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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Punia, Sushma Saharia, Ankur Ismail, Yaseera Petruccione, Francesco Bourdine, Anton V. Demidov, Vladimir V. Morozov, Oleg G. Yin, Juan Singh, Ghanshyam Tiwari, Manish Optoelectronics and Photonics Research Lab Department of Electronics and Communication Engineering Manipal University Jaipur Jaipur303007 India Department of Physics Stellenbosch University Stellenbosch7600 South Africa School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7602 South Africa Stellenbosch South Africa Department of Communication Lines Povolzhskiy State University of Telecommunications and Informatics 23 Lev Tolstoy Street Samara Oblast443010 Russia JSC Scientific Production Association State Optical Institute Named after Vavilov S.I. 36/1 Babushkin Street St. Petersburg192171 Russia Department of Photonics and Communication Links Saint Petersburg State University of Telecommunications Named after M.A. Bonch-Bruevich 22 Bolshevikov Avenue St. Petersburg193232 Russia Department of Radiophotonics and Microwave Technologies Kazan National Research State University named after A.N. Tupolev-KAI 10 Karl Marx Street Kazan420111 Russia Division of Quantum Physics and Quantum Information University of Science and Technology of China 99 Xiupu Road Pudong District Shanghai200093 China Department of ECE Malaviya National Institute of Technology Jaipur Jaipur302017 India

ISBN: (纸本)9789819784639

This work is intended to present a nested Photonic Crystal Fiber (PCF) for OAM mode propagation. The proposed PCF comprises of a dense flint SF6 and a very light flint LLF1. The finite-element method (FEM) is used for the modal analysis to obtain propagating HE/EH modes. The proposed fiber has shown flat dispersion during the propagation of multiple orbital angular momentum (OAM) modes. Furthermore, the designed PCF’s nonlinearity (gamma) and confinement loss (CL) are calculated to prove the practical feasibility of the design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photonic crystal fibers

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

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

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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

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From the digital data revolution to digital health and digital economy toward a digital society: Pervasiveness of Artificial Intelligence

arXiv

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arXiv 2020年

作者： Emmert-Streib, Frank Predictive Society and Data Analytics Lab Faculty of Information Technology and Communication Sciences Tampere University Finland Institute of Biosciences and Medical Technology Tampere Finland

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and utilization of these data. Fortunately, recent breakthroughs in deep learning algorithms complement nowadays machine learning and statistics methods for an efficient analysis of such data. Furthermore, advances in text mining and natural language processing, e.g., word-embedding methods, enable the processing of large amounts of text data from diverse sources such as governmental reports, blog entries in social media or clinical health records of patients. In this paper, we present a perspective on the role of artificial intelligence in these developments and discuss potential problems we are facing in a digital society. Copyright © 2020, The Authors. All rights reserved.

关键词： Industry 4.0

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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

Deep reinforcement learning of event-triggered communication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

关键词： Deep learning Automation Conferences Reinforcement learning Computer architecture Numerical simulation Timing

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Dynamic Neural Assimilation: A Deep Learning and data Assimilation Model for Air Quality Predictions

SSRN

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SSRN 2023年

作者： Tučkus, Nikodemas D’Elia, Ilaria Chinnici, Marta Zollo, Maurizio Guo, Yi-Ke Arcucci, Rossella Data Science Institute Imperial College London United Kingdom Department of Computing Imperial College London United Kingdom Department of Energy Technologies and Renewable Sources Division of Information and Communication Technology ENEA Rome Italy Leonardo Centre on Business for Society Imperial College Business School United Kingdom Hong Kong University of Science and Technology Hong Kong Department of Earth Science and Engineering Imperial College London United Kingdom Imperial College London United Kingdom

Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing air quality is of the utmost importance to protect human health and the environment. Different tools are available, from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels but they are often computationally very expensive which makes them poorly *** this paper, we developed a novel model called Dynamic Neural Assimilation (DNA) integrating Recurrent Neural Networks (RNN) and data Assimilation (DA) methods to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating the relationship with industrial statistics. We analysed the advantages of the DNA system in terms of both accuracy and efficiency. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy from the European Environment Agency (EEA) and simulated results derived from the air quality modelling system AMS-MINNI (Atmospheric Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level). We analysed air pollution data in Italy from the years 2003-2010 and studied its association with nearby industries. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

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IEEE International Conference on Big data

作者： Ling Jin Xiaodan Xu Yuhan Wang Kaveh Farokhi Sadabadi Alina Lazar Duleep Rathgamage Don Zachary Needell C Anna Spurlock Mahyar Amirgholy Mona Asudegi Energy Technology Area Berkeley National Laboratory Berkeley CA Center for Advanced Transportation Technology University of Maryland College Park MD Department of Computer Science and Information Youngstown State University Youngstown OH School of Data Science and Analytics Kennesaw State University Marietta GA Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory Berkeley CA Civil and Environmental Engineering Department Kennesaw State University Marietta GA Office of Transportation Policy Studies Federal Highway Administration Washington DC D.C.

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level.

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database Intrusion Detection Systems (DIDs): Insider threat detection via behavioural-based Anomaly detection systems - A Brief Survey of concepts and approaches

arXiv

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arXiv 2020年

作者： Khan, Muhammad Imran Foley, Simon N. O'Sullivan, Barry Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Ireland Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal information. database Intrusion detection systems, specifically behavioural-based database intrusion detection systems, have been shown effective in detecting insider attacks. This paper presents background concepts on database intrusion detection systems in the context of detecting insider threats and examines existing approaches in the literature on detecting malicious accesses by an insider to database Management Systems (DBMS). Copyright © 2020, The Authors. All rights reserved.

关键词： Relational database systems

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Privacy interpretation of behavioural-based anomaly detection approaches

arXiv

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arXiv 2020年

作者： Khan, Muhammad Imran Foley, Simon N. O’Sullivan, Barry Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Ireland Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

This paper proposes the notion of ‘Privacy-Anomaly Detection’ and considers the question of whether behavioural-based anomaly detection approaches can have a privacy semantic interpretation and whether the detected anomalies can be related to the conventional (formal) definitions of privacy semantics such as k-anonymity. The idea is to learn user’s past querying behaviour in terms of privacy and then identifying deviations from past behaviour in order to detect privacy violations. Privacy attacks, violations of formal privacy definition, based on a sequence of SQL queries (query correlations) are also considered in the paper and it is shown that interactive querying settings are vulnerable to privacy attacks based on query sequences. Investigation on whether these types of privacy attacks can potentially manifest themselves as anomalies, specifically as privacy-anomalies was carried out. It is shown that in this paper that behavioural-based anomaly detection approaches have the potential to detect privacy attacks based on query sequences (violation of formal privacy definition) as privacy-anomalies. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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data, power and bias in artificial intelligence

arXiv

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arXiv 2020年

作者： Leavy, Susan O’Sullivan, Barry Siapera, Eugenia Insight Centre for Data Analytics School of Information and Communication Studies University College Dublin Ireland School of Computer Science and Information Technology University College Cork Ireland

Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

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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

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