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

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Mapping System to Interprete a Fracture Zones in Seismic data Based on an Autoencoder and Generative Adversarial Networks

Mapping System to Interprete a Fracture Zones in Seismic Dat...

引用

2023 international conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Vokhmintsev, A.V. Khristodulo, O.I. Polyakova, L.Y. Chelyabinsk State University Institute of Information Technology Chelyabinsk Russia Ugra State University Institute of Digital Economy Khanty-Mansiysk Russia Ufa University of Science and Tecnology Geoinformation System Departmrnt Ufa Russia Kumertau Branch of Orenburg State University Faculty of Full-Time and Part-Time Education Kumertau Russia

ISBN: (纸本)9781665475952

Our research was aimed at developing methods and algorithms for identifying and mapping fracture zones of the Jurassic and pre-Jurassic complexes based on seismic data and methods of machine learning and artificial intelligence. In this paper it were developed and tested: the feature selection algorithm based on an autoencoder for interpreting data on seismic slices with low intraclass and high interclass variability, as well as the algorithm for synthesizing new images based on generative adversarial networks and a seismic database. The experiments carried out have shown that the algorithms have good accuracy and speed of operation, which makes them potentially suitable for processing large volumes of seismic information. The proposed autoencoder for candidate images with a high recall value both for the search variant with the micro-averaging metric and for the search with the macroaveraging metric. The automatic interpretation of seismic data on seismic slices of the Salymskaya well was carried out. The system for processing, analyzing and classifying 3D seismic information was developed based on the proposed methods and algorithms. The optimal predictive model was tuned based on machine learning and artificial intelligence methods. © 2023 IEEE.

关键词： Mapping

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Blockchain-Based Decentralized Authority for Complex Organizational Structures Management

Blockchain-Based Decentralized Authority for Complex Organiz...

引用

29th international conference on Parallel and Distributed computing (Euro-Par)

作者： Jarosz, Kamil Wojtyczek, Patryk Slota, Renata G. AGH Univ Krakow Krakow Poland

ISBN: (纸本)9783031488023;9783031488030

Collaboration based on data sharing within global networks of autonomous organizations requires complex user and group structures. Decentralized group management provides greater resilience and independence compared to classical approaches with centralized architectures. We propose a blockchain-based solution that ensures consistency of global user and group structures, allowing them to be updated collectively by a set of authorized organizations. The evaluation showed the blockchain-related costs of performing changes to the structures.

关键词： data sharing decentralized management collaborative systems blockchain

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Walking to Authenticate: Identifying Robust Behavioral Biometrics from Step Count data 15

Walking to Authenticate: Identifying Robust Behavioral Biome...

引用

15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Chen, Zhen Shi, Keqin Sun, Weiqiang Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798350308693

Step count data, readily available from smartphones and wearable devices, offers valuable insights into a user's physical activities and lifestyle patterns, positioning it as a viable contender for integration into biometric authentication systems. In this study, we introduce Step Count Print (SCP), a novel behavioral biometric derived from coarse-grained minute-level step count data, featuring daily step count distribution to capture an individual's unique physical activity pattern. We conduct an ablation study with data from 100 users collected over a five-year period, utilizing mainstream machine learning algorithms. Our experimental results demonstrate SCP's non-redundancy in user authentication scenarios, achieving an impressive average accuracy of $92.3 \%$. We showcase the universality and effectiveness of SCP across various classification algorithms through user accuracy histograms. Our proposed strategy holds promise for user-friendly and reliable biometric authentication, leveraging step count data to enhance security and usability in diverse applications. © 2023 IEEE.

关键词： Biometrics

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Distributed Attacks Classification Based on Radical Basis Function and Particle Swarm Optimization In Hypervisor Layer 6

Distributed Attacks Classification Based on Radical Basis Fu...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Shukla, Aditya Kumar Sharma, Ashish GLA University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350346961

The term 'cloud computing' refers to a concept for on-demand provisioning of computer resources (including hardware and software) through the Internet. Cloud computing's primary benefits lie in the time and money savings it may provide to IT departments. While cloud computing offers many benefits It also raises many safety considerations. These include data privacy, data availability, data placement, data security credential, license, access management, network security, web data protection, virtual devices protection, etc. Recently, machine learning has shown to be an effective tool for safeguarding the cloud. In order to automate the process of identifying cloud assaults with better accuracy than any other technology, machine learning algorithms are trained on numerous legitimate datasets to develop models. To mitigate the effects of DDoS invasions, our research suggests implementing a detection method at the hypervisor layer. As a means of detecting and categorizing DDoS attacks in the communication between virtual computers, the suggested detection method combines the particle swarm optimization (PSO) with radial basis function (RBF). High classification accuracy against DDoS attacks is the focus of our investigation and recommended method.. © 2023 IEEE.

关键词： data privacy

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Loan Prediction Using Machine Learning

Loan Prediction Using Machine Learning

引用

2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sarisa, Hemanth Kumar Khurana, Varun Koti, Venkat Chandu Garg, Neha Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering India

ISBN: (纸本)9798350344387

Loan prediction is a significant problem in the banking industry. Using Technology, will change and make some improvements in Loan prediction by bank employees manually. By using "Machine learning "in loan prediction. The machine learning system will check the data for a loan of the applicant and the data will be checked so fast and without any human error. So, it will help to improve technology in Bank sectors. By this, processing the data will be checked quickly, making the data output in less time. It helps both the applicant and the bank *** learning algorithms can be used to analyze historical data and make accurate predictions about whether a loan application will be approved or not. In this study, we propose a machine learning-based loan prediction model that utilizes various features such as credit score, income, loan amount, and loan term. We explore different classification algorithms, including logistic regression, decision tree, and random forest, to build the loan prediction model. Our experimental results show that the random forest algorithm outperforms other models with an accuracy rate of 83.45%. The proposed model can help banks and financial institutions streamline their loan approval process, reduce manual effort, and minimize the risk of bad loans. © 2023 IEEE.

关键词： data Pre-processing Decision Tree K-Neighbours Classifier Machine Learning Python Random Forest Classifier

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Construction of cloud detection sample library based on Landsat-8 data and semi-supervised learning

Construction of cloud detection sample library based on Land...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhou, Jianlin Zhao, Bowen Feng, Xiaoxing Sun, Yaxing College of Electronic Engineering and Optoelectronic Technology Nanjing University of Science and Technology Nanjing210000 China Engineering Training Centre Nanjing University of Science and Technology Nanjing210000 China

ISBN: (纸本)9781510671881

Cloud detection of remote sensing images is an important preprocessing step for remote sensing image applications. While machine learning (deep learning) methods improve the accuracy of cloud detection in remote sensing images, they put forward higher requirements for the quantity and quality of data sets. However, if you want to reconstruct the dataset by yourself, you generally need to select the training samples by manual visual interpretation, which will consume a lot of manpower and material resources. Previous researches mainly focused on the optimization of cloud detection algorithms, and few studied the extraction of training samples. In order to construct a cloud detection dataset, this paper proposes an erosion and diversion-based semi-supervised learning (EDSL) model. Fmask algorithm is used to automatically obtain cloud masks from Landset-8 remote sensing images to construct a cloud detection dataset. The results show that the dataset constructed by EDSL model is better than Fmask algorithm. The precision of the dataset constructed by EDSL model reaches 0.93, which exceeds Fmask. At the same time, we use SVM algorithm to conduct experiments on this data set, and the results are equivalent to the data set constructed by human vision, which proves the scientific nature of this method. © 2023 SPIE. All rights reserved.

关键词： Landsat

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Transformers, Tables and Frame Semantics 17

Transformers, Tables and Frame Semantics

引用

17th IEEE international conference on Semantic computing (ICSC)

作者： Ramirez, Mario Bogatu, Alex Paton, Norman W. Freitas, Andre Univ Manchester Dept Comp Sci Manchester Lancs England Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9781665482639

Transformer-based language models are able to capture linguistic patterns at scale by encoding both syntactic and semantic dimensions of natural language representations with the aim of achieving language understanding. While Transformers have been adapted for generating table embeddings, less research effort has been dedicated to investigating the extent to which these models can encode table semantics. To address this limitation, we propose a method to transfer knowledge from pre-trained natural language models to encode schema-level relationships and analyze the resulting model with respect to two schema-related tasks in different data scenarios.

关键词： data lakes deep learning natural language processing semantics structured data table understanding transformers

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Automation of EEG data processing with HPC Community Cloud 24

Automation of EEG Data Processing with HPC Community Cloud

引用

24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Gorodnichev, Maxim A. Nalepova, Elizaveta D. Merkulova, Ekaterina A. Rudych, Pavel D. Savostyanov, Alexander N. Scientific Research Institute of Neurosciences and Medicine Icm and Mg Sb Ras Novosibirsk State University Novosibirsk State Technical University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Scientific Research Institute of Neurosciences and Medicine Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9798350336870

We study and further develop an approach based on the computational models formalism to automation of data processing pipelines in a specific application domain-neurophysiology. A computational model is a formal specification of a set of variables (in a mathematical sense) of an application domain and a set of operations with their input and output variables. If values of some variables are given, the operations that have these variables as inputs can compute values of their output variables, these variables serve as inputs for other operations and, thus, some subset of variables can obtain their values. This way, all possible/meaningful computational scenarios in the domain can be defined and algorithms (scenarios) for particular computational problems can be automatically derived by forward chaining. In this work, we apply the approach to building a framework for managing data and computations in neurophysiology. Brain studies produce large amounts of data such as electroencephalograms, functional magnetic tomography images, questionnaires, and results of other medical tests. There is a need for systematic management of these data, specification of data processing pipelines, implementing these pipelines on high performance computing systems, representing the process of computing and the results with a graphic user interface. We build a sample computational model to represent a limited set of scenarios that can be reused and further extended in a regular way by adding more variables and operations, propose a framework for management of data and computations, automation of building graphical user interfaces. A prototype implementation of the framework based on the developed computation model is a practical output of this work. © 2023 IEEE.

关键词： Electroencephalography

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Securing data in the Image Using SHA & ECC 2

Securing Data in the Image Using SHA & ECC

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Hamsanandhini, S. Balusubramanie, P. Abinaya, A.B. Kongu Engineering College Cse Tamilnadu Perundurai India Kongu Engineering College Computer Technology - Pg Tamilnadu Perundurai India

ISBN: (纸本)9798350347579

Networks have expanded globally, and information is now available digitally. Due to the volume of data being exchanged online, data security has become a difficult problem. Cryptography provides integrity, confidentiality, non-repudiation, and authenticity to the secret data. Symmetric & asymmetric cryptographic techniques are used today for hiding any confidential information from unauthorized persons. The combination proposed is the asymmetric algorithm (ECC) and a hash function (SHA). This research study introduces a new technique for encrypting and decrypting text that uses elliptical curve cryptography (ECC) to improve data security and a secure hash function (SHA-3) to ensure data integrity. When compared to the previous method for text encryption (RSA), the suggested solution of SHA with ECC is more effective. Compared to other cryptographic algorithms, it offers a higher level of security with a smaller key size. © 2023 IEEE.

关键词： Hash functions

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AIC-based Distributed Photovoltaic data Compression Sensing Method 8

AIC-based Distributed Photovoltaic Data Compression Sensing ...

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8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Shi, Zhan Li, Bo Yang, Zhihua Wang, Xiuzhu Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Communication Management Department Guangdong China Guangdong Electric Power Communication Technology Co. Ltd. Science Innovation Department Guangdong China

ISBN: (纸本)9798350302455

In view of the high-speed data transmission requirements of the new generation of power line carrier communication in distributed photovoltaic information access, data compression sensing is required to improve. In this paper, we study an AIC-based distributed photovoltaic data compression sensing method. Based on the adaptive characteristics of K-singular value decomposition (K-SVD) algorithm, the data sparsity is dynamically estimated to realize the adaptive dynamic adjustment of data acquisition. AIC algorithm is used to reconstruct data, which greatly improves the accuracy of data reconstruction. Information aggregation based on compressed sensing is constructed to achieve high efficiency data aggregation under the condition of low sampling rate. Simulation results show the superior performance of the proposed algorithm in the data transmission volume and energy consumption. © 2023 IEEE.

关键词： data compression

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