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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是381-390 订阅

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Incremental Abbreviation Detection in Clinical Texts 8

Incremental Abbreviation Detection in Clinical Texts

引用

Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Vo, Chau Cao, Tru Ho Chi Minh City Univ Technol Vietnam Natl Univ HCMC Fac Comp Sci & Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam Eastern Washington Univ Spokane WA USA

ISBN: (纸本)9781728107882

In this paper, incremental abbreviation detection in clinical texts is considered for the practical context where new clinical texts are added, processed, and exploited over time. We propose a parameter-free semi-supervised method, named nonThreshold-ST, based on Self-training and C4.5. It inherits the prediction capability and simplicity of Self-training, while exploiting additional instances that are the most confidently predicted for enhancing the training dataset in the parameter-free configuration manner. The experimental results show that it outperforms its base supervised learning method on different English clinical text sets. Moreover, its Recall, Precision, F-measure, and Accuracy are among the highest values as compared to those of some other semi-supervised learning methods.

关键词： Clinical Text Analysis Abbreviation Detection Incremental mining Semi-Supervised learning

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Efficiency Improvement of Deepening data Application of State Grid Power under machine learning Algorithm 3

Efficiency Improvement of Deepening Data Application of Stat...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Xing, Wei Wu, Tong Wu, Botao State Grid Customer Service Center Tianjin Dongli300309 China

ISBN: (纸本)9798350381931

In the process of deepening the data processing of State Grid Power, a machine learning algorithm is proposed to solve the problem that the data efficiency of State Grid Power cannot be effectively improved. Using mechanical learning theory to classify the power data of State Grid and find mining strategies for all kinds of data;Then, under the guidance of machine learning theory, we find the best data efficiency improvement strategy. The results show that the machine learning algorithm can improve the accuracy of data processing and shorten the time of data deepening. © 2024 IEEE.

关键词： Efficiency

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Discerning Art Works through Active machine learning 3

Discerning Art Works through Active Machine Learning

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yu, Zihao Grinnell College GrinnellIA50112 United States

ISBN: (纸本)9781665459112

Scene classification is a popular and important question in computer vision and has been developed in different areas. Applying computer vision to artworks has become a popular topic in recent years. However, the traditional random sampling to identify the artworks through machine learning requires a large data set and, therefore, a higher cost to get a solid result. This paper compares random sampling and active learning (uncertainty sampling) performance using a data set (8446 paintings) of the 50 most influential painters in Europe from the 13th to the 20th century. and then propose that the active learning strategy can build a stronger model that requires smaller data sets. The active learning model can be further improved through training in larger data sets and applied in the artwork recognition for artificial intelligence.. © 2022 IEEE.

关键词： Computer vision

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Proceedings of the 2020 IEEE 3rd international conference on data Stream mining and Processing, DSMP 2020

Proceedings of the 2020 IEEE 3rd International Conference on...

引用

3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

ISBN: (纸本)9781728132143

The proceedings contain 88 papers. The topics discussed include: information processing system for detection impurity in technical oil based on laser;hybrid machine learning system for solving fraud detection tasks;speech signal structuring method for biometric personality identification;kln: a deep neural network architecture for keypoint localization;training neural network over encrypted data;forecasting nonlinear nonstationary processes in machine learning task;the algorithmic classification trees;development of an adaptive module of the distance education system based on a hybrid neuro-fuzzy network;new approaches in the learning of complex-valued neural networks;trainable neural networks modeling for a forecasting of start-up product development;deep neo-fuzzy neural network and its accelerated learning;and two approaches to machine learning classification of time series based on recurrence plots.

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Accurate Fall Detection Using 3-Axis Accelerometer Sensor And MLF Algorithm 3

Accurate Fall Detection Using 3-Axis Accelerometer Sensor An...

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3rd international conference on pattern Analysis and Image Analysis (IPRIA)

作者： Jahanjoo, Anice Tahan, Marjan Naderan Rashti, Mohammad Javad Shahid Chamran Univ Ahvaz Fac Engn Comp Engn Dept Ahvaz Iran

ISBN: (纸本)9781509064540

Nowadays, with the growing population of elderly people, the number of elderly without caregivers at home has also increased. It is clear that an elderly living alone at home is at higher risk of severe damage, due to potential delays in notifying caregivers and providing care at healthcare facilities. This especially becomes critical in case of high-risk incidents such as stroke or heart attack. To address this issue, an increasing number of methods have been proposed that employ various fall detection algorithms for elderly people. In this paper, we propose a new algorithm to detect falls, using a multi-level fuzzy min-max neural network. The proposed algorithm is compared with three other machine-learning algorithms (MLP, KNN, SVM). The main focus of this paper is on the effect of dimensionality reduction with using the Principal Component Analysis (PCA) method inside the proposed algorithm. The evaluations show that the multi-level fuzzy min-max neural network provides a high level of accuracy with a small number of dimensions. This is in contrast to the other algorithms, where accuracy is further lowered after applying dimensionality reduction. The performance evaluation of this algorithm on a public dataset obtained using accelerometer sensor data with using three dimensions indicates an accuracy of 97.29% for the sensitivity metric and 98.70% for the specifity metric.

关键词： fall detection triaxial accelerometer fuzzy minmax neural networks

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Naive Bayes classifier: True and estimated errors for 2-class, 2-features case

Naive Bayes classifier: True and estimated errors for 2-clas...

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3rd IEEE international conference on Intelligent Systems

作者： Hoare, Zoe Univ Wales Sch Informat Bangor LL57 1UT Gwynedd Wales

ISBN: (纸本)9781424401956

The low error rate of Naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided.

关键词： machine learning pattern recognition Naive Bayes error rates

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Comparative Study of Motion recognition with Temporal Modelling of Electromyography for Thumb and Index Finger Movements aiming for Wearable Robotic Finger Exercises 3

Comparative Study of Motion Recognition with Temporal Modell...

引用

3rd IEEE international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Singhvi, Shradha Ren, Hongliang Natl Univ Singapore Dept Biomed Engn Singapore Singapore VIT Univ Dept Sensors & Biomed Technol Vellore Tamil Nadu India

ISBN: (纸本)9781538670668

The project aims to perform pattern recognition of thumb and index linger gestures from the Electromyography (EMG) recordings acquired by a recently introduced External Wearable device. On the basis of the selected time domain features as reviewed based on classification performance, machine learning techniques, such as K-nearest neighbour (KNN), Support Vector machine (SVM), Discriminant Analysis etc. are compared to choose a suitable model for recognition of same and different finger movements. The recognition model obtained for a set of six hand-finger gestures shows an accuracy of 80 - 86% in KNN model for two Different movements of Thumb and index linger and about 82 - 88% in SVM model for two same movements of index finger and thumb using single myo armband. The trained model obtained from single myo armband was also tested with data from double myo armbands. As a result, the accuracy obtained was in a range of 66-82% for various gestures. The post-analysis results are promising and competent evidence for available literature and for developing user-friendly medical devices. The purpose of analyzing the following gestures using Myo armband is to implement a suitable model for creating an intuitive human-machine interface like robotic Hand exoskeleton for rehabilitation purposes.

关键词： Myo armband EMG Signals machine learning Classification

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An Unsupervised Emotional Scene Retrieval Framework for Lifelog Videos 3

An Unsupervised Emotional Scene Retrieval Framework for Life...

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3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Nomiya, Hiroki Morikuni, Atsushi Hochin, Teruhisa Kyoto Inst Technol Sakyo Ku Kyoto 6068585 Japan

ISBN: (纸本)9781479941735

In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods require a large amount of training data to construct a recognition model. Adopting such methods for large-scale video databases is unrealistic because preparing sufficient training data requires considerable human efforts. We introduce an unsupervised machine learning framework to solve this issue by making it possible to construct a facial expression recognition model without any training data set. The proposed method is evaluated through an emotional scene detection experiment. A prototype of the emotional scene retrieval system based on the proposed emotional scene detection method is implemented.

关键词： Lifelog video retrieval facial expression recognition clustering ensemble learning

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Improving Human Activity recognition Through Multisensor data Fusion Techniques in Wireless Sensor Networks 3

Improving Human Activity Recognition Through Multisensor Dat...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Wireless Sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Wireless sensor network(WSN) human activity recognition (HAR) multi-sensor datasets data fusion machine learning classification Covariance Intersection (CI) Kalman Filter (KF)

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KiL 2023: 3rd international Workshop on Knowledge-infused learning 23

KiL 2023: 3rd International Workshop on Knowledge-infused Le...

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29th ACM SIGKDD conference on Knowledge Discovery and data mining (KDD)

作者： Gaur, Manas Tsamoura, Efthymia Sreedharan, Sarath Mittal, Sudip Univ Maryland Baltimore Cty Baltimore MD 21228 USA Samsung AI Res Cambridge England Colorado State Univ Ft Collins CO 80523 USA Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (纸本)9798400701030

Recent prolific advances in artificial intelligence through the incorporation of domain knowledge have constituted a new paradigm for AI and data mining communities. For example, the human feedback-based language generation in ChatGPT (a large language model (LLM)), the use of Protein Bank in DeepMind's AlphaFold, and the use of 23 rules of safety in DeepMind's Sparrow have demonstrated the success of teaming human knowledge and AI. In addition, the knowledge retrieval-guided language modeling methods have strengthened the association between knowledge and AI. However, translating research methods and resources into practice presents a new challenge for the machine learning and data/knowledge mining communities. For example, in DARPA's Explainable AI seminar, the need for explainable contextual adaptation is seen as the 3rd phase of AI, facilitating the interplay between data and knowledge for explainability, safety, and, eventually, trust. However, policymakers and practitioners assert serious usability and privacy concerns that constrain adoption, notably in high-consequence domains, such as cybersecurity, healthcare, and other social good domains. In addition, limitations in output quality, measurement, and interactive ability, including both the provision of explanations and the acceptance of user preferences, result in low adoption rates in such domains. This workshop aims to accelerate our pace towards creating innovative methods for integrating knowledge into contemporary AI and data science methods and develop metrics for assessing performance in various applications.

关键词： Neurosymbolic AI Knowledge-infused learning Explainable AI Safe AI Language Models Games Programming Languages

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