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Research on human-machine interaction method of smart grid based on meta-learning 4

Research on human-machine interaction method of smart grid b...

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2022 4th international conference on Energy, Power and Grid, ICEPG 2022

作者： Wu, Feng Liu, Junjun Wang, Chen Ji, Yonghuan Cui, Chen Beijing Zhongdian Feihua Communication Co. Ltd. Beijing100070 China

As the size of the power grid continues to grow, the workload of dispatchers is becoming more and more powerful, and dispatchers alone cannot meet the daily dispatching communication needs. Voice human-machine interaction is a key technology in the field of artificial intelligence. this article proposes a smart grid human-machine interaction method based on meta-learning, this article proposes to use meta-learning to train language models and first-order MAML to optimize the initial model parameters. Experimental results show that after 20 epochs of training using less data, the speech recognition WER is reduced to 1.64% and the SER is reduced to 9.78%, which is better than the traditional hidden Markov model on WER and SER, and with the increase of training times, the recognition performance is gradually improved, and there is no overfitting phenomenon. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Comparative Analysis on Prediction Model of Hate Speech Detection Based on Social Media data Using Artificial Intelligence 4

Comparative Analysis on Prediction Model of Hate Speech Dete...

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4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Kaware, Pravin D. Raut, Anjali B. Department of Computer Science & Engineering H.V.P.M's College of Engineering & Technology Amravati India

ISBN: (纸本)9798331530402

the exponential expansion of social media networking platforms has magnified the dissemination of hate speech, thereby requiring strong recognition systems to minimize its influence. the present work offers a comparative examination of many prediction models for the recognition of hate speech using social media data, employing Artificial Intelligence (AI) methodologies. the paper intends to assess the efficiency of several machine learning (ML) and Deep learning (DL) algorithms for accurately detecting hate speech. the various strategies are evaluated using known datasets and measures including accuracy, recall, precision, and Fl-score. In addition, useful insights on the most effective methods for identifying hate speech are presented. the findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. Al-based hate speech detection apps improve accuracy and efficiency. Such systems are critical for creating a safer and more inclusive online. © 2025 IEEE.

关键词： Deep learning

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Self-Supervised learning for Point Clouds through Multi-crop Mutual Prediction

Self-Supervised Learning for Point Clouds through Multi-crop...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Changyu Zeng Wei Wang Jimin Xiao Anh Nguyen Yutao Yue Institute of Deep Perception Technology JITRI Wuxi China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom

As the demand for high-level autonomous driving continues to grow, efficient processing of large amount of point cloud data from LiDAR sensors has become a severe issue. Despite its great success, it is extremely difficult, if not impossible, to apply supervised learning in many real-world applications with natural scenes lacking annotations. In addition, removing label dependency and improving algorithm generalization ability are the current key research challenges in 3D deep learning. We propose a crop-based self-supervised learning framework for point clouds called Multi-Crop Mutual Prediction (MCMP). By cropping the original point clouds into different number of multiscale patches and making patches from the same object as close as possible through dynamic updates of online prototypes, the encoder is well able to maintain consistency between both local and global features. MCMP framework is model-agnostic so it is convenient to integrate it into encoders of any architectures. We pre-train three commonly used point cloud feature extraction networks on a single object-level dataset MondelNet40 and evaluate MCMP on three downstream tasks, namely, object classification, partial segmentation and semantic segmentation. Experimental results show that MCMP outperforms the previous crop-based self-supervised approaches in the object classification and semantic segmentation tasks and produces comparable results with the supervised benchmark methods.

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Pre and Post Harvesting using Deep learning Techniques: A comprehensive study 4

Pre and Post Harvesting using Deep Learning Techniques: A co...

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4th international conference on Recent Trends in Computer Science and Technology, ICRTCST 2021

作者： Patil, Nisha Deepak Tiwari, Rajesh Kumar Kumar, Ajit Jjt University Computer Science And Engineering Rajasthan India Rvs College Of Engineering And Technology Computer Science And Engineering Jamshedpur India

ISBN: (纸本)9781665466332

the paper aims to provide a comprehensive view about the use of deep learning Pre-harvest and Post-Harvest management in viticulture. Traditional approach targets manual aid in Pre and Post-harvest Quantitative and Qualitative analysis of post-harvest and pre-harvest management. the traditional approach has its disadvantages like an undue time consuming, Chances of manual errors and Recordkeeping maybe a voluminous task. the observational study which aims to eliminate technical discrepancies by using deep learning ensures benefits like AI-assisted algorithm and image processing ensures accuracy in data reading and analysis, automatic fruit quality monitoring reduces "Farm-to-Table"time provides better marketing quality for grapes and Optimum utilization of supply chains. We'll look at how machine literacy can be used in current vineyard operations and processes to yield industry-applicable quality. the recurrent neural network is a deep knowledge classifier that may be used to make a system that can identify grapes rested on their quality. Deep learning networks like AlexNet, GoogleNet, and VGG16 (Very Deep Convolutional Networks for Large-Scale Picture recognition) could enhance image segmentation and classification of grape bunches while also resolving approach negligence. the study concludes by demonstrating how deep literacy can be used for pre-and post-harvest operations in the viticulture industry. © 2022 IEEE.

关键词： Computer vision

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Image Synthesis with Single-type patterns for Mixed-type pattern recognition on Wafer Bin Maps 4

Image Synthesis with Single-type Patterns for Mixed-type Pat...

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4th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Byun, Yunseon Baek, Jun-Geol Korea University Department of Industrial and Management Engineering Seoul Korea Republic of

ISBN: (纸本)9781665458184

To increase the productivity, it is important to manage yield and reduce defects in the semiconductor industry. One of the efforts is to identify defect patterns and control the cause factors that affects the defects. Many engineers inspect the quality of each chip and check the defect pattern on the wafer bin maps. To get the accurate and consistent classification results regardless of the level for domain knowledge or experience of engineers, deep learning-based models have recently been studied. Since most previous studies aim to classify the single-type defect patterns, it is needed to consider the mixed-type defect patterns together. Also, they require a lot of labeled data to train the deep learning-based classification model. However, defects occur extremely rarely in actual manufacturing process. therefore, the method securing the higher accuracy in a situation where enough labeled data are not given is needed. this paper proposes a deep convolutional generative adversarial network for wafer map synthesis (DCGAN-WS) which generates the mixed-type patterns by synthesizing the single-type pattern and adding the pixel-wise summation. To maintain the characteristics of the binary pixel of the wafer bin maps, a thresholding technique is added. MixedWM38 dataset is used for the experiments, and it was verified that the mixed-type patterns were synthesized well. It helps to construct more robust model for single-type pattern classification and to generate the mixed-type patterns that have not occurred before. In the future, it is expected that this model addresses the problem of the lack of labeled data for defect pattern classification models. © 2022 IEEE.

关键词： Generative adversarial networks

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Electronic Music Signal Identification Model Based On machine learning Algorithm

Electronic Music Signal Identification Model Based On Machin...

引用

Emerging Technology (INCET), international conference for

作者： Li Dongfang School of Music University of Jinan Jinan China

the main idea of this model is to recognize sound signals and classify them into musical instruments. the working principle of the model is that different sounds have different characteristics and can be used for classification. the algorithm is based on machine learning algorithm, which can learn from data and improve performance over time. the concept of the model is to automatically identify music signals so that they can be used as inputs to other models. the model is based on machine learning algorithm and uses artificial neural network (ANN) and machine learning technology. Artificial neural network is a computer algorithm that simulates the working mode of human brain neurons. machine learning algorithms are more complex than artificial neural networks, which use different types of layers to process data and learn from it. they have been successfully applied in many fields, such as image recognition, speech recognition, text classification and so on.

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Human Brain Wave Concentration pattern Prediction Design Concept 4

Human Brain Wave Concentration Pattern Prediction Design Con...

引用

4th international conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Rahmad, Iwan Fitrianto Zarlis, Muhammad Universitas Sumatera Utara Faculty of Computer Science Medan Indonesia

ISBN: (纸本)9781665453950

By not concentrating on someone while driving or working this can lead to serious accidents, with the increasing number of road accidents becoming one of the most important problems that cannot be ignored. Traffic accidents are one of the leading causes of death in Indonesia, with human error playing a major role. According to Police data on the electronic media site ***, it is stated that in Indonesia an average of 3 people die every hour due to traffic accidents. therefore, using the concept of predicting the concentration pattern of human brain waves to reduce the accident rate while driving by using a machine learning approach. To detect a person's concentration, it can be done by measuring existing brain waves using sensors, there are several waves found in the human brain including alpha, beta, delta, theta, gamma waves. features using the frequency method and time method as well as a combination of frequency and time after using the PCA method to select features, so that the input from the classification method will be better. © 2022 IEEE.

关键词： Brain

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Deep learning for Diagnosis of Endometrial Cancer and Atypical Endometrial Hyperplasia

Deep Learning for Diagnosis of Endometrial Cancer and Atypic...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Aihua Zhao Xin Zhu Xin Du Wenwen Wang Wenqing Ma Shixuan Wang Graduate School of Computer Science and Engineering The University of Aizu Fukushima Japan Department of Gynecology Maternal and Child Hospital of Hubei Province Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Obstetrics and Gynecology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Endometrial cancer(EC) is the most common and rapidly increasing female cancer globally. Atypical endometrial hyperplasia (AEH) is a precancerous condition of EC. Although hysteroscopy serves as the primary modality for diagnosing lesions, it relies on the subjective judgment of hysteroscopists. therefore, this study proposed a computer-aided diagnostic system utilizing the EfficientNet network as a baseline, incorporating ParNet attention mechanism and class weighting to accurately classify EC/AEH from benign lesions. this study included 49,556 hysteroscopy images from 1,237 cases as a training set and 3,412 hysteroscopy images from 85 cases as a testing set. AUC, accuracy, sensitivity, specificity, PPV, Kappa, and F 1 -Score of the proposed method are 0.941, 89.4%, 93.7%, 87.1%, 73.3%, 0.755, and 0.8225, respectively. the proposed model may be used as a computer-aided tool for the diagnosis of EC/AEH.

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Sign Language recognition Using Deep learning 4

Sign Language Recognition Using Deep Learning

引用

4th international conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Nirmala, M. New Horizon College of Engineering Computer Science and Engineering Bangalore India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

the ability to converse with hearing and deaf persons has always been difficult for those who are tongue-Tied. In this paper, we can see different methods which are introduced to help them to communicate effectively. there are many human interpreters or assistant tools to help them communicate, but each person cannot afford that aid. the only mode of communication for them is sign language. therefore, the project's primary goal is to assist those individuals by providing a system that will recognize the signs, translate them into text, and enable them to lead a normal social life. Previously, a method including hand detection had been developed as a learning tool for novices in sign language. the system was developed using a method based on skin color modeling known as explicit skin-color space thresholding. the specified range of skin tones will distinguish between pixels, or the hand, and non-pixels, or the background. the photos were given as input to a model called the CNN a deep learning algorithm. We will be implementing this project using Keras to train the images. this document provides information on a variety of projects/research on sign language detection in the domains of machine learning, deep learning, and image depth data. this study considers a number of the numerous problems that must be overcome in order to overcome this problem, as well as the future scope. © 2022 IEEE.

关键词： Pixels

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Metagenomic Face: Applying machine learning to Facial Microbiome data for pattern recognition and Disease Classification

Metagenomic Face: Applying Machine Learning to Facial Microb...

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Intelligent Technologies (CONIT), international conference on

作者： M S Guru Prasad Raghavendra M Devadas Vani Hiremani Afsar Jahan J Praveen Gujjar Aryan Dahiya Department of Computer Science and Engg Graphic Era (Deemed to be University) Dehradun India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework’s efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis.

关键词： Support vector machines Measurement Logistic regression Metagenomics Microorganisms Accuracy machine learning algorithms

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