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重庆邮电大学学报（自然科学版） 2010年第6期22卷 720-723,760页

作者： Sankar K Pal Indian Statistical Institute Calcutta Indian

machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computing and granular computing is widely used in many fields,such as in protein sequence analysis and biobasis determination,TSM and Web service classification Etc.

关键词： machine intelligence rough sets information granules rough-fuzzy case generation protein sequence analysis and biobasis determination TSM web service classification

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Determining human-coronavirus protein-protein interaction using machine intelligence

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Medicine in Novel Technology and Devices 2023年第2期18卷 123-142页

作者： Arijit Chakraborty Sajal Mitra Mainak Bhattacharjee Debashis De Anindya J.Pal Bachelor of Computer Application Department The Heritage AcademyKolkataIndia Department of Computer Science and Engineering Heritage Institute of TechnologyKolkataIndia Department of Biotechnology Heritage Institute of TechnologyKolkataIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataIndia University of Burdwan BurdwanIndia

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities globally.Recent research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral pathogenesis.However,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical interactions.This article elucidates the host-viral PPI through machine Learning(ML)lenses and validates the biological significance of the same using web-based tools.ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work.The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment analysis.Consequently,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

关键词： Coronavirus Ensemble learning machine intelligence Protein-protein interaction

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AI2.0时代的数字学术及其范式变革

How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities

图书馆学情报学 2024年第3期

作者：金家琴刘炜

数字学术是数字技术和方法介入学术研究过程的研究范式。以ChatGPT为代表的生成式人工智能的突破，标志着通用人工智能的到来，有学者称之为AI2.0时代，也使得科学研究从数据密集型向计算密集型过渡，从而进入以人工智能介入研究过程的... 详细信息

数字学术是数字技术和方法介入学术研究过程的研究范式。以ChatGPT为代表的生成式人工智能的突破，标志着通用人工智能的到来，有学者称之为AI2.0时代，也使得科学研究从数据密集型向计算密集型过渡，从而进入以人工智能介入研究过程的第五范式，越来越多的研究过程将有人工智能参与，甚至主导。与数据驱动型研究有所不同的是，大模型不仅可以更加高效甚至“自动”进行数据获取、提取、管理和分析等工作，而且有可能实现海量科学数据的“理解”，从而发现新的规律和趋势。大模型不仅可以帮助科学家更好地理解数据，还可以为科学研究提供更加全面和深入的视角，同时，它还能应用于多种相关场景，帮助科学家发现数据或成果中的错误和缺陷，从而提高科学研究的可靠性和可重复性。当然，随着人工智能技术的广泛应用，也带来了一些挑战。对于图书馆而言，面向科研的服务将更加学术化和技术化，除了基础设施、科研交流环境建设和数据支撑服务之外，还将以AI思维和数字化方法与工具更加深入地介入科研过程，并在保护数据的隐私和安全、避免数据泄露和滥用，以及数据伦理方面提供必要的支持。

关键词： AI2.0 数字学术科研范式大语言模型 ChatGPT 机器智能 AI2.0 Digital scholarship Research paradigm Large language model ChatGPT machine intelligence

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Digital Communications and Networks 2023年第6期9卷 1351-1362页

作者： Jinfang Jiang Chuan Lin Guangjie Han Adnan MAbu-Mahfouz Syed Bilal Hussain Shah Miguel Martínez-García Department of Internet of Things Engineering Hohai UniversityChangzhou213022China Software College Northeastern UniversityShenyang110819China Council for Scientific and Industrial Research(CSIR) Pretoria0184South Africa School of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLE113TTUK Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburgSouth Africa

The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

关键词： Industrial internet of things(IIoT) Industry 4.0 Artificial intelligence(AI) machine intelligence Software-defined networking(SDN)

Clause-level Relationship-aware Math Word Problems Solver

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machine intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence research.Existing math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level relationship.To this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher level.Specifically,in the lower-level processes,we split problems into clauses according to their natural division and learn their semantics.In the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local view.Then,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global view.Next,we enhance the representation of clauses based on the learned clause-level dependency relationships.In expression generation,we develop a tree-based decoder to generate the mathematical expression.We conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Multi‐branch angle aware spatial temporal graph convolutional neural network for model‐based gait recognition

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IET Cyber-Systems and Robotics 2022年第2期4卷 97-106页

作者： Liyang Zheng Yuheng Zha Da Kong Hanqing Yang Yu Zhang College of Control Science and Engineering Zhejiang UniversityHangzhouChina

Model‐based gait recognition with skeleton data input has attracted more attention in recent years.The model‐based gait recognition methods take skeletons constructed by body joints as input,which are invariant to changing carrying and clothing conditions.However,previous methods limitedly model the skeleton information in either spatial or temporal domains and ignore the pose variety under different view angles,which results in poor performance for gait recognition.To solve the above problems,we propose the Multi‐Branch Angle Aware Spatial Temporal Graph Convolutional Neural Network to better depict the spatial‐temporal relationship while minimising the interference from the view angles.The model adopts the legacy Spatial Temporal Graph Neural Network(ST‐GCN)as its backbone and relocates it to create independent ST‐GCN branches.The novel Angle Estimator module is designed to predict the skeletons'view angles,which enables the network robust to the changing views.To balance the weights of different body parts and sequence frames,we build a Part‐Frame‐Importance module to redis-tribute them.Our experiments on the challenging CASIA‐B dataset have proved the efficacy of the proposed method,which achieves state‐of‐the‐art performance under different carrying and clothing conditions.

关键词： deep learning machine intelligence vision

Robot intelligence for Real World Applications

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Chinese Journal of Electronics 2018年第3期27卷 446-458页

作者： LIU Yunhui ZHENG Fan GUO Ruibin WANG Jiangliu NIE Qiang WANG Xin WANG Zerui T Stone Robotics Institute The Chinese University of Hong Kong National University of Defense Technology

This paper presents a brief review on recent work on machine intelligence for real-world applications of robots.To act in a real world environment,a robot should possess a broad sense of intelligence including speech,perception,reasoning,action,etc.In this paper,we particularly deal with the intelligence involving action or body motion.The intelligence related to robot action/motion can be classified into two categories:manipulation intelligence and mobility intelligence.The manipulation intelligence means the skill/intelligence of reliably manipulating objects according to tasks and the mobility intelligence corresponds to the ability of autonomously moving,or flying,and or jumping in a natural environment.Human-robot interaction is another important topic for real-world applications.In addition to reviewing the ma jor approaches,this paper also gives an overview on our efforts in these important topics.

关键词： machine intelligence Simultaneous localization and mapping(SLAM) Medical robots Humanrobot interaction

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

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Computer Systems Science & Engineering 2023年第12期47卷 2921-2937页

作者： Shariar Md Imtiaz Ki-Chul Kwon F.M.Fahmid Hossain MdBiddut Hossain Rupali Kiran Shinde Sang-Keun Gil Nam Kim Department of Information and Communication Engineering Chungbuk National UniversityCheongju-siChungcheongbuk-do28644Korea Department of Electronic Engineering University of SuwonHwaseong-siGyeonggi-do18323Korea

This article describes a novel approach for enhancing the three-dimensional(3D)point cloud reconstruction for light field microscopy(LFM)using U-net architecture-based fully convolutional neural network(CNN).Since the directional view of the LFM is limited,noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds.The existing methods suffer from these problems due to the self-occlusion of the model.This manuscript proposes a deep fusion learning(DL)method that combines a 3D CNN with a U-Net-based model as a feature extractor.The sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing.A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature fromthe directional sub-aperture LF data cube.For the enhancement of the depthmap,dual iteration-based weighted median filtering(WMF)is used to reduce surface noise and enhance the accuracy of the reconstruction.Generating a 3D point cloud involves combining two key elements:the enhanced depth map and the central view of the light field image.The proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing(HCI)and real-world LFM datasets.The results are compared with different state-of-the-art methods.The structural similarity index(SSIM)gain for boxes,cotton,pillow,and pens are 0.9760,0.9806,0.9940,and 0.9907,respectively.Moreover,the discrete entropy(DE)value for LFM depth maps exhibited better performance than other existing methods.

关键词： 3Dreconstruction 3Dmodeling point cloud depth estimation integral imaging light filedmicroscopy 3D-CNN U-Net deep learning machine intelligence

基于因果模型的复杂工业过程数据驱动软传感器自动特征选择方法

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Engineering 2023年第3期22卷 82-93页

作者： Yan-Ning Sun 秦威 Jin-Hua Hu Hong-Wei Xu Poly Z.H.Sun School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Artificial intelligence Shanghai Jiao Tong UniversityShanghai 200240China

关键绩效指标(KPI)的软感知在复杂工业过程的决策中起着至关重要的作用。许多研究人员已经使用尖端的机器学习(ML)或深度学习(DL)模型开发出了数据驱动的软传感器。此外,特征选择是一个关键的问题,因为一个原始的工业数据集通常是高维的... 详细信息

关键绩效指标(KPI)的软感知在复杂工业过程的决策中起着至关重要的作用。许多研究人员已经使用尖端的机器学习(ML)或深度学习(DL)模型开发出了数据驱动的软传感器。此外,特征选择是一个关键的问题,因为一个原始的工业数据集通常是高维的,并不是所有的特征都有利于软传感器的发展。一个完美的特征选择方法不应该依赖于超参数和后续的ML或DL模型。相反,它应该能够自动选择一个特征子集进行软传感器建模,其中每个特征对工业KPI都有独特的因果影响。因此,本研究提出了一种受因果模型启发的自动特征选择方法,用于工业KPI的软感知。首先,受后非线性因果模型的启发,本研究将该方法与信息论相结合,以量化原始工业数据集中每个特征和KPI之间的因果效应。然后,提出了一种新的特征选择方法,即自动选择具有非零因果效应的特征来构造特征的子集。最后,利用所构造的子集,通过AdaBoost集成策略开发KPI的软传感器。通过对两个实际工业应用的实验证实了该方法的有效性。在未来,该方法也可以应用于其他工业过程,以帮助开发更先进的数据驱动的软传感器。

关键词： Big data analytics machine intelligence Quality prediction Soft sensors Intelligent manufacturing

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A study on preterm birth predictions using physiological signals,medical health record information and low-dimensional embedding methods

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IET Cyber-Systems and Robotics 2021年第3期3卷 228-244页

作者： Ejay Nsugbe Oluwarotimi William Samuel Ibrahim Sanusi Mojisola Grace Asogbon Guanglin Li Nsugbe Research Labs SwindonUK Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldSheffieldUK

Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.

关键词： cybernetics decision-making decision-tree classifier machine intelligence machine learning sensor fusion