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Anisotropy of Electronic Spin Texture in the High-Temperature Cuprate Superconductor Bi_(2)Sr_(2)CaCu_(2)O_(8+δ)

Chinese Physics Letters 2023年第3期40卷 45-48页

作者：刘文晶查鹤鸣顾根大沈晓萍叶茂乔山 State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Condensed Matter Physics and Materials Science Department Brookhaven National LaboratoryUptonNew York11973USA State Key Laboratory of Surface Physics Department of PhysicsFudan UniversityShanghai 200433China School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China

Seeking new order parameters and the related broken symmetry and studying their relationship with phase transition have been important topics in condensed matter ***,by using spin-and angle-resolved photoemission spectroscopy,we confirm the helical spin texture caused by spin-layer locking in the nodal region in the cuprate superconductor Bi_(2)Sr_(2)CaCu_(2)O_(8+δ)and discover the anisotropy of spin polarizations at nodes alongΓ–X andΓ–Y *** breaking of C_(4)rotational symmetry in electronic spin texture may give deeper insights into understanding the ground state of cuprate superconductors.

关键词： symmetry superconductor breaking

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Efficient Detection of Knee Osteoporosis Using the Swin Transformer on X-Ray Images

Efficient Detection of Knee Osteoporosis Using the Swin Tran...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Sultana, Rahat Zaman, Muhammad Ivković, Nikola Cengiz, Korhan Rafaqat, Iqra Akhunzada, Adnan The University of Lahore Faculty of Computer Science Sargodha Pakistan Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of Zagreb Faculty of Organization and Informatics Varaždin Croatia Biruni University Department of E&e Engineering Istanbul Turkey University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367560

Knee Osteoporosis (KOP) is a skeletal disease that is caused by low bone mineral density and the degradation of bone tissue. It increases the risk of bone fractures in the knee region and is commonly seen in older people but usually ignored due to its silent nature. Osteoporosis is mostly diagnosed via X-ray images and Dual-Energy X-ray Absorptiometry (DXA) scans that can be difficult to read due to their sheer volume the subtle variations. This study aims to develop a Deep Learning (DL) utilizing Swin Transform model that can accurately recognize osteoporosis in knee X-ray images at an early stage. The dataset used for this research comprises knee X-rays labeled as either 'normal' or 'osteoporotic,' with image preprocessing including resizing to standard dimensions, converting the images into tensors, and normalizing pixel values to improve model performance. The model is trained and validated on arranged dataset, characterized by metrics such as accuracy, precision, recall or F1-score that gives an idea how well our model performs for prediction as acknowledged for its performance in image classification tasks. The results demonstrate that the Swin Transformer model has an overall accuracy of 89.38% on the data set for identifying osteoporosis features on knee X-ray images. This approach demonstrates the potential of the Swin Transformer model to serve as an effective tool for early osteoporosis diagnosis, offering improved accuracy and reliability compared to traditional convolutional neural networks (CNNs). © 2024 IEEE.

关键词： Diseases

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Tree Data Structures and Efficient Indexing Techniques for Big Data Management: A Comprehensive Study 26

Tree Data Structures and Efficient Indexing Techniques for B...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Samoladas, Dimitrios Karras, Christos Karras, Aristeidis Theodorakopoulos, Leonidas Sioutas, Spyros Department of Computer Engineering and Informatics University of Patras Rion Greece Department of Management Science and Technology University of Patras Koukouli Patras Greece

ISBN: (纸本)9781450398541

In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletion, and search. In this work, we survey B+ Tree, QuadTree, kD Tree, R Tree, and others along with efficient indexing techniques for big data management in order to provide a generic overview of the field to readers. Ultimately, we provide some indexing experiments as per insert operations and response times. © 2022 ACM.

关键词： Information management

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Estimating Person Location in Bathroom with Spatial Ultrasound and Variational autoencoder

Estimating Person Location in Bathroom with Spatial Ultrasou...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Sato, Shun Yasuda, Yuto Ohara, Ryotaro Hamabe, Riku Genda, Takayuki Imanaka, Shoya Izumi, Shintaro Kawaguchi, Hiroshi Kobe University Graduate School of System Informatics Kobe Japan Kobe University Department of Computer Science and Systems Engineering Kobe Japan Kobe University Graduate School of Science Technology and Innovation Kobe Japan

ISBN: (纸本)9798350346459

In bathrooms, a fall can lead to severe injuries or even drowning. While taking preventive measures against such accidents is crucial, it's equally important to swiftly detect and request rescue when they occur. In such scenarios, knowing the exact location of the person in the bathroom becomes critical. However, concerns related to privacy and costs make the use of cameras and lidar impractical. To address these issues, we propose a method for estimating the 3D location of a person within the bathroom using ultrasound correlation values as input for a deep neural network. To validate our approach, we conduct experiments using two sensors in this study. The results are promising, as we achieved a mean square error of just 10.48 cm. © 2023 IEEE.

关键词： Location

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Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

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SN Computer science 2024年第1期5卷 13页

作者： Kumar, Ajay Singh, Rajiv Ranjan Chatterjee, Indranath Sharma, Nikita Rana, Vaishali Department of Computer Science and Informatics Central University of Himachal Pradesh Himachal Pradesh Dharamshala India Department of Cyber Security and Networks Glasgow Caledonian University Glasgow Scotland United Kingdom Department of Computer Engineering Tongmyong University Busan South Korea School of Technology Woxsen University Hyderabad India

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges. © 2023, The Author(s).

关键词： Blockchain Healthcare IoT Neuroadaptive Smart contracts

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Transforming Cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： Cybersecurity

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Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data science Models 11

Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Al-Khowarizmi Ramlan, Fitria Wulandari Efendi, Syahril Lubis, Arif Ridho Riza, Ferdy Amrullah Sary, Yoshida Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia National University Ireland College of Engineering and Informatics Galway Ireland Universitas Sumatera Utara Department of Computer Science Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering & Informatics Medan Indonesia Universitas Muhammadiyah Sumatera Utara Department of Information System Medan Indonesia

ISBN: (纸本)9798350305968

The crude palm oil (CPO) industry is highly competitive due to fluctuating and unpredictable prices. To accurately predict future CPO prices, a forecasting technique is necessary. This can be achieved through various regression techniques and data mining approaches. This paper proposes a Multivariate Adaptive Regression Splines (MARS) model, which utilizes the ECoS machine learning algorithm to obtain optimal forecasting values. This research mainly focuses on forecasting CPO prices using the ECoS-MARS approach. The functional basis (BF) validation has yielded an accuracy of 0.0532%. Furthermore, validation has been conducted to achieve accurate forecasting values, resulting in an accuracy of 0.11714%. The best parameter combination for this forecasting model consists of a learning rate of 0.6 for both learning rates 1 and 2, a sensitivity threshold of 0.3, and an error threshold of 0.05. © 2023 IEEE.

关键词： Forecasting

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A fast parallel classification model for the diagnosis of cancer

Recent Advances in Computer Science and Communications

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Recent Advances in Computer science and Communications 2021年第1期14卷 291-301页

作者： Jain, Divya Singh, Vijendra Department of Computer Science and Engineering School of Engineering and Technology The Northcap University Gurugram India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

Background: In this era of voluminous data, there is a need to process data speedily in less time. Moreover, it is also essential to diminish the dimensionality of data as well as to apply parallel computations for classification. SVM is a prominent classification tool and is currently one of the most popular state-of-the-art models for solving various classification problems that makes use of parallel computations to speed up its processing. Objective: To develop a fast, promising classification system using optimized SVM classifier with hybridized dimensionality reduction for the diagnosis of cancer disease. Methods: The proposed approach comprises of two stages – the first stage presents a hybrid approach to reduce the dimensionality of cancer datasets, and the second stage presents an efficient classification method to optimize the SVM parameters and improve its accuracy. To lessen the execution time, the proposed approach uses GPUs to concurrently run different processes on machine workers. Results: The proposed method with the combination of dimensionality reduction & parallel classification using optimized SVM classifier is found to give excellent results based on ‘Classification Accuracy’, ‘Selected Features’ and ‘Execution Time’. Conclusion: The experimental findings with benchmark datasets indicate that the proposed diagnostic model yields significant improvement in terms of execution time when compared to the conventional approach. The proposed model can assist doctors and medical professionals in the quick selection of most significant risk factors for the diagnosis and prognosis of cancer diseases. © 2021 Bentham science Publishers.

关键词： Classification (of information)

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