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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3348 条记录，以下是781-790 订阅

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and data Science, ARTE2022 held at 33rd international conference on database and Expert Systems Applications, DEXA 2022

6th International Workshop on Cyber-Security and Functional ...

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and data Science, ARTE2022 held at 33rd international conference on database and Expert Systems Applications, DEXA 2022

ISBN: (纸本)9783031143427

the proceedings contain 40 papers. the special focus in this conference is on database and Expert Systems Applications. the topics include: Synthetic data in Automatic Number Plate recognition;An Untold Tale of Scientific Collaboration: SCCH and AC 2 T;on the Creation and Maintenance of a Documentation Generator in an Applied Research Context;towards the Digitalization of Additive Manufacturing;Twenty Years of Successful Translational Research: A Case Study of three COMET Centers;data Integration, Management, and Quality: From Basic Research to Industrial Application;building a YouTube Channel for Science Communication;introduction of Visual Regression Testing in Collaboration Between Industry and Academia;vibration Analysis for Rotatory Elements Wear Detection in Paper Mill machine;applying Time-Inhomogeneous Markov Chains to Math Performance Rating;introducing data Science Techniques into a Company Producing Electrical Appliances;a Technology Transfer Portal to Promote Industry-Academia Collaboration in South-Tyrol;fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing;sending Spies as Insurance Against Bitcoin Pool mining Block Withholding Attacks;risks in DeFi-Lending Protocols - An Exploratory Categorization and Analysis of Interest Rate Differences;battling the Bullwhip Effect with Cryptography;Reporting of Cross-Border Transactions for Tax Purposes via DLT;securing File System Integrity and Version History Via Directory Merkle Trees and Blockchains;taxation of Blockchain Staking Rewards: Propositions Based on a Comparative Legal Analysis;comparison Framework for Blockchain Interoperability Implementations;A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME;towards Strategies for Secure data Transfer of IoT Devices with Limited Resources;application of Validation Obligations to Security Concerns;mode Switching for Secure Edge Devices;a Lifecycle Framework for Semantic Web machine Learn

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Equation Attention Relationship Network (EARN): A Geometric Deep Metric Framework for learning Similar Math Expression Embedding 25

Equation Attention Relationship Network (EARN): A Geometric ...

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25th international conference on pattern recognition (ICPR)

作者： Ahmed, Saleem Davila, Kenny Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo CSE Dept Buffalo NY 14260 USA

ISBN: (纸本)9781728188089

Representational learning in the form of high dimensional embeddings have been used for multiple pattern recognition applications. there has been a significant interest in building embedding based systems for learning representations in the mathematical domain. At the same time, retrieval of structured information such as mathematical expressions is an important need for modern IR systems. In this work, our motivation is to introduce a robust framework for learning representations for similarity based retrieval of mathematical expressions. Given a query by example, the embedding can find the closest matching expression as a function of euclidean distance between them. We leverage recent advancements in image-based and graph-based deep learning algorithms to learn our similarity embeddings. We do this first, by using unimodal encoders in graph space and image space and then, a multi-modal combination of the same. To overcome the lack of training data, we force the networks to learn a deep metric using triplets generated with a heuristic scoring function. We also adopt a custom strategy for mining hard samples to train our neural networks. Our system produces rankings similar to those generated by the original scoring function, but using only a fraction of the time. Our results establish the viability of using such a multi-modal embedding for this task.

关键词： Mathematical Information retrieval Semi-supervised learning Graph matching

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Advances in Adversarial Attacks and Defenses in Intrusion Detection System: A Survey

Advances in Adversarial Attacks and Defenses in Intrusion De...

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4th international conference on Science of Cyber Security (SciSec)

作者： Mbow, Mariama Sakurai, Kouichi Koide, Hiroshi Kyushu Univ Dept Informat Grad Sch Informat Sci & Elect Engn Fukuoka Japan Kyushu Univ Res Inst Informat Technol Cyber Secur Ctr Fukuoka Japan

ISBN: (纸本)9789811977688;9789811977695

machine learning is one of the predominant methods used in computer science and has been widely and successfully applied in many areas such as computer vision, pattern recognition, natural language processing, cyber security etc. In cyber security, the application of machine learning algorithms for network intrusion detection system (NIDS) has seen promising results for anomaly detection mostly with the adoption of deep learning and is still growing. However, machine learning algorithms are vulnerable to adversarial attacks resulting in significant performance degradation. Adversarial attacks are security threats that aim to deceive the learning algorithm by manipulating its predictions, and Adversarial machine learning is a research area that studies both the generation and defense of such attacks. Researchers have extensively worked on the adversarial machine learning in computer vision but not many works in Intrusion detection system. However, failure in this critical Intrusion detection area could compromise the security of an entire system, and need much attention. this paper provides a review of the advancement in adversarial machine learning based intrusion detection and explores the various defense techniques applied against. Finally discuss their limitations for future research direction in this emerging area.

关键词： Adversarial attack Cyber security Intrusion detection machine learning Deep learning Poisoning attack Evasion attack

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Spatial-Temporal Graph Neural Networks Fusing Multiple data 4

Spatial-Temporal Graph Neural Networks Fusing Multiple Data

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Xu, Haonan Xue, Wenfang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China

ISBN: (纸本)9798350321951

Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. the application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. the experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods. © 2022 IEEE.

关键词： Investments

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machine learning-based Prediction Mechanism of Repeated Purchase Behavior of E-commerce Customers

Machine Learning-based Prediction Mechanism of Repeated Purc...

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Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Shuyuan Li College of Economics and Management Jilin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

A machine learning-based prediction model was developed to understand the repeated purchase behavior of e-commerce customers. A data mining process and the logistic regression algorithm were used in the model. data preprocessing such as data cleaning and feature engineering was conducted for training the model. the model was evaluated in the experiment for accuracy, precision, recall, and area under the receiver operating characteristics curve to ensure comprehensive assessment. A fusion approach was used to combine multiple machine-learning models to enhance prediction accuracy and robustness. Bagging, boosting, stacking, and voting were employed to leverage the strengths of different models for the robust prediction of the model. the model allows e-commerce companies to effectively predict and enhance customer retention.

关键词： Logistic regression Accuracy Stacking data preprocessing Predictive models Prediction algorithms data models

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Detecting Irregular Contexts Using Image recognition and data Analysis for Elderly Monitoring

Detecting Irregular Contexts Using Image Recognition and Dat...

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Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Sinan Chen Masahide Nakamura Kiyoshi Yasuda Kobe University Kobe Japan Kobe University RIKEN Center for Advanced Intelligence Project Tokyo Japan Kobe University Kobe Japan Japan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

In developed countries including Japan, the population is rapidly aging, and dealing with the increasing number of people requiring care is an urgent issue. Assistive technology (AT) can solve problems in medical care, welfare, and home care issues with engineering technology. Previous studies proposed sensors and physical devices for the elderly, but individualized detection and analysis have not yet been realized. thus, we developed proposed a method for detecting and analyzing situations in the daily life of the elderly. the time-series data was analyzed using an image recognition pre-trained model with lightweight machine learning. the model helps to improve quality of life (QoL) and promote a stable daily life for the elderly at home.

关键词： Image recognition Sociology Medical services machine learning data models Sensors Internet of things

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A Systematic Survey on COVID 19 Detection and Diagnosis by Utilizing Deep learning Techniques and Modalities of Radiology 2022

A Systematic Survey on COVID 19 Detection and Diagnosis by U...

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14th international conference on Contemporary Computing, IC3 2022

作者： Agrawal, Shrishtee Singh, Abhishek Tiwari, Abhishek Mishra, Anushri Tripathi, Abhinandan Department of Computer Science and Engineering Buddha Institute of Technology Gorakhpur India

ISBN: (纸本)9781450396752

One of the most difficult aspects of the present COVID19 pandemic is early identification and diagnosis of COVID19, as well as exact segregation of non-COVID19 individuals at low cost and the sickness is in its early stages. Despite their widespread use in diagnostic centres, diagnostic approaches based solely on radiological imaging have flaws given the disease's novelty. As a result, to evaluate radiological pictures, healthcare practitioners and computer scientists frequently use machine learning and deep learning models. Based on a search strategy, from November 2019 to July 2020, researchers scanned the three different databases of Scopus, PubMed, and Web of Science for this study. machine learning and deep learning are well-established artificial intelligence domains for data mining, analysis, and pattern recognition. Deep learning in which data is passed through many layers and automatically learning the composition of each layer from large dataset and it enables a new way that evaluates the complete image without human guidance to discern which insights are valuable, with applications ranging from object detection to medical image. Deep learning with CNN may have a significant effect on the automatic recognition and extraction of crucial features from X-ray and CT Scan images related to Covid19 analysis. According to the results, models based on deep learning possess amazing abilities to offer a precise and systematic system for detecting and diagnosing COVID19. In the field of COVID19 radiological imaging, deep learning software decreases false positive and false negative errors in the identification and diagnosis of the disease. It is providing a once-in-a-lifetime opportunity to provide patients with quick, inexpensive, and safe diagnostic services while also reducing the epidemic's impact on nursing and medical staff. © 2022 ACM.

关键词： Computerized tomography

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Gesture recognition Technology in Smart Gloves Enhanced by machine learning

Gesture Recognition Technology in Smart Gloves Enhanced by M...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： thangam S Nithin Kandi Dorai Sai Charan Vijay Gosu J. Jesy Janet Kumari Virupaxappa G Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. the entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. this fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive.

关键词： Hands Flexible printed circuits Heuristic algorithms machine learning Gesture recognition Sensor fusion Real-time systems Sensor systems Reliability Intelligent sensors

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Privacy-Preserving Deep learning Models for Human Activity recognition in Edge Computing Environments

Privacy-Preserving Deep Learning Models for Human Activity R...

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Artificial Intelligence and Signal Processing (AISP), INDIA, international conference on

作者： Kambala Vijaya Kumar Challari Pranitha Annam Tanuja Pagolu Indira PriyaDarsini Eswar Patnala Vinay Kumar Dasari Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

the increase of wearable devices and smart sensors led to increase of human activity recognition system; this plays a vital role in various domains like healthcare and mainly we need to take further modifications on education, sports and security. Human activity recognition system raises privacy concerns, it often involves the collection of sensitive data of users, there is an approach to modernizing human activity recognition system by using machine learning techniques and data identification and lattice based methods to ensure privacy while restructuring and managing unwanted data or activities in the recognition process. Human activity recognition system involves the continuous monitoring and analyzing the individual activities. To address this, we can use machine learning approaches like deep learning models which improve the accuracy of activity recognition while minimizing the intrusion on the user privacy. Here we use machine learning for accurate the activity recognition and deepdeep learning techniques for preserving privacy and restructuring of unwanted activities. Combining these techniques we can safeguard user privacy in an increasingly data centric world. In this paper we are seeing as the negligible of human exercises from the pictures or recordings taken from the different public where the high goal can be distinguished and getting the exceptionally low goal and protection can be stowed away from such pictures or recordings.

关键词： Deep learning Privacy data privacy Accuracy Recurrent neural networks Recording Human activity recognition Convolutional neural networks Wearable devices Sports

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ShoppingGPT: A GPT-based Product Recommendation Dialogue System

ShoppingGPT: A GPT-based Product Recommendation Dialogue Sys...

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

作者： Renjie Yu Yujing Guan Yuancheng Zhan Dept. of Computer and Information Technology Guangxi University of XingJian College of Science and Liberal Arts Nanning China Dept. of School of Big Data and Artificial Intelligence Guangxi University Of Finance and Economics Nanning China Dept. Global Institute of Software Technology of NanNing University NanNing University Nanning China

this article introduces a shopping recommendation dialogue system called ShoppingGPT, which aims to help people get comprehensive product recommendations based on individual needs and preferences. the system can leverage existing large-scale generative pre-trained models with the ability to communicate and understand human language to provide more detailed and comprehensive product recommendations through multiple conversations with users. By constructing the shopping recommendation dataset and using the large-scale language model fine-tuning methods, we successfully fine-tuned on a large-scale language model and transformed the shopping recommendation task into a sequential labeling task to adapt to our shopping recommendation dialogue system. And we design a specific method for unlabeled prompt, which uses the basic language knowledge of large-scale language models to output product information for unsupervised annotation for product recommendation. In the experiment, we evaluate several existing language model fine-tuning techniques and explore the generalization ability of the ShoppingGPT system on unlabeled samples. We also compared models before and after fine-tuning to see what the models learned from the data. In the discussion section, we provide a detailed analysis of the advantages and limitations of ShoppingGPT and its potential applications in the shopping field. In addition, we explore the potential security challenges that ShoppingGPT can pose and discuss how to address them. Finally, we discussed how to improve ShoppingGPT to make it perform better and more secure.

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