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A Novel Approach on Yoga Posture Identification Using machine learning 6

A Novel Approach on Yoga Posture Identification Using Machin...

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6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Rajgure, E.G. Patidar, Raghavendra University of Technology Electronics & Communication Engineering Jaipur India

ISBN: (纸本)9798350359817

the era of globalization has increased beyond expectations and to fulfil it, every individual has to face many challenges and stressful situations. To keep stress away from us many doctors and health experts suggest suitable yoga, pranayama, diet along with appropriate medicine. In recent times yoga exercise with postures estimation analysis of is followed everywhere because Practicing yoga helps to rise flexibility, improves muscle strength and boost respiratory system. In addition to fostering spiritual growth, yoga has several positive physiological impacts on the body. Yoga strengthens the body and the psyche when practiced regularly. Yoga is not only a physical exercise but also keeps our mental health intact. Yoga helps to maintain self-discipline and self-awareness. But because it is hard to verify the precision of the yoga poses, practitioners won't gain fully from the exercises. In this research work, we are presenting a scientific analysis related to yoga posture recognition using systematic approach of artificial intelligence and machine learning. the objective is to verify the accuracy of the poses when practicing yoga. We are developing embedded wearable sensor-based device to acquire the time series data from sensors and by using suitable algorithm different postures get recognized. In addition to this we have also studied the benefits of practicing yoga in the improvements of various health parameters. © 2023 IEEE.

关键词： machine learning

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Enhanced Classification Models for Heart Disease Prediction in Precision Medicine 6

Enhanced Classification Models for Heart Disease Prediction ...

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6th international conference on Informatics and data-Driven Medicine (IDDM)

作者： Boyko, Nataliya Dosiak, Iryna Lviv Polytech Natl Univ Stepana Bandera St 12 UA-79000 Lvov Ukraine

this investigation endeavors to scrutinize and enhance machine learning (ML) algorithms tailored for medical diagnostics, specifically in the prognostication of cardiac pathologies. the focal point of this study encompasses medical indicators such as electrocardiograms (ECGs), diagnostic outcomes, and symptomatic manifestations, examining their pivotal role in the efficacious identification of cardiac ailments. the research delves into mathematical data mining models and their classification synergies, constituting the core subject of inquiry. the scholarly contribution of this work lies in the refinement of ML methodologies, thereby bolstering the precision in cardiac disease diagnostics. From a practical standpoint, the research aspires to forge a sophisticated classification schema, poised to mitigate the incidence of diagnostic inaccuracies. the discourse underscores the pertinence of ML algorithm deployment in the medical domain, particularly for cardiac diagnostics. A meticulous review and critique of extant scientific literature and analogous studies have been conducted, alongside an evaluation of mathematical and informational frameworks. the study delineates fundamental classifiers and their collective ensembles, with a pronounced focus on discerning the associative patterns between cardiac conditions and various indicators. the research illuminates the impact of demographic factors, such as age and smoking habits, as well as comorbidities, laboratory metrics, symptomatic expressions, test outcomes, and ECG readings on cardiac diagnostic processes. It also contrasts the symptomatic disparities between genders in both cardiac patients and healthy cohorts. An array of classification methodologies and their ensembles have been employed to facilitate the automated diagnosis of cardiac diseases. Extensive trials utilizing diverse ML classification techniques have been executed. the investigation extends to feature selection algorithms and data transformation tec

关键词： technology processing analysis machine learning algorithms classification diagnosis heart diseases

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Leveraging Artificial Intelligence and data mining to Identify Chinese Dialects: A Comparative Analysis of Tone and Segment Importance 6

Leveraging Artificial Intelligence and Data Mining to Identi...

引用

6th international conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Zhou, Xin Wu, Manting Wang, Hongyan School of Foreign Languages Shenzhen University The Guangming and Galaxy Primary School South China Normal University Shenzhen China Shenzhen University School of Foreign Languages Shenzhen China The Netherlands Studies Center Shenzhen University School of Foreign Languages Shenzhen China

ISBN: (纸本)9781665491259

Studies based on big data and deep learning models have been a hot topic in the development of dialect recognition technology. However, the recognition correct rate always fails to reach 100%. To find out the salient features for the recognition of different dialects, a perception experiment was held based on the three dialects of Mandarin Chinese in Liaoning Province. the stimuli consisted of three sets of 30 frequent utterances from Mandarin Speech Perception (MSP) materials. then, each utterance was manipulated in two ways, i.e., tonally and spectrally. In the tonal dimension the natural fundamental frequency was replaced by a constant f0 of 100 Hz, which removes all melodic information from the utterances. In the second dimension, the original recordings were low-pass filtered using a digital filter with a cut-off frequency of either 1000 Hz or 300 Hz (default smoothing constant of 100 Hz). Next, from each of these dialect areas one male speaker was selected to record the testing materials in their own dialect. And ten eligible subjects were recruited from each dialect region to select the dialects they thought they hear. the results showed that: 1. Segments play a more important role in dialect recognition. 2. the relative contribution of tone varies in different situations. the findings could contribute to further improving the accuracy of dialect recognition technology. © 2023 IEEE.

关键词： Digital filters

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Efficient Unsupervised learning for Plankton Images 26

Efficient Unsupervised Learning for Plankton Images

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

作者： Alfano, Paolo Didier Rando, Marco Letizia, Marco Odone, Francesca Rosasco, Lorenzo Pastore, Vito Paolo Univ Genoa MaLGa DIBRIS Genoa Italy INFN Sez Genova Genoa Italy Italian Inst Technol Genoa Italy MIT CBMM 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. the adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. the proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties.

关键词： Image segmentation Microorganisms Pipelines Neural networks Sociology Computer architecture Feature extraction

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Domain Adapted Few-Shot learning for Breast Histopathological Image Classification 10th

Domain Adapted Few-Shot Learning for Breast Histopathologica...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Mohanta, Anindita Roy, Sourav Dey Nath, Niharika Bhowmik, Mrinal Kanti Tripura Univ Comp Sci & Engn Dept Agartala 799022 Tripura India New York Inst Technol Biol & Chem Sci Dept New York NY 10023 USA

ISBN: (纸本)9783031451690;9783031451706

the inaccessibility of high numbers of annotated data and imbalance dataset's classes is a common phenomenon in medicalmicroscopic image datasets. Also, collecting data from the same domain is a challenging task in microscopic imaging (e.g. same magnification factor, staining method, cancer type, imaging modality). A Conventional Deep learning model with lack of labeled training data and domain shift could result in a misdiagnosis for such datasets. To overcome this problem, we formulated a domain adaptive few-shot learning (DA-FSL) problem and measured the perception capability of DA-FSL benchmark for medical histopathological image classification (especially for breast histopathological imaging). DA-FSL model overcomes a few-shot learning problem with domain shift by training the network on available source domain data but being able to test on target domain data with less number of available samples. DA-FSL models are validated on two publicly available breast cancer histopathological image datasets i.e., BreakHis and BreastCancer_IDC_Grade (BCIG). the presented DA-FSL framework has shown promising outcomes on both datasets. For performance evaluation we compared DA-FSL framework with other transfer learning and few-shot learning methods with different settings.

关键词： Breast Cancer Histopathological Images Few-Shot learning Domain Adaptation Classification

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Modern Experiment Management Systems Architecture for Scientific Big data 6th

Modern Experiment Management Systems Architecture for Scient...

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6th international conference on Software Testing, machine learning and Complex Process Analysis, TMPA 2021

作者： Kaida, Anastasiia Savelev, Aleksei National Research Tomsk Polytechnic University Lenina Ave. 30 Tomsk634050 Russia

ISBN: (纸本)9783031504228

Experiment management systems (EMS) have more than twenty-five years of history. From small desktop prototypes to large-scale distributed systems, they become more and more complicated. the new chapter of EMS was uncovered with the age of Big data surrounded by a special ecosystem to extract, analyze and store data. the big data ecosystem considers new elements that must be taken into account to expand the functionality for EMS to support all data lifecycle stages. One of the challenges is to highlight the key points of a huge variety of EMS evolving through time. Such systems do not usually follow a unified pattern because of special needs for each project. this paper introduces the conceptual high-level architecture as an example of a unified pattern of building EMS for big data ecosystems. the architecture does not consider to be used with the grid computing approach. © 2024, Springer Nature Switzerland AG.

关键词： Big data

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A machine learning Framework Based on ACO-EMD-CKAN for Bearing Fault Detection 6

A Machine Learning Framework Based on ACO-EMD-CKAN for Beari...

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6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Zhou, Qingqing China University of Mining and Technology Electrical Engineering and Automation Xuzhou China

ISBN: (纸本)9798350389418

Rolling bearings are the core components of rotating machinery, so their health condition is directly related to safe production. However, there are still some problems in the traditional fault diagnosis of rolling bearings with low accuracy, which is difficult to adaptively extract typical traits. therefore, a new method for motor rolling bearing fault diagnosis based on ACO-EMD-CKAN is proposed in this work. Firstly, the data is optimized by Ant Colony Optimization (ACO) to determine the optimal number of Intrinsic Mode Function (IMF) values. then, denoising is performed through Empirical Mode Decomposition (EMD), and more prominent feature vectors are extracted. Next, these vectors are sent to the Convolution Kolmogorov-Arnold Network (CKAN). After passing through three layers of convolution and three layers of maximum pooling, they are finally sent to the Kolmogorov-Arnold Network (KAN) layer to perform adaptive feature extraction of the signal. Compared with the common bearing fault diagnosis methods, the proposed ACO-EMD-CKAN model has the highest accuracy, reaching 9 8. 8 8 %. the experimental results verify the feasibility of the model for effective classification of bearing faults, and provide important application value for efficient and reliable bearing fault detection in the future. © 2024 IEEE.

关键词： Ant Colony Optimization Convolution Kolmogorov-Arnold Network Empirical Mode Decomposition fault diagnosis rolling bearing

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Sentiment Analysis for Product Reviews Using Deep learning Techniques 6

Sentiment Analysis for Product Reviews Using Deep Learning T...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Yadav, Neeraj Kumbhar, Riya Goves, Angelo Coelho, Chrisel Rebello, Irwin Mahale, Siddesh Wadekar, Ameya Aswale, Shailendra Goa University Information Technology PCCE Verna India

ISBN: (纸本)9798350304480

Sentiment analysis or opinion mining, is the process of identifying and extracting subjective information from source materials. It is important in the context of user-generated content, such as tweets or Facebook posts, or Amazon product reviews, to understand the overall sentiment towards a certain topic or brand. this can provide valuable insights for businesses and organizations looking to improve their online reputation and engagement with customers. Traditionally, sentiment analysis has been handled through manual methods such as reading through and categorizing individual pieces of content. However, with the advent of big data and machine learning, automated methods have become more prevalent in the field. Methods such as natural language processing and machine learning algorithms are commonly used in sentiment analysis. this research paper focuses on a hybrid method for sentiment analysis on product reviews. Compared to other methods of deep learning and machine learning this hybrid approach excels in overall performance. © 2023 IEEE.

关键词： Deep learning machine learning Product Reviews Sentiment Analysis Social Media

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theoretical Evaluation of Ensemble machine learning Techniques 5

Theoretical Evaluation of Ensemble Machine Learning Techniqu...

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

作者： Shah, Milind Kantawala, Harsh Gandhi, Kinjal Patel, Rohini Patel, Kinjal A. Kothari, Ankita Department of Computer Science and Engineering Gujarat Vadodara India Department of Computer Engineering G H Patel College of Engineering and Technology Gujarat Vallabh-Vidhyanagar India Gujarat Law Society University Faculty of Computer Applications and Information Technology Gujarat Ahmedabad India

ISBN: (纸本)9781665474672

the use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. these strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. this study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared. © 2023 IEEE.

关键词： Adaptive boosting

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Self-Supervised Image Denoising with Subsampling and Residual learning 6

Self-Supervised Image Denoising with Subsampling and Residua...

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

作者： Liu, Lingjun Li, Yuying Wu, Weirui Xie, Zhonghua Huizhou University School of Computer Science and Engineering Huizhou China

ISBN: (纸本)9798350325485

Image denoising based on deep learning has made more extensive development by using a large amount of data for network training, however, it is difficult to obtain clean images without noise in actual scenarios, leading to the emergence of self-supervised deep learning technique. In this step, we raise two questions: how to improve the performance of self-supervised learning and how adaptive is the self-supervised method to various networks. In response to these issues, we propose a self-supervised image denoising scheme which generates image training pairs by subsampling the noisy image twice, and combines U-Net and ResNet to form an effective denoising network. As a result, we improve the denoising performance of the latest self-supervised method Neighbor2Neighbor by building a deeper convolutional network while avoiding gradient explosion through careful setting of training parameters. the experimental results show that the proposed method improves the PSNR value by 0.3 dB and SSIM value by 0.004 on average compared to the original algorithm, and also verifies the robustness of the training strategy of Neighbor2Neighbor algorithm to different network structures. © 2023 IEEE.

关键词： Image denoising

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