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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Underwater Plankton Image Enhancement Method Based on Local Histogram Equalization

Underwater Plankton Image Enhancement Method Based on Local ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Jin Yang Wenqiang Fan Longwen Fu Chunlei Xia School of Physics and Electronic Information Yantai University Yantai China Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Classification and identification of marine plankton are important for marine ecological environment monitoring. Due to the tiny size of individual plankton and the weak light intensity in the marine environment, it is challenging for the imaging system to capture a clear image of plankton in the flowing water. Sometimes causing weak brightness and noise interference in the collected images. To this end, we propose an underwater image enhancement method based on local histogram equalization to remove noise while improving image clarity effectively. the proposed method improves the brightness and contrast in HSI color space, and preserves the color information (hue and saturation) in H and S channels. Experiments were carried out on the underwater plankton images. the enhancement performance of the proposed method was evaluated by commonly used metrics and compared with the well-known image enhancement methods, including a deep learning-based method. the experimental results showed that the proposed method was superior to the other methods in noise reduction, structure similarity, and perceptual quality. this work presents a simple and effective method for underwater plankton image enhancement. the proposed enhancement method could provide high-quality images for implementing automatic plankton classification.

关键词： Measurement Histograms Image color analysis Plankton Brightness Noise reduction pattern recognition Colored noise Image enhancement Monitoring

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A Review on data Balancing Techniques and machine learning Methods

A Review on Data Balancing Techniques and Machine Learning M...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Gaurav Parmar Rimi Gupta Tejas Bhatt G.J. Sahani Brijeshkumar Y. Panchal Hiren Patel Dept. of Computer Engineering S.V.I.T Vasad Gujarat Software Developer TÜV SÜD South Asia Pvt. Ltd

Researchers in the discipline of data mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. this research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher’s work learned to determine the most effective method for disseminating the data. there will be a discussion of ways to enhance categorization abilities in the conclusion. the future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations.

关键词： Distributed databases machine learning data mining

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Named Entity recognition for Protecting Sensitive data using Hybrid CNN

Named Entity Recognition for Protecting Sensitive Data using...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Sheela Gowr. P Kumar. N Department of Computer science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. the named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. the proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. the proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall.

关键词： Deep learning Training Radio frequency databases data security Forestry Predictive models

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5th international conference on Big data and Security, ICBDS 2023

5th International Conference on Big Data and Security, ICBDS...

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5th international conference on Big data and Security, ICBDS 2023

ISBN: (纸本)9789819743865

the proceedings contain 18 papers. the special focus in this conference is on Big data and Security. the topics include: Development of U-net Neural Network for Biomedical Images with Big data;Research on Implementation of BADGE Active learning to Increase Defect Detection Effectiveness;stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection;design of Human Resource Optimal Allocation Model for Electric Power Construction Enterprises Based on machine learning;research on the Construction of Digital Education Cloud Resource database Based on Ant Colony Algorithm;simulation of Short-Term Prediction Model of Real Estate Price Index Based on Neural Network Algorithm;PLMR: An Efficient Pre-trained Model for Aircraft Maneuver recognition;blockchain-Based WeChat Small Program Platform: A New Type of data Security and Management Solution;a Survey of Edge Computing Resource Allocation and Task Scheduling Optimization;an Algorithm for Analyzing Psychological Characteristics in Music Education Based on Markov Model;enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions;internet Public Opinion Evolution Model and Simulation Based on Big data and Grey Clustering Algorithm Under New Media Environment;simulation of Regional Financial and Economic Development Decision Model Based on Decision Tree Algorithm;simulation Research on Pre-alarm Model of Enterprise Shared Financial Crisis Based on Genetic Algorithm;research on Accurate Poverty Alleviation Algorithm of Rural Industry Based on Association Rules mining;preface.

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Exploring the Frontiers of Security: Comprehensive Biometric Security Solution with Multiple Modalities Enhanced by machine learning

Exploring the Frontiers of Security: Comprehensive Biometric...

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Emerging Technology (INCET), international conference for

作者： S Raghavendra M J Prasanna Kumar S V Shashikala Dept. of CSE BGS Institute of Technology Adichunchanagiri University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Individuals' physiological or characteristics of conduct are used by biometric systems to identify and authenticate users. Systems with these attributes fall into two categories: unimodal and multimodal. the former suffers from a few of issues that affect system accuracy, including as noisy data, intra-class variance, inter-class similarity, non-universality, and spoofing. Conversely, multimodal systems, have demonstrated a notable improvement in identification and verification success rates. Multimodal systems detect and process two or more physiological or behavioural features. this study uses fingerprint, iris, and ear data to develop a bimodal biometric sensing technique. DNN Methods of machine learning are applied in the recognition process, and the research explores the design and functioning of bimodal systems.

关键词： Biometrics (access control) System performance Authentication machine learning Physiology Real-time systems Sensors

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Comparison of deep learning and hand crafted features for mining simulation data 25

Comparison of deep learning and hand crafted features for mi...

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25th international conference on pattern recognition (ICPR)

作者： Georgiou, theodoros Schmitt, Sebastian Baeck, thomas Pu, Nan Chen, Wei Lew, Michael Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Honda Res Inst Europe GmbH Offenbach Germany

ISBN: (纸本)9781728188089

Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes, airplanes parts etc. the output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated. Automated data analysis methods are warranted but a non-trivial obstacle is given by the very large dimensionality of the data. A flow field typically consists of six measurement values for each point of the computational grid in 3D space and time (velocity vector values, turbulent kinetic energy, pressure and viscosity). In this paper we address the task of extracting meaningful results in an automated manner from such high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data, i.e. predicting drag and lift forces applied on an airfoil. We also propose an adaptation of the classical hand crafted features known from computer vision to address the same problem and compare a large variety of descriptors and detectors. Finally, we compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches. Our results show that the deep learning-based methods, as well as hand crafted feature based approaches, are well-capable to accurately describe the content of the CFD simulation output on the proposed dataset.

关键词： Flow fields

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Advancements in Sensitive Information recognition through Blockchain Technology

Advancements in Sensitive Information Recognition Through Bl...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Ming Zhang Mingtian Peng Wenyang Cui Lin Zhang Travelsky Technology Limited Beijing China College of Cyber Science Naikai University Tianjin China Chinese Institute of New Generation Artificial Intelligence Development Strategies Naikai University Tianjin China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

In the face of an increasingly digitized and informationized society, biometric technologies, including fingerprint recognition and facial recognition, have penetrated into our lives. However, sensitive information data stored using traditional database technology still faces data security issues during storage and use, such as leakage, tampering, and identity theft. On this premise, this article designs a blockchain based airport sensitive information security identification method, which ensures the integrity and security of sensitive information data during storage and use through the characteristics of blockchain decentralization, tamper resistance, and traceability. this article selects Ethereum blockchain as the platform, facial recognition technology based on deep learning as the identity authentication method, smart contracts as the specific implementation technology, and airports as the specific application scenarios of this technology. this article first outlines the research progress of facial recognition technology and blockchain technology in information recognition technology. Secondly, the specific implementation path of this method was introduced. then, the functionality and performance of the method were tested through written test cases, and the security of the method was analyzed. Finally, a summary of the work in this article was made, and further improvement and optimization directions were proposed.

关键词： Deep learning databases Face recognition data security Smart contracts Memory Airports Blockchains Secure storage Optimization

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Liu, Wei Xu, Rong-Yao Qiao, Lang-Chao Wang, Jin-Lu Gao, Bao-Hong Yang, Xin-Gang Feng, Wen-Tao School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Division of Monopoly Administration Shaanxi Branch of China National Tobacco Corporation China

ISBN: (纸本)9781450396899

the number of cigarette-related packages and normal packages is often extremely unbalanced in practice, this characteristic must be considered in the express-related counterfeit cigarette crime prediction, otherwise the performance of prediction model on unknown packages will be affected badly. In this paper, an approach of using SMOTE-ENN technology to deal with imbalanced express data and establish the express-related counterfeit cigarette crime prediction model is proposed. In the preprocessing stage, original data sets are structured by Chinese word segmentation method. In feature calculation and analysis stage, the spatio-temporal features of express data are calculated and analyzed, and the optimal features are further obtained by feature reduction algorithm named PCA. Next, based on optimal features, balanced data are obtained by mixed sampling. Finally, the classifier model was trained and optimized to predict the probability of a package containing counterfeit cigarettes in express data including 2851 normal samples and 285 cigarette-related samples randomly selected from 3077 cigarette-related samples. the performance of all classifiers is improved after using the SMOTE_ENN to balance datasets, and the indicators are above 98%. Experimental results demonstrate feasibility, effectiveness of the proposed approach. © 2022 ACM.

关键词： Crime

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5th international conference on Big data and Security, ICBDS 2023

5th International Conference on Big Data and Security, ICBDS...

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5th international conference on Big data and Security, ICBDS 2023

ISBN: (纸本)9789819743896

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16th international Workshop on data Privacy Management, DPM 2021, and 5th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2021 held in conjunction with ESORICS 2021

16th International Workshop on Data Privacy Management, DPM ...

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16th international Workshop on data Privacy Management, DPM 2021, and 5th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2021 held in conjunction with ESORICS 2021

ISBN: (纸本)9783030939434

the proceedings contain 23 papers. the special focus in this conference is on data Privacy Management. the topics include: Towards a Formal Approach for data Minimization in Programs (Short Paper);Virtual ASICs: Generalized Proof-of-Stake mining in Cryptocurrencies;asymmetric Asynchronous Byzantine Consensus;using Degree Centrality to Identify Market Manipulation on Bitcoin;Augmenting MetaMask to Support TLS-endorsed Smart Contracts;smart Contracts for Incentivized Outsourcing of Computation;anonymous Sidechains;filling the Tax Gap via Programmable Money;Impact of Delay Classes on the data Structure in IOTA;synthesizing Privacy-Preserving Location Traces Including Co-locations;Secure Static Content Delivery for CDN Using Blockchain Technology;lattice-Based Proof-of-Work for Post-Quantum Blockchains;blockchain-Based Two-Factor Authentication for Credit Card Validation;homomorphic Decryption in Blockchains via Compressed Discrete-Log Lookup Tables;quantitative Rubric for Privacy Policy Analysis;rethinking the Limits of Mobile Operating System Permissions;interdependent Privacy Issues Are Pervasive Among third-Party Applications;SPGC: An Integrated Framework of Secure Computation and Differential Privacy for Collaborative learning;a k-Anonymised Federated learning Framework with Decision Trees;anonymizing machine learning Models;a New Privacy Enhancing Beacon Scheme in V2X Communication;Foreword from the DPM 2021 Program Chairs.

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