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Predicting Coronary Heart Disease Through machine Learning Algorithms 3rd

Predicting Coronary Heart Disease Through Machine Learning A...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafillou, Vassilios Electrical and Computer Engineering Department University of Peloponnese Patras Greece Health Physics and Computational Intelligence Lab Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation Lab Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9783031655210

machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the functioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tailored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early prediction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical database with 78001 records. Our ultimate goal is to develop an expert Artificial intelligence system and an Artificial Neural Network that can predict the development of coronary heart disease by employing intelligent knowledge-mining algorithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random Forest, Decision Trees and Neural Networks. In our experimental analysis, the Decision Tree and Neural Network achieved an accuracy of 90.08% and 84.56% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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HCI international 2021 - Late Breaking Posters 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Constantine Stephanidis Margherita Antona Stavroula Ntoa

ISBN: (数字)9783030901790

ISBN: (纸本)9783030901783

This two-volume set CCIS 1498 and CCIS 1499 contains the late breaking posters presented during the 23rd international conference on Human-Computer Interaction, HCII 2021, which was held virtually in July 2021.;The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. Additionally, 174 papers and 146 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work” (papers and posters).

关键词： User Interfaces and Human Computer Interaction Computer Systems Organization and Communication Networks Artificial intelligence Computer Applications Computer Imaging, Vision, pattern recognition and Graphics Data Structures and Information Theory

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EXplainable Artificial intelligence Improves EEG-Based Cognitive Workload Assessment Induced by Fine Motor Activity in Neurosurgeons 3

EXplainable Artificial Intelligence Improves EEG-Based Cogni...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Arpaia, Pasquale De Luca, Matteo Calce, Anna Della Carone, Giovanni Castelli, Nicolò Duran, Dunja Gargiulo, Ludovica Moccaldi, Nicola Nalin, Marco Perin, Alessandro Piccolo, Salvatore Puttilli, Cosimo Visani, Elisa Naples Italy Fondazione Irccs Istituto Neurologico Carlo Besta Milan Italy Cerro Maggiore Italy

ISBN: (纸本)9798350378009

Cognitve workload associated with fine motor activity in neurosurgeons was monitored by using a wearable electroen-cephalographic (EEG) device. The most informative EEG features were selected by means of an explainable Artificial intelligence (XAI) algorithm. XAI represents a promising novel approach in this application field and offers new opportunities for extracting information from EEG data beyond traditional statistical and machine Learning-based methods. Six neurosurgeons performed the Purdue Pegboard Test (PPT) at two difficulty levels related to low or high cognitive load. EEG signals were acquired with an eight dry electrode device. Absolute powers in six different frequency bands of interest were explored. Three most involved EEG features resulted from SHapley Additive exPlanations (SHAP) methods, namely absolute power in delta band on C3 and Fz channels and the absolute power in theta band on Fz. Summary plots showed a decrease of the three identified EEG features in the high cognitive load task. These findings demonstrate the potential of Artificial intelligence-supported wearable EEG solutions to monitor cognitive load over time, to track the cognitive load of trainee neurosurgeons and to design adaptive training courses. © 2024 IEEE.

关键词： Personnel training

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A novel robustness image watermarking scheme based on fuzzy support vector machine

A novel robustness image watermarking scheme based on fuzzy ...

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international conference on Computer Science and Information Technology (CSIT)

作者： Lei Li Wen-Yan Ding Jin-Yan Li Pattern Recognition and Intelligence System College of Automation Nanjing University of Posts and Telecommunications Nanjing China

In this paper, a novel fuzzy support vector machine based image watermarking scheme is *** the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this article, Firstly, we split the given image into 8 * 8 block, then calculated for each sub-block of the texture features as input vectors to train the support vector machine. The image sub-block is divided into two categories( one category is “-1” represents a weak texture, the other is “+1” represents a strong texture), and we make the strong texture as more important category in this paper. Therefore, given its larger fuzzy membership than the weak texture's. From the relevant theory of watermarking technology we know that a strong local image texture can tolerate more of the watermark information. In order to enhance watermark robustness and embedding more watermark information in the host image, we improve the accuracy of classifying one class(a strong texture). If the data points belonging to the class of strong texture are incorrectly categorized into the weak texture class, the amount of embedded watermarks are reduced and robustness(the ability of resisting attacks) of the image is decreased. Results show that this algorithm has good robustness against common image attacks.

关键词： Gaussian noise Watermarking Support vector machines Robustness

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Augmented Reality and Artificial intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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Three dimensional monocular human motion analysis in end-effector space

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7th international conference on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2009

作者： Hauberg, Søren Lapuyade, Jerome Engell-Nørregård, Morten Erleben, Kenny Steenstrup Pedersen, Kim eScience Center Dept. of Computer Science University of Copenhagen Denmark

ISBN: (纸本)3642036406

In this paper, we present a novel approach to three dimensional human motion estimation from monocular video data. We employ a particle filter to perform the motion estimation. The novelty of the method lies in the choice of state space for the particle filter. Using a non-linear inverse kinematics solver allows us to perform the filtering in end-effector space. This effectively reduces the dimensionality of the state space while still allowing for the estimation of a large set of motions. Preliminary experiments with the strategy show good results compared to a full-pose tracker. © 2009 Springer.

关键词： Motion estimation

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Exploration of Contour Coding and Its Application in Digital Image Processing 3

Exploration of Contour Coding and Its Application in Digital...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Little Femilin Jana, D. Kadam, Prema S. Sharma, Neeraj Garg, Avni Acharjya, Kalyan Venkadeshwaran, K. Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Photograph processing is a discipline of laptop science that concentrates on manipulating digital photos and using algorithms to recognize and beautify the photo's quality. Contour coding is a powerful approach used in virtual picture processing that allows one to increase, shop, and examine virtual snapshots from a combination of strategies like aspect detection, morphological processing, and segmentation. Contour coding aims to reduce complex virtual pix to a fixed of fundamental symbolic features that may be created, stored, and manipulated without loss in quality or element. Applications of contour coding are specially used for item reputation, segmentation, spatial pattern reputation, and scientific photograph processing. This newsletter offers the theory and fundamentals of contour coding and explores its software in actual-world virtual picture processing scenarios. We speak about the significance of contour coding and present a short evaluation of existing algorithms and improvement techniques used to achieve virtual photograph processing desires. We additionally gift the advances and challenges of contour coding and how it could form the future of digital picture processing. © 2023 IEEE.

关键词： challenges development fundamentals processing segmentation

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Application of 3d models in intelligent visual inspection of product anomalies 3

Application of 3d models in intelligent visual inspection of...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Kong, Feifei Lu, Yunxia Du, Fuzhou School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Satellite Manufacturing Factory Co. Ltd. Beijing China

ISBN: (纸本)9781510681804

Due to its advantages of non-contact, high efficiency, and digitization, machine vision has been extensively utilized in product appearance anomaly inspection. The advancement of deep learning has significantly propelled the digitization and intelligence process of visual inspection. Nonetheless, the current learning paradigm still heavily relies on a large quantity of high-quality training samples, and exhibits limited capability in detecting anomaly categories beyond the training set (also known as open-set detection). Aiming at these challenges and inspired by the model-driven digital inspection idea, this article advocates for the thorough exploration of the auxiliary potential of 3D models in visual inspection. Diverging from the traditional paradigm, which mainly leverages digital models as data management centers, this article explicitly highlights two additional critical functions of 3D models: knowledge carriers and detection references. The efficacy of the proposed approach is demonstrated through case studies. © 2024 SPIE.

关键词： Smart manufacturing

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Using EEG Brain Signals to Predict Children's Learning Performance During Technology-assisted Language Learning 3

Using EEG Brain Signals to Predict Children's Learning Perfo...

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3rd IEEE international conference on Human-machine Systems, ICHMS 2022

作者： Nakov, Nikolas Alimardani, Maryam Tilburg University Department of Cognitive Science and Artificial Intelligence Tilburg Netherlands

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Brain-computer interface (BCI) systems can provide an objective evaluation of a learner's performance based on their brain signals. Having an accurate assessment of the learning process can facilitate the adaptation of the educational setting to improve the effectiveness of learning. The current study aimed at developing BCI classifiers that can accurately predict the learning performance of children by investigating temporal and spectral characteristics of their brain signals. EEG brain activity collected from children while learning a second language was analyzed by extracting two types of features: frequency-domain features using power spectral density (PSD) analysis and time-domain features using detrended fluctuation analysis (DFA). Two machine learning models (i.e., SVM and KNN) were trained with either PSD features or both PSD and DFA features. It was expected that adding a time-domain feature would improve the prediction accuracy of the models. However, the models with only frequency-domain features performed significantly better, suggesting that EEG temporal correlations may not reflect learning processes. Despite this, both approaches yielded high prediction performance, with SVM reaching 84% accuracy in predicting the learning performance of children. These findings highlight the potential of neurotechnology to be employed in learning analytics to refine traditional methods of academic performance measurement and ultimately achieve personalized learning. © 2022 IEEE.

关键词： Electroencephalography

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A new modeling method for on-line handwriting recognition

A new modeling method for on-line handwriting recognition

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international conference on Document Analysis and recognition

作者： G. Menier G. Lorette P. Gentric IRISA Rennes France Laboratoires d''Electronique Philips Limeil-Brevannes France

This paper introduces and discusses the concept of stable and shared information and its application in a new modeling method based on the selection of features. The model constructed is used for the automatic detection of scriptor-independent information. The selected features are treated as functions in order to allow a continuous interpretation of the script signal. This proposed representation permits the joint interpretation of on-line and off-line information. The paper then goes on to present some experimental results.

关键词： Handwriting recognition Data mining Feature extraction Character recognition pattern recognition pattern analysis Keyboards Mice Information analysis Graphics

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