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Two-dimensional computer Modelling of Contaminant Migration in Unsaturated Porous Media with Nanoparticles

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Two-dimensional Computer Modelling of Contaminant Migration ...

International computer Sciences and Information Technologies (CSIT)

作者： Viktor Zhukovskyy Nataliia Zhukovska Jan Perhac Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine Department of Computers and Informatics Technical University of Kosice Kosice Slovak Republic

ISBN: (纸本)9798350334326

The complicated physical problem of computer modeling pollutant migration in the zone of suspended water with nanoparticles to the catcher filter is discussed in this study. The brand-new mathematical model of nonlinearity in two dimensions is offered. The generalized Darcy-Clute laws for mass and moisture movement in various subregions of soil are taken into careful consideration in the model. The finite difference method was used to find a numerical solution to the boundary value problem and to provide an algorithm for computer implementation. Utilizing a newly created Nanosurface program solution, computer modeling was carried out along with the analysis of numerical experiments. The nanoparticle influence was demonstrated.

关键词： Nanoparticles Cloud computing Boundary value problems Computational modeling Soil Predictive models Mathematical models

Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

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Prediction New Cases of COVID-19 in Indonesia Using Vector A...

2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021

作者： Danistya, Daffa Rahmansyah Qaulifa, Fahira Ramadani, Yovi Adhi Nurma Yulita, Intan Ardisasmita, Mulya Nurmansyah Agustian, Dwi Research Center for Artificial Intelligence and Big Data Padjadjaran University Bandung Indonesia Department of Electrical Engineering Brawijaya University Malang Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Department of Informatics Engineering Semarang University Semarang Indonesia Department of Computer Science Padjadjaran University Bandung Indonesia Department of Public Health Padjadjaran University Bandun Indonesia

ISBN: (纸本)9781665408905

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.

关键词： Long short-term memory

Dynamics of cold random hyperbolic graphs with link persistence

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arXiv 2022年

作者： Zambirinis, Sofoclis Hartle, Harrison Papadopoulos, Fragkiskos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus Network Science Institute Northeastern University BostonMA02115 United States

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω ∈ [0, 1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

Multi-platform Process Flow Models and Algorithms for Extraction and Documentation of Digital Forensic Evidence from Mobile Devices

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arXiv 2022年

作者： Ocen, Gilbert Gilibrays Bongomin, Ocident Mugeni, Gilbert Barasa Makau, Mutua Stephen Semwogerere, Twaibu Department of Computer Engineering Faculty of Engineering Busitema University Tororo Uganda Department of Manufacturing Textile and Industrial Engineering School of Engineering Moi University Eldoret Kenya Directorate of Innovation Research and Development Communication Authority of Kenya Nairobi Kenya Department of Computer Science School of Computing and Informatics Meru University of Science and Technology Meru Kenya

The increasing need for the examination of evidence from mobile and portable gadgets increases the essential need to establish dependable measures for the investigation of these gadgets. Many differences exist while detailing the requirement for the examination of each gadget, to help detectives and examiners in guaranteeing that of any kind piece of evidence extracted/ collected from any mobile devices is well documented and the outcomes can be repeatable, a reliable and well-documented investigation process must be implemented if the results of the examination are to be repeatable and defensible in courts of law. In this paper we developed a generic process flow model for the extraction of digital evidence in mobile devices running on android, Windows, iOs and Blackberry operating system. The research adopted survey approach and extensive literature review a s means to collect data. The models developed were validate through expert opinion. Results of this work can guide solution developers in ensuring standardization of evidence extraction tools for mobile devices. © 2022, CC BY.

关键词： Extraction

Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

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arXiv 2023年

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen China The Department of Computer Science Hong Kong Baptist University Hong Kong The Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://***/Keke921/GCLLoss. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Various Modulation Types Classification on the Air by Convolutional Neural Network 23

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Various Modulation Types Classification on the Air by Convol...

Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Yousif I Hammadi Mokhalad Abdulameer Kadhim Alsaeedi Omar Abdulkareem Mahmood Aws Zuhair Sameen Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature; however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. The suggested CNN network is constructed mainly of six-Convolutional layers. The overall number of trainable parameters in the proposed network is 1,757,962. Thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities.

关键词：

Time-Domain Channel Estimation for Extremely Large MIMO THz Communications with Beam Squint

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TechRxiv

TechRxiv 2023年

作者： Vlachos, Evangelos Kaushik, Aryan Eldar, Yonina C. Alexandropoulos, George C. Industrial Systems Institute ATHENA Research and Innovation Centre Rio-Patras26504 Greece School of Engineering and Informatics University of Sussex United Kingdom Faculty of Mathematics and Computer Science Weizmann institute of Science Israel Department of Informatics and Telecommunications National Kapodistrian University of Athens Athens15784 Greece

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the Terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures, which leads to frequency selectivity, a problem known as beam squint. Multi-carrier transmission schemes which are usually deployed to address this problem, suffer from high peak-to-average power ratio, which is specifically dominant in THz communications where low transmit power is realized. Diverging from the usual approach, we devise a novel channel estimation problem formulation in the time domain for single-carrier (SC) modulation, which favors transmissions in THz, and incorporate the beam-squint effect in a sparse vector recovery problem that is solved via sparse optimization tools. In particular, the beam squint and the sparse MIMO channel are jointly tracked by using an alternating minimization approach that decomposes the two estimation problems. The presented performance evaluation results validate that the proposed SC technique exhibits superior performance than the conventional one as well as than state-of-the-art multi-carrier approaches. © 2023, CC BY.

关键词： Channel estimation

Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

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arXiv 2023年

作者： Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-Ming Huang, Hui Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China

The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We replace a portion of feature maps from tail classes with those belonging to head classes. These fused features substantially enhance the diversity of tail classes. Both theoretical analysis and practical experimentation demonstrate that H2T can contribute to a more optimized solution for the decision boundary. We seamlessly integrate H2T in the classifier adjustment stage, making it a plug-and-play module. Its simplicity and ease of implementation allow for smooth integration with existing long-tailed recognition methods, facilitating a further performance boost. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is available at https://***/Keke921/H2T. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Exploiting optimum acoustic features in COVID-19 individual's breathing sounds

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Exploiting optimum acoustic features in COVID-19 individual'...

2021 International Research Conference on Smart Computing and Systems engineering, SCSE 2021

作者： Milani, M. G. Manisha Ramashini, Murugaiya Murugiah, Krishani Chamal, Lanka Geeganage Shamaan Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei Brunei Uva Wellassa University Department of Computer Science and Informatics Sri Lanka Pavendar Bharathidasan College of Engineering and Technology Department of Biotechnology India School of Engineering Sri Lanka Technological Campus Sri Lanka

ISBN: (纸本)9781665409650

The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors;thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes;statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals. © 2021 IEEE.

关键词： COVID-19