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Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution

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arXiv 2022年

作者： Wang, Xiwen Zhou, Rui Ying, Jiaxi Palomar, Daniel P. The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Shenzhen Research Institute of Big Data Shenzhen China The Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Department of Electronic and Computer Engineering Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Kowloon Hong Kong

Since Markowitz’s mean-variance framework, optimizing a portfolio that strikes a trade-off between maximizing profit and minimizing risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the portfolio’s return, a.k.a. the mean and variance, which are sufficient to characterize a Gaussian distribution. However, it is broadly believed that the first two moments are not enough to capture the characteristics of the returns’ behavior, which have been recognized to be asymmetric and heavy-tailed. Although portfolio designs involving the third and fourth moments, i.e., skewness and kurtosis, have been demonstrated to outperform the conventional mean-variance framework, they present non-trivial challenges. Specifically, in the classical framework, the memory and computational cost of computing the skewness and kurtosis grow sharply with the number of assets. To alleviate the difficulty in high-dimensional problems, we consider an alternative expression for high-order moments based on parametric representations via a generalized hyperbolic skew-t distribution. Then, we reformulate the high-order portfolio optimization problem as a fixed-point problem and propose a robust fixed-point acceleration algorithm that solves the problem in an efficient and scalable manner. Empirical experiments also attest to the efficiency of our proposed high-order portfolio optimization framework, which presents low complexity and significantly outperforms the state-of-the-art methods by 2 ∼ 4 orders of magnitude. Copyright © 2022, The Authors. All rights reserved.

关键词： Profitability

Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

Study and Trend Prediction of Covid-19 cases in India using Deep Learning Techniques 1

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Study and Trend Prediction of Covid-19 cases in India using ...

1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Shrivastava, V.K. Kumar, Ajay Shrivastava, A. Tiwari, A. Thiru, K. Batra, R. Department of Big Data Analytics Adani Institute of Digital Technology Management Gujrat Gandhinagar382423 India Department of Mechanical Engineering SGT University Haryana Gurugram122505 India Department of Electronics and Tele Communication Engineering IIIT Odisha Bhubaneswar751003 India Department of Information Technology Madhav Institute of Technology and Science Gwalior474005 India United World School of Computational Intelligence Karnavati University Gujrat Gandhinagar383422 India Reenu Batra Department of Computer Science and Engineering SGT University Haryana Gurugram122505 India

The novel coronavirus or officially known as SARS-CoV 2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused a severe pandemic over the world affecting not only the economy of the countries but also the lifestyle of the people worldwide. As on 31.12.2020, Covid-19 (coronavirus disease) has infecting more than 10266674 people and causing about 148738 deaths in India. It has been seen through various statistics of various countries that the number of Covid-19 cases grows exponentially as the number of test increases then after some period, the rate of new cases decreases. In this research paper, researchers have created deep learning-based model to predict the curve of the new Covid-19 cases vs the total number of tests conducted in India. There is still lockdown in some part of the country while some states have partially relaxed the rules and some states totally lifted the lockdown. Predicting the number of new cases and their trend can help in deciding what is the optimal time to release the lockdown. It will also help in determining when the coronavirus will loosen its grip from India. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting

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arXiv 2023年

作者： Spadon, Gabriel Kumar, Jay Eden, Derek van Berkel, Josh Foster, Tom Soares, Amilcar Fablet, Ronan Matwin, Stan Pelot, Ronald Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Industrial Engineering Department Dalhousie University HalifaxNS Canada Departement de Genie Mathematique et Electrique IMT Atlantique Brest France Department of Comp. Sci. and Media Tech. Linnaeus University Vaxjo Sweden Institute of Computer Science Polish Academy of Sciences Warsaw Poland DHI Water & Environment Inc. Ottawa Canada

This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS) data using feature fusion for problem shifting. We have developed a deep auto-encoder model and a phased framework approach to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data as input. To this end, we fuse the spatiotemporal features from the AIS messages with probabilistic features engineered from historical AIS data referring to potential routes and destinations. As a result, we reduce the forecasting uncertainty by shifting the problem into a trajectory reconstruction problem. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations;the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada. Copyright © 2023, The Authors. All rights reserved.

关键词： data fusion

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

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Federated Learning-Based Intelligent Indoor Smoke and Fire D...

Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

Corrections to “A Deep Learning Framework for the Classification of Brazilian Coins”

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IEEE Access 2024年 12卷 70000-70000页

作者： Debabrata Swain Viral Rupapara Amro Nour Santosh Satapathy Biswaranjan Acharya Shakti Mishra Ali Bostani Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India College of Engineering and Applied Sciences American University of Kuwait Salmiya Kuwait Department of Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India Department of Computer Engineering-AI & Big Data Analytics Marwadi University Rajkot Gujarat India

In the above article [1] , ChatGPT was used to improve the English writing in the abstract and in Section II: Literature Survey. As per IEEE PSPB policy, the use of content generated by AI in an article shall be disclosed in the acknowledgments section of any article submitted to an IEEE publication. The purpose of this correction is to address the lack of disclosure in the published article.

关键词： Surveys Deep learning Writing Chatbots Artificial intelligence