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Enhanced Classification of Gravitational Wave Images using CNN Model

Enhanced Classification of Gravitational Wave Images using C...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Aditya Sharma Inderdeep Kaur Paurav Goel Master of Engineering - Artificial Intelligence Chandigarh University Chandigarh India Department of Computer Science Chandigarh University Chandigarh India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The discovery of gravitational waves resulting from the merger of binary black holes revolutionized physics and astronomy. Amid signal complexity and noise capture, data performance is critical. This work presents a convolutional neural network (CNN) algorithm using real-time data from the "Gravity Spy" dataset at https://***/datasets/tentotheminus9/gravity-spy-gravitational-waves. A combination of CNN model and Fourier transform accuracy of gravitational wave signal expression. It is worth noting that frequency domain signal analysis is better than time domain signal analysis in capturing signal characteristics. Experimental results demonstrate the effectiveness of CNN in improving signal recognition by achieving an accuracy of 94.63%. This research helps advance the exploration of the void by proposing a deeper study, thus promoting a deeper understanding of astrophysical phenomena.

关键词： Deep learning Analytical models Accuracy Gravitational waves pattern classification Real-time systems Convolutional neural networks

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Research on Artificial Intelligence (AI) recognition Automated soft-Shell Crab Breeding System under “Internet +” Technology

Research on Artificial Intelligence (AI) Recognition Automat...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Lei Zheng Mingmin Gong Zhenyang Li Mingming Yang School of Business Shenzhen Institute of Technology Shenzhen China School of Information Engineering Wuhan College Wuhan China School of Information and Communication Shenzhen Institute of Technology Shenzhen China

This project aims to use artificial intelligence technology to raise crabs in land equipment, so as to easily obtain soft-shell crabs, to increase the supply of new aquatic products in the market, to meet the needs of market consumption upgrading, and finally to achieve the goal of economic benefits. This project is based on artificial intelligence to identify automatic soft-shell crab cultivation technology, combined with scientific breeding management methods, the general crab to soft-shell crab cultivation. The equipment uses the Internet of Things, big data, cloud computing, computer vision and other technologies to design five systems, including soft shell crab mounting identification system, water quality monitoring system, three-dimensional breeding system, automatic feeding system, integrated control system, to realize the local and remote all-round intelligent monitoring of soft-shell crab breeding base. In this paper, STC15F2K60S2 as the core of the design of MCU based hardware platform is presented. RS485 communication mode is used to collect data of each sensor in real time, and multiple environmental parameters can be monitored in real time. Using video surveillance technology, the pond can be secured, and the pond can be photographed on the surface and underwater. The feeding device is controlled by PLC and the feeding machine and aerator are controlled intelligential. The network uses ESP8266 wireless network to realize network connection to AP. Through the establishment of the “server” management software, we enable users to remote query through the computer browser, mobile APP and other ways in the case of network.

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Instance Segmentation in Mobile computing Environments for Identification of Specific Characteristics in Endangered Species 17

Instance Segmentation in Mobile Computing Environments for I...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Ribeiro, Morgana C. O. Monteiro, Rhayane S. Moreira, Mario W. L. Rodrigues, Joel J. P. C. Fed Inst Educ Sci & Technol Ceara IFCE Aracati CE Brazil Fed Univ Piaui UFPI Teresina PI Brazil Inst Telecomunicacoes Aveiro Portugal

ISBN: (纸本)9781728186160

The segmentation of instances is a key topic in image processing and computer vision. There are numerous applications such as medical image analysis, video surveillance, image compression among others, in which its algorithms show significant results. Due to the COVID-19 pandemic, most countries have been affected mainly by their economy. In the food production sector, including fishing and aquaculture, it was no different. In this context, this research has as main objective to contribute to the 2030 Agenda for Sustainable Development suggested by the United Nations (UN), through a fish detection and segmentation model based on the framework Detectron2, optimizing the time of professionals in identifying specific characteristics of a particular species. To achieve this objective, this research seeks to facilitate the recognition of patterns of parts of the fish from the segmentation of instances and to stimulate scientific research in the area through the morphological information collection of certain species. The results present an accuracy, based on the Intersection over Union (IoU) indicator, of 88.4%, providing an effective solution for the collection of these characteristics.

关键词： Mobile computing pattern recognition Instance Segmentation Convolutional Neural Networks Detectron2

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Detection of Region-of-Interest Based on Noise Filtering in High-Speed Images 1

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4th international conference on Innovative computing, IC 2021

作者： Jang, Seok-Woo Anyang University Anyang14028 Korea Republic of

ISBN: (数字)9789811642586

ISBN: (纸本)9789811642579

Relatively inexpensive cameras have become common that can film even minute movements of moving objects at high speeds. This paper introduces a method of removing noise pixels abnormally generated from the ultra-high-speed images entered, and then detecting the skin color area of a person, the area of interest that represents personal information from the images of noise removed. The proposed algorithm eliminates noises by applying bilateral filters from the high-speed images that are first accepted. Then, using color models created through learning, the human skin area, which is an area of interest, is accurately extracted from the image of noise removed. The experimental results show that the proposed algorithm effectively removes noises from the high-speed images entered and then robustly detects the area of interest, the skin color area. The method introduced in this study is expected to be useful in many applications related to pattern recognition, such as image analysis and understanding, video pre-processing, noise removal, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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Comprehensive Analysis of Deep Learning for Accurate Knee Joint Segmentation and Classification

Comprehensive Analysis of Deep Learning for Accurate Knee Jo...

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Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Bhagyashree Maharana Avinash Kumar Sharma Khushi Samridhi Yojna Arora Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida U.P. India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Knee joint segmentation and classification are critical tasks in medical imaging, having applications in diagnosis, treatment planning, and surgical navigation. The intricate architecture of the knee joint and the variation in knee joint imaging, however, can make these tasks difficult. Deep learning has become a viable method for classifying and segmenting knee joints. From massive knee joint picture datasets, deep learning models may be trained to recognize intricate patterns, and they can produce state-of-the-art performance on certain tasks. In order to divide and classify knee joints, this paper discusses current developments in deep learning. The paper talks about the various training approaches and deep learning architectures applied to these challenges. Furthermore, the paper provides an extensive summary of the cutting-edge functionality of deep learning.

关键词： Deep learning Training Image segmentation Accuracy Navigation Surgery Bones Planning pattern recognition Tumors

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Innovative Deep Learning Techniques for Solving Complex Computer Vision Challenges and Enhancing pattern recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： T. Jayachitra S. Sathya Ramesh P S Jothikumar R Gouri M. S Mohammed Waheeduddin Hussain Department of Electrical Electronics and Communication Engineering Sharda University Greater Noida India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani India Department of Smart Computing Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Shadan College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Baselios Mathews - II College of Engineering Sasthamcotta Kollam India Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications.

关键词： Deep learning Computer vision Visualization Accuracy Network topology Neural networks Data augmentation Topology pattern recognition Object recognition

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Combining Attention Mechanism and Local-Global Features Association Network for Vehicle Re-identification 11

Combining Attention Mechanism and Local-Global Features Asso...

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11th international conference on computing and pattern recognition, ICCPR 2022

作者： Li, Cai-Xia Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Vehicle re-identification (Re-ID) aims to retrieve the target vehicle from a large dataset composed of vehicle images captured by multiple cameras. Most vehicles are difficult to recognize in the environment of low resolution, occlusion, and viewpoint change, which brings challenges to vehicle Re-ID. Existing work usually uses additional attribute information to distinguish different vehicles, such as color, viewpoint, and model. However, this requires expensive manual annotation. Therefore, we propose a three-branch network based on attention mechanism and local-global feature association (AM-LGFA) to improve the accuracy of vehicle Re-ID. In the global branch, the global features of the vehicle are extracted. A multi-scale channel attention module is introduced into the attention branch to suppress irrelevant information and extract important channel features. The features extracted from the backbone are divided into different stripe features in the horizontal direction in the local branch. Then connect each stripe feature with the global information to enhance the context between features. Finally, the features extracted from the three branches are concatenated as the feature representation of the test phase. The experimental results show that the features extracted by the AM-LGFA network are complementary. The effectiveness of this method is verified on two challenging public datasets, VehicleID and VeRi-776. © 2022 ACM.

关键词： Vehicles

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Application of pattern recognition in Computer Image Processing

Application of Pattern Recognition in Computer Image Process...

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international conference on Multi-modal Information Analytics, MMIA 2021

作者： Yang, Pei Xi’an Fanyi University Xi’an Shaanxi China

ISBN: (纸本)9783030748135

The application of computer image processing technology is more and more extensive, which is involved in many fields, and improves the image quality. pattern recognition technology is a new recognition technology extended from artificial intelligence technology, which plays a very important role in computer image processing. It can not only improve the efficiency of image processing, but also ensure the accuracy of the image. This paper first introduces the pattern recognition technology, especially its advantages, then analyzes its application principle, and finally launches the practical application and development of pattern recognition technology. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： pattern recognition

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Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction 29

Cluster-aware Pseudo-Labeling for Supervised Open Relation E...

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29th international conference on Computational Linguistics, COLING 2022

作者： Duan, Bin Wang, Shusen Liu, Xingxian Xu, Yajing Pattern Recognition & Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

Supervised open relation extraction aims to discover novel relations by leveraging supervised data of pre-defined relations. However, most existing methods do not achieve effective knowledge transfer from pre-defined relations to novel relations, they have difficulties generating high-quality pseudo-labels for unsupervised data of novel relations and usually suffer from the error propagation issue. In this paper, we propose a Cluster-aware Pseudo-Labeling (CaPL) method to improve the pseudo-labels quality and transfer more knowledge for discovering novel relations. Specifically, the model is first pre-trained with the pre-defined relations to learn the relation representations. To improve the pseudo-labels quality, the distances between each instance and all cluster centers are used to generate cluster-aware soft pseudo-labels for novel relations. To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data. Experimental results on two public datasets demonstrate that our proposed method achieves new state-of-the-arts performance © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： Extraction

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AffectPro: Towards Constructing Affective Profile Combining Smartphone Typing Interaction and Emotion Self-reporting pattern 22

AffectPro: Towards Constructing Affective Profile Combining ...

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24th ACM international conference on Multimodal Interaction, ICMI 2022

作者： Hari, Satchit Ajay, N. Sarcar, Sayan Sen, Sougata Ghosh, Surjya Bits Pilani Goa India Birmingham City University United Kingdom

ISBN: (纸本)9781450393904

The ubiquity of smartphones and the widespread usage of text entry by soft keyboard in different instant messaging applications (e.g., WhatsApp, FB messenger) have opened the possibilities of inferring emotions from longitudinal typing data. To build this emotion inference engine, we apply machine learning models on features extracted from user's typing patterns (not content). However, one major challenge encountered while developing the emotion inference model is the requirement of individual training data as typing patterns are often person-specific. In this paper, we investigate the possibility of combining typing pattern with emotion self-reporting to identify a group of similar users so that the training data among these users can be shared to fulfill the requirement of personalized dataset. We develop a framework AffectPro, which quantifies the typing interaction behavior (e.g., typing speed, error rate) and self-reporting pattern (e.g., emotion state transition probability) to construct the affective profiles of users. We evaluated AffectPro in a 6-week in-the-wild study involving 28 users, who used an Android application encompassing a custom keyboard to perform all their typing activities, and to report their instantaneous emotions. We extracted different typing signatures and self-report behavior details from the collected dataset (≈ 5000 typing sessions, ≈ 108 hours of typing data) to construct the affective profile of users. Our results demonstrate similarity across users in terms of typing signature, emotion self-reporting pattern, and a combination of both;which can be leveraged to share training data among similar users to overcome the challenges of personalized data collection. © 2022 ACM.

关键词： Smartphones

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