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A Survey on Evolutionary Computation Based Drug Discovery

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Industrial Image Anomaly Detection Method Based on Improved MAE 28

Industrial Image Anomaly Detection Method Based on Improved ...

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28th International Conference on Automation and computing, ICAC 2023

作者： He, Hui Xu, Yuanping Zhang, Chaolong Guo, Benjun Xu, Zhijie Jin, Jin Kong, Chao Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom

ISBN: (纸本)9798350335859

This study explores an improved Masked Autoencoder (MAE) model for anomaly detection in common industrial scene images. Industrial image anomaly detection, as an important research topic in computer vision, can detect abnormal data deviations from normal expected behavior and ensure the normal operation of various systems. In actual industrial scenarios, the scarcity of abnormal samples, the cost of labeled data, and the lack of prior knowledge about anomalies make unsupervised learning methods widely used in the field of image anomaly detection. However, most unsupervised learning methods currently require the application of large-scale datasets. In order to make unsupervised learning applicable to small-scale industrial image datasets, we replaced the backbone network with Swin Transformer based on the Masked Autoencoder method, while improving its mask strategy and optimizer. The effectiveness and advantage of the proposed method are demonstrated through an experimental comparison with existing model, the results show that this method is better than MAE model pretrained on ImageNet, and outperforms other unsupervised learning models in multiple categories. © 2023 IEEE.

关键词： Anomaly detection

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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A novel observation points‐based positive‐unlabeled learning algorithm

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1425-1443页

作者： Yulin He Xu Li Manjing Zhang Philippe Fournier‐Viger Joshua Zhexue Huang Salman Salloum Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) ShenzhenChina College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Computing National University of SingaporeSingaporeSingapore

In this study,an observation points‐based positive‐unlabeled learning algorithm(hence called OP‐PUL)is proposed to deal with positive‐unlabeled learning(PUL)tasks by judiciously assigning highly credible labels to unlabeled *** proposed OP‐PUL algorithm has three ***,an observation point classifier ensemble(OPCE)algorithm is constructed to divide unlabeled samples into two categories,which are temporary positive and permanent negative ***,a temporary OPC(TOPC)is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive ***,a permanent OPC(POPC)is finally trained based on the combination of original positive samples,permanent positive samples and permanent negative *** exhaustive experimental evaluation is conducted to validate the feasibility,rationality and effectiveness of the OP‐PUL algorithm,using 30 benchmark PU data *** show that(1)the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and(2)the permanent positive samples have a consistent probability distribution with the original positive ***,a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL *** demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

关键词： artificial intelligence datamining,machine learning

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Frequency Filtering Network for Undersampled MRI Reconstruction 2

Frequency Filtering Network for Undersampled MRI Reconstruct...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Chen, Xinren Liu, Zhaohui Xiong, Dongping Ouyang, Lijun Zhang, Xiaozhi University of South China School of Computing/Software Hengyang China University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350331417

Magnetic resonance imaging (MRI) is an imaging technology widely used in medical clinical diagnosis. Nevertheless, it always takes a long imaging time to obtain the high spatial resolution MR image. Image reconstruction has been playing a crucial role for accelerated MRI. Recently, deep neural networks show potential to significantly speed up MRI. However, these deep learning methods usually learn the feature information in the spatial domain. Although some works have attempted to utilize the frequency information from the k-space to reconstruct MR images, they still suffer from the inability to consider the global information. To address these limitations, in this work, we propose a new residual filter channel attention network (RFCAN) for undersampled MRI reconstruction. Specifically, the proposed model consists of a novel filter channel attention (FCA) in the frequency domain and the multiscale cascade convolution in the spatial domain to learn both the dual-domain features. This model employs the filtering instead of the traditional self-attention mechanism in Transformer to learn k-space information. It can avoid the limitations of the Transformer window to better learn the global information. We also introduce a frequency channel attention to fine-tune the filtering results to better balance the global and local information. Experiment results show that our model consistently outperforms other state-of-the-art methods and can achieve high-quality MRI reconstruction. © 2023 IEEE.

关键词： channel attention deep learning Frequency filtering network MRI reconstruction

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Magnetic resonance image reconstruction based on two-stage progressive network 3

Magnetic resonance image reconstruction based on two-stage p...

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3rd International Conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Shen, Lingyun Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

ISBN: (纸本)9798350359961

In clinical applications, medical image reconstruction is crucial for extracting complementary information and restoring image quality. However, existing deep learning-based reconstruction methods suffer from the following two problems: first, they ignore the frequency-domain information specific to magnetic resonance imaging, and second, most of the traditional networks are single-end-To-end networks, which lack effective attention to feature information. We suggest a unique two-stage progressive network (TSPNet) to solve these issues. Our approach employs two lightweight Unets as subnetworks for progressive recovery, and the network parts are carefully designed. During the feature extraction phase, we creatively create a Dual Domain Feature Extraction Module (DDFEM) to effectively extract rich features. Subsequently, we create a hybrid attention module (HAM) to effectively process and integrate the retrieved data. Experimental results show that our model outperforms other state-of-The-Art methods and reconstructs images with stronger texture information and superior visual quality. © 2023 IEEE.

关键词： Convolutional neural network deep learning Fourier Transform Medical Image Reconstruction

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MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy 2

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Xie, Xinyu Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Electrical Engineering Hengyang China University of South China School of Computing/Software Hengyang China

ISBN: (纸本)9798350331417

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality. © 2023 IEEE.

关键词： Detail-aware Fine-grained information Fourier Transform Multimodal medical image fusion

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Frequency Domain-based Matching and Integration Network for Multi-contrast MRI Reconstruction 2

Frequency Domain-based Matching and Integration Network for ...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Wang, Lijuan Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Computing/Software Hengyang China University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350331417

Multi-contrast magnetic resonance (MR) images are crucial for diagnosing diseases and analysis in clinical practice, yet their acquisition often entails long scanning procedures. Recently, exploiting shared information among multi-contrast MR images yields favorable results in their reconstruction. However, most studies simply concatenate the multi-contrast information without effective matching and fusion mechanisms to mitigate the impact of redundant features. Furthermore, the feature extraction backbone networks tend to lose high-frequency details like textures during downsampling. To promote the development of this research field, we develop a matching and fusion framework for multi-contrast MR reconstruction and design a feature extractor that explores frequency-domain information. Firstly, our feature extractor captures both long-range and short-range dependencies in the target and reference images, acquiring high- and low-frequency feature information. Then, we introduce a progressive feature matching mechanism to identify features in the reference image that resemble those in the target. Additionally, we propose an innovative fusion strategy that leverages non-local Fourier self-attention and cross-attention mechanisms to precisely combine the semantic feature from multi-contrast MR images. Extensive experiments validate the superior performance of our method in multi-contrast MR image reconstruction, surpassing other state-of-the-art methods. © 2023 IEEE.

关键词： Feature fusion Feature matching Frequency domain Multi-contrast MRI reconstruction

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Multiscale Spectral–Spatial Capsule Neural Network for Hyperspectral Image Classification 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Weiye Xu, Yuanping Xu, Zhijie Kong, Chao Niu, Xuemei Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Deep learning models have recently shown good performance in the hyperspectral remote sensing image classification tasks. In particular, a capsule network (CapsNet) was introduced as a powerful alternative to convolutional neural networks (CNNs). The CapsNet adopts vector neuron and encode the spatial relationship of features in an image, which exhibits encouraging performance. Motivated by CapsNet, this paper presents a novel capsule network based on multiscale spectral–spatial features to improve the performance for hyperspectral images (HSIs) classification. First, multi-scale features are extracted from the hyperspectral data in 1D spectral, 2D spatial and 3D spatial-spectral cubes, and their primary capsule neurons are constructed separately. Then, a multi-head attention mechanism is introduced to model the association of all primary capsule neurons from multiple dimensions to efficiently extract finer-grained multi-scale spatial-spectral information. Finally, the multiscale capsule neurons are updated using a dynamic routing agreement to obtain more discriminative high-level capsule features thus improving the classification accuracy. Experimental results on two commonly used HSIs datasets (Indian Pines, Pavia University) demonstrate that the proposed model can achieve better performance compared with other state-of-the art deep-learning-based approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neurons

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