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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

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Edge Computing: Architecture, Implications and Future with Latest Trends 3

Edge Computing: Architecture, Implications and Future with L...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Thakkar, Krish Patel, Akash Budhrani, Aishwariya Patel, Khushi Patel, Sachin Nayak, Amit Department of Information Technology Gujarat Changa388421 India Department of Computer Science & Engineering Gujarat Changa388421 India Department of Computer Engineering Gujarat Changa388421 India

ISBN: (纸本)9798350322842

As a result of the proliferation of Internet-connected smart devices brought about by the explosive growth of the Internet of Everything (IoE), lower bandwidth load, slower response, bad security, and poor privacy are just some of the issues that have arisen with data growth. Edge computing solutions have arisen as a response to the limitations of traditional cloud computing in meeting the data processing demands of modern societies. Edge computing, the new era of distributed computing, differs from cloud computing in that it places greater emphasis on being physically proximate to both the user and the data itself. The primary focus of this article is a critical examination of the associated literature and findings around edge computing. Edge computing is first briefly compared with cloud computing. Then, briefly discuss edge computing's architecture, followed by major challenges with edge computing and ending with latest trends with edge computing. © 2023 ieee.

关键词： computer architecture

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Machine learning-based predictive analytics and big data in the automotive sector 3

Machine learning-based predictive analytics and big data in ...

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

作者： Lourens, Melanie Sharma, Seema Pulugu, Revathy Gehlot, Anita Manoharan, Geetha Kapila, Dhiraj Durban University of Technology Faculty of Management Sciences South Africa Mriirs Department of Computer Applications Haryana Faridabad India Narsimha Reddy Engineering College Department of Computer Science and Engineering Telangana State Hyderabad500100 India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Business Telangana India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

data science and machine learning's inherent capacities to automatically learn and optimize processes and products are making them more indispensable to the automotive industry of the future. This essay will define data science and machine learning and draw parallels between the two fields. A definition of automatic optimization is provided, and its value as a tool when paired with data analytics is shown. It illustrates how these technologies are currently being applied in the industry and presents examples using the important subprocesses in the automotive value chain. The sector is only beginning to explore the many of applications for these innovations, therefore we utilize futuristic use cases to illustrate their transformative potential. The article concludes by demonstrating how these technological advancements may increase productivity in the automotive industry and fortify the sector's customer focus across the board, from product and creation process to customers and their interaction with the product. © 2023 ieee.

关键词： Automotive industry

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Research on College Student Employment Market Information Mining Based on Grey Theory 3

Research on College Student Employment Market Information Mi...

引用

3rd ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wang, Yue Computer Engineering and Science Academy Anhui University of Science & Technology Huainan China

ISBN: (纸本)9798350317022

This paper employs an integrated approach of data mining and grey theory to comprehensively analyze college student employment market dynamics from 2019 to 2023. Establishing an employment information data model and a grey relational model, the study introduces algorithms for processing certification, awards, and employment indices. A customized annual employment index algorithm, utilizing clustering strategies, is designed for college student employment attributes. Utilizing the grey relational model for binary and continuous attributes, the research identifies key factors significantly correlated with college student employment outcomes, including academic performance, certification qualifications, awards, gender, ethnicity, and language proficiency. The findings underscore the pivotal influence of academic achievements and certifications on employment prospects, further unveiling the nuanced impact of demographic factors. These results not only provide valuable insights for job seekers and recruiters but also contribute to a deeper understanding of the dynamic college student employment landscape, facilitating more informed decision-making strategies. © 2023 ieee.

关键词： College Student Employment data mining Employment information Grey theory Relational degree

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Spectral Feature Selection Via Low Rank Decomposition and Local Preservation 3

Spectral Feature Selection Via Low Rank Decomposition and Lo...

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3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Jiang, Luxi Chen, Xiuhong College of Artificial Intelligence and Computer Science Jiangnan University Wuxi Jiangsu Province China

ISBN: (纸本)9798350335972

Feature selection is an important step in data mining and pattern recognition tasks. In unsupervised cases, feature selection becomes more difficult due to the lack of labels in the samples. Therefore, this paper proposes a spectral feature selection (JLrdLP) algorithm that combines low rank decomposition and local preservation. First, the model predicts pseudo-labels using spectral clustering, which jointly learns the cluster labels and feature selection matrix, enabling the algorithm to select more discriminative features. In order to realize the selection of data features, the feature selection matrix is guaranteed to be sparse by L_2,1 -norm minimization constraint;the projection matrix is decomposed into the product of two low-rank matrices to reveal the global correlation of data features;The idea is to preserve the local geometry of the data when transforming the original data into a low-dimensional space. Finally, the experimental results on multiple real datasets show that the above has better robustness, thus improving the effect of the classification. © 2023 ieee.

关键词： Feature Selection

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Cloud-Based data Protection and Secure Backup Solutions: A Comprehensive Review of Ensuring Business Continuity 3

Cloud-Based Data Protection and Secure Backup Solutions: A C...

引用

3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Suman, Om Prakash Saini, Lakshya Kumar Kumar, Sandeep Dr. B R Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9798350300710

Information has become so crucial to businesses in the modern world that they require data recovery services more frequently. Nowadays, a significant amount of electronic information is generated, necessitating the need for data recovery services. Cloud computing has introduced a new type of computing platform that produces a substantial amount of data on the primary cloud, leading to an increased demand for data recovery services. The development of effective and efficient demand requires data recovery methods. The recovery method is designed to let the user access data from any backup server when a server's data is lost and it cannot deliver data to the user. To achieve this, various techniques have been developed. In this paper, we will explore various data backup and recovery methods to comprehend the uniqueness of each approach and compare the advantages and disadvantages of each method. © 2023 ieee.

关键词： Recovery

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data Quality Assessment Using Benford's Law and Excel 3

Data Quality Assessment Using Benford's Law and Excel

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Shalini, T. Shanker Rao, T Lilly Shyamili, T. Rao, T Shanker IIT West Bengal Kharagpur721302 India Narmada College of Science and Commerce Zadeshwar Electronics Department Gujarat Bharuch392011 India MNIT CSE Department Rajasthan Jaipur India Narmada College of Science and Commerce Ex-Physics Department Zadeshwar Gujarat Bharuch392011 India

ISBN: (纸本)9781665494007

In the computer era, data is growing exponentially. So, the data quality and anomaly has become a major concern. It is very important and necessary to keep the scientific data free from fraud. application of Benford's law (BL) has become very effective method for detection of fake data and also checks the data irregularities in various fields. This paper will elaborately discuss the four statistical tests called as goodness-of-fit tests applied to the four data sets with different sample sizes. The data sets comprise the land acquired by the government to broaden the roads/lanes and published in the National leading newspaper. The graphical approach of conformance to Benford's law might be very clear and evident, however four fit index methods, namely Chi-square (χ2)Calc goodness of fit-values, MAD test and RMSE test are used to prove the conformity between the data and *** results of all the tests indicate no irregularities in the published data and shows the conformity of the data with the BL. © 2023 ieee.

关键词： Statistical tests

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FedOES: An Efficient Federated Learning Approach 3

FedOES: An Efficient Federated Learning Approach

引用

3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Li, Yue Liu, Zengjin Huang, Yunfa Xu, Peiting School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798350335972

Federated Learning (FL) is a distributed approach for performing machine learning tasks. It prevents data sharing by aggregating the models trained by distributed clients on the central server, thereby maintaining data privacy. However, a major challenge in FL architecture is the high communication costs incurred by clients in multiple communication rounds, which can create communication bottlenecks. Additionally, the issue of non-independent and identical distribution (Non-IID) data poses a significant challenge. To address these challenges, our paper proposes a method based on in-cluster training and top-k gradient sparsification. The clients adopt the in-cluster training strategy to mitigate the negative effects of Non-IID data, while sparse gradient is implemented on both the clients and the server to reduce communication costs. Our experiments demonstrate that our proposed approach, FedOES, outperforms federated averaging in common FL scenarios. It not only improves communication efficiency, but also achieves excellent performance in scenarios where the data is Non-IID. © 2023 ieee.

关键词： Privacy-preserving techniques

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Homomorphic Encryption: Hands Inside the Gloves 3

Homomorphic Encryption: Hands Inside the Gloves

引用

3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Singh, Vaibhav Kant Chauhan, Aditya Singh Singh, Ayush Thakur, Raja A Central University Guru Ghasidas Vishwavidyalaya Department of Computer Science and Engineering Bilaspur India

ISBN: (纸本)9798350343632

Homomorphic encryption is a groundbreaking cryptographic technique that enables secure data operations on encrypted data without revealing the underlying information. This study explores various forms of homomorphic encryption, including labelled, somewhat, and fully homomorphic encryption, and provides practical insights into its implementation using the RSA algorithm. Further, this study also discusses about the real-time applications of homomorphic encryption, such as secure data sharing in government administration, and emphasizes its potential to revolutionize data security and privacy in a wide range of domains. This research study underscores the ongoing advancements in this field, highlighting the promise it holds for the future of secure and privacy-preserving data handling. © 2023 ieee.

关键词： Ciphertext Decryption Encryption Error Morphism Noise Plaintext

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Leveraging towards Multi-objective IoT application Placement in Fog Computing Environment 3

Leveraging towards Multi-objective IoT Application Placement...

引用

3rd ieee international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Apat, Hemant Kumar Sattarapu, Pranati Dash, Rudra Narayan Goswami, Veena Mohanty, Sagarika Barik, Rabindra K. Kiit University School of Computer Application Bhubaneswar India Woxsen University School of Technology Hyderabad India Kiit University School of Electrical Engineering Bhubaneswar India National Institute of Technology Department of Cse Rourkela India

ISBN: (纸本)9798350304732

Internet of Things (IoT) is one of the most talked-about innovations of recent years because it paves the way for novel interactions between devices and people that can improve daily life. Interconnected devices are generating an unprecedented amount of data due to the growth of IoT devices. When it comes to processing the data of IoT applications, a new paradigm known as fog computing is developing as an intriguing alternative. To leverage the maximum benefit optimal deployment of services into the fog computing devices has paramount importance. To find the optimal placement of services into the heterogeneous fog computing is a NP complete problem. In the present work, we implement genetic algorithm with Multi-objective IoT applications placement to minimise the three important parameters i.e. makespan, energy and cost. It also presents a comparison with the placement of single objective IoT applications. The findings indicate that employing Multi-objective placement approaches yields greater benefits in comparison to utilising a single objective placement strategy. © 2023 ieee.

关键词： application placement Fog computing IoT Multi-objective single objective

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Disease Prediction Using Random Forest Classifier by Machine Learning application 3

Disease Prediction Using Random Forest Classifier by Machine...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Singh, Sumit Mohd, Noor Shukla, Tanmay Sharma, Jaya Chopra, Aarushi Graphic Era Deemed to Be University Computer Science & Engineering UK Dehradun India

ISBN: (纸本)9798350322842

Accurate disease prediction is a critical task in healthcare, yet it can be particularly challenging due to the complexity of the human body and the multitude of factors that contribute to the onset of various diseases. Fortunately, machine learning algorithms such as the random forest classifier have been shown to be valuable tools in predicting diseases. This research study aims to explore the application of random forest classifiers in disease prediction, specifically by analyzing their performance in predicting different types of diseases using various symptoms. Our study found that the random forest classifier is a powerful and reliable tool for disease prediction, producing precise and trustworthy results. With its proven efficacy and versatility, the random forest classifier is poised to become an essential part of the disease prediction toolkit for healthcare professionals. © 2023 ieee.

关键词： Decision trees

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