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共 3244 条记录，以下是421-430 订阅

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Analysis of the Mechanism of Generative Adversarial Networks learning Based on Gray Image 3

Analysis of the Mechanism of Generative Adversarial Networks...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Geng, Chaozhi 21st Century International School Beijing100080 China

ISBN: (纸本)9781510661301

machine learning has a crucial role in people's lives. machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results. © 2023 SPIE.

关键词： Generative adversarial networks

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Predicting Laptop Prices in the Tunisian Market Using data mining and machine learning Methods 3

Predicting Laptop Prices in the Tunisian Market Using Data M...

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3rd IEEE international conference on Signal, Control and Communication, SCC 2023

作者： Balti, Ala Yassin, Mannai Lakhoua, Mohamed Najeh Sayadi, Mounir University of Tunis National High School of Engineering of Tunis Ensit Research Laboratory LR13ES03 Sime Tunisia University of Carthage National Engineering School of Carthage ENICarthage Tunisia University of Carthage National Engineering School of Carthage ENICarthage Research Laboratory Smart Electricity & Ict SEICT Tunisia

ISBN: (纸本)9798350326390

The laptop industry has undergone swift expansion and technological enhancements, resulting in a diverse array of laptop models and capabilities to cater to a broad spectrum of consumer demands. However, for businesses, the task of identifying the most suitable laptop that aligns with specific requirements can be quite formidable. To tackle this challenge, this study introduces a solution centered around predicting laptop prices through the application of supervised machine learning, with a particular focus on utilizing the Gradient Boost method. The system attains an impressive predictive accuracy of 94% when compared against actual market prices. Moreover, this investigation delves into the connections between laptop prices and various attributes including model, RAM, storage types (HDD/SSD), GPU, and CPU. By making use of this system and comprehending these correlations, enterprises can streamline the laptop selection process. This research significantly contributes to the domain of laptop price prognostication, providing enterprises with the tools to make well-informed decisions and optimize their laptop acquisition strategies. © 2023 IEEE.

关键词： Forecasting

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Evaluation of the Performance of Methods for Classifying EEG Signal Processing 63

Evaluation of the Performance of Methods for Classifying EEG...

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63rd international Scientific conference on Information-Technology-and-Management-Science of Riga-Technical-University (ITMS)

作者： Litvinenko, Anna Simanovics, Janis Riga Tech Univ Inst Radio Elect Riga Latvia

ISBN: (数字)9798350399851

ISBN: (纸本)9798350399851

Over the past few decades the popularity of artificial neural networks has been continuously increasing in the fields of machine learning, data mining and pattern recognition. However, it has been suggested that in many scenarios artificial neural networks do not always deliver the best performance when compared to other data classification methods, like tree-based models. This article evaluates and compares the performance of different classification algorithms and methods employing electroencephalography data. Both artificial neural networks and classic methods are benchmarked and compared.

关键词： artificial neural networks electroencephalography machine learning random decision forests support-vector machines tree-based models

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data mining for news content: a case of Cantonese opera news topic modelling analysis 3

Data mining for news content: a case of Cantonese opera news...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Wang, Bifeng Xu, Xiaotong Chen, Haocih Xie, Xinyi Chen, Jiawei Liu, Qian Fu, Yong School of Education GuangZhou Academy of Fine Arts Guangdong Guangzhou510006 China School of Journalism and Communication Jinan University Guangdong Guangzhou510632 China School of Literature Zhejiang University Zhejiang Hangzhou310058 China College of Liberal Arts Jinan University Guangdong Guangzhou510632 China

ISBN: (纸本)9781510661301

Topic modelling approach is widely used for text data mining in NLP(Natural Language Processing). Text mining has been used for analysis of ICH (intangible cultural heritage), where Cantonese opera is a representative ICH of Lingnan culture. This study retrieved news content on Cantonese Opera and used machine learning analysis (LDA topic modelling) method to find out the distribution of the topics. Four main themes are concluded: the development, cooperation, and inheritance of Cantonese opera(taken up to 45.1% in all data);The traditional form(23.5%);Innovative forms(18.3%);Education and cultural inheritance of Cantonese opera(13.2%). This research further explored how to better promote Cantonese opera by analysing the topics as well as the data, and suggested that emphasis should be placed on the innovation of traditional elements in Cantonese opera, keeping them close to life, and education. © 2023 SPIE.

关键词： machine learning

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A study on license plate recognition based on convolutional neural network 3

A study on license plate recognition based on convolutional ...

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Shi, Xiangui Wang, Xiaole Chen, Tao Qian, Cong Liu, Yiming School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781510661363

With the rapid development of advanced technologies such as artificial intelligence and Internet of Things, intelligent traffic management has been widely used. License plate recognition technology is particularly important in intelligent traffic management as the unique identification of vehicles, but due to the influence of natural environment, recognition angle, image clarity, size of license plate and other factors, the traditional license plate recognition technology is less robust and difficult to accurately identify the number plate number. In this paper, we describe a license plate recognition technique implemented by constructing Convolutional Neural Networks (CNN), which is able to recognize license plate numbers more accurately and has certain significance for further research on deep learning in the field of intelligent traffic management. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image processing

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

3rd International Conference on Artificial Intelligence, Rob...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

ISBN: (纸本)9789819721993

The proceedings contain 33 papers. The special focus in this conference is on Artificial Intelligence, Robotics, and Communication. The topics include: Design and Research of UAV Fire Emergency and Intelligent Inspection System in the Valve Hall of Converter Station;Development Overview and Application Prospects of Advanced UUVs;bisporus Mushroom 3D Reconstruction for Digital Twin of Smart Factory;Research on Automatic Music Generation Based on LSTM;specific Emitter Identification Method Based on Aggregation Model with Metric learning in Complex Field;fake Comment Detection Based on Generative Adversarial Networks;Shipping Risk Assessment Ship Inspection Method Based on Improved YOLOv5;spatiotemporal Prediction of Harmful Algal Blooms Based on Spatiotemporal Attention and Transformer;large-Scale Information Dissemination Algorithm Based on Random Mapping;Smoke Detection in Complex Scenery Using Improved Faster-RCNN;CoT-Enhanced CNN-ViT Fusion with Adaptive Token learning for Precise Facial Expression recognition;evaluation Method of Cognitive Level Based on machine learning and Wearable Device;ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion;real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence;traffic Light Detection Based on Deep learning;robot Navigation Based on Reinforcement learning: An Overview;active Control Technology of Supply Chain Intelligent Work Robot Based on Speech Enhancement Technology;research on Security Technology of Power Dispatching data Network Based on Deep learning;An Improved RRT Path Planning Method Incorporating Deep Reinforcement learning for Space Multi-arm Robot;a Classification Denoising Algorithm Based on 3D Point Cloud Curvature;Comparative Analysis of the Effect of KPCA in PSO-GRU Combination Model.

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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language 1

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4th international conference on Deep learning Theory and Applications (DeLTA)

作者： Bogdanoski, Konstantin Mishev, Kostadin Simjanoska, Monika Trajanov, Dimitar Ss Cyril & Methodius Univ Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje North Macedonia iReason LLC 3rd Macedonian Brigade 37 Skopje North Macedonia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech recognition (ASR) models for low-resource languages. The study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. The paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. The study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. The findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. The research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. These findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.

关键词： machine learning Deep learning Automatic speech recognition Inference metric ASR pipeline Speech-to-text Wav2Vec2 ESPNet NeMo Natural language processing Macedonian language Low-resource languages Word error rate WER Character error rate CER

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Exploring machine learning Applications for Additive Manufacturing 3rd

Exploring Machine Learning Applications for Additive Manufac...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Chouhan, Kshitij Gupta, Sumit Chaudhary, Vijay Gupta, Pallav Kumar, Sundeep Department of Mechanical Engineering ASET Amity University Uttar Pradesh Noida201313 India Management Studies Engineering College Ajmer Rajasthan305025 India

ISBN: (纸本)9789819918935

The use of data and algorithms to open the ways that human learn, which gradually increase the accuracy, is all considered under a branch of computer science and machine learning known as machine learning (ML). This is a rapidly growing field of data science majorly includes the use of trained algorithms and statistical methods to make classifications, which uncovering basic insights of data mining projects. AM is a relatively new technology that has the potential to drastically alter the future of digital manufacturing. In recent years, machine learning (ML) has attracted a lot of attention in AM because of its amazing performance in data tasks including classification, regression, and clustering. One of the primary justifications is this very fact. The complexity of production systems leads to a variety of problems with regard to design principles, standardization, and quality control. There is a growing need, however, for more sophisticated and superior products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D printing

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FairdR: Ensuring Fairness in Mixed data of Fairly and Unfairly Treated Instances 1

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3rd CAAI international conference on Artificial Intelligence (CICAI)

作者： Liu, Yuxuan Kuang, Kun Zhang, Fengda Wu, Fei Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Fairness has emerged as a crucial topic in data mining and machine learning applications, driven by ethical and legal considerations. It is important to recognize that not all samples are treated unfairly, resulting in data heterogeneity in fairmachine learning. Existing fair models primarily focus on achieving fairness across all heterogeneous data, yet they often fall short in ensuring fairness within specific subgroups, such as fairly treated and unfairly treated data. This paper presents a novel problem of training a fair model on heterogeneous data, aiming to achieve fairness for both types of data, with a particular emphasis on the unfairly treated subset. To address this challenge, an effective approach is to recover the distribution of both fairly and unfairly treated data. In this study, we adopt the Structural Causal Model (SCM) to model the heterogeneous data as a mixture of causal structures. Leveraging the perspective of SCM, we propose a framework called FairdR, which utilizes the Hirschfeld-Gebelein-R ' enyi (HGR) correlation to accurately recover the distribution of both fairly and unfairly treated data. FairdR can serve as a pre-processing method for other fair machine learning models, providing protection for the unfairly treated members. Through empirical evaluation on synthetic and real-world datasets, we demonstrate that the presence of heterogeneous data can introduce unfairness in previous algorithms. However, FairdR successfully recovers the distribution of fairly and unfairly treated data, thus improving the fairness of downstream algorithms when dealing with heterogeneous data.

关键词： Fairness Causality Heterogeneity

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From Wrist to World: Harnessing Wearable IMU Sensors and TinyML to Enable Smart Environment Interactions 3

From Wrist to World: Harnessing Wearable IMU Sensors and Tin...

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3rd international conference on AI-ML Systems, AIMLSystems 2023

作者： Saha, Bidyut Samanta, Riya Ghosh, Soumya Kanti Roy, Ram Babu Indian Institute Of Technology Kharagpur India

ISBN: (纸本)9798400716492

In this demonstration paper, we introduce a pioneering method to elevate smart environment interactions using IMU sensor data from smartwatches or fitness trackers. Central to our approach is the classification of hand gestures, facilitating a seamless synergy between human gestures and device interactions. Through the integration of Tiny machine learning (TinyML) techniques, our system expands wearables' functionalities beyond traditional fitness monitoring to include gesture-based controls. The process comprises data collection, model training, and subsequent deployment to the wearable's microcontroller (MCU) for on-device machine learning inference. Such on-device computing ensures data privacy, as no data is transmitted externally, and guarantees low latency for real-time responses. Preliminary evaluations showcase the system's effectiveness in tasks like controlling PowerPoint presentations, managing table lamp operations, and interpreting specific hand gestures for device commands. Notably, our system achieves commendable gesture recognition accuracy while optimizing resource usage, underscoring its vast potential in diverse smart environment settings. © 2023 Owner/Author.

关键词： machine learning

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