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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Bai, Hao Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computer Science and Technology ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

In this paper, we propose a new generic sampling classification method based on granular Ball sampling because of good robust noise immunity and possessing properties that can be classified as an undersampling method, called Granular Ball Center-based Common Under-sampling. The method uses adaptively generated Ball to cover the data space, and the sampled data are supported by the centroid properties of the Ball. Unlike the boundary sampling of granular ball sampling, this method mainly uses the center points to smooth out the noise, which not only can clearly represent the attributes, but also can reduce some computational effort, and in most of the time, it can obtain almost the same accuracy as the original model, or even can perform better. Compared with undersampling, it can be better adapted to the task of unbalanced classification as well as non-unbalanced classification. The above can show that the method is able to be generalized without the restriction of specific classifiers and also has a strong adaptability to the dataset. The method can also be effectively used as an undersampling method for unbalanced classification because it is supported by the centroid property, and the method has a speedup effect for most classifiers. These advantages form a strong advantage of the method in improving classifier performance. © 2022 IEEE.

关键词： Cloud computing Adaptation models Computational modeling big data Task analysis

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Lon-Chen Hung Chun-Cheng Wei Hung-Jen Chen Chih-Fu Yang Wei-Min Cheng Department of Electronic Engineering Lunghwa University of Science and Technology School of Software and Big Data Changzhou College of Information Technology Department of Computer Information and Network Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.

关键词： Uncertainty Target tracking Trajectory tracking Simulation Neural networks Manipulators Stability analysis

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Estimating Channels by Transmitting Pilots from Reconfigurable Intelligent Surface

Estimating Channels by Transmitting Pilots from Reconfigurab...

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IEEE International Conference on Communications (ICC)

作者： Yanze Zhu Yang Liu Qingqing Wu Qingjiang Shi School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University Shenzhen Research Institute of Big Data Shenzhen China

The rising reconfigurable intelligent surface (RIS) is a promising technology and a multitude of literature focuses on its channel estimation (CE), which is a critical and challenging task. Most existing works adopt a type of “cascaded channel” training scheme, where the “two-hop” channel cascaded by RIS is treated as one and measured by one shot. As unveiled by the latest researches, however, the concatenated channel suffers from severe fading loss and hence seriously degrades the CE precision. To resolve this difficulty, this paper proposes a novel training scheme. Specifically, being equipped with one transmit RF chain, the RIS broadcasts pilot signals to all other devices during the training period. This novel scheme can overcome double fading loss at a low hardware cost. The pilot design of the newly proposed training scheme is a difficult quartic optimization problem and we develop a gradient descent (GD) based solution to resolve it. Numerical results verify the effectiveness of our solution and demonstrate our training scheme can significantly outperform the traditional cascaded channel training method.

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Suppressing Beam Squint Effect For Near-Field Wideband Communication Through Movable Antennas

arXiv

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arXiv 2024年

作者： Zhu, Yanze Wu, Qingqing Liu, Yang Shi, Qingjiang Chen, Wen Department of Electronic Engineering Shanghai Jiao Tong University 200240 China School of Information and Communication Engineering Dalian University of Technology Dalian China School of Software Engineering Tongji University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China

In this correspondence, we study deploying movable antenna (MA) array in a wideband multiple-input-single-output (MISO) communication system, where near-field (NF) channel model is considered. To alleviate beam squint effect, we propose to maximize the minimum analog beamforming gain across the entire wideband spectrum by appropriately adjusting MAs’ positions, which is a highly challenging task. By introducing a slack variable and adopting the cutting-the-edge smoothed-gradient-descent-ascent (SGDA) method, we develop algorithms to resolve the aforementioned challenge. Numerical results verify the effectiveness of our proposed algorithms and demonstrate the benefit of utilizing MA array to mitigate beam squint effect in NF wideband system. © 2024, CC0.

关键词： Near field communication

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Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network

Joint Scheduling and Beamforming Design in Traffic-Aware RIS...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Li, Aichen Liu, Yang Wu, Qingqing Shi, Qingjiang Zhao, Jun Dalian University of Technology School of Information and Communication Engineering China University of Macau Department of Electrical and Computer Engineering China Tongji Univ. Shanghai Shenzhen Research Institute of Big Data School of Software Eng. Shenzhen China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9781665459754

This paper considers exploiting reconfigurable intelligent surface (RIS) in a multi-user multi-access mobile computing (MEC) system. To precisely model the traffic behaviors of the tasks, we consider queuing procedures at both mobile users and the cloud server. This consideration leads to concatenation of queues and makes our problem much more difficult than existing literature since analytic expression of the objective is impossible. Our goal is to jointly optimize the task scheduling, RIS configuration and computation resource allocation to improve the MEC network's overall computation rate in the long run while maintaining the stability of the queues. To tackle this highly challenging stochastic problem, we develop a Lyapunov guided deep reinforcement learning (DRL) solution, which borrows the Lyapunov optimization theory to stabilize queues and at the same time can effectively optimize the intractable objective in an online manner. Numerical results will be presented to verify the effectiveness of our solution and the performance boost due to the deployment of RIS. © 2022 IEEE.

关键词： Edge computing

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Crop Yield Prediction based on Attention-LSTM Model

Crop Yield Prediction based on Attention-LSTM Model

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IEEE International Conference on Orange Technologies (ICOT)

作者： Zirui Xiong Xin Guo Danjue Zhang Shih-Pang Tseng Yan Ji Xuan Sun School of Computer University of South China Hengyang China Department of Artificial Intelligence Shanghai Pudong Vocational and Technical College China School of Software and Big Data Changzhou College of Information Technology Changzhou China School of Information Science and Technology Sanda University China Department of Integrated Media Technology and Operation Shanghai Pudong Vocational and Technical College China

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

With the widespread application of big data and artificial intelligence technologies in the agricultural sector, accurate prediction of crop yields has become crucial, not only for food security but also for significantly enhancing agricultural management efficiency. This paper delves into various prediction models, including traditional machine learning algorithms and deep learning-based time series prediction models, with a particular focus on the integration of the attention mechanism with Long Short-Term Memory (LSTM) networks. By constructing an LSTM model fused with the attention mechanism (Attention Mechanism LSTM Model, Attention-LSTM), this study significantly improves the accuracy of crop yield predictions, which is of great significance for ensuring the stability of food supply and optimizing the allocation of agricultural *** the experimental section, a large dataset encompassing factors such as fertilizer usage and soil properties was used to rigorously test the Attention-LSTM model. The results showed that, compared to models such as BP neural networks, GRU, BILSTM, and LSTM, the Attention-LSTM exhibited excellent performance in key evaluation metrics including RMSE, MSE, and MAPE, achieving significant improvement in prediction accuracy and enhancement of generalization *** contribution of this paper lies in the development of the Attention-LSTM model, which, with its efficient and precise crop yield prediction capabilities, provides a powerful tool for enhancing the level of agricultural intelligence and optimizing agricultural production decisions. It also showcases the vast potential of deep learning technologies in smart agriculture.

关键词： Analytical models Accuracy Attention mechanisms Time series analysis Neural networks Predictive models data models Stability analysis Crop yield Long short term memory

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