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Machine learning in materials genome initiative:A review

Journal of Materials Science & technology 2020年第22期54卷 113-122页

作者： Yingli Liu Chen Niu Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and TechnologyKunming650500China Light Alloy Research Institute Central South UniversityChangsha410083China Chinese Academy of Engineering Beijing100088China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China Chengdu MatAi Technology Co. LtdChengdu610041China

Discovering new materials with excellent performance is a hot issue in the materials genome *** experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

关键词： Materials genome initiative(MGI) Materials database Machine learning Materials properties prediction Materials design and discovery

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management

Analyzing the Potential Benefits of Data Mining for Customer...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Aravind A R Shilpa M. Katikar Kalyan Acharjya G. Ezhilarasan Surendra Yadav Abhinav Mishra Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty.

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Implementing Long Short-Term Memory Network for Enhancing Hyper Spectral Time Series Modeling

Implementing Long Short-Term Memory Network for Enhancing Hy...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Muktha Eti Jyoti Shekhawat Manish Nagpal Srisainath R Pranali G. Chavhan Rakesh Kumar Yadav Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This take a look at proposes using lengthy quick-time period reminiscence (LSTM) networks as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the use of this recurrent neural community approach, the model can account for the temporal dating among the hyper spectral spectral time collection variables. additionally, with this utility, the version can research from the spectral time series over a longer time period. This study examined the efficacy of the carried out LSTM network with the aid of comparing the results to the Hyper Spectral Time collection model performance performed prior to using this method. The effects recommend that the addition of the LSTM network accelerated the accuracy of the version through about 25%. This generation has the potential to substantially beautify the accuracy of temporal records inside the discipline of faraway sensing records analysis. In conclusion, this examine indicates that LSTM networks are a promising tool for boosting the predictive energy of HSTS models. long quick-term memory Networks (LSTM) are a type of Recurrent Neural Networks (RNNs) that have been applied to many duties in synthetic Intelligence including language information, herbal language processing, and image processing. more recently, they had been used to enhance hyper spectral time series modeling. The shape of LSTM permits it cope with and keep lengthy-term sequences of statistics, making it particularly suitable for modeling hyper-spectral time series. At whenever step, an LSTM community takes as input a vector representing the hyper-spectral values, and outputs a vector which represents the intermediate representation of the time series. these intermediate representations are then used to generate the output at every next time step. LSTM networks are for his or her ability to capture lengthy-time period dependencies, and because of their structure, they are additionally nicely-appropriate to hyper-spectral time collecti

关键词： Performance evaluation Recurrent neural networks Shape Time series analysis Predictive models Vectors Robustness

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Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

arXiv

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arXiv 2022年

作者： Wei, Shoulin Li, Yadi Lu, Wei Li, Nan Liang, Bo Dai, Wei Zhang, Zhijian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Faculty of Science Kunming University of Science and Technology Kunming650500 China

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveysa) © 2022, CC BY.

关键词： Surveys

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Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications

arXiv

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arXiv 2025年

作者： Zhao, Ruojing Xu, Yifei Yang, Songjie Chen, Hua Assi, Chadi School of Telecommunications Engineering Xidian University China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China Concordia University MontrealQCH3G 1M8 Canada

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system’s spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system’s ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system’s performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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A DDPG Hybrid with Graph Scheme for Resource Management in Digital Twin-assisted Biomedical Cyber-Physical Systems

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IEEE Transactions on Network and Service Management 2025年

作者： Xu, Yi-Han Ding, Rong-Xing Xu, Xiao-Ren Zhou, Ding Zhou, Wen Qinghai University School of Computer Technology and Application Xining810016 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Universiti Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China

In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs. © 2004-2012 IEEE.

关键词： Resource allocation

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Effects of chelating agents on the sol-gel synthesis of nano-zirconia:Comparison of the Pechini and sugar-based methods

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International Journal of Minerals,Metallurgy and Materials 2020年第5期27卷 693-702页

作者： Faramarz Kazemi Farzin Arianpour Mahdiar Taheri Ali Saberi Hamid Reza Rezaie Department of Mining and Metallurgy Amirkabir University of TechnologyTehranIran Research and Application Center Kastamonu UniversityKastamonuTurkey College of Engineering and Computer Science The Australian National UniversityCanberraAustralia Materials Processing Faculty of Engineering ScienceUniversity of BayreuthBayreuthGermany School of Metallurgy and Materials Engineering Iran University of Science and TechnologyTehranIran

This study focused on the comparison of the Pechini and sugar-based combustion synthesis methods to produce nano-zirconia. Zirconium hydroxide was utilized as metal precursor and citric acid, sucrose, and fructose were used as chelating agents, followed by calcination at 500, 600, and 700°C in air, respectively. Characterization was conducted by thermal analysis, specific surface area measurement, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning and transmission electron microscopy. When sucrose and citric acid were used as chelating agents during synthesis, mixtures of monoclinic and tetragonal phases were formed after calcination at 600 and 700°C. In the fructose samples, the tetragonal structure was the unique characterized phase. The tetragonal parameters in the fructose samples were determined using the diffraction data and the lattice parameter ratio was proven to increase with the temperature increase. Compared with the citrate and sucrose samples, the largest specific surface area(27 m2·g-1) and smallest particle size(39.1 nm) were obtained for the fructose sample after calcination at 700°C. The study revealed the formation of single-phase stabilized tetragonal zirconia using fructose as chelating agent after calcination at 500°C, and the presence and formation mechanism of stabilized tetragonal phase were also discussed on the basis of the X-ray and electron diffraction studies.

关键词： zirconia nanopowder sol-gel sugar Pechini chelation

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A reputation-based approach using consortium blockchain for cyber threat intelligence sharing

arXiv

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arXiv 2021年

作者： Zhang, Xiaohui Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Computer Technology Application Key Laboratory of Yunnan Province Kunming China

The CTI (Cyber Threat Intelligence) sharing and exchange is an effective method to improve the responsiveness of the protection party. Blockchain technology enables sharing collaboration consortium to conduct a trusted CTI sharing and exchange without a trusted centralized institution. However, the distributed connectivity of the blockchain-based CTI sharing model proposed before exposes the systems into byzantine attacks, the compromised members of partner organizations will further decrease the accuracy and trust level of CTI by generating false reporting. To address the unbalance issues of performance in speed, scalability and security, this paper proposes a new blockchain-based CTI model, which combines consortium blockchain and distributed reputation management systems to achieve automated analysis and response of tactical threat intelligence. In addition, the novel consensus algorithm of consortium blockchain that is fit for CTI sharing and exchange introduced in this paper. The new consensus algorithm is called "Proof-of Reputation" (PoR) consensus, which meets the requirements of transaction rate and makes the consensus in a creditable network environment through constructing a reputation model. Finally, the effectiveness and security performance of the proposed model and consensus algorithm is verified by experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Cybersecurity

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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DGLNet: A Cross-Modal Global-Local Collaborative Network for Chest Radiology Report Generation

SSRN

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SSRN 2022年

作者： Liu, Lijun Wang, Lizhen Liu, Li Peng, Wei Fu, Xiaodong Huang, Qingsong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science and Engineering School of Information Science and Engineering Yunnan University Kunming650228 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

Automatic chest radiology report generation is crucial for improving diagnostic efficiency, enhancing diagnostic quality, and reducing misdiagnoses or missed diagnoses. Existing approaches either resize the high-resolution radiology image into smaller sizes or segment it into multiple patches for separate processing, which may lose fine local details or global contextual information, and the accuracy of report generation will be limited. In addition, the direct fusion of visual features with sentence features reduces the accuracy of aligning fine-grained visual features with text features. In this paper, we focus on designing a cross-modal global-local collaborative network to capture more local fine details and global contextual information, which comprises a global-to-local network and a local-to-global network that takes the resized entire image and segmented image patches as respective inputs. The former handles the extraction of global visual features, the fusion of global and local visual features, and the fusion of visual features into report sentences. The latter implements the extraction of local visual features, the fusion of local to global visual features, and the fusion of visual features to report words. Experimental results on two publicly available datasets, IU X-Ray and MIMIC-CXR, show that our proposed network outperforms state-of-the-art image captioning-based methods in most evaluation metrics. © 2022, The Authors. All rights reserved.

关键词： Extraction

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