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

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pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

Pattern Discovery in an EEG Database of Depression Patients:...

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14th international conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

the ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： data mining

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Understanding Autism Using machine learning: A Structural MRI Study 26

Understanding Autism Using Machine Learning: A Structural MR...

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

作者： Ali, Mohamed T. ElNakieb, Yaser Shalaby, Ahmed Elnakib, Ahmed Mahmoud, Ali Zai, Huma Ghazal, Mohammed Barnes, Gregory El-Baz, Ayman Univ Louisville Bioengineering Dept Louisville KY 40292 USA Abu Dhabi Univ Abu Dhabi U Arab Emirates Univ Louisville Dept Neurol Louisville KY 40202 USA Univ Louisville Pediat Res Inst Louisville KY 40202 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the cortical regions of ASD subjects. Every cortical feature receives a score corresponding to their contribution in diagnosing a subject to be ASD or typically developed (TD). Scores are determined by hyper-optimized machine learning (ML) classifiers. An early personalized diagnosis of ASD becomes possible by the proposed CAD system. the proposed framework implements multiple stages including cerebral cortex extraction from structure MRI (sMRI). Moreover, the proposed framework identify the altered brain regions. We can summarize this framework in the following procedures: i) Cerebral cortex segmentation, ii) Parcellation of the cortex to Desikan-Killiany (DK) atlas;iii) Annotating brain regions which are associated with ASD;iv) Blocking for the confounding effect of both age and sex;v) Tailoring ASD neuroatlases;vi) Classifying ASD using neural networks (NN). We utilized Autism Brain Imaging data Exchange (ABIDE I) dataset to test the proposed framework. the proposed achieved a balanced accuracy score of 97% +/- 2%. In this study, we demonstrate the ability to describe specific developmental patterns of the brain in autism using tailored neuro-atlases, as well as, developing an objective CAD system using morphological features extracted from sMRI scans.

关键词： Neuroimaging Autism Image segmentation Cerebral cortex Design automation Magnetic resonance imaging machine learning

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Can Large Language Models Recognize and Respond to Student Misconceptions? 6th

Can Large Language Models Recognize and Respond to Student M...

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6th international conference on Adaptive Instructional Systems (AIS) Held as Part of the 26th international conference on Human-Computer Interaction (HCII)

作者： Smart, Francis Bos, Nathan D. Bos, Jaelyn T. Michigan State Univ E Lansing MI 48824 USA Johns Hopkins Univ Baltimore MD USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9783031606083;9783031606090

Expert human tutors can observe learner mistakes to understand their misconceptions and procedural errors. Highly capable, but opaque large language models have shown remarkable abilities across numerous domains, and may be useful for adaptive instruction in a variety of ways. Working with publicly available data from the National Assessment of Educational Progress, (388 questions selected from 4th, 8th and 12th grade math and science) we examined these three questions: 1) Do language models find the same problems difficult as students do? We found statistically significant, but small similarities in performance that differ somewhat by model. 2) Do language models have the same pattern of errors as students? Our findings reveal that, under the "minimal " prompts, the models often mirror students in choosing the same incorrect answers. However, this alignment decreases when prompt models used "chain of thoughts". 3) Can language models interpret and explain students' wrong answers? We presented frequently-chosen wrong answers toNAEP items to GPT-4 and an experienced science teacher, and compared their explanations. there was a good correspondence between these explanations, with 81% being fully or partially in agreement. Discussion focuses on how these capabilities can be used for test design and adaptive instruction.

关键词： Large Language Model Difficulty Prediction Distractor Prediction Distractor Analysis Item Analysis Question Difficulty Large Language Models machine learning Natural Language Processing

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using data mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial recognition;crime data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Cut the peaches: image segmentation for utility pattern mining in food processing 22

Cut the peaches: image segmentation for utility pattern mini...

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22nd IEEE international conference on data mining (ICDM)

作者： Chiaro, Diletta Prezioso, Edoardo Izzo, Stefano Giampaolo, Fabio Cuomo, Salvatore Piccialli, Francesco Univ Naples Federico II Dept Math & Applicat R Caccioppoli Naples Italy

ISBN: (纸本)9798350346091

the progress achieved in the field of information and communication technologies, particularly in computer science, and the growing capacity of new types of computational systems (cloud/edge computing) significantly contributed to the cyber-physical systems, networks where cooperating computational entities are intensively linked to the surrounding physical environment and its on-going operations. All that has increased the possibility of undertaking tasks hitherto considered to be an exclusively human concern automatically: hence the gradual yet progressive tendency of many companies to adopt artificial intelligence (AI) and machine learning (ML) technologies to automate human activities. this papers falls within the context of deep learning (DL) for utility pattern mining applied to Industry 4.0. Starting from images supplied by a multinational company operating in the food processing industry, we provide a DL framework for real-time pattern recognition applied in the automation of peach pitters. To this aim, we perform transfer learning (TL) for image segmentation by embedding seven pre-trained encoders into multiple segmentation architectures and evaluate and compare segmentation performance in terms of metrics and inference speed on our data. Furthermore, we propose an attention mechanism to improve multiscale feature learning in the FPN through attention-guided feature aggregation.

关键词： industry 4.0 predictive maintenance utility pattern mining image segmentation attention mechanism

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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PCA based machine learning techniques for Heart Disease Diagnosis System 4

PCA based Machine Learning techniques for Heart Disease Diag...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Vijayalakshmi, K. Lakshmi, K. Deeba, K. Ashika Devi, R. Reva University School Of Csa Bangalore India

ISBN: (纸本)9798350384628

Prominent and preliminary information mining has spread to a great extent globally with innovative real-time applications. It rapidly addresses the challenges in marketing, e-business, retail, education, health sectors, etc., in different areas and determination. Medical services are among those that need quick recommendation services and diagnosis through technological upgrades. In any situation, effective investigation apparatus is limited in identifying abandoned patterns in data. With the application of machine learning, this work aims to provide a model with better performance measured based on various classification metrics like precision and ***: the proposed work applies data preprocessing techniques like label encoding, standardization scaling and principal component analysis on the heart diagnosis dataset. Further, the model is built using various classification algorithms like SVM, KNN, Random Forest, Logistic Regression and decision tree and evaluated on unseen ***: the alignment of predictive machine learning techniques within the same dataset has been examined to detect heart disease based on two binary class labels: yes and no. the observed results show better performances for Decision Tree on unscaled data and PCA (both 80%), Logistic regression with PCA (86%), Random Forest on Scaled and PCA (both 88%), KNN and SVM on PCA with scaled data (86% & 87%). the Random Forest model surpasses all other models with more accuracy. © 2023 IEEE.

关键词： Decision trees

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Minimal Solvers for Point Cloud Matching with Statistical Deformations 26

Minimal Solvers for Point Cloud Matching with Statistical De...

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

作者： Flood, Gabrielle Tegler, Erik Gillsjö, David Heyden, Anders Aström, Kalle Lund Univ Ctr Math Sci Lund Sweden

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. this is addressed within the field of robotics and is crucial for multi-robot SLAM. there are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. the solvers use either three or four point matches. these solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.

关键词： Point cloud compression three-dimensional displays Simultaneous localization and mapping Shape Merging machine learning Task analysis

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Cannabis Revolutionizing VOC Detection: Advanced Sensors and machine learning Innovations 4th

Cannabis Revolutionizing VOC Detection: Advanced Sensors and...

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4th international conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Ayat, Yassine El Moussati, Ali El Aouni, Abdelaziz Mir, Ismail Oujda60000 Morocco

ISBN: (纸本)9789819606436

this paper details the conception of an electronic nose tailored for the precise detection of Volatile Organic Compounds (VOCs) emitted by cannabis and tobacco. Leveraging the Grove—Gas Sensor V2, equipped with four chemically sensitive sensors, our device offers accurate detection capabilities. through rigorous experimentation, we have demonstrated its efficacy in identifying VOCs from cannabis resins and tobacco with exceptional accuracy and reliability. Employing advanced machine learning algorithms, we’ve developed an odor recognition model to interpret sensor data effectively. Notably, our focus on performance optimization includes integrating stabilization techniques to minimize external factor-induced variations, enhancing detection accuracy and reproducibility. this innovation represents a significant step forward in VOC characterization technology, promising diverse applications across industrial and healthcare sectors. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic nose

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