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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是21-30 订阅

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Revolutionizing Requirements Elicitation: Deep learning-Based Classification of Functional and Non-Functional Requirements 5

Revolutionizing Requirements Elicitation: Deep Learning-Base...

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Hanna, Grace Boyar, Nathan Garay, Nathan Maleki, Mina University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States

ISBN: (纸本)9798350355925

the requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. this paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. this DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. these results indicate the capability for deep learning techniques to play a vital role in elicitation. © 2024 IEEE.

关键词： Software design

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Long-tailed Oracle recognition Based on Attention and Multi-Expert learning 5

Long-tailed Oracle Recognition Based on Attention and Multi-...

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Yang, ZhongYuan Ibrahim, Ghalip Xu, Xuebin Yadikar, Nurbiya Ubul, Kurban School of Computer Science and Technology Xinjiang University Urumqi China School of Experimental Equipment Division Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

Oracle bones, one of the earliest and most influential writing systems in China, have significantly contributed to the fields of archaeology and paleography. Despite the scarcity of oracle bone data available for scanning and the limited number of experts, the introduction of convolutional neural networks(CNNs) hasgreatly improved the efficiency and accuracy of automatic oracle bone recognition. However, issues such as data quality and category imbalance continue to constrain the development of oracle bone recognition techniques. To address these problems, this study proposes an approach based on an attention mechanism and a multi-expert learning network. First, a new attention mechanism is introduced to help the model recognize and enhance the stroke and radical regions that are critical for categorization decisions. Second, a multi-expert learning strategy integrates the decisions of multiple independently trained sub-models (experts) to achieve higher overall performance than a single model. Experimental results show that the proposed algorithm significantly improves oracle bone recognition, with an average accuracy improvement of 7.90% on the Oracle-AYNU dataset and 9.28% on the OBC306 dataset compared to the baseline model. © 2024 IEEE.

关键词： Convolutional neural networks

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machine learning Based Method for mining Anomaly Features of Network Multi Source data 5th

Machine Learning Based Method for Mining Anomaly Features of...

引用

5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Ma, Lei Yang, Jianxing Li, Jingyu Beijing Polytechnic Beijing100016 China

ISBN: (纸本)9783031505768

Aiming at the problem that the current network multi-source data anomaly diagnosis is not effective, this paper proposes a method of network multi-source data anomaly feature mining based on machine learning. First of all, a multi-source data feature recognition model is built based on the multi-level structure of machine learning. then, the network multi-source data feature classification algorithm is designed and optimized to identify and locate the abnormal data features based on the classification results. Finally, the network multi-source data abnormal data screening model is constructed to mine the abnormal characteristics. the experimental results show that this method has high practicability and accuracy, and fully meets the research requirements. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： machine learning

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Digitization Workflow for data mining in Production Technology applied to a Feed Axis of a CNC Milling machine 5

Digitization Workflow for Data Mining in Production Technolo...

引用

5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Drowatzky, Lucas Maelzer, Mauritz Wejlupek, Kim A. Wiemer, Hajo Ihlenfeldt, Steffen TUD Dresden Univ Technol IMD D-01062 Dresden Germany

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. the first hurdle is to obtain an analysiscapable data set. this is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. the related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. data and documentation are provided alongside this publication. (c) 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 5th international conference on Industry 4.0 and Smart Manufacturing.

关键词： Condition Monitoring machine learning Digitization Production Engineering Production Technology Anomaly Detection Classification Usable Artificial Intelligence UAI data mining

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Hybrid Prediction of Daily COVID-19 Infections Using machine learning and SIR Model Integration 5

Hybrid Prediction of Daily COVID-19 Infections Using Machine...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Phoomvuthisarn, Suronapee Ruchikachorn, Puripant Chulalongkorn University Department of Statistics Bankgkok Thailand

ISBN: (纸本)9798350355925

the COVID-19 pandemic has highlighted significant challenges in predicting the daily infectious individuals, a critical aspect for managing public health responses. Accurate prediction models are essential, but epidemic models, alongside traditional statistical and machine learning models require extensive data, which may have accurate predictions but may not be timely enough to handle a pandemic immediately in real-life scenarios. this paper explores a hybrid approach, utilizing machine learning to estimate the basic reproduction number (Ro) and integrating it into the SIR (Susceptible-Infected-Recovered) model for more reliable predictions during the early stages of a pandemic using limited data. this study evaluates the effectiveness of this approach using historical COVID-19 data from thailand and compares it to existing methods. the proposed hybrid model is evaluated against traditional SIR models with fixed Ro and various machine learning models, including Random Forest Regressor, RNN, LSTM, and GRU. Prediction accuracy is measured by mean absolute error (MAE). Our approach demonstrates superior performance in predicting daily infectious individuals during the first wave of COVID-19 in thailand, even with limited early-stage data, and effectively adapts to changing epidemic dynamics influenced by policy responses. Results indicate that the hybrid model significantly outperforms both traditional SIR models and other machine learning approaches, showcasing its robustness and adaptability in real-world pandemic management scenarios. © 2024 IEEE.

关键词： Prediction models

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Research on the Stability of pattern recognition Collaborative Algorithms Based on data mining 5

Research on the Stability of Pattern Recognition Collaborati...

引用

5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Fan, Xue College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798331527662

the contemporary era and world of big data has seen the stability of advanced pattern recognition and collaborative algorithms that are critical in the practical process of data mining. this research study primarily delves into the factors that significantly enhance algorithms' stability and optimized function. this factor is focused on ensuring reliable and consistent recognition models and patterns. Based on this context, this research primarily focuses on including data mining techniques to bolster collaborative algorithms' optimized functions and robustness. through a comprehensive analysis of a myriad of data sets, factors that significantly influence the stability of these algorithms are underscored. Findings, therefore, indicate that incorporating pattern recognition and collaborative algorithms based on data mining significantly improves the algorithmic stability of a system. thus, the research findings validate the extensive and empirical simulation and tests that efficiently maintain stability in a pattern recognition model. © 2024 IEEE.

关键词： Artificial Intelligence Attention Mechanism Collaborative Algorithms data mining Stability pattern recognition

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Detecting Feet's Bending patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. the smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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An Overview of Automatic Speech recognition Based on Deep learning and Bio-Signal Sensors 5th

An Overview of Automatic Speech Recognition Based on Deep Le...

引用

5th international conference on data Science, machine learning and Applications

作者： Venkatesh, N. Krishna, K. Sai Geetha, M. P. Dave, Megha R. Kapila, Dhiraj SR Univ Sch CS & AI Warangal Telangana India Anurag Univ Elect & Commun Engn Hyderabad 500072 Telangana India Sri Eshwar Coll Engn Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India Sankalchand Patel Univ Comp Engn Swami Sachchidanand Polytech Coll Visnagar 38431 Gujarat India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the process of producing speech is complex and includes a number of biosignals in addition to acoustics. In order to overcome the limitations of conventional speech processing in particular and to gain a better understanding of the process of speech creation in general, these biosignals can be used. they originate from the articulators, the movement of the articulator muscles, the connections within the brain, and the brain itself. With an emphasis on speech production, recognition, and volitional control, we discuss artificial mouth techniques in this review that make use of a variety of sensors, including gyros, images, 3-axial magnetic sensors, electromyography as EMG, electroencephalography as EEG, electropalatography as EPG, electromagnetic articulography as EMA, permanent magnet articulography as PMA, and articulator electromyography. Before classifying them into taxonomy, we evaluate the flow of several voice recognitionrelated deep learning technologies, including visual speech recognition and silent speech interface. We conclude by talking about ways to address the communication issues that persons with speech impairments face as well as upcoming deep learning research.

关键词： Bio signals deep learning speech recognition EMG EMA

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Application of machine learning and data mining Techniques for Accurate Cloud Burst Prediction 5

Application of Machine Learning and Data Mining Techniques f...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Siddique Ibrahim, S.P. Venkata Harika, N. Mohitha Reddy, N. Srija, K. Sahithi, P.Shiva Kohima, A. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331518097

Cloudbursts are often characterized by intense rainfall over smaller areas, during catastrophic floods and landslides in vulnerable areas. Traditional models and meteorological departments struggle to predict the occurrences due to unstable weather variations. Approximate forecasting plays an important role in meteorology;it will influence the environment and people as it decreases the impact of cloudbursts. this study leverages the power of data mining and machine learning techniques to present the cloudburst prediction system, improving accuracy and providing early warnings. All the data contained is real time and factual. machine learning techniques like neural networks, decision trees, along with the old meteorological data and satellite imagery are used to find the events and patterns of cloudbursts in the data. data mining is used to preprocess the data, remove inconsistencies, smooth the data to find the associative rules, which tells us which combination of data items lead to cloudbursts. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Detection of Sunspots Using machine learning Techniques 5

Enhanced Detection of Sunspots Using Machine Learning Techni...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Panup, Wanida Bumrungkit, Acharaporn Kaewthongrach, Rungnapa Sutthana, Jaruwan Channumsin, Sittiporn Space Technology Research Center Chonburi Thailand

ISBN: (纸本)9798350355925

In this work, we develop a method for sunspot detection based on the YOLOv5 and YOLOv8 networks. We apply this method to a dataset of HMIIF solar disk images from the Solar Dynamics Observatory (SDO) satellite, renowned for its comprehensive collection of sun images. Sunspot detection is crucial for predicting solar flares and potential coronal mass ejections (CMEs), which can have significant effects on the Earth. By automating this detection process, our aim is to provide a reliable tool for continuously monitoring these phenomena and studying their evolution over time. Our experimental findings demonstrate that YOLOv8 models outperform YOLOv5, particularly YOLOv8n, which offers enhanced detection capabilities and promising improved object detection across various applications. © 2024 IEEE.

关键词： machine learning

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