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检索条件"任意字段=2022 International Conference on Algorithms, Microchips and Network Applications"

共 1155 条记录，以下是1-10 订阅

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international conference on algorithms, microchips and network applications

International Conference on Algorithms, Microchips and Netwo...

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2022 international conference on algorithms, microchips and network applications

ISBN: (纸本)9781510653290

The proceedings contain 91 papers. The topics discussed include: prediction of blood oxygen saturation based on deep learning;military communication mode optimization algorithm based on fuzzy multi-objective decision making;improved sparrow search algorithm combining adaptive mutation and proportion adjustment strategy;temporal and spatial analysis of the impact of epidemic situation on tourists' emotional fluctuation in China based on text mining;neural network quantization algorithms based on weights and activation values;solving multi depot school bus routing problem based on simulated annealing algorithm;research on machine learning classification algorithms for diabetes data;bilateral personalized attention recommendation algorithm for user data based on neural network;a particle swarm algorithm combined with flower pollination algorithm;analysis of energy storage cost in Zhejiang province base on the life cycle kwh cost algorithm;and research on the architecture of energy big data operation service system and algorithm simulation based on decision tree.

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Second international conference on algorithms, microchips, and network applications, AMNA 2023

Second International Conference on Algorithms, Microchips, a...

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2nd international conference on algorithms, microchips, and network applications, AMNA 2023

ISBN: (纸本)9781510664791

The proceedings contain 56 papers. The topics discussed include: quality evaluation method of agricultural talents training based on improved random forest algorithm;prediction of traffic accident impact range based on CatBoost ensemble algorithm;design of power grid full-service data monitoring platform based on cloud computing;an intelligent algorithm integrated with fit algorithms for solving the one-dimensional bin packing problem under multiple length restrictions;desensitization method of image data in the Internet of vehicles based on instance segmentation;construction and analysis of tourism mapping based on data mining and Apriori algorithm;chemical process fault classification based on improved marine predator optimization random forest;path planning for ships avoiding movable obstacles based on improved greedy algorithm;and an analysis algorithm for real-time monitoring of campus crowd density based on campus wireless network logs.

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Third international conference on algorithms, microchips, and network applications, AMNA 2024

Third International Conference on Algorithms, Microchips, an...

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3rd international conference on algorithms, microchips, and network applications, AMNA 2024

ISBN: (纸本)9781510680098

The proceedings contain 79 papers. The topics discussed include: a local flooding-based survivable routing algorithm for mega-constellations networks with inclined orbits;research on HOV lane route layout method based on heuristic algorithm;a distributed on-demand routing algorithm for large-scale low earth orbit constellation;exploiting sequence characteristics for long-term workload prediction in cloud data centers;DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment;a lightweight car damage detection algorithm;a privacy preserving carbon quota trading and auditing method;TCSE-ResNet50 mixed-signal identification algorithm for joint spectrum and quartic spectrum;research on speed profile generation of train automatic driving planning based on improved genetic algorithm;and an efficient 3D point cloud classification approach via persistent homology.

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Investigation into art education course suggestion algorithms using a lightweight gradient boosting machine and deep belief network 2

Investigation into art education course suggestion algorithm...

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2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： He, Lei Xinjiang University Xinjiang Urumqi830046 China

ISBN: (纸本)9781510689053

The rapid development of artificial intelligence has not only profoundly changed all walks of life, but also had a profound impact on the field of education. Schools across the country are also actively organizing and carrying out online teaching and research activities through various online teaching platforms. However, the current course recommendation method has a certain knowledge lag and the computational efficiency needs to be improved. In response to the above problems, this paper designs a feature extraction method based on a deep belief network, and designs an intelligent aesthetic education course recommendation algorithm in combination with an improved lightweight gradient boosting machine. The research results show that the research method only needs 20 iterations, and the corresponding loss values of the training set and the validation set converge to 0.23 and 0.08 respectively. Compared with the current latest algorithm, the accuracy of the research method is 98.6% when it is stable, and the calculation time is at least 6.799s/epoch, and the F1 value is as high as 86.62%. The above research results show that the research method can not only improve the efficiency of acquiring aesthetic education knowledge content, but also promote the diversified channels for receiving knowledge and flexible learning methods. © 2025 SPIE.

关键词： Curricula

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Potential applications and Prospects of TV Director Talent Cultivation in the Context of Artificial Intelligence algorithms 24

Potential Applications and Prospects of TV Director Talent C...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Liang, Jing Cao, Chang Wang, Zhiguo Xie, Yumeng Li, Hongjie Zhu, Xingyu Network and Educational Technology Center Jinan University Guangdong Guangzhou China Office of Scientific Research and Experimental Development Jinan University Guangdong Guangzhou China

ISBN: (纸本)9798400709999

With the widespread adoption of AI algorithms and the transformative impact of 5G and 4K/8K technologies in media production, the training of television directing talent faces new challenges and opportunities. This study explores the potential applications of AI in practical teaching, particularly in multi-camera switching, real-time image processing, and automated editing. In parallel, the evolution of online live broadcasting, accelerated by post-pandemic media dynamics and heightened viewer expectations, necessitates a sophisticated skill set for TV directors. As crucial figures in live broadcasts, TV directors must navigate these technological advancements and the changing aesthetic landscape. This paper proposes a comprehensive, hybrid teaching model that integrates AI technologies with Electronic Studio Production (ESP) and Electronic Field Production (EFP) methods. By doing so, it aims to enhance students' practical skills, problem-solving abilities, and collaborative capabilities. Our findings highlight the importance of immersive practical training in preparing students for the evolving demands of TV directing in the context of new media. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Television studios

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Pavement Crack Segmentation using Incomplete Supervision network 24

Pavement Crack Segmentation using Incomplete Supervision Net...

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3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wu, Wenxiu Jin, Yihui Fang, Zhihua Zhang, Jianfeng Fan, Xiaqi Song, Xiaoliang Port and Transportation Management Center Zhejiang Jinhua China Lanxi Transportation Construction Investment Group Co. Ltd. Zhejiang Jinhua China Lanxi Tongda Highway Maintenance Engineering Co. Ltd. Zhejiang Jinhua China Lanxi SHUNDA Road and Bridge Engineering Co. Ltd. Zhejiang Jinhua China

ISBN: (纸本)9798400707308

Challenges such as the lack of baseline pavement disease databases and limited segmentation accuracy and speed impact the performance of pavement disease segmentation and its practical engineering applications. To explore the feasibility of incomplete supervised learning, an experimental study generated inaccurate labels in batches by combining traditional image segmentation algorithms with manual methods, and the Mean Teacher model was tested. For imprecise supervised learning, the test F1 accuracy reached 0.572 after adding imprecise labels to the training set of self-collected data, although external imprecise labels were not feasible. In incomplete supervised learning, the Mean Teacher model showed significant advantages, achieving a test accuracy (MIoU) of 0.796 and an F1 score of 0.596 on self-collected data. Using unlabeled external images can enhance network accuracy in semantic segmentation tasks. With a small number of accurately labeled images, incomplete supervised learning can achieve substantial prediction accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Multi-task Heterogeneous Framework for Semi-supervised Medical Image Segmentation 17th

Multi-task Heterogeneous Framework for Semi-supervised Medic...

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17th international conference on Intelligent Robotics and applications

作者： Cao, Jinghan Fan, Huijie Fu, Shengpeng Xu, Ling Chen, Xi'ai Lin, Sen Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang 110159 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China China Med Univ Dept Med Oncol Hosp 1 Shenyang Peoples R China

ISBN: (纸本)9789819607730;9789819607747

Although recent advancements in fully supervised learning have led to notable successes in medical image segmentation, acquiring high-quality pixel-wise annotations from experts in the medical imaging domain continues to pose a significant challenge. Therefore, semi-supervised learning (SSL) is receiving increasing attention in medical image segmentation. Most existing semi-supervised learning (SSL) algorithms typically apply varying levels of perturbations to the student and teacher models, utilizing Exponential Moving Average (EMA) for parameter transfer. However, these methods are limited by the constraints of homogeneous networks, which can lead to the problem of model cognitive bias, restricting further exploration of the performance of dual-network models. We propose a novel Multi-task Heterogeneous Framework (MHF) aimed at correcting network biases and enhancing the performance of semi-supervised medical image segmentation. The MHF introduces two different sub-networks, each performing predictions for different tasks, to explore the potential of heterogeneous networks. Specifically, we use two heterogeneous sub-networks for different task predictions: one sub-net for segmentation prediction and the other for level set function prediction. In addition, we blend pseudo-labels from various modalities based on the Dice scores of the sub-nets. The merged pseudo-labels are then utilized to compute the unsupervised loss. Experimental results on the CT medical image dataset demonstrate that our method outperforms several state-of-the-art methods.

关键词： Multi-task Heterogeneous network Semi-supervised Medical Image Segmentation

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Multi-scale Traffic Camera Image Detection network Based on Improved YOLOv8 21st

Multi-scale Traffic Camera Image Detection Network Based on...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Peng, Zhihao Qi, Xinyuan Wu, Sheng Shang, Jianga Yang, Linquan China University of Geosciences Wuhan430074 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819601240

With the extensive deployment of traffic cameras across cities, utilizing these devices for vehicle detection has become essential for efficient traffic management. However, variations in vehicle sizes and distances between vehicles and cameras lead to inconsistent scaling of vehicles in the images, which presents a challenge for feature extraction. Furthermore, conventional object detection algorithms fail to fully utilize the rich feature information across multiple layers. To solve these issues, we develop a novel and advanced multi-scale traffic YOLO (MT-YOLO), based on the YOLOv8 model. Firstly, we propose the spatial multi-scale (SMS) attention mechanism to improve the extraction capability of spatial multi-scale vehicle features and aggregate these features for dynamic spatial modulation. Secondly, we develop the multi-scale dilation transposition CARAFE (MDT-CARAFE) upsampling method, which utilizes dilated convolutions to enhance the receptive field at different scales during the upsampling process. Lastly, we augment the network with a detection head and replace the original detection head with the adaptively spatial feature fusion(ASFF) detection head, which dynamically selects and fuses multi-layer feature maps, enabling the network to better capture multi-scale information. Our experiments demonstrate that the MT-YOLO model greatly improves results on the traffic camera detection dataset., achieving an mAP@0.5 of 80.9%, with a precision of 92% and recall of 93%. These results surpass the performance of leading detection algorithms. In addition, the model takes only 12.5 milliseconds to complete an inference, which makes it highly deployable and valuable for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Portability of Multiphysics applications on Heterogeneous Modular Supercomputers 15th

Portability of Multiphysics Applications on Heterogeneous ...

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15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Caviedes-Voullième, Daniel Koh, Seong-Ryong Poll, Stefan Suarez, Estela Arakawa, Takashi Nakajima, Kengo Sumimoto, Shinji Jülich Supercomputing Centre Forschungszentrum Jülich Jülich Germany CliMTech Inc. Chiba Japan The University of Tokyo Kashiwa Chiba Japan Kobe Japan Department of Computer Science University of Bonn Bonn Germany

ISBN: (纸本)9783031857027

The Modular Supercomputer Architecture (MSA) integrates various processing units, with compute modules tailored for specific algorithms, forming a unified heterogeneous system. Modules operate as parallel clustered systems, interconnected via a common or federated network. Optimized resource management allows flexible node selection, aiming for better performance. Hardware heterogeneity poses challenges for developers, who must port and optimize codes for various configurations. This study evaluated the performance of two applications-TSMP (regional Earth-system simulation) and mAIA (fluid dynamics)-across two MSA platforms (DEEP, Wisteria/BDEC-01), considering both hardware and software configurations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supercomputers

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Neuro-Symbolic Integration for Open Set Recognition in network Intrusion Detection 23rd

Neuro-Symbolic Integration for Open Set Recognition in Net...

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23rd international conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Bizzarri, Alice Yu, Chung-En Jalaian, Brian Riguzzi, Fabrizio Bastian, Nathaniel D. University of Ferrara Ferrara Italy University of West Florida PensacolaFL United States Institute for Human and Machine Cognition PensacolaFL United States United States Military Academy West PointNY United States

ISBN: (纸本)9783031806063

Open Set Recognition (OSR) addresses the challenge of classifying inputs into known and unknown categories, a crucial task where labeling is often prohibitively expensive or incomplete. This is particularly vital in applications like network Intrusion Detection Systems (NIDS), where OSR is used to identify novel, previously unknown attacks. We propose a neuro-symbolic integration approach that combines deep learning and symbolic methods, enhancing deep embedding for clustering with custom loss functions and leveraging XGBoost’s decision tree algorithms. Our methodology not only robustly addresses the identification of previously unknown attacks in NIDS but also effectively manages scenarios involving covariance shift. We demonstrate the efficacy of our approach through extensive experimentation, achieving an AUROC of 0.99 in both contexts. This paper presents a significant step forward in OSR for network intrusion detection by integrating deep and symbolic learning to handle unforeseen challenges in dynamic environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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