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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis 20

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Hussain, Khadim Azhar, Muhammad Lee, Bumshik Iqbal, Asma Affan, Muhammad Ullah Khan, Sajid Comsats University Islamabad Department of Computer Science Sahiwal Pakistan Hong Kong Shue Yan University Department of Applied Data Science Hong Kong Hong Kong Chosun University Department of Computer Science and Software Engineering Korea Republic of Tsinghua University Department of Computer Science China University of Lakki Marwat Department of Computer Science Pakistan

ISBN: (纸本)9798350395785

In this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named 'ASAnalyzer', which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COVID-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods. © 2023 IEEE.

关键词： Classification (of information)

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Residual Convolutional Graph Neural Network with Subgraph Attention Pooling

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Tsinghua science and Technology 2022年第4期27卷 653-663页

作者： Yutai Duan Jianming Wang Haoran Ma Yukuan Sun Information and Communication Engineering Department Tiangong UniversityTianjin 300387China Computer Science Department Tiangong UniversityTianjin 300387China Software Engineering Department Tiangong UniversityTianjin 300387China Center for Engineering Intership and Training Tiangong UniversityTianjin 300387China

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational ***,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification *** this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features ***,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph *** strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual *** feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level *** messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for *** experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

关键词： graph neural network graph pooling information loss

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of the impact of college students' behavior on employment rate 3

Prediction of the impact of college students' behavior on em...

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2022 3rd International Conference on computer Information and Big Data Applications, CIBDA 2022

作者： Liu, Xiang Zhang, Yikuan Department of Computer Science Software Engineering Institute of Quangzhou Guangzhou China Department of Electronic Software Engineering Institute of Quangzhou Guangzhou China

ISBN: (纸本)9783800758760

With the rapid development of artificial intelligence and the continuous improvement of prediction methods, analyzing and researching the changing trend of student employment, optimizing the direction of teaching improvement, and improving the employment rate of students have a good role. This paper mainly introduces several key factors that affect employment based on the behavior of college students, establishes a BP neural network employment rate prediction model, and predicts the employment rate of recent graduates by training and learning the employment rate of recent graduates. Comparing the training results with the real employment rate, the error is less than 2%, which shows that the employment rate prediction model based on BP neural network has practical value. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Employment

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Visual object tracking:Progress,challenge,and future

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The Innovation 2023年第2期4卷 19-20页

作者： Libo Zhang Heng Fan Institute of Software Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Engineering University of North TexasDentonTX 76206USA Nanjing Institute of Software Technology Nanjing 210000China

Visual object tracking aims to continuously localize the target object of interest in a video *** one of the most fundamental problems in computer vision,visual object tracking has a long list of critical applications including video surveillance,autonomous driving,human-machine interaction,augmented reality,robotics,etc.,in which the tracking system provides the capacity to report target positions in real time for subsequent visual *** the past decades,visual object tracking has been extensively explored and has witnessed considerable progress,especially in deep-learning-based tracking.

关键词： Visual visual computer

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MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTUAL COLONOSCOPY 18

MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTU...

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18th International Conference on Interfaces and Human computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Ma, Ming Sun, Chenyang Lei, Na Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University United States School of Software Technology Dalian University of Technology China International School of Information Science & Engineering Dalian University of Technology China

ISBN: (纸本)9789898704580

Colorectal polyps can evolve into colon cancer over time. Early screening or detection of colon polyps using computer-aided detection (CAD) techniques along with removal of the polyps can lower the risk of colon cancer. As an alternative to optimal colonoscopy (OC), virtual colonoscopy (VC) has the advantage of non-invasiveness in the detection of colon polyps. Recent decades have witnessed rapid advancement in VC based polyp detection and false positive (FP) reduction techniques using machine learning or deep learning methods. However, the majority of existing methods are based on the computation using computed tomography (CT) images of patient data, which are in some cases very computationally expensive, particularly in the scenario of necessity of large dataset for model training. In this paper, we propose a novel automatic polyp detection method termed Mesh-PolypDet using the surface meshes of colorectal data. The initial detection of polyp candidates is performed on 3D colon surface meshes, and the subsequent FP reduction is carried out using the visual transformer and conformal mapping. Experiments are conducted to evaluate the performance of the proposed method in comparison with other existing methods. © 2024 Interfaces and Human computer Interaction. All Rights Reserved.

关键词： Conformal mapping

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Over time analysis of the codon usage of SARS-CoV-2 and its variants

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Computational and Structural Biotechnology Journal 2025年 27卷 2034-2050页

作者： Davidson, Alma Parr, Marina Totzeck, Franziska Churkin, Alexander Barash, Danny Frishman, Dmitrij Tuller, Tamir Department of Biomedical Engineering Tel Aviv University Tel Aviv 6997801 Israel Department of Bioinformatics School of Life Sciences Technical University of Munich Freising Germany Department of Software Engineering Sami Shamoon College of Engineering Beer-Sheva84100 Israel Department of Computer Science Ben-Gurion University Beer-Sheva8410501 Israel Sagol School of Neuroscience Tel Aviv University Tel Aviv 6997801 Israel

As viruses evolve over time with their host, they adapt to multiple cellular functions to ensure efficient long-term transmission. Their ability to survive and function efficiently depends on optimizing their genetic code to effectively recruit the host's gene expression machinery, particularly the translation machinery. Codon usage bias (CUB) measures the level of adaptation at the codon level, considering multiple factors such as the host's tRNA *** estimating the adaptation scores of viruses to their host, we can gain insight into the changes that occur on the genomic level throughout their evolution. In our study, we propose tracking the viral evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using CUB to estimate adaptation. By comparing different strains and analyzing changes over time, we demonstrate an increased adaptation of the Omicron SARS-COV-2 variant. In addition, we observe fluctuations in CUB scores, with periods of decreased and then increased adaptation over time, offering detailed insights at a fine temporal resolution. This analysis further contributes to understanding how different mutations influence viral adaptation, as well as the evolution of other human-infecting viruses. © 2025 The Authors

关键词： Coronavirus

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Broadcasting and Three List Subtraction 17th

Broadcasting and Three List Subtraction

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819644476

Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. In the scope of this paper, we will mainly focus on determining the broadcast time and the optimal broadcasting scheme for graphs. Determination of the broadcast time of a node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few classes of networks. In this paper, we will consider networks that can be represented as k-path graphs. We will pose a new problem, called 3 list subtraction, and discuss its relation to the broadcast time problem on k-path graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interconnection networks (circuit switching)

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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computer Systems science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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