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International Computer science and Engineering Conference (ICSEC)

作者： Nattapong Tongtep Thatsanee Charoenporn College of Computing Prince of Songkla University Phuket Thailand Asia AI Institute Faculty of Data Science Musashino University Tokyo Japan

In this paper, we propose a method and web application to reduce wordiness in Thai by adapting pattern-based analysis with wordiness initial corpus. The small amount of Thai text is gathered to create the initial corpus for grammatically analyzing the patterns of Thai wordiness. 18 wordiness have been decided to intensely analyze the co-occurrence, patterns contexts and construction rules. And 99 wordiness patterns are discovered for the experiments. The experiment has been done with 100 documents from Thai National Corpus by 15 students. For the user satisfaction which is analyzed from the questionnaire, users are highly satisfied with the use of the proposed system. And the wordiness reduction result of the experiment shows the effectiveness of the wordiness identification and reduction which the accuracy is 86.51%. The 13.49% of errors come from word segmentation and unknown words. Wordiness automatically corpus will be developed for further experiment in the future.

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Forest Mapping from Hyperspectral Image Using Deep Belief Network

Forest Mapping from Hyperspectral Image Using Deep Belief Ne...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xianxian Luo Songya Xu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Faculty of Educational Science Quanzhou Normal University Quanzhou China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Forest type is very important in the management of the forest ecosystem. Previous literatures demonstrate that deep belief network (DBN) gain the excellent classification performance. This research attempts to classify the forest type from hyperspectral image using DBN. To begin with, the Restricted Boltzmann Machines and deep belief networks are briefly reviewed. Then, the effect of network depth and the number of hidden units on the accuracy and Kappa efficient is discussed. Furthermore, forest type is classified using DBN by Python language, comparing with typical classification methods, such as support vector machine (SVM). The experimental results show that the number of layers is 3 and the number of nodes is 256 are the optimal network structure for forest type identification. The overall accuracy is 85.8% and the coefficient is 0.785, which is better than the classification performance of support vector machine. In general, deep belief network has the potential to be the dominant method of forest mapping by hyperspectral image.

关键词： Forestry Support vector machines Hyperspectral imaging Feature extraction Machine learning Training

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Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference

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Optik 2022年 270卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid Sathishkumar K Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

In power systems engineering, early faults detection, as well asa location in networks is a major challenge that results in damage and revenue to facilities and equipment, energy loss .The reason for the delay in detection is that most network operators rely on data/complaints supplied by customers without having a system in place to check and verify whether the information is misleading or *** paper proposed the novel fault detection in power system operators for sub-station equipment by a convolutional neural network (CNN)for detecting and relegating blemish of the network by training voltage and power values by analyzing the circuit breakers of the network. Then, at that point, discrete wavelet change (DWT) has been coordinated with the preparation part to get qualities of voltage and current signs in numerous recurrence groups of the organization at the sub-stations and this will bean master arrangement of issue location. The fault locations have been done using a fuzzy interference-based fault location system (FI-FLS) of the network. By this fault detection and locator, the aberrant and mundane conditions of the networks has been relegated and located. The simulation results show feasibility, effectiveness, average precision. The fault located by this proposed method is line to ground, line to line and, phase fault predicted on the fault parameter *** proposed approach attains 96 % average accuracy for the detection that shows the efficiency of the performance.

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CyberSentry: Enhancing SCADA Security through Advanced Deep Learning and Optimization Strategies

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International Journal of Critical Infrastructure Protection 2025年

作者： Alaa O. Khadidos Adil O. Khadidos Shitharth Selvarajan Taher Al-Shehari Nasser A Alsadhan Subhav Singh Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS1 3HE Leeds U.K Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Chitkara Centre for Research and Development Chitkara University Himachal Pradesh-174103 India Division of research and development Lovely Professional University Phagwara Punjab India

SCADA systems form the core of infrastructural facilities, including power grids, water treatment facilities, and industrial processes. Changing cyber threats present increasingly sophisticated attacks against which traditional security models inadequately protect SCADA systems. These traditional models usually have drawbacks in the way of inadequate feature selection, inefficiency in detecting most attacks, and suboptimal parameter tuning, which cause vulnerabilities and reduce resilience in systems. This paper presents CyberSentry, a new security framework designed to overcome limitations so as to provide robust protection for SCADA systems. These three modules makeup CyberSentry: the RMIG feature selection model, tri-fusion net for attack detection, and Parrot-Levy Blend Optimization (PLBO) for parameter tuning. The Recursive Multi-Correlation-based Information Gain (RMIG) feature selection model enhances accuracy in detection by optimizing the set of fatal features through recursive multi-correlation analysis by Information Gain prioritization. The Tri-Fusion Net combines anomaly detection, signature-based detection, and machine learning classifiers to enhance the detection versatility and robustness. The PLBO module ensures efficient and dynamic tuning for the parameters through undocumented Parrot and Levy optimization techniques. The proposed CyberSentry framework integrates, within a unified architecture, anomaly detection, signature-based detection, and machine learning classifiers to enhance the security of SCADA systems against diverse cyber threats. Features extracted in this manner are analyzed using machine learning classifiers that exploit their predictive capabilities for robust threat classification. The proposed approaches are fused within the Tri-Fusion Net to complement each other in areas where the separate methods lack certain strengths. This, therefore, ensures broad threat detection, as is validated by extensive testing with various datasets

关键词： SCADA Systems Security Cyber-Attack Intrusion Detection Deep Learning Feature Selection and Optimization

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 647 LNNS卷 ix-x页

作者： Abraham, Ajith Hong, Tzung-Pei Kotecha, Ketan Ma, Kun Mishra, Pooja Manghirmalani Gandhi, Niketa Faculty of Computing and Data Science FLAME University Maharashtra Pune India Scientific Network for Innovation and Research Excellence Machine Intelligence Research Labs AuburnWA United States National University of Kaohsiung Kaohsiung Taiwan University of Jinan Jinan China Symbiosis International University Pune India Mishra Scientific Network for Innovation and Research Excellence Machine Intelligence Research Labs Kerala Mala India

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A comparison of deep learning-based techniques for solving partial differential equations

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AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

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FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

arXiv

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arXiv 2024年

作者： Chen, Long-Fei Tsai, Chao-Wei Li, Jian-Yang Yang, Bin Li, Di Duan, Yan Hsia, Chih-Hao Pan, Zhichen Qian, Lei Quan, Donghui Jiang, Xue-Jian Li, Xiaohu Zhao, Ruining Zuo, Pei School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang550025 China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311100 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China School of Atmospheric Sciences Sun Yat-sen University Guangdong Zhuhai China Núcleo de Astronomía Facultad de Ingenieríay Ciencias Universidad Diego Portales Chile Department of Astronomy College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Department of Electronic and Optical Engineering Space Engineering University Beijing China Laboratory for Space Research Faculty of Science The University of Hong Kong Hong Kong Xinjiang Astronomical Observatory Chinese Academy of Sciences No. 150 Science 1-Street Urumqi830011 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Sciences University of Chinese Academy of Sciences Beijing100049 China

We used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets, C/2020 F3 (NEOWISE), C/2020 R4 (ATLAS), C/2021 A1 (Leonard), and 67P/Churyumov-Gerasimenko during or after their perihelion passages. Thousands of molecular transition lines fall in this low-frequency range, many attributed to complex organic or prebiotic molecules. We conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the data. Although several molecules have been detected at high frequencies of great than 100 GHz in comets, our results confirm that it is challenging to detect molecular transitions in the L-band frequency ranges. The non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines, which could be caused by the masers or non-LTE effects. Although the line strengths are predicted to be weak, for FAST, using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecules

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Using Apriori Algorithm on Students' Performance data for Association Rules Mining

Using Apriori Algorithm on Students' Performance Data for As...

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2nd International Seminar on Education Research and Social science(ISERSS 2019)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students' achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.

关键词： data mining apriori algorithm association rules students' achievement analysis

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Vehicle's Density Prediction Based on History data of e-Toll in PT Jasamarga Pandaan Tol Using Hidden Markov Model

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： N I Asrori N Iriawan W S Winahju Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Indonesia

The Gempol-Pandaan toll is a strategic center for regional development. In 2017, the number of vehicles passing through this toll has increased. This condition could impact on traffic jam and road damage because of passes by many cars, especially trucks with loads that exceed their capacity. Therefore, it is necessary to do a vehicle's density prediction not only from the Gempol toll gate but also from Kejapanan, Bangil and Rembang toll gates that will exit through Pandaan toll gate. In this research, vehicle's density is viewed from the probability and number of each vehicles category I, II, III, IV, V. The origin gate and vehicle category can not be observed directly, but the car must pass the toll gate to do tapping the e-toll card, so the method used in this research is the Hidden Markov Model (HMM). Two estimate methods for HMM parameters are employed in this research, the Expectation-Maximization (EM) algorithm and Bayesian approach. The result shows that HMM parameters estimation using Bayesian is better than the EM algorithm. The Bayesian estimated parameter values are closer to the input parameters so that the model is more representative to explain the vehicle's density prediction.

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