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Lightweight Conditional Model Extrapolation for Streaming data under Class-Prior Shift 26

Lightweight Conditional Model Extrapolation for Streaming Da...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Tomaszewska, Paulina Lampert, Christoph H. Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland Inst Sci & Technol Austria ISTA Machine Learning & Comp Vis Grp Klosterneuburg Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. the main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multi-class classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier's bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.

关键词： continual learning non-stationary data incremental training meta-learning class-prior shift

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

5th International Conference on Entrepreneurship, Innovation...

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5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

ISBN: (纸本)9789819716814

the proceedings contain 41 papers. the special focus in this conference is on Entrepreneurship, Innovation, and Leadership. the topics include: A Study on Indian Digital Banking Online Customer Experience;a Comparative Analysis and Statistical Inference of Diabetes Cases;impact of Gamification on Student learning: An Empirical Evidence;Measuring Impact of Generative AI in Software Development and Innovation;Sustainability in the Digital Era: Case Study of a Security Equipment Manufacturing SME;Using Monte Carlo Methods for Retirement Simulations of the 401K and IRA;a Novel Classification Scheme for Credit Card Fraud Detection Using data mining;Analyzing the Impact of UPI on Supply Chain Performance: A Natural Language Processing Approach with Generative Pre-trained Transformers;mental Health: Prediction and Analysis of Anxiety, Depression, Stress and Happiness Using machine learning;detecting Diabetes Retinopathy through machine learning;extracting Relevant Features for Software Transplantation;robust Method for Accessing IoT Devices and Blockchain for Secure data Management;comparative Analysis of Financial datasets Predictive Modeling Using Feature Extraction Methods;Cancer Detection Techniques: An Overview of Traditional and AI-Based Methods and their Comparative Analysis;the Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced learning Experiences;employing machine learning Models in Prediction of Harmful Gases from Agri-Waste;emerging Trends and Perspectives on Challenges and Opportunities in Cloud Computing: A Systematic Literature Review;tagTrackr: A Smart Asset Tracking Solution;Assessing the Effectiveness of the ELSA App for English Language learning of Indian Native Speakers;safeMeds: Electronic Health Records Using Blockchain;depression Detection Using Linear Regression Model.

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Skill Factor as a Key Indicator of Digital Dexterity in Emerging Countries: Cluster Analysis Approach 5

Skill Factor as a Key Indicator of Digital Dexterity in Emer...

引用

5th international conference on data Analytics for Business and Industry, ICDABI 2024

作者： Alhumaid, Fahad Elamir, Elsayed University of Bahrain Management and Marketing Sakhair Bahrain

ISBN: (纸本)9798350368710

Cluster analysis is a data mining method that organizes similar objects based on their attributes. It is an unsupervised and quantitative form of classification that works on unlabeled data. One functional way to earn resilience and sustainability is through digital transformation which depends on digital skills. therefore, in this article, the cluster analysis is used to segment 39 emerging countries based on their skills factor, which is a key indicator of digital skill in fourth industrial revaluation (4IR) era. We have chosen cluster analysis because it has several advantages, such as identifying homogeneous groups of countries in terms of skills factor, facilitating the study of digital dexterity in complex system that include several dimensions, showing readiness of countries to the adoption of 4IR and enhancing cooperation opportunities among countries. We do not aim to explore the connection between skills factor and digital variable. We only focus on using skill factor as a key indicator of digital skills in emerging countries by clustering the countries into four groups according to their skills. this can assist the decision-makers to evaluate, improve and compare the digital skills, training policies and strategies with other countries. © 2024 IEEE.

关键词： Adversarial machine learning

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes 6

The Performance Analysis of Several Machine Learning Methods...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wu, Yuanyuan Xia, Zhuomin Liu, Huizhou Huang, Mengxing Zhang, Yu Hainan University College of Information and Communication Engineering Haikou570228 China Hainan University College of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350325485

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine's open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods. © 2023 IEEE.

关键词： Decision support systems

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Facial Emotion recognition Using machine learning Algorithms: Methods and Techniques 1

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5th international conference on data and Information Sciences, ICDIS 2023

作者： Gupta, Akshat Department of CSE National Institute of Technology Goa India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

the interpretation of a person’s state of mind relies heavily on their facial expressions. the identification of facial expressions requires the categorization of a number of different feelings, such as neutral, happy, angry. this use is becoming increasingly significant in day-to-day living. Identifying emotions can be accomplished through a variety of strategies, such as those based on machine learning and artificial intelligence (AI) techniques. Methods such as deep learning and image classification are utilised in order to recognise facial expressions and categorise them in accordance with the corresponding photographs. For the purpose of training expression recognition models, a variety of datasets are utilised. this article provides a comprehensive analysis of the facial recognition systems and techniques that have been utilised in the past, as well as those that have been developed more recently, together with the methodology behind them and some examples of their applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Emotion recognition

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Named Entity recognition for Protecting Sensitive data using Hybrid CNN 5

Named Entity Recognition for Protecting Sensitive Data using...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

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

ISBN: (纸本)9781665474672

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. © 2023 IEEE.

关键词： Natural language processing systems

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

5th International Conference on Advances in Information Comm...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

ISBN: (纸本)9789819761029

the proceedings contain 86 papers. the special focus in this conference is on Advances in Information Communication Technology and Computing. the topics include: Universal Representation theory—Rigour II;revolutionizing Smart Cities: A data-Driven Traffic Monitoring System for Real-Time Traffic Density Estimation and Visualization;Assessing the Influence of Self-service Technology on WOM: the Role of Customer Satisfaction;Universal Representation theory—Applications II;enhancing Performance of machine learning Models in Healthcare: An Analytical Framework for Assessing and Improving data Quality;evaluating the Enhancement of Cognitive Skills and Acquisition of New Skills in an Environment of Different Video Game Genres;hySeg-Net: A Robust Interactive Hybrid Technique for Image Segmentation and Classification in Hand Gesture recognition;web Vulnerability Scanner;digitalization of Educational Resources in Pre-tertiary Education in Ghana: Assessing Impact, Challenges, and Opportunities in Enhancing learning Outcomes;interconnected through Robotic System in English Foreign Language Education;transferability Cybersocialization as a Digital Civility through Artificial Intelligence-Based Design thinking Mindset to Modern Pedagogy;fog Computing in Healthcare: Enhancing Recommendations and Real-Time Monitoring;enhancing Bank Consumer Retention: A Comprehensive Analysis of Churn Prediction Using machine-learning and Deep learning Techniques;a Contemporary Model of Kazakh National Pedagogy in the Era of Technology: theory-Based Design Framework;the Future of Post-harvest Logistics: the Cold Chain Practices with Blockchain, IoT, and FinTech;diagnosing Sexually Transmitted Disease in Females: Leveraging Dimensionality Reduction and machine learning;a Systematic Review of Optimal Task Scheduling Methods Using machine learning in Cloud Computing Environments;Intelligent Urban Futures: Exploring AI and machine learning Applications in Smart Cities.

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Multi-scale feature pyramid perception for micro-expression recognition based on domain adaptation 5

Multi-scale feature pyramid perception for micro-expression ...

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5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Wang, Zhihao School of Artificial Intelligence Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9781510682863

Micro-expressions (MEs) are unintentional, brief facial expressions with the potential to reveal hidden or suppressed emotions, significantly contributing to the study of human emotions and psychological states. Despite this, the brief duration and subtlety of MEs make micro-expression recognition (MER) a difficult task. this paper introduces a domain adaptation based multi-scale feature pyramid perception framework for MER, namely domain adaptation multi-scale feature pyramid perception network (DAMFPP-Net), extracting common feature representations between MEs and macro-expressions (MaEs) and transferring them to MER task. Specifically, the network extracts multi-scale features of both MEs and MaEs by multi-scale feature pyramids construction and perception module. then, domain adaptation is utilized to reduce the differences in feature distributions between the two, thus obtaining a shared emotional representation. Finally, the common features are transferred to MER task to adapt to the unique characteristics of MEs. Our approach demonstrates state-of-the-art performance through extensive experiments on three public micro-expression datasets. © 2024 SPIE.

关键词： Contrastive learning

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data Poisoning Attack by Label Flipping on SplitFed learning 5th

Data Poisoning Attack by Label Flipping on SplitFed Learning

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Gajbhiye, Saurabh Singh, Priyanka Gupta, Shaifu Indian Inst Technol Jammu Nagrota India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9783031235986;9783031235993

In the distributed machine learning scenario, we have Split learning (SL) and Federated learning (FL) as the popular techniques. In SL, the model is split between the clients and the server for sequential training of clients, whereas in FL, clients train parallelly. the model splitting in SL provides better overall privacy than FL. SplitFed learning (SFL) combines these two popular techniques to incorporate the model splitting approach from SL to improve privacy and utilize the generic FL approach for faster training. Despite the advantages, the distributed nature of SFL makes it vulnerable to data poisoning attacks by malicious participants. this vulnerability prompted us to study the robustness of SFL under such attacks. the outcomes of this study would provide valuable insights to organizations and researchers who wish to deploy or study SFL. In this paper, we conduct three experiments. Our first experiment demonstrates that data poisoning attacks seriously threaten SFL systems. Even the presence of 10% malicious participants can cause a drastic drop in the accuracy of the global model. We further perform a second experiment to study the robustness of two variants of SFL under the category of targeted data poisoning attacks. the results of experiment two demonstrate that SFLV1 is more robust than SFLV2 the majority of times. In our third experiment, we studied untargeted data poisoning attacks on SFL. We found that untargeted attacks cause a more significant loss in the global model's accuracy than targeted attacks.

关键词： SplitFed learning Federated learning Adversarial machine learning Label flipping data poisoning

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Exploring the Open World Using Incremental Extreme Value machines 26

Exploring the Open World Using Incremental Extreme Value Mac...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Koch, Tobias Liebezeit, Felix Riess, Christian Christlein, Vincent Koehler, thomas E Solut GmbH Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. this work introduces a modification of the widely known Extreme Value machine (EVM) to enable open world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. this reduces the training time by a factor of 28. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. the proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.

关键词： classification and clustering online learning and continual learning

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