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共 20248 条记录，以下是4931-4940 订阅

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Image super-resolution algorithm based on multi-dimensional Swin Transformers 3

Image super-resolution algorithm based on multi-dimensional ...

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3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Zheng, Qinan Xu, Huahu Bian, Minjie School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Data Group Co. Ltd Shanghai China

ISBN: (纸本)9798350359961

Numerous studies have demonstrated the Swin Transformer performs well in image super-resolution tasks. However, Swin Transformer typically divides the input image into fixed size blocks (such as 48x48) and independently processes each block, causing it to overlook a wider area. Furthermore, many deep learning super-resolution technologies face the challenge of computationally intensive operations. To address these issues, we propose a new NSTSR model consisting of four parts: data preprocessing layer, primary feature extraction layer, advanced feature extraction layer, and image generation layer. The advanced feature extraction layer consists of multiple STNBs (Swin Transformer N-segment blocks). We have introduced a technique called N-Segments, which applies the N-Gram contextual method for natural language processing tasks to low-level visual tasks. In our work, N-Segments refers to neighboring local windows in Swin, distinct from the traditional text analysis definition where N-Segments represents a continuous picture segment. Leveraging sliding-WSA, the N-Segments interact with each other, enabling the expansion of the regions seen to restore degraded pixels effectively. The experimental results demonstrate that the NSTSR model exhibits excellent performance compared to existing advanced methods while maintaining the computational efficiency structure. © 2023 IEEE.

关键词： component data Augmentation Image Super-resolution N-Segments Swin Transformer

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Prediction of Traffic Participant Behavior in Driving Environment Based on Counterfactual Inference 3rd

Prediction of Traffic Participant Behavior in Driving Envi...

引用

3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Huang, Yajie Wang, Fang Zhu, Tong College of Transportation Engineering Chang’an University Xi’an China

ISBN: (纸本)9789819710867

To travel safely and efficiently in complex traffic scenarios, autonomous vehicles need to be able to accurately predict the intentions and future behaviors of surrounding traffic participants. Nowadays, the behavior prediction of autonomous driving is mostly achieved using machine learning and deep learning models. However, due to the complexity and opacity of these models, it is difficult to meet the transparency requirements of autonomous driving systems in terms of interpretability. Therefore, by changing the input data, that is, using the mask to add or delete the traffic participants in the driving scene and modify the operation of the lane structure, so as to explore the main factors affecting the behavior prediction and further improve the interpretability of the model. To verify the interpretability of counterfactual inference operations for behavioral prediction methods, this paper conducts experiments on a large-scale autonomous driving public dataset nuScenes. Through a detailed analysis of the experimental results, we can conclude that the proposed method is interpretable for the behavioral prediction results. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Forecasting

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Deep learning Based Text to SQL Conversion on WikiSQL dataset: Comparative Analysis 3

Deep Learning Based Text to SQL Conversion on WikiSQL Datase...

引用

3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Lahoti, Kushal Paryani, Muskan Patil, Archana College of Engineering Pune Department of Computer Engineering Pune India

ISBN: (纸本)9781665482684

Due to tremendous expansion in the banking, business, and IT sectors, data is growing at an exponential rate these days, and the majority of it is stored in relational databases. In order to access and alter information, users must be proficient with languages such as SQL (Structured Query Language). However, the customers are unfamiliar with SQL. As a result, a number of different models have been presented to date to convert English queries into SQL for extracting data from databases to achieve human performance on the dataset. It is an approach toward developing an end-to-end solution that may provide an intuitive text interface for many data sources within the corporate ecosystem, allowing them to obtain real-time data visualization or insights. Some of the models developed to date have already surpassed the 90 % test accuracy on the WikiSQL dataset. However, more research is needed to develop a largely scalable model with higher accuracy on custom and unknown databases. The purpose of this paper is to shed light on different strategies involved in this domain and to understand the contrast between them. We mainly focus on those factors and aspects that impact the performance and accuracy of the task significantly. This paper presents comprehensive research on the most prominent algorithms and analyses them based on different factors. © 2022 IEEE.

关键词： data visualization

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Prediction of Loan Approval of Customers Based on Credit Score Using Machine learning 1

引用

3rd international conference on Information Technology, InCITe 2023

作者： Gupta, Navya Sharma, Deeksha Alka Aggarwal, Vrinda Singh, Veronica Bansal, Poonam Malik, Kiran Bachelor of Technology IGDTUW New Delhi India Artificial Intelligence Data Sciences IGDTUW New Delhi India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

Lenders receive loans from investors in the lending industry with the intention of returning interest. Credit is required and helps banks decide whether to provide a loan to a person or not by forecasting the risk that they would default on their debt. The money and time risk associated with selecting a safe individual has been reduced. This is accomplished by exploring Big data for historical data of the borrowers, after which the system is trained using the machine learning model that generates the most precise result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Machine learning Based Preemptive Diagnosis of Parkinson's Disease Using Saudi Clinical data: A Preliminary Case Study on Saudi Arabia dataset 3

Machine Learning Based Preemptive Diagnosis of Parkinson's D...

引用

3rd international conference on intelligent data Science Technologies and Applications, IDSTA 2023

作者： Olatunji, Sunday O. Alzahrani, Abdulaziz Alsubaie, Abdulaziz Aldahlan, Osamah Alnafea, Ibrahim Alamri, Abdullah Basheer Ahmed, Mohammed Imran Alam Khan, Mohammad Aftab Almuhaidib, Abdulrahman Alhiyafi, Jamal College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Kettering University Department of Computer Science FlintMI48504 United States

ISBN: (纸本)9798350339253

Parkinson's disease is a degenerative neurological condition that gradually worsens over time and currently has no known cure. It affects the nervous system and can cause thinking difficulties, loss of automatic movements, and problems with balance. Given its impact on societies and individuals alike, detecting Parkinson's disease early can help reduce the incidence and severity of the condition. Therefore, this study aims to predict Parkinson's disease preemptively to avoid complications from the disease. The study employed two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN), to predict the disease. GridSearchCV with 10-folds was utilized to improve the accuracy of the models. In addition, Sequential Forward Feature Selection (SFFS) was employed to reduce the feature number and increase accuracy. The results showed that, using 27 features, K-NN performed best in terms of accuracy (81%), recall (81%), and precision (82%). © 2023 IEEE.

关键词： Support vector machines

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Vehicle Pedestrian Detection Method Based on Improved YOLOv5 Algorithm 3

Vehicle Pedestrian Detection Method Based on Improved YOLOv5...

引用

3rd international conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Chen, Zhao-Hui Ling, Xiao-Ming National Engineering Research Center for Technology of Environmental Deposition Lanzhou Jiaotong University Lanzhou730000 China Key Lab of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730000 China

ISBN: (纸本)9781510670945

Vehicle pedestrian detection is a key aspect in driver assistance systems, which need to accurately detect all vehicle pedestrian targets on the roadway in order to ensure driving safety. To solve the problem of low accuracy in vehicle pedestrian target detection, this paper proposes a vehicle pedestrian detection method based on the improved YOLOv5 algorithm. In this paper, the initial anchor boxes of the dataset are re-clustered by the K-means clustering algorithm, and the CIOU loss function and DIOU_nms, are applied to the YOLOv5 algorithm to improve the target recognition effect and reduce the false and missed detection rate of small targets. The experimental results show that the mAP@0.5 of the improved YOLOv5 algorithm is improved by 1.85%. © 2023 SPIE.

关键词： Deep learning

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Research on Soccer Player Tracking Algorithm Based on Deep learning 3rd

Research on Soccer Player Tracking Algorithm Based on Deep L...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Bai, Hongding Yuanyuan, Chai Cheng, ZhenHua Yunnan Engineering Vocational College Yunnan Kunming650304 China Sports Department Modern College of Northwest University Shaanxi Xi’an710130 China

ISBN: (纸本)9783031631382

Target tracking technology is of great significance in football game video, and is the basis of high-level semantic tasks such as video summary generation, player motion analysis, game strategy formulation and football event detection. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly including correlation filtering and deep learning, but none of them can achieve high accuracy player tracking for soccer game *** recent years, AI and computer vision have become hot topics, attracted the close attention of a large number of experts and researchers, and triggered an upsurge of extensive and in-depth research. This paper studies the soccer player tracking algorithm based on deep learning. A football player tracking scheme based on deep learning is proposed: a convolutional neural network is built to extract the rich visual features of players in football game video, and the network is trained on a large number of data sets containing similar objects, which improves the ability of the algorithm to identify the same team members. The main objective of the project is to develop a system that can track the position and movement of players in real time, which will be used for live broadcast purposes. In order to achieve this goal, we designed a system using deep learning technology and computer vision algorithm. We also use some advanced technologies, such as 3D graphics processing unit (GPU) and field programmable gate array (FPGA). © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Graphics processing unit

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High dimensional time series anomaly detection based on generated adversarial network and spatial correlation 3

High dimensional time series anomaly detection based on gene...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Pan, Junru Liang, Ying Chen, Zexin School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

The production data collected by a large number of sensors in actual industrial production have multiple sources, high collection frequency and multiple dimensions, and the existing cleaning methods are difficult to accurately model the changeable time series. Therefore, a high dimensional time series cleaning method based on generative adversarial network and spatial correlation is proposed. Firstly, the time series with multiple dimensions are piecewise aggregated data processing. Secondly, the correlation analysis is carried out by mathematical statistical method, and the weighted repair is made after the abnormal value is found in the sequence. Finally, the long and short term memory cycling neural network training data set was used to complete the data cleaning, and the accuracy rate, recall rate and F1 score were calculated to evaluate the abnormal detection performance. The experimental results show that the cleaning efficiency and accuracy of this method are significantly improved. © 2023 SPIE.

关键词： Time series

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Neuro-Symbolic Approach for Tantrum Monitoring and Prevention in Individuals with Autism Spectrum Disorder: A Protocol for Virtual Agents 3

Neuro-Symbolic Approach for Tantrum Monitoring and Preventio...

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3rd international conference on Hybrid Human-Artificial Intelligence, HHAI 2024

作者： Monaldini, Andrea Department of Computer Science University of Pisa Pisa Italy Department of Information Engineering Computer Science and Mathematics University of l'Aquila Italy

ISBN: (纸本)9781643685229

Autism Spectrum Disorder (ASD) poses unique challenges, with individuals often experiencing difficulties in emotional regulation, leading to disruptive behaviors such as tantrums. This research protocol outlines the development of an innovative system designed to monitor and prevent tantrums in individuals with ASD, employing a neuro-symbolic approach. The proposed system integrates neural networks and symbolic reasoning to improve understanding and prediction of tantrum episodes. Leveraging real-time physiological and behavioral data, collected through wearable devices and user input, the system employs machine learning algorithms to detect patterns indicative of imminent tantrum events. Furthermore, a symbolic reasoning system interprets these patterns, taking into account individualized factors. The outcomes of this study are anticipated to contribute valuable insights to the growing field of technology-assisted interventions for neurodevelopmental disorders. © 2024 The Authors.

关键词： learning algorithms

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th international conference on intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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