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Power Grid Encrypted data Range Filtering Scheme Based on Order-revealing Encryption 21

Power Grid Encrypted Data Range Filtering Scheme Based on Or...

引用

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： Qiao, Hongyi Peng, Cong Luo, Min Wu, Po He, Debiao Wuhan University School of Cyber Science & Engineering Wuhan China Electric Power Research Institute State Grid Henan Electric Power Company Zhengzhou China

ISBN: (纸本)9798350329223

As the power Internet of Things (IoT) system accelerates, the IoT end devices within the power grid show an explosive growth, and generate massive electricity consumption data. While the diverse encryption technologies currently employed in the existing power grid system offer a degree of data privacy protection, they also make it difficult to filter and screen the data of the original node in the link. In addition, the traditional cryptographic technology can not resist the attack of uploading invalid data from the malicious terminal in the case that the power terminal is controlled by a malicious adversary. To address this issue, this paper presents an efficient bilinear pairing-free order-revealing encryption scheme (r-ORE) based on the modified GQ protocol, and constructs a power grid encrypted data range filtering scheme to realize the comparison of ciphertext without disclosing plaintext information. The scheme allows the IoT terminals in the power grid, such as smart meters, to encrypt the electricity consumption data before sending it to the edge server, and to filter the range of encrypted data through the query range ciphertext generated by the data center. The experimental evaluation shows that our r-ORE surpasses Cash et al.'s scheme in terms of execution costs, and its extended scheme is practical in the power grid environment. © 2023 IEEE.

关键词： Internet of things

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Resume Screening-Testing for data Stability

Resume Screening-Testing for Data Stability

引用

2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Wahedna, Asim Vakil, Adit Shah, Somil Kelkar, Vishakha V. Shrivastava, Ishan Dwarkadas J.Sanghvi College of Engineering Department of Electronics and Telecommunication Mumbai400056 India

ISBN: (纸本)9798350319200

Resume parsing and evaluation are critical tasks for human resources teams, and with the surplus of candidates applying to jobs in today's time, the process of resume evaluation is both manual and time consuming. The process of resume parsing has been traditionally done manually, which is time-consuming and prone to errors. However, with the advancements in Natural Language processing (NLP), automated resume parsing has become a popular research area. In addition to parsing, the advent of machine learning has allowed resume screening to be automated, through the use of key words and named entities. In this technical paper, we provide a comprehensive analysis of pre-existing research on resume parsing and evaluation frameworks, and provide a detailed overview of the different NLP techniques used for resume parsing, such as Named Entity Recognition, along with the various machine learning algorithms used for screening resumes, such as cosine similarity. We then implement our own models using resume datasets provided by a third-party firm, and analyze the stability and consistency of output data for each model using frequency distribution graphs, statistical averages, etc. in order to determine the best model for our given dataset. This paper provides a number of valuable graphs and observations that can be helpful for researchers and practitioners in the field of recruitment, machine learning. © 2023 IEEE.

关键词： e-recruitment Hiring Information Retrieval Job Seekers Machine learning Natural Language processing Resume Analysis Resume Evaluation Resume quality assessment Resume Recommendation Resume Similarity

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FedEntropy: Efficient Federated Learning for Non-IID Scenarios Using Maximum Entropy Judgment-based Client Selection 21

FedEntropy: Efficient Federated Learning for Non-IID Scenari...

引用

作者： Ling, Zhiwei Yue, Zhihao Xia, Jun Wang, Ting Chen, Mingsong Lian, Xiang East China Normal University MoE Engineering Research Center of SW/HW Co-design Tech. and App. Shanghai China Kent State University Department of Computer Science Kent United States

ISBN: (纸本)9798350329223

Although various techniques have been proposed for Federated Learning (FL) to address its problem of low classification accuracy in non-IID scenarios, most of them neglect both i) distinct data distribution characteristics of heterogeneous devices (i.e., clients), and ii) benefits and hazards of local models for global model aggregation. In this paper, we present FedEntropy, an efficient FL approach with a novel two-stage dynamic client selection scheme that fully takes the above two factors into account. Unlike existing FL methods, FedEntropy firstly selects clients with high potential for benefiting global model aggregation in a coarse manner, and then further filters out inferior clients from such selected clients by using our proposed maximum entropy judgment method. Based on the pre-collected soft labels of the selected clients, FedEntropy only aggregates those local models that can maximize the overall entropy of their soft labels, thus effectively improving global model accuracy while reducing the overall communication overhead. Comprehensive experimental results on well-known benchmarks demonstrate both the superiority of FedEntropy and its compatibility with state-of-the-art FL methods. © 2023 IEEE.

关键词： client selection Federated Learning maximum entropy judgment non-IID data distribution

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Machine Learning-Based ALS Diagnosis Using Gene Expression data

Machine Learning-Based ALS Diagnosis Using Gene Expression D...

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2023 RIVF international conference on computing and Communication Technologies, RIVF 2023

作者： Vu, Duc-Long Le, Hai-Chau Posts and Telecommunications Institute of Technology Department of Data engineering Hanoi Viet Nam

ISBN: (纸本)9798350315844

Amyotrophic Lateral Sclerosis (ALS) is a rare disease that debilitates the body of a patient with few treatment methods. The biological insight involved in ALS remains elusive and faces challenges in making decisions about diagnosis. Recently, gene expression has been valuable for overcoming obstacles in analysis and providing accurate diagnosis out-comes in many diseases. In this work, we presented a new method concentrating on biomarker selections including three different procedures of gene reduction. We retrieved datasets and conducted differential expression analysis to identify the gene markers that vary significantly. We then apply filter and embedded methodology for gene selection with the Maximum Relevance Minimum Redundancy (MRMR) followed by the least absolute shrinkage and selection operator (LASSO) regression. Various machine learning algorithms corresponding with a set of gene combinations are then estimated using a series of cross-validation procedures. The optimal gene subset corresponding with the machine learning algorithms is then validated separately on the testing dataset and considered the best model based on the receiver operating characteristic (ROC) curve. Finally, a combination including 22 potential genes with Logistic Regression algorithms is considered the most effective method for diagnosis of ALS with an AUC score of 87.90% which is dominant in comparison with other current methods. © 2023 IEEE.

关键词： Gene expression

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Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks 4

Traffic Sign Detection in the Digital Era: Leveraging Convol...

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4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Kothai, G. Anbumozhi, Anna Abirami, R. Kavin, R.A. Karthikeyan, M. Kalyani, T. Gokulpandian, M. TamilNadu Coimbatore India SRM Institute of Science and Technology Department of Data Science and Business TamilNadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies TamilNadu Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Virudhunagar India

ISBN: (纸本)9798350313826

Road safety depends heavily on traffic signs, and as there are more vehicles on the road, the demand for precise traffic sign recognition systems rises. The development of machine-learning algorithms to predict and recognize traffic signs in real-time has been the main focus of research. An overview of the state of advancement in image analysis and classification for recognizing road signs is given in this article. It also goes through their benefits and drawbacks in various traffic situations. Managing various lighting and weather situations, the necessity for real-time processing, and future connection with autonomous driving systems are just a few of the difficulties that are highlighted in the paper. This study seeks to provide guidance to professionals working in the areas of autonomous driving, computer vision, and machine learning who recognize traffic signs. © 2023 IEEE.

关键词： Traffic signs

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Secure Auditing with Encrypted Trace Logs in Multi-Cloud Storage Management 1

Secure Auditing with Encrypted Trace Logs in Multi-Cloud Sto...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Gnanasudharsan, A. Meena, P. Guru Raghavendra, S. Sreevarshan, S. Rithika, S.R. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350316469

Cloud computing revolutionizes the way resources are utilized, offering a platform where they can be dynamically allocated based on demand. This on-the-fly data processing and storage capability has led various providers to offer both free and premium storage solutions. However, with a multitude of providers in the market, accessing data scattered across them can be cumbersome. Hence, the emergence of multi-cloud storage management tools, which provide a unified interface for users to seamlessly access data without juggling between multiple providers. Though authenticated access safeguards data, maintaining its integrity requires oversight of user activities. This calls for the implementation of a Trace Log that meticulously records each action. Such logs, while crucial for auditing, are sensitive and can be vulnerable. To bolster their security, encryption techniques can be implemented, using unique user-specific keys. This not only enhances the log's security but also ensures controlled, authenticated access. A proposal for a robust, secure, and efficient system to manage and monitor data across multiple cloud platforms. © 2023 IEEE.

关键词： Authentication Cloud computing data processing Encryption Multi-Cloud Storage Management Streamline Trace Log

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Distributed log collection for business processes based on ELK architecture 2

Distributed log collection for business processes based on E...

引用

2nd IEEE international conference on Electrical Engineering, Big data and algorithms, EEBDA 2023

作者： Zong, Yize North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-cale Stream Data Beijing China

ISBN: (纸本)9781665462532

When it comes to executing service solutions in a distributed environment, the service solution is considered as a workflow split into many sub-processes that are scheduled to run independently and autonomously on each processing engine. Each organization's machines have its own set of limitations. They can't write too much code or rebuild the system, so they'll have to rely on a lightweight, low-code strategy to expand the service solution's process activities to collect valuable logs. As a result, we created a log collection system for the case, as mentioned earlier. The listener class is used to insert some functional code that sends the logs to the messaging middleware. Then, the service call records, or log data, are transmitted to the central administration platform. The ELK (Elasticsearch, Logstash, Kibana) framework, commonly used in log analysis nowadays, is then used to perform real-time data processing. Finally, the logs are visually displayed to provide intuitive feedback on log information and to make system maintenance and adjustment easier. © 2023 IEEE.

关键词： Middleware

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Hyper Lattice Structure for data Cube Computation

Hyper Lattice Structure for Data Cube Computation

引用

international conference on data Analytics and Management, ICDAM 2022

作者： Phogat, Ajay Kumar Mann, Suman Maharaja Surajmal Institute of Technology GGSIPU New Delhi110058 India

ISBN: (纸本)9789811976148

In the current business scenario, a quick and accurate response is required for business decisions. data warehouse consists of huge volumes of data repositories. This information is utilized to develop insights for decision-making queries on data, besides it takes a long time to process, resulting in longer response times. This response time must be lowered in order to make effective and efficient decisions. The computation of data cubes is a momentous task in data warehouse design. Pre-computation may significantly reduce the response time and also improve online analytical processing efficiency by computing partially or all or part of a data cube. However, such computing is difficult since it may need a substantial amount of time and storage space. Many authors have suggested algorithms for efficient data cube computation for reducing the time computation and storage cost. In this paper, we have proposed a framework that uses a heuristic approach for data cube computation in the hyper lattice structure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data warehouses

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Descriptive Statistical Analysis and Discretization of Academic data for Machine Learning Techniques 10

Descriptive Statistical Analysis and Discretization of Acade...

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Balwinder Gupta, Anu Singla, Ravinder K. Panjab University Chandigarh India

ISBN: (纸本)9789380544472

Every educational institute gathers voluminous data, which includes student enrolment data, student attendance, classroom participation, assessment results, course information, etc. Basic data analysis tools and techniques are not adequate to process and analyze a large volume of academic data. Machine learning or data mining techniques have been applied by researchers which require pre-processing of the dataset. The discretization technique is commonly applied during data pre-processing to transform continuous feature values into discrete ones. The academic datasets are mostly continuous and hence require discretization. The paper attempts to apply descriptive statistical analysis to academic data to gain a better insight and understanding of the dataset. The observations obtained from descriptive analysis such as range, skewness, median, and mode are further applied in the discretization of the dataset. In the discretization process, both supervised and unsupervised methods have been used to convert continuous numeric values into discrete or nominal values. It has been found from the experiments that the CSForest algorithm has generated the best model for academic performance prediction. The proposed work has achieved a percentage accuracy of 97.10 % and outperformed the previous study. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Statistical methods

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Research on teaching quality evaluation system based on data mining technology

Research on teaching quality evaluation system based on data...

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2023 international conference on Intelligent Systems, Communications, and Computer Networks, ISCCN 2023

作者： Huang, Dongxian Liu, Tingting National University Manila1008 Philippines St.Paul University Manila Manila1004 Philippines Changjiang Polytechnic Wuhan430074 China

ISBN: (纸本)9781510666238

The task of quickly finding target information from a huge database is required in the era of big data. Association rule algorithms, which mainly carry out research in terms of dimensionality of data, abstraction level and types of processing variables, have become an effective method for solving data mining problems. data mining techniques can discover a lot of information hidden behind a large amount of data and provide effective data support for important decisions. Association rule-based data mining techniques are one of the most important research areas, and the use of association rule algorithms can identify the key factors affecting the research object. In this paper, the classical association rule algorithm Apriori and its improvement algorithm are studied to investigate the factors influencing teaching quality in vocational institutions using data mining techniques. The article combines the association rule algorithm of data mining technology and database technology, and constructs a database matching the association rule mining through the process of data collection and data pre-processing. The key factors of teaching quality are analyzed by using association rule algorithm, and the teaching quality evaluation system based on data mining technology is studied in depth. © 2023 SPIE. All rights reserved.

关键词： data mining

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