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3rd International Conference on artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Kang, Di Hao, Haiyan School of Smart Tourism Hainan College of Software Technology Qionghai571400 China School of Computing and Artificial Intelligence Hainan College of Software Technology Qionghai571400 China

ISBN: (纸本)9798350376173

To succeed in the market, it is necessary to have an accurate and timely grasp of the market situation and development, and to formulate plans and allocate resources based on current market demand and development trends, in order to meet market needs and gain competitive advantages. The original market planning and decision-making relied on manual writing and experience, making it difficult to meet the requirements of the constantly developing market economy. Therefore, with marketing as the mainstay and a pragmatic attitude, the station has established a 5G network based analysis and prediction system for goods sales information, following the path of technological innovation. The system uses computers to input, store, manage, retrieve, process, analyze, and predict sales information. Its biggest feature is the organic integration of market information both inside and outside the site. A comprehensive and efficient analysis and processing were conducted to meet the needs of actual work, and the future development direction was predicted, truly achieving a targeted understanding of oneself. The experimental results showed that the data transmission speed of the marketing information intelligent system based on 5G technology for prediction and analysis was at least about 125Mbps, while the data transmission speed of the marketing information intelligent system based on traditional systems for prediction and analysis was always maintained at around 25Mbps. © 2024 IEEE.

关键词： 5G mobile communication systems

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A dynamic displacement map based on deep Q-network to assist the rendering of stylised 3D models

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International Journal of Wireless and Mobile computing 2023年第3-4期24卷 226-234页

作者： Zheng, Hao Yang, Houqun Huang, Mengshi Wang, Yizhen School of Computer Science and Technology Hainan University Haikou Hainan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu Sichuan China College of Physics and Electronic Engineering Hainan Normal University Haikou Hainan China

In the process of rendering with NPR, there will often be a problem of reduced perception of the NPR effect caused by the fixed spatial structure of the static mesh. Therefore, this research first establishes a convolutional neural network to conduct supervised training on numerous hand-drawn target stylised images, and perform continuous angle recognition through stylised images then output its angle vector. Secondly, the displacement map is generated in realtime by inputting the observation angle through the fully connected neural network model. After that, the real-time displacement map is sampled to dynamically generate the deformation of the model. In the end, the goal of breaking the model's sense of space and enhancing the NPR rendering effect can be achieved. Besides, the experimental results of this study verify the effectiveness of the method. © 2023 Inderscience Enterprises Ltd.

关键词： Neural network models

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning

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High-Confidence computing 2024年第2期4卷 96-104页

作者： Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao 266580China School of Computing and Artificial Intelligence Xipu CampusSouthwest Jiaotong UniversityChengdu 611756China

Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication *** paper proposes FedQMIX,a node selection algorithm based on multi-agent reinforcement learning(MARL),to address these ***,we observe a connection between model weights and data distribution,and a clustering algorithm can group clients with similar data distribution into the same ***,we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward,penalizing the use of more communication rounds and thereby improving the communication efficiency of ***,experiments show that FedQMIX can reduce the number of communication rounds by 11%and 30%on the MNIST and CIFAR-10 datasets,respectively,compared to the baseline algorithm(Favor).

关键词： Communication efficient Federated learning MARL

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Prediction of Marketing Information Intelligent System Based on 5G Network

Prediction of Marketing Information Intelligent System Based...

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artificial intelligence and Autonomous Robot Systems (AIARS), International Conference on

作者： Di Kang Haiyan Hao School of Smart Tourism Hainan College of Software Technology Qionghai China School of Computing and artificial intelligence Hainan College of Software Technology Qionghai China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

关键词： Computers Technological innovation 5G mobile communication Decision making Writing Market research Data communication Intelligent systems Predictive analytics Pragmatics

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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ADCL: an adaptive dual contrastive learning framework based on MHGAT and VAE for cell type deconvolution in spatial transcriptomics

ADCL: an adaptive dual contrastive learning framework based ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Shilin Zhang, Qingchen Tianjin University College of Intelligence and Computing Tianjin China Hainan University School of Computer Science and Technology Haikou China Shenyang Normal University Software College Shenyang China

ISBN: (纸本)9798350337488

Cell type deconvolution is a critical mission in spatial transcriptomics. We propose an adaptive dual contrastive learning framework (ADCL) based on MHGAT and VAE for cell type deconvolution. For spatial transcriptomic data, we construct unsupervised contrast learning module based on the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. For scRNA-seq data, we utilize a variational autoencoder (VAE) to reconstruct gene expression matrix and reduce the impact of noise. Finally, we construct contrastive learning deconvolution module to learn probability matrix to achieve cell type deconvolution. Experiments on two real datasets and one simulated dataset prove that ADCL outperforms current state-of-the-art approaches. © 2023 IEEE.

关键词： Cell type deconvolution Contrastive learning MHGAT Spatial transcriptomics VAE

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Empowering Semantic Segmentation with Selective Frequency Enhancement and Attention Mechanism for Tampering Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第6期5卷 3270-3283页

作者： Xu, Xu Lv, Wenrui Wang, Wei Zhang, Yushu Chen, Junxin Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Nowadays, massive amounts of multimedia contents are exchanged in our daily life, while tampered images are also flooding the social networks. Tampering detection is therefore becoming increasingly important for multimedia integrity, and it is generally realized by designing specific convolutional neural networks. From a new perspective, this article proposes two pluggable modules for empowering existing semantic segmentation models for tampering detection. First, a selective frequency enhancement (SFE) module is developed to suppress the semantic information and selectively enhance the tamper information. Second, a boundary enhanced attention (BEA) module is designed to highlight the edge information of tempered area. Our SFE and BEA modules are combined with five mainstream semantic segmentation networks for performance evaluation. The experiment results demonstrate that our modules are able to empower the semantic segmentation networks for tampering detection, and their combinations even perform better than state-of-the-art algorithms in certain datasets. © 2020 IEEE.

关键词： Semantics

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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Quantization and Deployment Study of Classification Models for Embedded Platforms 28

Quantization and Deployment Study of Classification Models f...

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28th International Conference on Automation and computing, ICAC 2023

作者： Huang, Zihan Jin, Jin Zhang, Chaolong Xu, Zhijie Xu, Yuanping Kong, Chao Wen, Qin Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld United Kingdom College of Artificial Intelligence Neijiang Normal University

ISBN: (纸本)9798350335859

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios. © 2023 IEEE.

关键词： Calibration

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