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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Hu, Ao Lin, Yunhan Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9781510680425

To solve the problem of ship heave motion in harsh marine environments, which affects the positioning accuracy and safety of its robotic arm, this paper adopts a PID parameter optimization method based on Tent cold light source improved sparrow search ***, utilizing Tent chaotic mapping to enhance the diversity of sparrow populations, and inspired by the fluorescence attraction effect in the firefly algorithm, this paper perturbs sparrows with a cold light source strategy to obtain better positions and improve their search ***, simulation experiments were conducted to optimize the PID control system using improved *** results showed that compared with the three population intelligent optimization algorithm, the improved algorithm had better convergence accuracy, system response speed, and ***, the improved algorithm was applied to the actual experiment of compensating for the heave direction at the end of a shipborne robotic *** comparison of the four algorithms in compensating for the height difference in the heave direction showed that the improved algorithm had the best heave compensation effect, reflecting the effectiveness of the algorithm in engineering *** provides a certain reference for the active compensation of swarm intelligence optimization algorithms in the heave direction of ships. © 2024 SPIE.

关键词： Robotic arms

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Learning to Cluster Person in the Video via Graph Convolution Network 13

Learning to Cluster Person in the Video via Graph Convolutio...

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13th International Conference on information technology in Medicine and Education, ITME 2023

作者： Li, Wei Liang, Sixin Bian, Ang Du, Guodong College of Computer Science Sichuan University Chengdu China School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350319156

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module;(ii) a pose-guided embedding module;(iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. © 2023 IEEE.

关键词： Convolution

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Earthquake Prediction Using Deep Neural Networks 8

Earthquake Prediction Using Deep Neural Networks

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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Bhargava, Bharat Pasari, Sumanta Birla Institute of Technology and Science Pilani Department of Computer Science and Information System Rajasthan Pilani India

ISBN: (纸本)9781665408165

Reliable prediction of earthquakes has numerous societal and engineering benefits. In recent years, the exponentially rising volume of seismic data has led to the development of several automatic earthquake detection algorithms through machine learning approaches. In this study, we propose a fully functional and efficient earthquake detector cum forecaster based on deep neural networks of long-short-term memory (LSTM) units. The model captures inherent temporal characteristics of earthquake data. For illustration, we consider an earthquake catalog from the Himalaya and its neighboring regions. The proposed LSTM model shows satisfactory performance for small to medium-sized earthquakes. We also implement a baseline artificial neural network (ANN) model to perform a suitable comparison. It is observed that both ANN and LSTM models fail to produce desired result for large events. © 2022 IEEE.

关键词： Long short-term memory

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Analysis and Forecasting of Blockchain-based Cryptocurrencies and Performance Evaluation of TBATS, NNAR and ARIMA 4

Analysis and Forecasting of Blockchain-based Cryptocurrencie...

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4th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2022

作者： Sadia, Iqra Mahmood, Atif Binti Mat Kiah, Laiha Azzuhri, Saaidal Razalli University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

The rapid growth of cryptocurrencies has gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. Therefore the intelligent management and under-standing the characteristics of cryptocurrencies are becoming more interesting. The price of cryptocurrencies are characterized by linear and nonlinear trend, seasonality and high volatility, which increases the risk factors for investors. This study ex-periments with three different time series forecasting methods, specifically considered for Cryptocurrencies price such as Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR) and Cardano (XRP), and devises a procedure to evaluate their performance. Time series data are collected and examined using descriptive statistics. In next step, the White Neural Network is used for Non-Linearity and Dickey-Fuller for nonstationary and correlation among different settings of datasets. Based on these analyses, we evaluate efficient financial forecasting models such as Autoregressive Integrated Moving Average (ARIMA), Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) and Neural Network Autoregressive (NNAR) with reference to different parameters configuration of these models. The performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criterion and models are ranked by statistical mean and standard deviation of MAPE values. The NNAR model gives minimum MAPE of 2.823 while the minimum convergence time of 4.9835s is observed with TBATS and hence, these are ranked at top amongst other models respectively. These results underpin that neural network-based models perform equally well on both types of nonlinear and linear financial data and, thus, have the potential to improve the impact of financial transaction and cryptocurrencies price bringing more innovation in the decision making process. © 2022 IEEE.

关键词： Errors

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A Comparative Analysis of Joomla, Drupal, Wordpress and ***: Exploring Features, Performance, and Suitability

A Comparative Analysis of Joomla, Drupal, Wordpress and ***:...

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2023 IEEE Asia-Pacific Conference on computer Science and Data Engineering, CSDE 2023

作者： Prasad, Ashwin Ashika Sharma, Neeraj Anand Kumar, Anal School of Science and Technology The University of Fiji Department of Computer Science and Mathematics Fiji College of Engineering Science and Technology Fiji National University Department of Computing Sciences and Information System Fiji

ISBN: (纸本)9798350341072

In the dynamic landscape of web development, the judicious choice of platform and structure plays a pivotal role. This comprehensive study conducts a thorough comparison of four prominent web development technologies: ***, Joomla, Drupal, and WordPress. With a primary focus on scrutinizing features, performance, and overall suitability, the research aims to provide valuable insights for developers, businesses, and decision-makers. By objectively evaluating the merits and limitations of each technology, this study seeks to empower stakeholders with essential knowledge, enabling them to make informed decisions at the onset of web development projects. © 2023 IEEE.

关键词： Decision making

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Detecting DTI Using Graph Embedding and Multi-Head Attention Mechanism

Detecting DTI Using Graph Embedding and Multi-Head Attention...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Chen, Yaoyu Yang, Wenli Yu, HaiPing Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China China Graphics Society School of Information Wuhan Vocational College Professional Committee of Graphics and Big Data Wuhan China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction has an important role in drug discovery, significantly expediting the drug design and development process. This paper proposes a new model, ERW-BiAN, that seamlessly integrates graph embedding representations with deep learning network. Specifically, we utilize an improved graph embedding model, ERW, to generate comprehensive feature vectors for each node within the knowledge graph. Subsequently, these feature vectors are fed into the BiAN model, which leverages an attention mechanism to assign precise weights to sequences. Experimental results demonstrate the superior accuracy and predictive prowess of ERW-BiAN in DTI prediction tasks. Furthermore, the case about COVID-19 shows that ERW-BiAN has the better application potential for predicting drug-target interactions. © 2024 IEEE.

关键词： Knowledge graph

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on information technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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Adversarial Recovery Network for Low-Light Person Re-Identification 23

Adversarial Recovery Network for Low-Light Person Re-Identif...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Liu Wang, Xiao Yuan, Xin Mao, Zhu Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology ChinaHubei Prov. Key Lab. of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)9781450398572

Person re-identification (re-ID), which is to connect pedestrians with the same identity in different scenarios, has been more widely used in case detection of security. The re-ID datasets and technology have contributed greatly to advancing community research. There are currently no available low-light datasets due to the difficulty of collecting and labeling images of people in low-light scenes. As a result, the vast majority of current re-ID methods do not have the recognition capability in low-light scenes. However, the recognition of persons in the low-light surveillance video has become an important issue to be solved for urban security, which plays an important role in improving the crime-solving rate of public safety departments and protecting people's lives and property. In this paper, we propose an Adversarial Recovery Network (ARN) that, unlike other approaches, enables a re-ID model with low-light recognition capability without the need for low-light data. Specifically, we first degrade the original images to generate multi-level low-light images;then obtain recovered images through the adversarial recovery module, and finally input the recovered images into the CNN network to extract pedestrian recognition knowledge. In addition, to validate the effectiveness of the proposed method, we also propose a low-light re-ID dataset (LOLR). Extensive experiments on LOLR have shown that our method outperforms other competitors by a wide margin. The dataset and code are available online at https://***/xl02111/ARN. © 2023 ACM.

关键词： Recovery

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