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14th international conference on Communication and network Security, ICCNS 2024

作者： Li, Aiguo Jia, Yanbo School of Computer Science and Technology Xi'an University of Science and Technology Shaanxi Xi'an China

ISBN: (纸本)9798400717994

When identifying metals with the same appearance and material non - destructively, the existing ultrasonic metal identification methods often overlook unknown - class identification, have poorly constructed data sets and limited practicality. This paper proposes an ultrasonic metal identification method based on the multi - scale fusion network of time - frequency features. It uses the multi - head multi - scale residual convolution module to extract and fuse time - frequency features, and combines the attention mechanism to process the fused features, which overcomes the over - fitting problem of a single time - domain feature and improves the generalization ability. An independent data set including training, validation and test sets is constructed. The training and validation sets each contain ultrasonic data of 12 test blocks, and the test set contains 13 classes (the 13th class is 5 test blocks of unknown categories). The test - set data are not used in model training to simulate actual scenarios. Experiments show that this method can efficiently identify metals in the self - built data set, and its effectiveness in classifying long sequences is verified on the UCR data set. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ultrasonic testing

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-network on 6G-enabled Mobile Edge networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th international conference on COMmunication Systems and networkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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LIGHT-YOLOv11: An Efficient S mall Object Detection Model for UAV Images 14

LIGHT-YOLOv11: An Efficient S mall Object Detection Model fo...

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14th IEEE international conference on Communication Systems and network Technologies, CSNT 2025

作者： Dewangan, Bhimendra Srinivas, M. Computer Science and Information Security National Institute of Technology Warangal India Computer Science and Engineering National Institute of Technology Warangal India

ISBN: (纸本)9798331531935

Object recognition in UAV images is very challenging due to the frequent appearance of numerous small and truncated entities. Object detection is an essential component of computer vision for accurate object localization and identification in complex and dynamic aerial scenarios. We introduce LIGHT-YOLOv11, a modified object detection model that integrates architectural enhancements to address the specific challenges of aerial imagery. To improve feature extraction and emphasize essential regions, the network’s initial block has been restructured into a Focus Block. Additionally, a Transformer block has been integrated into the backbone, replacing two blocks in the final architecture, to optimize spatial and contextual understanding. With these adjustments, detection accuracy and processing efficiency are remarkably balanced, especially for small and closely spaced objects. In-depth analyses of the VisDrone2019-DET dataset show that LIGHT-YOLOv11 outperforms other popular techniques, producing impressive outcomes across a range of situations. © 2025 IEEE.

关键词： Target drones

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Language identification using machine learning on social media text 4th

Language identification using machine learning on social med...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Raj, Rishav Ankita Dath, S. Sai Sholanki, Shivani Yadav, Anshuman Uchoi, Enjula Department of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9781032911571

One of the main areas researchers are looking into nowadays is how to change words from one language to another, known as transliteration. This is especially useful for cross-lingual applications where people need to translate words. Sometimes, people write in a mix of languages on social network platforms like X(twitter), Facebook, WhatsApp, which is called "mixed code language." To understand this kind of writing better, it’s important to figure out which language each word belongs to. In a recent study, a special type of deep learning model called Bidirectional Long Short-Term Memory (BLSTM) was introduced to analyze social media conversations in India. This model looks at the words that come before to figure out where each word comes from. Interestingly, the study found that using word embedding (a way of representing words) worked better than using character embedding. © 2025 the Author(s).

关键词： Adversarial machine learning

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Deformable Attention-Based Edge-Aware network for Single Image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ju Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368741

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality. © 2025 IEEE.

关键词： deformable attention edge-aware single image super-resolution window attention

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Enhancing urban mobility and providing way for ambulance using machine learning 4th

Enhancing urban mobility and providing way for ambulance usi...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Akanksha Gupta, Kashish Vaish, Khushi Sharma, Nikita Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9781032911571

In today’s world we have been facing problems due to increasing traffic on roads. This causes a lot of waste of time and increases stress level. So, this leads for creating a system which can intelligently handle traffic by itself modifying its timing in response to traffic density provide passage for ambulances using RCNN (Region-based Convolutional Neural network), webcam, NodeMCU controller, LEDs. The core idea revolves around traffic management through the assessment of traffic volume on each side of the road, with the aim of implementing smart traffic signal control based on this density information. The webcam captures real-time images of the automobiles on the road and send images to the NodeMCU microcontroller and then we apply RCNN algorithm and OpenCV to recognize and tally the count of automobiles on the lane and set traffic timing accordingly. If ambulance is detected traffic light turns green on that side and other side are turn red to provide passage for emergency vehicles. © 2025 the Author(s).

关键词： Ambulances

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Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (international Association of Engineers). All rights reserved.

关键词： Prediction models

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GGX-GAN: A Generative Adversarial network for Single-Image Material Appearance Editing with Physical Parameters 8

GGX-GAN: A Generative Adversarial Network for Single-Image M...

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8th international conference on computer Graphics and Virtuality, ICCGV 2025

作者： Wang, Shengyao Li, Hongsong School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781510689213

Material appearance editing methods based on generative adversarial network (or GAN) use perceptual parameters to control material appearance but are limited by the rating quality of training data. The data annotators were often confused by ambiguous perceptual parameters, leading to poor labeling or rating results. Instead, we choose physical parameters to achieve more interpretable and measurable material appearance editing. The image dataset rendered by a physically based renderer were used for GAN training and model validation, and the GGX BRDF model was chosen to control the glossiness of rendered objects. Then we embedded the BRDF model into the latent space of GAN and establishes a physical parameter control space of editing, allowing continuous material appearance tuning. Inverse rendering results of the images generated by the proposed GAN were analyzed to illustrate and evaluate its editing performance. It is shown that the proposed GAN can provide accurate, intuitive material appearance editing on both real-world photographs and rendered images. © 2025 SPIE.

关键词： Inverse problems

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Application of real-time processing of computer data based on digital media 24

Application of real-time processing of computer data based o...

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5th international conference on computer science and Management technology, ICCSMT 2024

作者： Zhang, Linqin Department of Computer and Arts Anhui Technical College of Industry and Economy Anhui Hefei China

ISBN: (纸本)9798400709999

With the popularization of computer technology in today's society, the science and technology in the field of digital media are constantly developing, which has actually changed people's original way of life and rest, and the study and work patterns in daily life have undergone earth-shaking changes. This paper analyzes the application of digital media technology in the computer data processing environment, and shows that the application of digital media technology can reflect more value with the support of computer environment, provide effective channels for it to show its own role, and bring more convenience to people's daily life. No matter learning knowledge or data processing, many problems existing in the past can be solved, and the real-time data processing speed can be fundamentally improved. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data centers

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Sentiment Analysis of Weibo Comments Based on Bi_directional Long Short-Term Memory network 24

Sentiment Analysis of Weibo Comments Based on Bi_directional...

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3rd international conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Li, Lianxiong School of Computer Science & Artifical Intelligence Lanzhou Institute of Technology Lanzhou Institute of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

In the problem of sentiment analysis in Weibo comments, contextual information has a significant impact on sentiment analysis. If contextual information cannot be considered, the accuracy of sentiment analysis will be low. The paper uses a Bi_directional Long Short-Term Memory Veural network(BiLSTM) model for sentiment analysis, considering the influence of contextual information to improve the accuracy of sentiment analysis in Weibo comments. To fully consider the impact of facial expression information on emotional orientation in Weibo comments, in the data preprocessing process, facial expression information is extracted from Weibo to retain it for later emotional judgment;Secondly, using CBOW model of Word2Vec to convert word vectors, Weibo comments can be represented in vector form;Finally, sentiment analysis was conducted using the BiLSTM network model to determine the emotional tendencies of Weibo comments. Compared with RNN and LSTM network models, this method has improved accuracy, recall, and F1 value. The experimental results indicate that the method used to implement sentiment analysis has significant practical value in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Long short-term memory

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