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Robust Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net 5

Robust Multi-Organ Nucleus Segmentation Using a Locally Rota...

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5th international conference on Medical Imaging with Deep learning (MIDL)

作者： Oreiller, Valentin Fageot, Julien Andrearczyk, Vincent Prior, John O. Depeursinge, Adrien HES SO Inst Informat Syst Sierre Switzerland CHU Vaudois Serv Nucl Med & Mol Imaging Lausanne Switzerland Ecole Polytech Fed Lausanne AudioVisual Commun S Lab LCAV Lausanne Switzerland

Locally Rotation Invariant (LRI) operators have shown great potential to robustly identify biomedical textures where discriminative patterns appear at random positions and orientations. We build LRI operators through the local projection of the image on circular harmonics followed by the computation of the bispectrum, which is LRI by design. this formulation allows to avoid the discretization of the orientations and does not require any criterion to locally align the descriptors. this operator is used in a convolutional layer resulting in LRI Convolutional Neural Networks (LRI CNN). To evaluate the relevance of this approach, we used it to segment cellular nuclei in histopathological images. We compared the proposed bispectral LRI layer against a standard convolutional layer in a U-Net architecture. While they performed equally in terms of F-score, the LRI CNN provided more robust segmentation with respect to orientation, even when rotational data augmentation was used. this robustness is essential when the relevant pattern may vary in orientation, which is often the case in medical images.

关键词： Local Rotation Invariance Convolutional Network Deep learning Segmentation Bispectrum

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A Traffic Sign recognition qnd Speech-BasedFeedback Generation System to Assist Drivers

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE international conference on

作者： Himangshu Sarma Navanath Saharia Department of Computer Science & Engineering IIIT Sri City Andhra Pradesh Data Engineering Lab CSE IIIT Senapati Manipur

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. the proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as “Drive slow, speed breaker ahead’, “Maximum Speed Limit is 80km/hr“, “School Ahead, drive slow”, “Don't take overtake” etc.

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Graph Analysis of the Ethereum Blockchain data: A Survey of datasets, Methods, and Future Work 5

Graph Analysis of the Ethereum Blockchain Data: A Survey of ...

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5th IEEE international conference on Blockchain (Blockchain)

作者： Khan, Arijit Aalborg Univ Aalborg Denmark

ISBN: (数字)9781665461047

ISBN: (纸本)9781665461047

Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous agents), ether (native cryptocurrency), tokens (digital assets), dApps (decentralized applications), and DeFi (decentralized finance). these key actors in the Ethereum interact with each other via transactions and contract calls. Given the highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in Ethereum blockchain. Recently, several research works performed graph analysis on the publicly available Ethereum blockchain data to reveal insights into its transactions and for important downstream tasks, e.g., cryptocurrency price prediction, address clustering, phishing scams and counterfeit tokens detection. In this work, we conduct an in-depth survey of the existing literature. We categorize them based on publication years, venues, core ranking, and authors' affiliations, data usage and graphs construction, graph mining and machine learning techniques employed, and the new insights derived by them. We conclude by discussing our recommendations on the future work. Our article will be useful to data scientists, researchers, financial analysts, and blockchain enthusiasts.

关键词： blockchain ethereum network analysis

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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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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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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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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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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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