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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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Research on LncRNA and Disease Associations Prediction Base on data mining 4

Research on LncRNA and Disease Associations Prediction Base ...

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2021 4th international conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2021

作者： Jia, Fengjiao Jiang, Shaohua Wu, Zaoyang Liang, Yigao College of Information Science and Engineering Hunan Normal University Changsha China

the accumulated experiments show that lncRNA has a role in biophysiological and case processes. Prediction of the relationship between diseases and lncRNA will contribute to clarify the etiology of diseases, develop new drugs and treat complex diseases. However, the traditional biological experiment method has long experiment period and high cost. therefore, based on existing biological data and biological experimental data, data mining techniques have been used to propose many models and methods to predict the lncRNA-disease correlation. this article will provide a specific introduction to lncRNA and disease-related databases, which summarizes some relevant predictions classical models (matrix factorization, heterogeneous networks, machine learning). At the end of the article, the problems of lncRNA and the current prediction of the disease are analyzed, and some ideas and help are provided for later researchers. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification 25

Soft Label and Discriminant Embedding Estimation for Semi-Su...

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25th international conference on pattern recognition (ICPR)

作者： Dornaika, F. Baradaaji, A. El Traboulsi, Y. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain

ISBN: (纸本)9781728188089

In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. the main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. the scarcity of labeled data may affect the performance of the semi-learning. this paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. the proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. the resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. these experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.

关键词： Graph-based embedding discriminant embedding soft labels semi-supervised learning pattern recognition image categorization

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

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

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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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On Some Scientific Results of the ICPR-2020

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pattern recognition AND IMAGE ANALYSIS 2021年第3期31卷 357-363页

作者： Gurevich, I. B. Moroni, D. Pascali, M. A. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia Pisa Natl Res Council Natl Res Council Italy Res Area Pisa Inst Informat Sci & Technol A Faedo I-56124 Pisa Italy

this special issue of PRIA is devoted to some scientific results and trends of the 25th international conference on pattern recognition (Virtual, Milano, Italy, January 10-15, 2021). Two important events of ICPR-2020 are represented in this special issue: (1) the paper of Professor Ching Yee Suen (Centre for pattern recognition and machine Intelligence, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada)-the recent winner of IAPR very prestigious K.S. Fu Prize for a year of 2020. the paper based on his lecture "From handwriting to human personality and facial beauty" presented at the ICPR 2020;(2) Special issue "ICPR-2020 Workshop "Image mining. theory and Applications." the analysis of the scientific contribution of IMTA-VII-2021 allows us to draw the following conclusions: (1) the construction of a unified mathematical theory of image analysis is still far from complete. (2) there is considerable interest in the development of new mathematical methods for analyzing and evaluating information presented in the form of images. (3) there is a tendency to expand the mathematical apparatus in the development of new methods of image analysis and recognition by involving in this process areas of mathematics that were not previously used in image analysis. (4) the gap between the capabilities of new mathematical methods of image analysis and recognition and their actual use in solving applied problems remains significant. (5) there is an excessive use of neural networks in solving applied problems of image analysis and image recognition, and quite often without proper justification and interpretation of the results. the special issue includes articles based on the workshop papers selected by the IMTA-VII-2021 Program Committee for publication in PRIA. the PRIA special issue "Scientific Resume of the 25th international conference on pattern recognition" is prepared by the National Committee for pattern recognition and Image A

关键词： automation of data analysis biomedical applications biomedical images conditional generative adversarial networks data mining descriptive image analysis face beauty identity-preserved image mining intellectual data analysis mathematical theory of image analysis mathematical theory of pattern recognition

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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...

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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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A Comparative Study on Fuzzy Clustering for Cloud Computing. Taking Web Service as a case

A Comparative Study on Fuzzy Clustering for Cloud Computing....

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Djellali, Choukri Adda, Mehdi Moutacalli, Mohamed Tarik Univ Quebec Rimouski Dept Comp Sci & Engn Rimouski PQ Canada

this study addresses the increasingly encountered challenge of data clustering. We present a comparative study to data clustering for cloud computing using Fuzzy C-MEANS and Adaptive Resonance theory. To reduce variance and improve generalization ability, we used a resampling method based on 10-fold cross-validation. the typical initialization scheme is applied to improve the convergence speed of training and thus, reach the optimal solution. Experimental results on cloud computing datasets showed that the typical initialization-based Fuzzy Adaptive Resonance theory model is effective and achieves improved accuracy for pattern recognition task compared to Fuzzy C-MEANS. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： data mining Deep learning Clustering Artificial Neural Network Fuzzy Logic Cross-Validation Cloud Computing Web Service

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Human Brain Wave Concentration pattern Prediction Design Concept 4

Human Brain Wave Concentration Pattern Prediction Design Con...

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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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