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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Intelligent Model for Predicting the Quality of Services Violation

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computers, Materials & Continua 2022年第5期71卷 3607-3619页

作者： Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs *** service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for *** users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and *** this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and *** paper also compares Simple-PSO and Parallel *** simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

关键词： Accountability particle swarm optimization mutant particle swarm optimization quality of service service level agreement

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Trust-aware Cloudlet Federation Model for Secure Service Selection

Trust-aware Cloudlet Federation Model for Secure Service Sel...

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Energy, Power, Environment, Control, and Computing (ICEPECC), International Conference on

作者： Vaneeza Shahid Muhammad Ziad Nayyer Usama Ahmed Department of Computer Science GIFT University Gujranwala Pakistan Department of Software Engineering Government College University Faisalabad Faisalabad Pakistan

The introduction of Edge computing has pushed the horizon to the edge of the network and the user’s proximity. The openness of data on the edge raised many issues about the security of data. Although edge computing is an extension of the cloud, besides this, its solutions to handle security threats are different from cloud computing because of its architectural dissimilarities. There is a need for a comprehensive mechanism to ensure the choice of secure service. For this purpose, the combination of trust would be a better approach to deal with the service selection tasks in edge computing. In this paper, a trust-aware cloudlet federation model has been proposed which ultimately helps in the safe service selection of an edge node. Before using a service ‘A’ of any edge node, the trust value of the edge node for service ‘A’ should be calculated, called Service Trust Value (STV). Then the edge nodes with a higher STV for service ‘A’ are filtered. Lastly, the model will select the edge node with a satisfactory value of trust for service ‘A’. This proposed model will be beneficial in secure service selection.

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks 19

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Bohai Peng, Kai Leung, Victor C.M. Xia, Yunni Huaqiao University College of Engineering Quanzhou China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Shenzhen University School of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Chongqing University School of Computers Chongqing China

ISBN: (纸本)9798350346558

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches. © 2022 IEEE.

关键词： Multiobjective optimization

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Weighted Poisson-disk Resampling on Large-Scale Point Clouds

arXiv

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arXiv 2024年

作者： Jiao, Xianhe Lv, Chenlei Zhao, Junli Yi, Ran Wen, Yu-Hui Pan, Zhenkuan Wu, Zhongke Liu, Yong-Jin College of Computer Science and Technology Qingdao University China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science & Technology Beijing Jiaotong University China School of Artificial Intelligence Beijing Normal University China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Poisson distribution

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Computing Salient Feature Points of 3D Model Based on Geodesic Distance and Decision Graph Clustering

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Recent Advances in computer science and Communications 2021年第8期14卷 2489-2494页

作者： Sun, Dechao Chen, Nenglun Wang, Renfang Liu, Bangquan Liang, Feng College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China Department of Computer Science Hong Kong University Hong Kong College of Digital Technology and Engineering Ningbo University of Finance & Economics Ningbo China

Introduction: Computing Salient Feature Points (SFP) of 3D models has important application value in the field of computer graphics. In order to extract the SFP more effectively, a novel SFP computing algorithm based on geodesic distance and decision graph clustering is proposed. Methods: Firstly, the geodesic distance of model vertices is calculated based on the heat conduction equation, and then the average geodesic distance and importance weight of vertices are calculated. Finally, the decision graph clustering method is used to calculate the decision graph of model vertices. Results and Discussion: 3D models in SHREC 2011 dataset are selected to test the proposed algorithm. Compared with the existing algorithms, this method calculates the SFP of the 3D model from a global perspective. Results show that it is not affected by model posture and noise. Conclusion: Our method maps the SFP of the 3D model to the 2D decision-making diagram, which simplifies the calculation process of SFP, improves the calculation accuracy and possesses strong robustness. © 2021 Bentham science Publishers.

关键词： Clustering algorithms

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An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

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computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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International Conference on computer Communication and Informatics (ICCCI)

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model

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IEEE Transactions on Services Computing 2025年第3期18卷 1445-1458页

作者： Yinchen Pan Jun Zeng Ziwei Wang Haoran Tang Junhao Wen Min Gao School of Big Data and software engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.

关键词： Trajectory Diffusion models Spatiotemporal phenomena Feature extraction Optimization Transformers Data mining Noise measurement Data models Correlation

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