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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 3282 条记录，以下是601-610 订阅

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Construction of a Japanese-Chinese Bilingual Corpus for learning Japanese Sentence patterns through Multi-Level Annotation 3

Construction of a Japanese-Chinese Bilingual Corpus for Lear...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Liu, Jun Zhuang, Luxuan Guangxi University School of Foreign Languages and Literatures Nanning China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

This work introduces the construction of a Japanese-Chinese bilingual corpus, aiming to help Chinese-speaking learners of Japanese as a second language (JSL) with their study of Japanese sentence patterns in Japanese grammar. For constructing this Japanese-Chinese bilingual corpus, methods of automatic identification of the Japanese sentence patterns were proposed by annotating a dataset on multiple levels for training a model based on Conditional Random Fields (CRF) machine learning algorithm. The multi-level annotation performed is Part-of-speech (POS) tagging, which includes the detailed information, such as surface form, dictionary form, pronunciation, difficulty level and etc. The well-trained model is applied to automatic identification and suggestion of the Japanese sentence patterns with different difficulty levels according to Japanese Language Proficiency Test (JLPT). We evaluated the model on a test dataset in order to show the effectiveness of our proposed methods. We also show how we used this model to construct a Japanese-Chinese bilingual corpus, and manually corrected the example sentences where the Japanese sentence patterns were identified incorrectly or not identified. This model can be effectively used for the Japanese language processing framework, which allows for a fast and easy application for the problem of Japanese grammar extraction. Moreover, the constructed Japanese-Chinese bilingual corpus can be applied as a useful computer-assisted language learning (CALL) tool for learning Japanese sentence patterns. © 2022 IEEE.

关键词： learning algorithms

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Vision-based Real Time Bangla Sign Language recognition System Using MediaPipe Holistic and LSTM 3

Vision-based Real Time Bangla Sign Language Recognition Syst...

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3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Foysol, Md. Walid Sajal, Sk. Estiaque Ahmed Alam, Mohammad Jahangir Daffodil International University Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798350338263

The Bangla Sign Language Detection System converts the Bangla sign language into text so that deaf-mute people can communicate with ordinary people. Deaf-mute people are quite detached from society because normal individuals and deaf-mute individuals have a communication gap. On the strength of technological welfare, it is now possible to capture any deaf-mute person's gesture, and with the help of machine learning, it can be converted into text. In this research, we adopt a development model for recognizing gestures that accommodates MediaPipe for extracting hands and posing landmarks and long short-Term memory (LSTM) to train and recognize the gesture. This will convert Bangla sign language gestures into readable text. The requirements analysis served as the foundation for our proposed model, which will be carried out in four stages: collecting data and processing it, training and testing the chosen neural network, and finally, real-Time testing. A gesture model is taught to recognize gestures with the help of a self-created dataset of Bangla sign language. The trained model successfully identifies the gesture, and the text equivalent of the gesture is displayed on screen. The purpose of this model is to help create a medium to communicate between normal people and the hearing impaired. © 2023 IEEE.

关键词： Long short-term memory

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The Analysis of Intelligent Real-Time Image Acquisition System Based on data mining 3

The Analysis of Intelligent Real-Time Image Acquisition Syst...

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Zhang, Xiaohuan School of Social and Political Sciences University of Glasgow United Kingdom

ISBN: (纸本)9781643684581

The purpose is to enhance the timeliness of news, provide real-time news for the audience, and attract more traffic for the news media, thereby helping news media grow financially and strengthening the network security communication efficiency and accuracy. First, an intelligent real-time image acquisition system is introduced, and its principle and advantages are analyzed. Second, an intelligent real-time image acquisition model based on DL (Deep learning) algorithm is established by combining DM (data mining) technology. Finally, the application of an intelligent image acquisition system in network communication is adjusted. The research results show that the combination of DL intelligent real-time image acquisition system with DM technology can effectively deal with various network communication scenarios, enhancing the accuracy and timeliness of network communication. The AlexNet model performs excellently in all aspects, with or without data enhancement or equalization. The recognition rate reaches 99.6% after data equalization and enhancement, and the AlexNet model can accurately classify news and non-news sceneries. This shows that the application of an intelligent real-time image acquisition system based on DM technology can effectively enhance the real-time performance of network communication. The results set an example for the application of relevant intelligent image acquisition technology in the journalism industry. © 2023 The Authors.

关键词： data mining

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Study on the classification method of gastrointestinal endoscope image based on CBAM and AlexNet 3

Study on the classification method of gastrointestinal endos...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Tong, Huifeng Zhou, Hua College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510667563

Gastrointestinal diseases are one of the important causes of gastrointestinal tumors, and also one of the serious public health problems that human beings are currently facing. Gastrointestinal inflammation and polyp lesions are the main ways to cause gastrointestinal cancer. Therefore, timely detection of gastrointestinal polyps and inflammation and other lesions through gastrointestinal endoscopy can effectively prevent corresponding cancer. In recent years, deep learning technology has played a huge role in computer-aided gastrointestinal endoscopic diagnosis, especially the convolutional neural network can recognize gastrointestinal lesions. This paper uses the public gastrointestinal endoscopy data set Kvasir (including 7 categories, 1000 images for each category), proposed and designed CBAM-AlexNet deep learning model, in which CBAM is the attention mechanism part, including channel attention and spatial attention, AlexNet is a deep learning network. The original features are input into the channel attention mechanism in CBAM to extract channel features, and then the output channel features are input into the spatial attention mechanism to extract spatial features, and finally the output spatial features are input into the AlexNet network for feature extraction and gastrointestinal disease data. The average accuracy rate, average accuracy rate, average recall rate and average F1Score of all categories of CBAM-AlexNet model were 91.36%, 91.34%, 91.39% and 0.913, respectively, and achieved good classification effect in seven categories of gastrointestinal endoscopy data, showing the superiority of the model. CBAM-AlexNet model has high specificity and sensitivity for the recognition of gastrointestinal polyps, which can assist doctors in the clinical diagnosis of gastrointestinal polyps quickly and reduce the probability of misdiagnosis, Some recognition errors caused by quality issues such as overexposure or underexposure of images will be improved in future wo

关键词： Endoscopy

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Utilization of machine learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. The early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. The cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. The toxicological study based on fish behavioral data is then summarized. © The Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic data 26

Towards Automatic Parsing of Structured Visual Content throu...

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

作者： Schoelch, Lukas Steinhaeuser, Jonas Beichter, Maximilian Seibold, Constantin Yang, Kailun Knaeble, Merlin Schwarz, Thorsten Maedche, Alexander Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.

关键词： Training Measurement Visualization Annotations Supervised learning Static VAr compensators Solids

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Design and Implementation of College Students’ Innovation and Entrepreneurship Experience System Based on Deep learning 3rd

Design and Implementation of College Students’ Innovation a...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Yang, Yi Jiangxi Institute of Applied Science and Technology Nanchang China

ISBN: (纸本)9783031631290

Cultivating students’ Set up a company leader ability is one of the main teaching tasks in universities. On the basis of encouraging teachers and students to participate in Set up a company leader teaching, students’ comprehensive quality and practical ability are improved, and more outstanding talents are trained for the society. According to the actual needs in students’ credit recognition of create a company, a fully functional experience system in students’ create a company based on B/S structure is constructed based on Java language. According to the support vector machine of big data analysis method, the risk assessment model in students’ entrepreneurship is established. At last, the functions of the system are realized by ASRNET technology, and the system is tested comprehensively from three aspects of interface, function and performance, and the errors found are corrected, so as to realize the platform and development of the whole system. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Students

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LIED: A Lightweight and Ensemble learning approach for fake face Detection 3

LIED: A Lightweight and Ensemble learning approach for fake ...

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3rd IEEE international conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Budhiraja, Rajat Kumar, Manish Das, M.K. Bafila, Anil Singh Singh, Sanjeev Institute of Informatics and Communication University of Delhi South Campus New Delhi India Indira Gandhi National Open University School of Computer and Information Sciences New Delhi India

ISBN: (纸本)9798350397819

For many years, machine learning problems have primarily been driven by the availability and quality of data. Being the key, data has been equally vulnerable and got a savior in the form of generative adversarial networks (GANs) which opened the floodgates for generating almost any type of real, yet synthetic data. Human face became one of the initial victims to this superior technology, where in highly realistic and convincing fake content is generated using deep learning technologies or ''DeepFakes''. Taking a giant leap forward from manipulating facial attributes, to be now able to swap expressions seamlessly and even generate new (non-existent) synthetic faces poses a grave threat not only to chosen few, but for the entire society. This upshoot has been reciprocated with significant efforts and investments for its detection, but the techniques are often marred with either lower accuracies, or, higher computation costs. This is where convolutional reservoir networks (CoRN) come to rescue owing to their lightweight nature, able to do ensemble feature extraction and its generalization ability. This paper investigates, implements and demonstrates the application of CoRN based architectures to the task of human fake face detection. The steep performance improvements as evident from our results further ratify the effectiveness of this approach, which is also shown to perform exceedingly well against smaller datasets. © 2023 IEEE.

关键词： Face recognition

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A New data Compression Method for Face recognition

A New Data Compression Method for Face Recognition

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xuansheng Wang Hongying Zheng Shenzhen Institute of Information Technology Shenzhen Guangdong Province China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Previous studies on feature extraction techniques frequently depended on converting data straight from high-dimensional regions into low-dimensional subspaces. However, these approaches often struggled to preserve the intricacies and nuances of the original data that are crucial for accurate analysis. In this groundbreaking study, we introduce a new data compression technique for feature extraction. This method meticulously compresses data samples into a more compact form while diligently preserving their pivotal characteristics, ensuring that no vital information is lost in the process. By achieving this delicate balance between compression and fidelity, our approach enables seamless integration with established dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), significantly enhancing their performance in face recognition tasks. Experimental evaluations underscore the efficiency and efficacy of our data compression method, demonstrating substantial gains in both computational efficiency compared to traditional methodologies, thereby paving the way for more practical and powerful applications in the field of pattern recognition and machine learning.

关键词： Training Dimensionality reduction Face recognition data compression Feature extraction Linear discriminant analysis Computational efficiency data mining Principal component analysis Software engineering

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LSTM Method for Human Activity recognition of Video Using PSO Algorithm 3

LSTM Method for Human Activity Recognition of Video Using PS...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Razmah, M. Senthil, G.A. Prabha, R. Divya, B. Sridevi, S. Naveen, A. Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

A most alluring method is Human Activity recognition (HAR) from video, cost-effective computer vision-based recognition research and it has wide range of applications such as Closed-Circuit Television (CCTV) surveillances, gaming and health care unit. The recognition of human activity labeled by the appearance and motions observed from the video. The rise of social informatization, the prominent information technology mentioned by machine vision is applied to many more scenes. The conventional methods of HAR facing problems such as slower recognition and precision rate are low. With the advancement of deep learning approach, the difficulties faced in HAR are addressed. For this analysis, various kinds of neural network have been recommended by researchers and they did HAR effectively. This study also proposes a neural network so called Long Term Short Memory (LSTM) combined with Particle Swarm Optimization (PSO) to predict human activities more accurately in a video. The dataset chosen for this study includes variety of actions including such as pushups, mixing, kayaking, playing basketball, drumming, etc. This approach improves the temporal and spatial dimensions to maximize the recognition rate. The accuracy obtained by processing UCF-50 dataset is relatively higher than the existing approach. © 2022 IEEE.

关键词： data mining

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