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Develop an unsupervised attention-based LSTM network algorithm for forecasting infectious disease

AIP Conference Proceedings 2023年第1期2977卷

作者： Mostafa Abotaleb Tatiana Makarovskikh Zainalabideen Albadran Ali J. Ramadhan System Programming Department South Ural State University Chelyabinsk Russia Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq

time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time series, weather forecasting, and medical diagnostics. In this study, we provide a model of the mechanism governing attention, which enables attended input to be provided to the model in place of actual input. In order for the model to produce more precise predictions, it seeks to demonstrate a fresh perspective on the data. The experiments were conducted with the (encoder-decoder) LSTM model as well to demonstrate the usefulness and superiority of the suggested strategy. The obtained results demonstrate that, when compared to the (encoder-decoder) LSTM base model, the proposed approach could reduce the mean square error (RMSE=9819.05), relative root mean square error (RRMSE=99.09), and coefficient of determination (R Square=0.96). The obtained results support the suggested approach’s efficacy, superiority, and importance in predicting SARS-CoV-2 infection cases.

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A Novel Solution to Enhance the Oxidative and Physical Properties of Cookies Using Maltodextrin-Based Nano-Sized Oils as a Fat Substitute

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ACS Omega 2025年第22期10卷 23111-23120页

作者： Raciye Meral Mehmet Mustafa Eki̇N Zafer Ceylan Aslıhan Alav Erol Kına Department of Food Engineering Faculty of Engineering Van Yüzüncü Yıl University Van 65080 Turkiye Özalp Vocational School Food Technology Program Van Yuzuncu Yıl University Van 65080 Turkiye Faculty of Science Department of Molecular Biology and Genetics Kutlubey Campus Bartin University Kutlubeyyazıcılar Village Bartin 74110 Turkiye Van Yüzüncü Yıl University Institute of Science Van 65080 Turkiye Computer Programming Program Özalp Vocational School Van Yüzüncü Yıl University Van 65100 Turkiye；Innovan Entrepreneurship Centre Van 65080 Turkiye

This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced cookies with nanoemulsions (FC) were produced. The addition of nanoemulsions increased the cookie diameter from 46.3 mm (control) to 56.1 mm and reduced the thickness, resulting in a desirable texture. Initial hardness values (30.3 and 45.8 N) were lower in nanoemulsion samples and remained reduced over a 90 day storage period. Black cumin oil-loaded nanoemulsions provided the lowest peroxide values (1.7, 2.7, and 2.4 mequiv O2/kg), maintaining oxidative stability during storage. Final free fatty acid (FFA) values ranged from 0.23% to 0.44% after storage. Thiobarbituric acid (TBA) values indicated slower lipid oxidation, with values ranging from 1.47 to 2.51 mg MDA/kg on day 0 and increasing to a maximum of 4.13 mg MDA/kg by day 90 in fat-reduced cookies. Among the tested formulations, nanoemulsions enriched with black cumin oil demonstrated the highest effectiveness, yielding enhanced oxidative stability and improved quality characteristics. This study presents an innovative strategy by utilizing maltodextrin-based nanoemulsions containing naturally antioxidant-rich oils as fat replacers, offering a clean-label alternative to improve the oxidative resilience and physical quality of cookies.

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A non-potential Target Function for Controlling the UAVs Group Flight in Presence of Concave Obstacles 5

A non-potential Target Function for Controlling the UAVs Gro...

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5th IEEE International Conference Actual Problems of Unmanned Aerial Vehicles Developments, APUAVD 2019

作者： Glazok, Olexiy M. Computer and Programming Engineering National Aviation University Dept. of Cybersecurity Kyiv Ukraine

ISBN: (纸本)9781728125923

A solution for the problem of controlling the unmanned quadrotor vehicles group flight in presence of obstacles of complex form is proposed. A traditional approach to construction of the target function does not solve the problem of control at presence of concave obstacles;the UAVs may get trapped in such a situation. The proposed target function contains the three terms. The first of them controls the UAVs' behavior with respect to attitude. The second is built on the base of a moving coordinate system bound to the trajectory of the group motion. The third addend is a standard sum that performs the function of repelling the UAVs one from another. Also, in the proposed approach the motion is bound to the virtual center of the group, instead of one UAV that is chosen to be a leader. This approach helps to avoid a discrepancy between desired and actual states of the group in case that the leader UAV experiences some problems with its motion. © 2019 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Neural networks for determining affinity functions

Neural networks for determining affinity functions

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Valery Dmitrievich Dmitrienko Alexander Yurievich Zakovorotniy Sergey Yurievich Leonov Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkov Ukraine

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

The Hamming neural network is an effective tool for solving the problems of recognition and classification of discrete objects whose components are encoded with the binary bipolar alphabet, and the difference between the number of identical bipolar components of the compared objects (vectors images) and the Hamming distance between them (Hamming distance is the number of mismatched bits in the binary vectors being compared) is used as the objects proximity measures. However, the Hamming neural network cannot be used to solve these problems in case the components of the compared objects (vectors) are encoded with the binary alphabet. It also cannot be used to evaluate the affinity (proximity) of objects (binary vectors) with Jaccard, Sokal and Michener, Kulzinsky functions, etc. In this regard, a generalized Hamming neural network architecture has been developed. It consists of two main blocks, which can vary being relatively independent on each other. The first block, consisting of one layer of neurons, calculates the proximity measures of the input image and the reference ones stored in the neuron relations weights of this block. Unlike the Hamming neural network, this block can calculate various proximity measures and signals about the magnitude of these proximity measures from the output of the first block neurons which are followed to the inputs of the second block elements. In the Hamming neural network, the Maxnet neural network is used as the second block, which gives out one maximum signal from the outputs of the first block neurons. If the inputs of the Maxnet network receive not only one but several identical maximum signals, then the second block, and, consequently, the Hamming network, cannot recognize the input vector, which is at the same minimum Hamming distance from two or more reference images stored in the first block. The proposed generalized architecture of the Hamming neural network allows using neural networks instead of the Maxnet network, whi

关键词： Biological neural networks Neurons Hamming distance computer architecture Image recognition programming Weight measurement

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Carrot and Stick approaches when managing Technical Debt

Carrot and Stick approaches when managing Technical Debt

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IEEE/ACM International Conference on Technical Debt (TechDebt)

作者： Terese Besker Antonio Martini Jan Bosch Computer Science and Engineering Software Engineering Chalmers University of Technology Göteborg Sweden University of Oslo Programming and Software Engineering Oslo Norway

When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, decrease the developers’ morale, and compromise the overall quality of the software. However, even if researchers and practitioners working in today’s software development industry are quite familiar with the concept of technical debt and its related negative consequences, there has been no empirical research focusing specifically on how software managers actively communicate and manage the need to keep the level of technical debt as low as *** paper aims to explore how software companies encourage and reward practitioners for actively keeping the level of technical debt down and also whether the companies use any forcing or penalizing initiatives when managing technical *** paper reports the results of both an online web-survey provided quantitative data from 258 participants and follow-up interviews with 32 industrial software practitioners. The findings show that having a TD management strategy can significantly impact the amount of TD in the software. When surveying how commonly used different TD management strategies are, we found that only the encouraging strategy is, to some extent, adopted in today’s’ software industry. This study also provides a model describing the four assessed strategies by presenting its strategies and tactics, together with recommendations on how they could be operationalized in today’s software companies.

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Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

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Expert Systems with Applications 2024年 238卷

作者： Das, Soumen Kumar Yu, Vincent F. Roy, Sankar Kumar Weber, Gerhard Wilhelm Department of Pure and Applied Sciences Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan Faculty of Engineering Management Poznan University of Technology Poznan Poland IAM METU Ankara Turkey

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. © 2023

关键词： Sustainable development

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A method for targeting unmanned aerial vehicles for a distributed intelligent control system for a group of dissimilar unmanned aerial vehicles

A method for targeting unmanned aerial vehicles for a distri...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Danylo Parkhomenko Oleksandr Rysovanyi Valery Burtsev Viktor Voronin Volodymyr Startsev Volodymyr Panchenko Doctoral student of the scientific and organizational department of Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of the faculty of anti-aircraft missile forces of Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substa...

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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Estimating Solar Power Plant Data Using Time Series Analysis Methods

Estimating Solar Power Plant Data Using Time Series Analysis...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Ebru Idman Emrah Idman Osman Yildirim Computer Programming Istanbul Arel University Istanbul Turkey Electronic Engineering Istanbul Arel University Istanbul Turkey

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

When meteorological data such as temperature, precipitation, weather events and economic data such as stock prices and exchange rates reach large levels, it may be necessary to analyze them with time series analysis methods. The aim of this research is to analyze the data of solar power plants with time series and make predictions for the future. To achieve this goal, solar panel data with historical depth will be collected, the collected data will be trained and predicted by various time series analysis methods and comparison will be made according to the prediction success among the related models. Methodology: With this study, using Python 3.6 and R 3.6.1, the time series estimation models were modeled with AR, ARMA, SARIMA, DES and TES, the difference between the real value and the predicted value of the data was found by the RMSE (Square Root of the Mean Square Error) method and it was seen which model has the best ability to estimate the dataset. In addition, with the trend and seasonality of the data, detailed information about the dataset was obtained with descriptive analysis and graphics. As a result, it was seen that using SARIMA or TES models in the datasets that show seasonal change in the light of the studies and estimations performed gives better results.

关键词： Predictive models Market research Time series analysis Data models Meteorology Estimation Prediction algorithms

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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