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Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

Enhancement of traditional clothes pattern recognation using...

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IEEE international conference on computing (ICOCO)

作者： Rangkuti, Abdul Haris Athala, Varyl Hasbi Luthfi, Naufal Fauzi Aditama, Syaugi Vikri Ramadhan, Muhammad Maulana Aslamia, Ayu Hidayah Bina Nusantara Univ Sch Comp Sci Bandung Indonesia

ISBN: (纸本)9781665436892

In general, Traditional clothes in Indonesia has its own characteristics to recognize the origin of the clothes. The formation of traditional clothes patterns in Indonesia from the crafts of remote communities in the regions of Indonesia. The article is that Indonesian citizens still find it difficult to distinguish traditional clothes that are scattered throughout Indonesia. To support this research, we use the Convolutional neural network method for types VGG16, VGG19 and MobileNetV2. To support the recognition process for a variety of traditional clothes patterns, the pattern processing process has started from preprocessing and image segmentation. This research was conducted on 5 types of experiments with supporting parameters, namely the number of training images and test images, rotation and scale of each image, image class and CNN parameters. After we conducted experiments on a collection of images that could be found in various scales and degrees, the average classification accuracy using VGG 16 was 79,23% in condition 2, but the highest classification accuracy was between 84,2%-92,5%. Meanwhile, if using VGG 19 the average classification accuracy is 79,95% with condition 1 for the highest accuracy between 92,6%-96,3%. If using mobilenetV2 the average classification accuracy is 83,44% for the highest accuracy between 87,3%-96,4%. Basically Research for traditional clothes pattern recognition using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 92%.

关键词： Traditional clothes Convolutional neural network VGG 16 VGG 19 MobilenetV2 classification accuracy

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17th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2022

17th International Conference on Soft Computing Models in In...

引用

17th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2022

ISBN: (纸本)9783031180491

The proceedings contain 64 papers. The special focus in this conference is on soft computing Models in Industrial and Environmental Applications. The topics include: An Extremal Optimization Approach to the Pairwise Connectivity Critical Node Detection Problem;dimensional Reduction Applied to an Intelligent Model for Boost Converter Switching Operation;Intuitionistic Fuzzy Sets in J-CO-QL + ?;assessing the Efficient Market Hypothesis for Cryptocurrencies with High-Frequency Data Using Time Series Classification;blockchain for Supply Chain Traceability with Data Validation;compression of Clustered Ship Trajectories for Context Learning and Anomaly Detection;DR Participants’ Actual Response Prediction Using Artificial Neural Networks;Non-linear Neural Models to Predict HRC Steel Price in Spain;first Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data;predictive Maintenance for Maintenance-Effective Manufacturing Using Machine Learning Approaches;virtual Sensor to Estimate Air Pollution Heavy Metals Using Bioindicators;regression Techniques to Predict the Growth of Potato Tubers;reliability-Sensitive Optimization for Provision of Ancillary Services by Tempo-Spatial Correlated Distributed Energy Resources;Predictive Maintenance of ATM Machines by Modelling Remaining Useful Life with Machine Learning Techniques;the Impact of Content Deletion on Tabular Data Similarity Using Contextual Word Embeddings;deep Learning-Based Dementia Prediction Using Multimodal Data;lightweight Models in Face Attribute recognition: Performance Under Oclussions;small Vessel Detection in Changing Seaborne Environments Using Anchor-Free Detectors on Aerial Images;Improving Malware Detection with a Novel Dataset Based on API Calls;identifying Places Using Multimodal Social Network Data;estimation of Lamb Weight Using Transfer Learning and Regression;monitoring Human Performance Through Deep Learning and Computer Vision in Industry 4.0;automatic Fish Size Estimation fr

关键词： Forestry

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A Facial Expression recognition System for Smart Learning Based on YOLO and Vision Transformer 2021

A Facial Expression Recognition System for Smart Learning Ba...

引用

7th international conference on computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Liang, Jingxin Wang, Dong Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781450389501

This paper proposes a facial expression recognition system for smart learning on classroom. Firstly, YOLO is used to extract face images of multiple students from high-resolution video;secondly, face images are preprocessed, then a self-attention based model named Vision Transformer (ViT) is used to recognize facial expressions;finally, the classified facial expression is used to assist teacher to analyze students' learning status, so as to provide suggestions for improving teaching effect. © 2021 ACM.

关键词： Students

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Weakly Supervised Medical Image Segmentation with soft Labels and Noise Robust Loss 1

引用

26th international conference on pattern recognition, ICPR 2022

作者： Felfeliyan, Banafshe Hareendranathan, Abhilash Kuntze, Gregor Wichuk, Stephanie Forkert, Nils D. Jaremko, Jacob L. Ronsky, Janet L. Department of Biomedical Engineering University of Calgary CalgaryAB Canada McCaig Institute for Bone and Joint Health University of Calgary CalgaryAB Canada Department of Radiology and Diagnostic Imaging University of Alberta EdmontonAB Canada University of Alberta EdmontonAB Canada Mechanical and Manufacturing Engineering University of Calgary CalgaryAB Canada

ISBN: (数字)9783031377426

ISBN: (纸本)9783031377419

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels. © 2023, Springer Nature Switzerland AG.

关键词： Diagnosis

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Spotting Fake News: Simplified LSTM Detection

Spotting Fake News: Simplified LSTM Detection

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Prajakta Tate Shreya Thorvat Amit Pawar Roshani Raut Anita Devkar Dept. of Information technology Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In the realm of combating misinformation, this paper presents a cutting-edge strategy that amalgamates two disparate datasets and sophisticated deep learning methodologies, primarily networks with long short-term memory (LSTM). Our innovative approach addresses the critical necessity for robust detection mechanisms amidst the rampant spread of fake news, particularly impacting social and political landscapes. Through meticulous experimentation, we have demonstrated substantial enhancements in accuracy and generalization, leveraging the inherent capabilities of LSTM networks and integrating diverse datasets sourced from reputable fact-checking entities and prominent social media platforms. Furthermore, we meticulously assess and benchmark our methodology against contemporary techniques, showcasing its superior efficacy in discerning authentic information from falsified narratives. Additionally, our research delves into exploring the potential synergies of user behavior analysis, social network scrutiny, and linguistic pattern recognition to further bolster detection performance. By advancing the field of bogus news detection, our study lays a formidable groundwork for mitigating the adverse consequences of disinformation in today's interconnected society pattern recognition to improve detection efficiency even more. Our study establishes a strong foundation for reducing the negative effects of misinformation in today's connected society by advancing the field of fake news detection.

关键词： Deep learning Resistance Measurement Technological innovation Accuracy Social networking (online) pattern recognition

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2021 international conference on Advanced Computer Applications, ACA 2021

2021 International Conference on Advanced Computer Applicati...

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2021 international conference on Advanced Computer Applications, ACA 2021

ISBN: (纸本)9781665435031

The proceedings contain 45 papers. The topics discussed include: another certain forms of soft compact and soft closed spaces;iris features extraction and recognition based on the local binary pattern technique;design and enhancement of localization system for radio frequency emitters based on doa techniques and triangulation centroid algorithm;ensemble-based noise filter for noisy and imbalanced data;blockchain and smart contracts to improve dental healthcare for children in primary school;recommender systems for e-commerce in online video advertising: survey;and spatial domain block based blind image watermarking for hardware applications.

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Implementing a Robust IoT Solution to Monitor Prone Position using Machine Learning Techniques 11

Implementing a Robust IoT Solution to Monitor Prone Position...

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11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Belay, Semagn Shifere Prince, T. Beyene, Tizazu Bayih Marew, Walelegne Yirdew Kebede, Andebet Dessiewu Woldia University School of Computing Woldia Institute of Technology Ethiopia College of Science and Arts University Department of Computer Science United Arab Emirates

ISBN: (纸本)9781665480383

It is essential to monitor sleeping patterns and position in order to learn about one's health state in order to enhance it and minimize sleep apnea. The use of a bed-mounted seismometer device to track the heart and respiratory patterns, as well as physical movement and attitude, while sleeping is discussed in this paper. The Internet of Things (IoT) and its gadgets and systems are becoming a part of everyday life. They are primarily used to track everyday tasks, particularly those that are relevant to individual health and wellness. As a matter of fact, and it's becoming increasingly important throughout the COVID-19 outbreak. A intelligent tracking and warning Internet of Things system known "NEF"was updated in this work to detect on-bed movement patterns, such as the prone position, using a variety of neural network models. On-bed movement patterns were recorded from seven different participants. The models derived using deep network were the most accurate when just the prone and supine postures were included. Random forest, on the other hand, produced the greatest total proportion of properly analysis refers to a set. Beddings such as pillows and blankets are expected to be investigated in greater depth in the future. © 2022 IEEE.

关键词： pattern recognition

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One-shot Distributed Algorithm for Generalized Eigenvalue Problem 22

One-shot Distributed Algorithm for Generalized Eigenvalue Pr...

引用

8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Lv, Kexin Sun, Zheng He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Quality and Business Operation Department Saic Motor Corporation Limited 489 Wei Hai Road Shanghai200041 China

ISBN: (纸本)9781450396110

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms both in the synthetic dataset and real-world datasets. © 2022 ACM.

关键词： Covariance matrix

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Applying Reservoir computing and Machine Learning Techniques for Image Enhancement in Biomedical Imaging

Applying Reservoir Computing and Machine Learning Techniques...

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international conference on Smart Communications and Networking (SmartNets)

作者： Andre Slonopas Adam Beatty Yenni Djajalaksana Department of Cybersecurity American Public University Sysytem (APUS) School of Security and Global Studies (SSGS) Charles Town WV International Council of E-Commerce Consultants (EC-Council) Academia Division Tampa FL

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

The paper presents a comprehensive study on the application of Machine Learning (ML) in enhancing the contrast of biomedical images. This research is pivotal in addressing the challenges of low visibility and detail in medical imaging, which are crucial for accurate diagnosis and treatment. The paper introduces innovative ML-based Histogram Equalization (ML - HE), leveraging deep learning algorithms and advanced image processing methods, to significantly improve the quality of biomedical images. These techniques enhance image clarity, detail, and overall contrast without compromising the integrity of the original data. This paper seeks to explore the integration of ML, specifically Reservoir computing, with traditional image enhancement methods, creating a synergistic approach that leverages the strengths of both ML and conventional techniques and expedites image enhancements in near real-time. This hybrid approach is shown to be more effective in handling diverse and complex imaging scenarios encountered in biomedical applications. The study also discusses the implications of these advancements for medical professionals, highlighting how ML-enhanced images can lead to more accurate diagnoses, better patient outcomes, and advancements in medical research. Overall, this paper sheds light on the transformative potential of ML in revolutionizing biomedical imaging, setting a new standard for image quality and diagnostic precision in healthcare.

关键词： Histograms Visualization Accuracy Machine learning algorithms soft sensors Thermocouples Speech recognition

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Spatial-Temporal Graph Neural Networks Fusing Multiple Data 4

Spatial-Temporal Graph Neural Networks Fusing Multiple Data

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Xu, Haonan Xue, Wenfang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China

ISBN: (纸本)9798350321951

Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. The application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. The experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods. © 2022 IEEE.

关键词： Investments

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