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SSRN 2022年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Departament of Industrial Engineering University of Chile Santiago Chile Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece

Selecting outstanding tech stocks for investment is challenging for any market investor. This challenge comes from the complexity caused by the dynamic and volatile nature of public policies and macroeconomic factors. Moreover, this task appears complex as an investor needs to decide from a diverse set of stocks, where performance ranking can conflict with another for different criteria, e.g., the contradiction between pure returns and risk performance. The paper proposes a novel model for stock evaluation and selection problems based on multi-source data fusion and decision-level fusion by multi-criteria decision-making (MCDM) techniques. The narrow criteria performance of a single source usually restricts a candidate's overall evaluation, especially on stock selection. Therefore, the proposed multi-source acquisition is promising to enhance reliability with reasonable data fusion, Furthermore, proper raw data processing and information integration by data fusion techniques are essential to obtain more accurate and concise data for decision-making support. Most important, the proposed multi-MCDM decision-fusion procedure can enhance the strength of a model to overcome the weakness of a single-performed model. Besides, we propose a customized loss function that improves the accuracy of time-series data prediction and increases the data's credibility. Meanwhile, the experiment shows that the proposed method reduces the prediction log error by 28\% in a best case on the experimental real-situation data and decreases the time-series data matching cost by 20\% between the prediction and observation. Finally, we conducted the real-world stock data selection by the proposed fusion-based model. The proposed model locates five potential preferred stocks, and the results are practical and effective, which are further justified through a detailed ablation study. Meanwhile, the time-series forecasting data got a lower error prediction than the baseline loss. © 2022, The Author

关键词： Data fusion

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Discovery of the high thermoelectric performance in low-cost Cu8SiSxSe6-x argyrodites

arXiv

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arXiv 2023年

作者： Parashchuk, Taras Cherniushok, Oleksandr Cardoso-Gil, Raul Tobola, Janusz Grin, Yuri Wojciechowski, Krzysztof T. Thermoelectric Research Laboratory Department of Inorganic Chemistry Faculty of Materials Science and Ceramics AGH University of Science and Technology Mickiewicza Ave. 30 Krakow30-059 Poland Max-Planck-Institut für Chemische Physik fester Stoffe Nöthnitzer Str. 40 Dresden01187 Germany Faculty of Physics and Applied Computer Science AGH University of Science and Technology Krakow30-059 Poland

Cu-based argyrodites have gained much attention as a new class of thermoelectric materials for energy harvesting. However, the phase transition occurring in these materials and low energy conversion performance limited their broad application in thermoelectric converters. In this work, we disclose a newly discovered highly efficient Cu8SiSxSe6-x argyrodite with stabilized high-symmetry cubic phase at above 282 K opening the practical potential of this material for the mid-temperature region applications. The temperature range broadening of the high-symmetry phase existence was possible due to the successful substitution of Se with S in Cu8SiSxSe6-x, which enhances the configurational entropy. The developed argyrodites show excellent thermoelectric performance thanks to the increased density of states effective mass and ultralow lattice thermal conductivity. Further tuning of the carrier concentration through the Cu-deviation improves the thermoelectric performance significantly. The dimensionless thermoelectric figure of merit ZT and estimated energy conversion efficiency η for Cu7.95SiS3Se3 achieve outstanding values of the 1.45 and 13 %, respectively, offering this argyrodite as a low-cost and Te-free alternative for the thermoelectric energy conversion applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Crystal structure

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Decision-making Support System for Predicting and Eliminating Malnutrition and Anemia

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Open Bioinformatics Journal 2023年 16卷

作者： Maasthi, Manasvi Jagadeesh Gururaj, Harinahalli Lokesh Ravi, Vinayakumar Basavesha, D. Almeshari, Meshari Alzamil, Yasser Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Karnataka Tumkur India Department of Diagnostic Radiology College of Applied Medical Sciences University of Ha'il Ha'il Saudi Arabia

Aims: This study investigates predicting and eliminating malnutrition and anemia using ML Algorithms and comparing all the methods with various malnutrition-based parameters. Background: The health of the nation is more important than the wealth of the nation. Malnutrition and anemia are serious health issues but the least importance is given to eradicate them. Objective: Proper nutrition is an essential component for the survival, growth, and development of infants, children, and women who in turn give birth to infants. Methods: In the proposed system, machine learning approaches are utilized to predict the malnutrition status of children under five years of age and anemia in men and women using old datasets and further providing a suitable diet recommendation to overcome the disease. Classification techniques are being used for malnutrition as well as anemia prediction. Results: Algorithms such as Naïve Bayes classifier (NBC), Decision Tree (DT) algorithm, Random Forest (RF) algorithm, and K-Nearest Neighbor (k-NN) algorithm are utilized for prediction. The results obtained by these algorithms are 94.47%, 85%, 95.49%, and 63.15%. When compared, Naïve Bayes and random forest algorithm provided efficient results for malnutrition and anemia, respectively. Conclusion: By testing and validating, preventive actions can be taken with the help of medical experts and dieticians to reduce malnutrition and anemia conditions among children and elders, respectively. © 2023 The Author(s).

关键词： Algorithm Dataset Deficiency Healthcare Naïve baye classifier Nutrition

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Optimizing Handwritten Numeral Recognition for English and Devanagari Using MNIST and CPAR Data

Optimizing Handwritten Numeral Recognition for English and D...

引用

Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Teekam Singh Tanupriya Choudhury Ketan Kotecha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification.

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Quantum spin systems: toroidal classification and geometric duality

arXiv

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arXiv 2024年

作者： Azimi-Mousolou, Vahid Bergman, Anders Delin, Anna Eriksson, Olle Pereiro, Manuel Thonig, Danny Sjöqvist, Erik Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-751 20 Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center StockholmSE-10691 Sweden KTH Royal Institute of Technology StockholmSE-10044 Sweden KTH Royal Institute of Technology StockholmSE-10044 Sweden Wallenberg Initiative Materials Science WISE Uppsala University Box 516 UppsalaSE-751 20 Sweden School of Science and Technology Örebro University ÖrebroSE-701 82 Sweden

Toroidal classification and geometric duality in quantum spin systems is presented. Through our classification and duality, we reveal that various bipartite quantum features in magnon-systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultra-fast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes. © 2024, CC0.

关键词： Ferromagnetic materials

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Reconstruction Error-Based Efficient Deep Neural Network for Diabetes Prediction in the Korean Population 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Amarbayasgalan, Tsatsral Li, Meijing Namsrai, Oyun-Erdene Ryu, Keun Ho Database Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of College of Information Engineering Shanghai Maritime University Shanghai China School of Engineering and Applied Sciences National University of Mongolia Ulaanbaatar Mongolia Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (数字)9789813367579

ISBN: (纸本)9789813367562

The World Health Organization (WHO) reported that diabetes is now one of the top ten causes of global mortality, and it is also highly ranked in Korea. The poor lifestyle, such as lack of physical activity, unhealthy diet, overweight, and tobacco usage, directly increases the risk of diabetes. It is asymptomatic at the early stage, and most patients may be diagnosed several years after onset. Over time, it can lead to cardiovascular diseases, kidney failure, blindness, and limb amputation. Nowadays, machine learning-based approaches can play an essential role in the healthcare domain for disease prediction. In this study, we have proposed a deep neural network (DNN) with a reconstruction error (RE)-based feature to predict diabetes. First, a deep autoencoder (DAE) model is used to calculate the RE-based new feature. Then, the DNN classifier learns from a combination of the new feature and other risk factors to predict diabetes. We have performed an experiment on the Korea National Health and Nutrition Examination Survey (KNHANES) dataset and compared with typical machine learning algorithms. As a result of our experiment, proposed RE-based DNN outperformed regular machine learning classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Deep neural networks

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Qubit fidelity under stochastic Schrödinger equations driven by colored noise

arXiv

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arXiv 2024年

作者： de Keijzer, Robert J.P.T. Visser, Luke Y. Tse, Oliver Kokkelmans, Servaas J.J.M.F. Department of Applied Physics Eindhoven University of Technology P. O. Box 513 Eindhoven5600 MB Netherlands Eindhoven Hendrik Casimir Institute Eindhoven University of Technology P. O. Box 513 Eindhoven5600 MB Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology P. O. Box 513 Eindhoven5600 MB Netherlands

Environmental noise on a controlled quantum system is generally modeled by a dissipative Lindblad equation. This equation describes the average state of the system via the density matrix ρ. One way of deriving this Lindblad equation is by introducing a stochastic operator evolving under white noise in the Schrödinger equation. However, white noise, where all noise frequencies contribute equally in the power spectral density, is not a realistic noise profile as lower frequencies generally dominate the spectrum. Furthermore, the Lindblad equation does not fully describe the system as a density matrix ρ does not uniquely describe a probabilistic ensemble of pure states {ψj}j. In this work, we introduce a method for solving for the full distribution of qubit fidelity driven by important stochastic Schrödinger equation cases, where qubits evolve under more realistic noise profiles, e.g. Ornstein-Uhlenbeck noise. This allows for predictions of the mean, variance, and higher-order moments of the fidelities of these qubits, which can be of value when deciding on the allowed noise levels for future quantum computing systems, e.g. deciding what quality of control systems to procure. Furthermore, these methods will prove to be integral in the optimal control of qubit states under (classical) control system noise. © 2024, CC BY.

关键词： Stochastic systems

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Enhanced Word Embedding with CNN using Word Order Information For Sentiment Analysis

Enhanced Word Embedding with CNN using Word Order Informatio...

引用

International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Peter Atandoh Zhang Fengli Paul Atandoh Hakeem Edward Mensah Acheampong Daniel Addo Michael Appiah-Twum Daniel Adu-Gyamfi Richard Safo Blankson Tohye Tewodros Giza Wi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Mathematics Mercer University Macon GA USA School of Resources and Environment University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology C. K. Tedam University of Technology and Applied Sciences Navrongo UE/R Ghana Summit Economics LLC Colorado USA

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7 % , 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE.

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Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning

arXiv

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arXiv 2024年

作者： Fan, Yuxin Fu, Lei Wang, Liyang Hu, Zhuohuan Cheng, Yu Wang, Yuxiang School of Engineering and Applied Science University of Pennsylvania Toronto Canada School of Computer Science and Engineering Changshu Institute of Technology Suzhou China Olin Business School Washington University in St. Louis St. LouisMO63130 United States San Francisco United States Mclean VA22102 United States New York NY United States

High-frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. The velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. The objective of this work is to optimise the real-time processing of data in high-frequency trading algorithms. The dynamic feature selection mechanism is responsible for monitoring and analysing market data in real time through clustering and feature weight analysis, with the objective of automatically selecting the most relevant features. This process employs an adaptive feature extraction method, which enables the system to respond and adjust its feature set in a timely manner when the data input changes, thus ensuring the efficient utilisation of data. The lightweight neural networks are designed in a modular fashion, comprising fast convolutional layers and pruning techniques that facilitate the expeditious completion of data processing and output prediction. In contrast to conventional deep learning models, the neural network architecture has been specifically designed to minimise the number of parameters and computational complexity, thereby markedly reducing the inference time. The experimental results demonstrate that the model is capable of maintaining consistent performance in the context of varying market conditions, thereby illustrating its advantages in terms of processing speed and revenue enhancement. © 2024, CC BY.

关键词： Financial markets

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Entanglement duality in spin-spin interactions

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Physical Review A 2022年第3期106卷 032407-032407页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran Department of Physics and Astronomy Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden

We examine entanglement of thermal states for spin-1/2 dimers in external magnetic fields. Entanglement transition in the temperature-magnetic-field plane demonstrates a duality in spin-spin interactions. This identifies a pair of dual categories of symmetric and antisymmetric dimers with each category classified into toric entanglement classes. The entanglement transition line is preserved from each toric entanglement class to its dual toric class. The toric classification is an indication of the topological signature of the entanglement, which bring about topological stability that could be relevant for quantum information processing.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum information theory Spin dynamics

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