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Gap estimation method for specular target based on binocular vision 9

Gap estimation method for specular target based on binocular...

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9th Symposium on Novel Photoelectronic Detection technology and applications

作者： Li, Ye Li, Dongxue Gao, Wenfei Xu, Kaiseng Qing, Dangsheng Zhang, Jinming Wang, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China Beijing Institute of Computer Technology and Application Beijing100854 China

ISBN: (纸本)9781510664432

OSR is a photosensitive material commonly used in aerospace photovoltaic displays, and it is difficult to detect the quality of OSR sheets due to its extremely high specular reflectivity. The mirror target is a common target in industrial production, such as the patch on the surface of photovoltaic cells, and its compact and neat arrangement affects the photosensitive quality. Therefore, the automatic perception and measurement of its gap is of great significance. In this paper, the gap measurement method of specular target based on binocular vision is proposed by taking the gap of a high reflectivity OSR sheet as the measurement target. In this method, the binocular camera is first calibrated to determine the relative geometric position of the binocular camera and its structural parameters. Then, the Canny algorithm that uses bilateral filtering instead of Gaussian filtering extracts the edge information in the image, and the LSD straight line detection algorithm is used to extract the extracted edge information in a straight line. Finally, the parallax map or depth map is obtained through the parallax change of the binocular camera, the virtual point is synthesized, and the fixed height constraint binocular matching method and triangulation method are used to solve the periodic blurring problem of boundary registration, and the gap width of the high reflectivity specular target is calculated. Binocular visual quality inspection experiments were designed to verify the feasibility and practicability of the proposed method. © 2023 SPIE.

关键词： Triangulation

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A fine-tuned vision transformer-based on limited dataset for facial expression recognition 9th

A fine-tuned vision transformer-based on limited dataset for...

引用

9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Febrian, Rio Huang, Ronald Richie Setiono, Nicholas Ramdhan, Dimas Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE Dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset. © 2024 The Authors.

关键词： Convolutional neural networks

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Implementation of Gamification to Help Teenagers Prevent Diseases 9

Implementation of Gamification to Help Teenagers Prevent Dis...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Haryanto, Michael Djaja, Fabian Putera Chrisandy, Michael Achmad, Said Moniaga, Jurike V Bina Nusantara University Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Adolescence is an important part of growing up to become an adult;keeping adolescent physique healthy is very important because poor dietary choices and a sedentary lifestyle will lead to diseases that they shouldn't have experienced yet. That is why teenagers need to be engaged to do more health- promoting behaviors, one way is by implementing gamification. This paper seeks to study which kind of gamification elements can be implemented from past research and whether the results were effective or not to prove whether gamification is a valid and effective strategy. At the same time, we are creating a gamified prototype for the mobile platform. The methodology starts with a systematic review of PubMed, MDPI, and Google Scholar to get a general idea of which gamification elements are the most implemented. Then a prototype will be made with the most used gamified elements from the systematic review. And then participants ranging from 10 - 19 years old will test the prototype and will give their opinions via a survey, From this, it is proven by our own gamified prototype survey results that using gamification as a way to engage teenagers is a fun and engaging way to do more health-promoting behaviors with a mostly positive feedbacks with the average rating of 8.33 out of 10 given from the participants. © 2024 IEEE.

关键词： Gamification

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The future of virtual reality: Prospect and problems 9th

The future of virtual reality: Prospect and problems

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Lie, Andrew Alfonso Buntoro, Owen Tamashi Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors. © 2024 The Authors.

关键词： Virtual environments

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Dual-Branch Subgraph-Pattern attention graph neural network for link prediction 4

Dual-Branch Subgraph-Pattern attention graph neural network ...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Ouyang, Zhongxuan Wang, Jinhua Computer Application Technology Computing Platform Department China Electronics Technology Group Corporation No. 32 Research Institute East China Computing Technology Research Institute Shanghai China

ISBN: (纸本)9781510686106

Graph data is a natural form of unstructured data, with nodes and edges representing entities and the relationships between them respectively. This type of data can be directly applied to various real-world scenarios. Graph Neural Networks (GNNs) have a natural advantage in handling graph-structured data, effectively processing the hierarchical structure of graphs to extract rich information. However, current GNNs have limitations in dealing with local information. In this paper, we discover a new attention-based network module that outperforms general GNNs. To better learn global-local information and overall hierarchical information, we propose a Dual-Branch Subgraph Pattern Attention Graph Neural Network (DB-SP-GAT). Experimental results demonstrate that DB-SP-GAT achieves superior link prediction performance across five benchmark datasets. © 2024 SPIE.

关键词： Graph neural networks

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Harnessing Deep Learning for Ocular Disease Diagnosis 9th

Harnessing Deep Learning for Ocular Disease Diagnosis

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Ryan, Jessica Nathaniel, Dave Andrew Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Vision impairment, often caused by preventable ocular diseases can be challenging to diagnose accurately and prone to human error. Automation using technology, particularly deep learning, offers a promising solution to aid in accurate and efficient disease detection. This study explores the use of different CNN models specifically VGG-16, VGG-19, ResNet-50, and ResNet-152v2, for detecting ocular diseases. Simple fine-tuning is applied to these models, and their performance is compared to identify the most effective model. The purpose is to show how different models contribute to establishing reliable illness detection systems. The results reveal that most of these models perform well with even minimal fine-tuning. Among the models, ResNet-152v2 achieved the highest training accuracy of 90.36% demonstrating its capacity to learn from the training data. In contrast, ResNet-50 offered a more balanced performance with marginally lower accuracy, making it a robust choice for general application. © 2024 The Authors.

关键词： classification deep learning ocular diseases

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Mental illness detection using sentiment analysis in social media 9th

Mental illness detection using sentiment analysis in social ...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Odja, Kasimirus Derryl Widiarta, Jasson Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts. © 2024 The Authors.

关键词： Neural network models

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Through the Lens: Unveiling the Power and Promise of Google's Visual Search technology 9

Through the Lens: Unveiling the Power and Promise of Google'...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Salim, Devano Hofker Susanto, Jose Juan Manalu, Sonya R. Shiddiqi, Hafizh Ash School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The digital landscape is undergoing profound changes, with visual search technology like Google Lens emerging as a pivotal force shaping our interaction with information. This research paper delves into Google Lens, a cutting-edge tool redefining how we engage with visual data. We comprehensively examine its functionalities, underlying principles, and real-world applications, particularly in education and library services. Through meticulous analysis, we evaluate its efficacy in addressing practical challenges and assess its accuracy, efficiency, and breadth of capabilities compared to other visual search technologies. By exploring existing research and the intricacies of Google Lens, this paper aims to provide a holistic understanding of this technology and its potential impact on how we access and interact with information in the real world. Finally, we explore the future of technology by contemplating the prospective roles of Google Lens across various industries and its potential for shaping the future of information access. © 2024 IEEE.

关键词： future of technology google lens visual search technology

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Investigating the use of Neural Networks in Enhancing Music Composition and Sound Quality

Investigating the use of Neural Networks in Enhancing Music ...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Gupta, Sachin Inzimam Manivasagam Sanskriti University Department of Management Mathura India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

An exciting new area of study in the world of music technology is the use of neural networks to the improvement of sound quality and the automation of musical creation. The goal of this research is to evaluate the potential of neural networks in boosting musical creation and reception. To determine whether algorithms based on neural networks can help us create groundbreaking music, we do extensive research into the field. First, the study presents a recurrent neural network (RNN)-based approach to composing melodic sequences of notes. This method draws on the remarkable pattern-recognition skills of neural networks to motivate richer musical production. Listening to a big library of pieces, the RNN model is able to learn how to construct new pieces that are enjoyable to human hearing. Synthesizing high-quality audio waveforms is crucial in modern music composition, and this study investigates the use of Generative Adversarial Networks (GANs) to this problem. The AudioWaveGAN approach utilizes GANs to produce high-quality sound, filtering out unwanted noise while also composing immersive pieces. Our study compares the proposed method to various established rivals. The criteria for evaluation are based on genuine conventional methods. The findings illustrate the superiority of the suggested technique in terms of originality, sound quality, and a combined score, emphasizing its potential to change music creation and production. This study represents a major advance toward using neural networks in music creation and audio processing. The research shows that these fresh methods open up exciting opportunities for artists, composers, and music producers who want to release hitherto unattainable wellsprings of inspiration and provide their listeners with fully enveloping aural journeys. © 2024 IEEE.

关键词： Generative adversarial networks

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Real Time Multiple-Object Detection Based on Enhanced SSD 2

Real Time Multiple-Object Detection Based on Enhanced SSD

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2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Jaiswal, Tarun Pandey, Manju Tripathi, Priyanka National Institute of Technology Department of Computer Application Raipur India

ISBN: (纸本)9781665458580

Object detection and recognition are widely employed in a variety of computer vision applications, and numerous techniques have been suggested in the literature. Object detection is one of the areas of computer vision that has seen a lot of achievement. In the last decade, various applications of computer vision have been presented. Some of these include autonomous cars, video surveillance, and scene interpretation. Visual recognition systems, including image categorization, localization, and detection, are at the heart of all of these applications and have gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance because of considerable advancements in neural networks, particularly deep learning (DL). Object detection techniques in real-time systems, on the other hand, necessitate minimal computing time and a thorough performance assessment prior to deployment. In this paper multiple image detection approach is used to identify and locate the object concerning their class label, and an enhanced feature map is used for object detection. All this is possible due to the recent advancement in DL along with image processing. These images, fed to the model, come from the video source. To tackle the problem related to the aspect ratio, the model uses the multi-scale feature map together with a distinct filter for diverse default boxes. © 2022 IEEE.

关键词： computer vision Visualization System performance Power control Object detection Streaming media Feature extraction

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