing, ICCC 2023

2023 International Conference on Control, Communication and ...

引用

5th international conference on Control, Communication and computing, ICCC 2023

ISBN: (纸本)9798350334128

The proceedings contain 139 papers. The topics discussed include: artificial level language: a library of computing engine for natural languages;fourth order sliding mode control of position for electro-hydraulic actuator;design of hexapod robot with adaptive gait transition on rough terrain;energy-efficient data aggregation schemes for low-power distributed wireless sensor networks using compressed sensing;adaptive neural attitude and altitude control of a quadrotor UAV with external disturbances via sliding mode control;an indigenous 32 bit pipelined processor for space applications;design of ins aided receiver integrity monitoring algorithm (ARIM) for GNSS integrated navigation systems;algebraic topology as an analytic tool to check lossless nature of geometric algorithms;implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage;a novel general purpose processing ASIC for inertial sensors;semi-autonomous non-singular terminal sliding mode control of a micro aerial robot for outdoor wall climbing operation;investigations on recent advancements in the fabrication of in-plane micro-supercapacitors as portable energy storage devices;Reswave-Net: a wavelet based residual U-Net for brain tumor segmentation and overall survival prediction;and bidirectional voltage level shifter with configurable voltage range.

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data Quality Assessment in Mobile Crowdsensing by Utilizing Psychology Effect 21

Data Quality Assessment in Mobile Crowdsensing by Utilizing ...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cheng, Zhehao Chen, Jiaoyan Liu, Jin Li, Mengyang Wuhan University of Science and Technology The School of Information Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Mobile crowdsensing has become a popular strategy for collecting physical information in smart cities. To ensure the efficiency and effectiveness of this approach, assessing the quality of sensing data is crucial. Typically, strategies for evaluating the quality of mobile crowdsensing data involve using correlated historical data from participants to make inferences about the current data quality or adjusting deviations in data quality through online feedback. However, these approaches may not perform well when both historical data and interactions with participants are unavailable, making the evaluation of data quality challenging. This work addresses the challenge of assessing the quality of sensing data when referable information is lacking. It introduces a psychological phenomenon where participants are more willing to provide truthful feedback when it doesn't compromise their privacy. Specifically, the system invites participants to engage in evaluating the quality of data uploaded by other participants after they have uploaded their own data. Finally, we evaluate the proposed strategy by applying it to the assessment of urban air quality. Experimental results demonstrate that the proposed strategy achieves higher data quality. © 2023 IEEE.

关键词： Air quality

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Blockchain Security Encryption to Preserve data Privacy and Integrity in Cloud Environment 10

Blockchain Security Encryption to Preserve Data Privacy and ...

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10th international conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Yeboah-Ofori, Abel Sadat, Sayed Kashif Darvishi, Iman University of West London School of Computing and Eng United Kingdom

ISBN: (纸本)9798350316353

Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest. © 2023 IEEE.

关键词： Cryptography

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A Systematic Review of data Science and Deep Learning Applications in Extracting Biological data 4

A Systematic Review of Data Science and Deep Learning Applic...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Praveena, Murala Paramesh, J. Bala, K. Bharath Institute of Higher Education and Research School of Computing Dept. of Cse Chennai600 073 India

ISBN: (纸本)9798350300093

This article provides a thorough analysis of data sciences and deep learning(DL) characters in data science emphasizing which one is needed out of DL for finer prediction has been as well addressed. This study gives a detailed correlation of disparate DL approaches employed in the biological sector (BS). Because of the appearance of dominant computers, advanced DL algorithms, and broad data production out of disparate industrial tools, a promising tomorrow in establishing solutions to the intricate issues in the BS, which remained formerly besides the hold of analytical solutions (AS) or numerical simulations (NS) is noticed. DL tools could include each particular within the log data and each data linked to the target data. Despite their constraints, these remain unlimited by constraining presumptions of AS or by specific data and/or power processing requisites of NS. This exhaustive work could provide an exceptional reference for DL implementations in this sector. Centered upon the review performed, it has been observed that DL approaches provide immense power in resolving issues in the BS' major fields incorporating prediction, classification, and clustering. © 2023 IEEE.

关键词： Genome

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Schema Integration on Massive data Sources 1

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23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Li, Tianbao Guo, Haifeng Yang, Donghua Li, Mengmeng Zheng, Bo Wang, Hongzhi Harbin Inst Technol Harbin Peoples R China ConDB Beijing Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

As the fundamental phrase of collecting and analyzing data, data integration is used in many applications, such as data cleaning, bioinformatics and pattern recognition. In big data era, one of the major problems of data integration is to obtain the global schema of data sources since the global schema could be hardly derived from massive data sources directly. In this paper, we attempt to solve such schema integration problem. For different scenarios, we develop batch and incremental schema integration algorithms. We consider the representation difference of attribute names in various data sources and propose ED Join and Semantic Join algorithms to integrate attributes with different representations. Extensive experimental results demonstrate that the proposed algorithms could integrate schemas efficiently and effectively.

关键词： Information integration Schema mapping Schema integration

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Integration of Cloud computing, IoT, and Big data for the Development of a Novel Smart Agriculture Model 3

Integration of Cloud Computing, IoT, and Big Data for the De...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Rao, Molli Srinivasa Modi, Sanjay Singh, Rajat Prasanna, K. Lakshmi Khan, Shakir Ushapriya, C. Dadi Institute of Engineering & Technology Anakapalle Department of Cse Visakhapatnam India Lovely Professional University Punjab Phagwara India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Agriculture Telangana Warangal India College of Computer and Information Sciences Riyadh Saudi Arabia University Centre for Research and Development Chandigarh University Department of Computer Science and Engineering Mohali140413 India St. Martin's Engineering College Department of Cse Telangana Secunderabad India

ISBN: (纸本)9798350399264

Since the traditional data paradigm cannot handle the volume of information generated by IoT (Internet - of -things) gadgets, cloud storage is now required. These data have been examined using big mining techniques. When evaluating the viability of smart agriculture, the Internet of Farmers must use technologies of information and communication (ICT) in their daily lives to get agricultural information. Crop growth observing, fertilizer classification and irrigation support systems use IoT. This article investigates and optimizes the large amounts of data produced during the farming process, but it seems to be analyzing data mining using the automated k-means system according to the maximum speed. The crop growth curve is intended to simulate the earliest K-means methodology. The experimental findings support the idea that clustering algorithms provide overall benefits in the F mutual information of 7.67% and just a decrease in the total period of 0.23 seconds is supported. Because it can more efficiently realize the operational processes of reliable information communication and data processing, the algorithm presented in this article has a major effect on advancing agronomic information technology. © 2023 IEEE.

关键词： Cloud computing

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Exploring The data Science 7

Exploring The Data Science

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Amodh Kapil, Akshat Ahlawat, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

data Science is a rapidly expanding field in technology that has attracted a lot of concentration recently due to the enormous influence it has on many different companies and domains. The field encompasses a wide range of techniques and tools aimed at extracting meaningful insights and knowledge from large and complex datasets. This study seeks to offer a thorough review of the field of data Science by exploring its history, fundamental concepts, and current trends. It also highlights the current trends and developments in the field, such as big data, cloud computing, and the Internet of Things (IoT). The paper concludes by discussing the future of data Science and the impact it will have on various industries, including healthcare, finance, and retail. © 2023 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Natural Language processing

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Architecting a big data-driven software architecture for smart street lighting 20

Architecting a big data-driven software architecture for sma...

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IEEE 20th international conference on Software Architecture (ICSA)

作者： Ali, Mubashir Scandurra, Patrizia Moretti, Fabio Blaso, Laura Univ Bergamo DIGIP Bergamo Italy ENEA Smart Cities & Communities TERIN SEN SCC Lab Rome Italy

ISBN: (纸本)9781665464598

Big data is one of the enabling technologies of the vision of Industry 4.0. Technological evolution is able to generate an increasing number of data. From the web, to social networks, from mobile devices to sensors, data is conveyed through the most disparate products of technology, whether physical or virtual. However, the transition from big data to smart data providing insights about information and issues that matter is not simple and obvious to achieve. The greater the amount of data and the more heterogeneous they are, the more complex their processing will be. The data, once collected, is processed by complex analytics algorithms and, to do this, considerable storage units and computing power are needed. In this paper, we describe our approach and experience at the Italian national agency ENEA in architecting a big data-driven software architecture for public street lighting. Such a software architecture is called ENEA PELL smart city platform (in brief, PELL SCP) and it is intended to collect, represent, control, predict, and possibly optimize the behaviour of public street lighting plants. In particular, we provide an overview of the analytics features that are being developed in collaboration with the University of Bergamo (Italy) to analyze electric energy data as collected by the PELL SCP.

关键词： Big data-driven software architecture smart street lighting smart city platforms PELL city analytics

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