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

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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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Software Entity Automated Visual recognition Method Based on Deep learning Algorithm 3

Software Entity Automated Visual Recognition Method Based on...

引用

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Gao, Hui Shandong Transport Vocational College Weifang China

Automated visual recognition is a process that utilizes computer vision technology to analyze, process, and recognize images and videos, thereby achieving automation. It is an important branch of computer vision technology, bringing great convenience to people's production and life. This article mainly studies software entity automated visual recognition methods based on deep learning algorithms. Combining image processing technology and deep learning theory, a visual recognition method based on convolutional neural networks is proposed. On this basis, the advantages and disadvantages of this method were analyzed, and improvement suggestions were proposed to address its shortcomings. The experimental results show that the visual recognition method based on deep learning algorithm exhibits excellent performance in terms of accuracy and robustness. © 2023 Elsevier B.V.. All rights reserved.

关键词： Automation

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Automatic Assessment System for English Pronunciation machine Quality Based on GloVe-CNN Algorithm 3

Automatic Assessment System for English Pronunciation Machin...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Tian, Tian School of International Business Communication Dongbei University of Finance and Economics Liaoning Dalian China

ISBN: (纸本)9798350317022

In recent years, people have paid more and more attention to machine quality assessment of pronunciation, and some progress has been made. For a long time in the past, people only paid attention to the symbols and phonetic information in mechanical language, but did not pay attention to whether the relationship between multiple corpus materials was accurate and reliable. Moreover, with the rapid development of computer technology, network communication technology, and the continuous maturity and improvement of digital coding research methods and theories, the English pronunciation machine evaluation system is also constantly being updated, and the quality evaluation level of pronunciation machines is getting higher and higher. This article aims to explore this method's assessment of users' English proficiency from three aspects: speech quality, grammatical structure and semantic features. First, this article introduces the problem of automatic assessment of speech quality and its related influencing factors;secondly, elaborates on the common measurement standards for judging whether the speaker is 'Chinese-English bilingual';finally, introduces the data mining result model based on machine learning algorithms, and explains the function used to construct sentence fuzzy sets. The overall accuracy of the system in this article is over 94%. Therefore, the system has an effective mechanism to detect and correct learners' errors in pronunciation of phonetic symbols. The system uses convolutional neural network to learn the complex relationship between pronunciation quality and features. CNN can effectively learn different levels of features and extract meaningful information for pronunciation quality assessment. © 2023 IEEE.

关键词： Assessment System Automatic Quality Assessment English Pronunciation GloVe-CNN Algorithm

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Research on deep learning-based facial expression recognition and its application in online learning state monitoring 3

Research on deep learning-based facial expression recognitio...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wang, Qisheng Yan, Xiaofei Wang, Yanqiu College of Information Science and Engineering Zaozhuang University Zaozhuang277160 China

ISBN: (纸本)9781510668522

This paper aims to research and develop a facial expression recognition algorithm based on convolutional neural networks (CNN) and apply it to a real-Time facial expression recognition system. Firstly, we introduce the Local Binary pattern (LBP) and CNN as commonly used facial recognition algorithms, discussing their characteristics and application areas. We then describe the experimental process in detail, conducting emotion recognition experiments using the FER2013 dataset and performing data preprocessing and augmentation steps. We design a CNN-based model architecture and set the parameters of the model. In the results and analysis section, we compare the performance of our model with other baseline models and discuss possible directions for improvement. Finally, we conclude that our algorithm achieves high accuracy in the experiments and provide prospects for future research and applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Local binary pattern

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Prediction techniques of spatiotemporal trajectory data: progress and framework 3

Prediction techniques of spatiotemporal trajectory data: pro...

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3rd international conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Qin, Wanting Ran, Dechao Ji, Mingjiang Tang, Jun Lao, Songyang National Innovation Institute of Defense Technology Chinese Academy of Military Science Beijing100071 China College of Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510680449

With the wide application of intelligent devices, a large amount of spatiotemporal trajectory data can be preserved and recorded. The application of spatiotemporal trajectories has penetrated every aspect of people's daily lives. It is of great applications value to predict the future position of a moving object by mining its motion law. data are the basis of the method, and thus, the form, source and characteristics of spatiotemporal trajectory data are discussed in this paper. Second, trajectory prediction technology is introduced, and its advantages, disadvantages and applications scope are summarized. Trajectory prediction technology can be divided into trajectory prediction methods based on constructing motion models, pattern mining and deep learning. Finally, the problems and potential research directions of trajectory prediction technology are discussed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Anomaly detection based on improved isolated forest 3

Anomaly detection based on improved isolated forest

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Cheng, Xinyun Liu, Zhe Tang, Ming Du, Yuanhan Xu, Chenwei Information and Communication Branch of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9781665490788

Anomaly detection is one of the hot research fields of machine learning and data mining, which is mainly used in fault diagnosis, intrusion detection, fraud detection and other fields. At present, there have been a lot of effective research work, especially the anomaly detection method based on isolated forest, but there are still many difficulties in processing high-dimensional data. A new anomaly detection algorithm k-nearest neighbor based isolation forest (KNIF) is proposed. In this method, the hypersphere is used as the isolation tool, the k-nearest neighbor method is used to construct the isolation forest, and the outlier calculation method based on distance is constructed. Sufficient experiments show that KNIF method can effectively detect anomalies in complex distribution environment and adapt to different distribution scenarios. © 2023 IEEE.

关键词： machine learning

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Improving Emotion recognition in Audio Signals: Leveraging Novel Features and Deep learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion recognition

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Applying Deep learning Strategies for Optimizing Real-Time data Analysis in machine learning 3

Applying Deep Learning Strategies for Optimizing Real-Time D...

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

作者： Mishra, Varun Dev, Sudhanshu Singh, Akhilendra Pratap Krishna, Gautham Geetha, B. Kadam, Rutuja S. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India

ISBN: (纸本)9798350319125

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization. © 2023 IEEE.

关键词： Deep learning

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Extensive Analysis of machine learning Techniques in the Field of Heart Disease 3

Extensive Analysis of Machine Learning Techniques in the Fie...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Rustagi, Tanvi Vijarania, Meenu K.R. Mangalam University Cse Haryana Gurugram India

ISBN: (纸本)9798350342338

CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that circulates filtered blood to each part of the body. The normal working of the heart is affected due to various conditions like Obesity, Hypertension, lifestyle etc. The large volume of heart diseases is avertable, but they continue to increase for the reason that protective measures are insufficient. To cure these life-threatening diseases, we need a more reliable and accurate system. machine learning procedures have been employed on numerous therapeutic datasets to industrialise the investigation of medical data. Recently, many researchers have started applying a number of machine learning techniques to assist the medical community and experts in the detection of heart-related *** technique like Support Vector machine,Artificial Neural Network and Random Forest are examples of data mining and machine learning approaches that are used to forecast heart disorders. This can help clinician to decide on the diagnosis and forecast of cardiac illness. The prime intention behind writing this research paper is to use machine learning procedures to forecast a patient's heart condition. © 2023 IEEE.

关键词： machine learning procedures Neural Network (NN) RF Supervised learning SVM

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Automated Plant Identification Through Deep learning with Particular Focus On Medicinal Plants 3

Automated Plant Identification Through Deep Learning with Pa...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Bhutada, Sunil Reddy, Ch. Sreeja Reddy, R. Sparsha Vaishnavi, S. Kumar, Goutham Sreenidhi Institute of Science and Technology Department of Information Technology Yamnampet Hyderabad India

ISBN: (纸本)9798350348347

Ecology, pharmacology, agriculture, and conservation require species identification. Identification is difficult when there are several identical species. Flora-with's 400,000 species need skilled identification. Dichotomous keys might take days to identify a species, especially in biodiversity hotspots. Non-scientists may struggle with this identifying process. Manual plant identification is difficult and slow, hence an automated system that extracts descriptive elements from raw visual plant data to identify and output the plant species is needed. The initiative suggests using leaves to identify plants instead than flowers, seeds, bark, or seeds. The project aims to create cloud-based item recognition software with a computer vision-based search engine to identify plants faster. Deep learning, a machine learning branch, has excelled in image recognition and classification. Deep learning is crucial to automated medicinal plant identification. Traditional medicine and botany require medicinal plant identification. Manual identification is slow and error-prone. Deep learning-based medicinal plant identification solves these problems. Deep learning algorithms can spot patterns and make predictions from enormous data sets by learning sophisticated relationships between input properties and output labels. Deep learning algorithms can examine leaf shape, texture, and colour to accurately identify plants from a large dataset of plant pictures. Deep learning models are scalable and may be readily integrated into mobile and internet applications, making automated medicinal plant identification solutions user-friendly and accessible. Deep learning in automated medicinal plant identification is a promising and growing field that could transform plant identification and traditional medicine research. This dissertation uses machine learning/deep learning (convolutional neural networks) to recognize plants in 2D pictures. © 2023 IEEE.

关键词： learning algorithms

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