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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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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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Deep learning for Diagnosis of Endometrial Cancer and Atypical Endometrial Hyperplasia

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

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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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Power System Fault Classification and Prediction Based on Intelligent Algorithms 4

Power System Fault Classification and Prediction Based on In...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Hu, Hongbin Wang, Yuqiang Han, Junfei Zhong, Ming Zhang, Yifan An, Haijun Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350308082

Power system fault classification and prediction based on intelligent algorithms is a method that utilizes machine learning and data mining techniques to classify and predict fault conditions in power systems. this method can effectively prevent and avoid the occurrence of faults in the power system by collecting a large amount of power system data and using intelligent algorithms for processing to predict the type and location of faults that may occur in the power system, thereby improving the reliability and stability of the power system. this paper mainly applies the method of algorithm comparison and experiment to analyze the correlation of power system fault classification and prediction. Experimental data show that the recall rate and accuracy of the adaptive moment estimation algorithm mentioned in this article are both above 0.9, ranking first. © 2023 IEEE.

关键词： machine learning

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Optimizing Crop Yield in Agriculture using data mining and machine learning Techniques

Optimizing Crop Yield in Agriculture using Data Mining and M...

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Emerging Technology (INCET), international conference for

作者： Rashmi Welekar Charanjeet Dadiyala Department of Computer Science and Engineering (Cyber Security) Shri Ramdeobaba College of Engineering and Management Nagpur India

the agricultural sector is complex, and farmers and agri-businesses face numerous decisions every day, influenced by various factors. Precise yield estimation is critical for effective agricultural planning. data mining methods provide practical and effective solutions for these problems. Agriculture has always been a target for prediction and estimation techniques. Environmental and climatic conditions, soil variability, nutrient input levels, fertilizer combinations, and commodity/market prices make it necessary for farmers and businesses to use critical in-formation to make informed decisions. this project focuses on analyzing agricultural conditions and scenarios to find optimal parameters and data to maximize crop yield using data mining and machine learning techniques like k-Nearest Neighbors (k-NN), Naïve Bayes, Support Vector machine (SVM), Linear Regression, etc. By mining crop, nutrient, soil, location, and climatic data and analyzing new, non-experimental data, the project aims to optimize yield and production, and make the agricultural sector more resilient to climatic change.

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Food Detection and recognition Using Deep learning - A Review 4

Food Detection and Recognition Using Deep Learning - A Revie...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Banerjee, Anushrie Bansal, Palak thomas, K.T. Christ University Department. of Data Science Lavasa Pune India

ISBN: (纸本)9781665474368

Studies show poor lifestyle choices and unhealthy eating patterns cause issues like obesity and other ongoing illnesses that raise the risk of heart attacks, such as hypertension, abnormal blood sugar levels, and diabetes. To improve this situation a lot of health apps have been built which use modern dietary monitoring systems that automatically evaluate dietary intake using machine learning and deep learning techniques rather. For these reasons indepth investigations on food detection, classification, and analysis have been conducted. Some of the top methods for automatic food recognition created have been discussed in this paper. We also propose an idea for detection of Indian food items using image classification. According to our findings of the papers we reviewed, convolutional neural networks (CNN) have been extensively been used in food detection as it has been giving better results compared to other models. We also observed that Vision transformers perform better in situations where the dataset is large and a hybrid model would give better accuracy. A review of potential applications for food image analysis, shortfalls in the area, and open issues concludes the paper. © 2022 IEEE.

关键词： Image classification

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Early diabetes risk prediction based on deep learning methods 4

Early diabetes risk prediction based on deep learning method...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Xu, Luyao He, Jianjun Hu, Yifang Chengdu University of Technology Chengdu China

ISBN: (纸本)9781665413220

Diabetes is a relatively common chronic disease, and there is a long asymptomatic stage. With the continuous development of deep learning methods, it has been widely used in various fields, and its application to the medical field has gradually become a hot spot. To prevent and reduce the incidence of diabetes, and for effective diagnosis and prediction of diabetes, a diabetes prediction model based on one dimensional convolutional neural network (lDCNN) is proposed. In order to verify the effectiveness of our proposed method, we compared it with the naive Bayes classifier and random forest. We analyzed the three methods according to several performance indicators of the confusion matrix. the experimental results verify that our proposed method has more advantages in terms of accuracy and precision, achieving the accuracy of 97.02%, which can effectively predict the diabetes. the proposed 1DCNN-based method has high clinical application value and promotion significance, providing ideas for the mining and analysis of other disease data in the medical industry today. © 2021 IEEE.

关键词： Forecasting

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