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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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machine learning-Based Cardiovascular Disease Prediction 1

引用

4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Kumar, Sudhanshu Yadav, Himanshu Kaur, Mandeep Goyal, Mayank Kumar Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

In contemporary times, cardiovascular diseases have emerged as a significant and critical cause of mortality for individuals. therefore, it would be a boon for human existence that there exists a technology with high accuracy capable of detecting these diseases at an earlier phase but it has emerged as a challenge for data processing techniques. It has been surveyed that machine learning algorithms are capable of analysing, predicting and generating judgements. Due to advancements in IoT a variety of sensors had been developed that had contributed towards the development of better ML methods. In few articles it is observed that cardiovascular diseases are anticipated by examining the symptoms. therefore, using machine learning techniques, a new and better solution in this domain is proposed that can predict cardiovascular diseases with better accuracy. the proposed article examines the probability of a cardiovascular disease in humans and classifies patient risk levels using various data mining techniques. this article brings out the analysis of various machine learning-based algorithms that are implemented to predict cardiovascular diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Unlocking Raw data: the Power of Micro-Inspection in Handwriting Analysis 4th

Unlocking Raw Data: The Power of Micro-Inspection in Handw...

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Xu, Yan Tang, Yufang Suen, Ching Y. Concordia University MontrealQCH3G 1M8 Canada Shandong Normal University Shandong Jinan250014 China

ISBN: (纸本)9789819787043

In the field of handwriting analysis, deep neural networks seemed to perform not as well as the classic machine-learning methods (such as the support vector machine). thus, we started the inspection from the source, that is raw data, the input of deep neural networks. One is random splits, it has a positive effect on test accuracy and loss, solving the small sample problem by creating more samples to make up for the lack of training samples. the other is preprocessing. We carried out experiments with and without preprocessing, and the results show that preprocessing operation is necessary especially binarization operations. Furthermore, the order of preprocessing operation also affects the final prediction result. Finally, we compared with state-of-the-art methods, and obtained competitive experimental results, discovering the power of micro-inspection in handwriting analysis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Fast Sparse Dynamic Time Warping 26

Fast Sparse Dynamic Time Warping

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

作者： Hwang, Youngha Gelfand, Saul B. LG Display Seoul South Korea Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs. this corresponds for example to quiet periods in speech or to a lack of physical activity. However, employing conventional DTW for such sparse time series runs a full search ignoring the zero data. Sparse dynamic time warping (SDTW) was previously developed which yields the exact DTW solution while reducing the time complexity by the order of a suitably defined sparsity ratio. this paper focuses on the development and analysis of a fast approximate algorithm for dynamic time warping based on the SDTW framework. We call this fast sparse dynamic time warping (FSDTW). this study includes numerical experiments which compare the performance and complexity of FSDTW with DTW, SDTW and other algorithms that approximate DTW for sparse time series. It is shown that FSDTW reduces the time complexity relative to SDTW by the order of the sparsity ratio with negligible error relative to the exact DTW distance.

关键词： Geometry Costs Heuristic algorithms Time series analysis machine learning Approximation algorithms pattern recognition

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HAR data Analysis: Unveiling the Potential of Federated CNN Models 4

HAR Data Analysis: Unveiling the Potential of Federated CNN ...

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4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Zaoui, Chaimae Benabbou, Faouzia University Hassan Ii Laboratory Of Modeling And Information Technology Faculty Of Sciences Ben m'Sick Casablanca Morocco

ISBN: (纸本)9798350309508

the Internet of things has seamlessly integrated into various facets of our daily lives, playing a pivotal role in sectors like security, transportation, smart homes, and healthcare. Across these domains, IoT employs numerous sensors that generate substantial data, traditionally stored in centralized infrastructures like Cloud Computing. However, this approach introduces challenges such as high latency, extended response times, and security concerns during data processing and analysis. In response to these challenges, federated learning, a revolutionary branch of artificial intelligence, has emerged. this paradigm shift allows machine learning techniques to benefit from both decentralized data and computing power, predominantly found in edge devices. this approach ensures a personalized user experience without compromising privacy. this paper introduces a federated Convolutional Neural Network (FedCNN) model and conducts a comparison between classical and federated learning for Human activities' recognition. After a state of art, different aggregation approaches were evaluated including Avg-CNN, Sgd-CNN, Prox- CNN, Med-CNN, and KRUM-CNN. Avg-CNN model outperformed with an accuracy of 93.87% on the HAR dataset, while Prox- CNN Med-CNN, Sgd-CNN, and KRUM-CNN achieved respectively 93.77%, 93.38%, 93.38% and 85.40%. © 2024 IEEE.

关键词： Cloud computing

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FEPC: Fairness Estimation Using Prototypes and Critics for Tabular data 26

FEPC: Fairness Estimation Using Prototypes and Critics for T...

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

作者： Giloni, Amit Grolman, Edita Elovici, Yuval Shabtai, Asaf Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A machine learning (ML) fairness estimator, which is used to assess an ML model's fairness, should satisfy several conditions when used in real-life settings. Specifically, it should: i) support a comprehensive fairness evaluation that explores all ethical aspects;ii) be flexible and support different ML model settings;iii) enable comparison between different evaluations and ML models;and iv) provide reasoning and explanations for the fairness assessments produced. Existing methods do not sufficiently satisfy all of the above conditions. In this paper, we present FEPC (Fairness Estimation using Prototypes and Critics for tabular data), a novel method for fairness assessment that provides explanations and reasoning for its assessments by using an adversarial attack and customized fairness measurement. Given an ML model and data records, FEPC performs a comprehensive fairness evaluation and produces a fairness assessment for each examined feature. FEPC was evaluated using two benchmark datasets (ProPublica COMPAS and Statlog datasets) and a synthetic dataset containing two features, one of which is biased and one of which is fair, and compared to existing fairness assessment methods. the evaluation demonstrates that FEPC satisfies all of the conditions, making it suitable for real-life settings, and outperforms existing methods.

关键词： Maximum likelihood estimation Prototypes machine learning Benchmark testing Cognition data models pattern recognition

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DBGAN: A data Balancing Generative Adversarial Network for Mobility pattern recognition 25th

DBGAN: A Data Balancing Generative Adversarial Network for M...

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25th international conference on Big data Analytics and Knowledge Discovery (DaWaK)

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity Coll Dublin Dublin D02 R123 Ireland Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783031398308;9783031398315

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. the results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. these results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

关键词： Mobility pattern recognition data imbalance Kernel embedding Sequence to image Generative adversarial networks Self-attention

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Predictive Modeling for Asthma Disease Detection: A Comparative Study of machine learning Algorithms 4

Predictive Modeling for Asthma Disease Detection: A Comparat...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Mukherjee, Arka Jena, Junali Jasmine Gourisaria, Mahendra Kumar Parui, Sricheta Patra, Sudhansu Shekhar Pattnayak, Parthasarathi School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar India School of Computer Applications KIIT Deemed to Be University Odisha Bhubaneswar India

ISBN: (纸本)9798331528171

Artificial Intelligence (AI) and machine learning (ML) models have proven to be scalable approaches for handling several biomedical problems. Recent availability of high-quality datasets which captures various factors contributing to the respiratory disease, have enabled the development of robust models that deliver high accuracy and precision scores in early detection of respiratory diseases. Asthma is a common respiratory illness which causes shortness of breath, coughing, wheezing etc. this paper focuses on asthma disease detection. It makes two primary contributions: (1) an empirical evaluation of their performance on an asthma disease detection dataset with data mining and pre-processing techniques, and (2) the identification of the most effective approach for asthma disease detection based on rigorous evaluation using metrics such as precision, accuracy, recall, F-1, and F-beta scores. Cat Boost Classifier was found to be the best model which predicted asthma disease with a 96.04% accuracy. © 2024 IEEE.

关键词： Contrastive learning

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Human action recognition based on smart sensors 4

Human action recognition based on smart sensors

引用

2024 4th international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Lin, Lanxin Sun, Di Sussex AI Institute Zhejiang Gongshang University Hangzhou China Department of Information and Electricity Zhejiang Gongshang University Hangzhou China

this paper focuses on human activity recognition using LSTM (Long Short-Term Memory) for handling time series data issues. Because LSTM can better deal with the correlation and long-term correlation of time-series data, the model is utilized to build a human-computer interaction action recognition system. the experimental steps include the following: first, the bracelet sensor obtains the human body acceleration signal to obtain a data set containing a variety of motion postures and preprocesses the data. then, LSTM is used to extract the features of the dataset, and the feature vector is utilized as the input to train the model iteratively. Finally, to improve the accuracy of recognition, a variety of machine learning models will be applied to compare the performance with the LSTM model. the experimental results indicate that the proposed model can better capture the acceleration changes of human motion in various life scenes, and can accurately distinguish various sudden action patterns of the human body in daily life. this also makes the human movement behavior recognition system proposed in this study more comprehensive. © Published under licence by IOP Publishing Ltd.

关键词： Contrastive learning

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machine-learning-Based Methods for Optimizing Smartphone Performance in Fitness Activity recognition 4

Machine-Learning-Based Methods for Optimizing Smartphone Per...

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4th international conference on Computer Science and Blockchain, CCSB 2024

作者： Zhang, Xun Lin, Can Han, Xiaoyang Yuan, Junqing Yu, Zengyi Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350356502

With the expanding application areas of IoT, smart fitness has become an area of growing interest. IoT based fitness trackers are already playing an important role in the smart fitness domain. this paper presents a hierarchical Human Activity recognition (HAR) framework based on smartphone sensors and an IoT fitness activity assistance system, aiming to build a tool to support fitness training without the requirement of a personal trainer. To begin with, we propose a novel hierarchical recognition framework and creatively introduce labels related to fitness activities. We use the SMOTE algorithm for data augmentation and evaluate the performance of six ML classifiers for fitness activity recognition with different feature subsets, and the experimental results clearly demonstrate the significant advantage of the hierarchical recognition strategy in terms of accuracy. After that we focus on the recognition performance of transitional postures. Comprehensive analyses reveal that SVM and RF models excel in recognizing Continuous Activities for real-time fitness tracking, while RF and KNN are effective for Counted Activities, ideal for transitional posture recognition. Additionally, we conduct ablation experiments with time-domain, frequency-domain, and time-frequency-domain features, which show that the model exhibits superior performance when time-frequency-domain features are used simultaneously. Finally, we delve into classifier performance in specific activities by introducing confusion matrices in order to find the most accurate classifier for a specific activity. the results highlight that specific classifier selection tailored to the activity type can significantly enhance the precision and reliability of fitness tracking systems. © 2024 IEEE.

关键词： Active learning

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