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International Conference on Informatics and Computing (ICIC)

作者： Delfia Nur Anrianti Putri Raden Bagus Muhammad AdryanPutra Adhy Wijaya Nailfaaz Wahyono Computer Science Undergraduate Program Faculty Of Mathematics And Natural Science Sleman Indonesia Department of Computer Science and Electronics Faculty Of Mathematics And Natural Science Sleman Indonesia

Urban Green Space (UGS) plays a vital role in maintaining the balance of urban ecosystems. In Yogyakarta City, urban development and an increase in population accompanied by a decrease and insufficiency in the proportion of UGS correlate with rising urban temperatures. Monitoring the availability of UGS can serve as a foundation for decision-making in managing the city’s UGS. With advancements in technology and computing in the field of computer Vision (CV), it’s possible to recognize UGS from the geometric patterns of satellite images. This study proposes a vegetation segmentation method using satellite images and a Deep Learning (DL) method with the U-Net architecture to estimate the availability of UGS. Evaluation results show that the image dataset without preprocessing with the Red Green Blue (RGB) color channel gives an average Intersection over Union (IoU) of 80.74%. These results are better than data that underwent grayscaling and contrast stretching preprocessing. Based on a case study conducted in three sub-districts in the Yogyakarta region, the constructed U-Net model gave an average IoU evaluation of 60.88%. These results demonstrate the model’s capability to provide a reference in calculating the UGS area at the sub-district level, which can then be utilized by the Yogyakarta City government to analyze the availability of UGS more efficiently. With this analysis, the government can make strategic decisions to manage UGS and create a sustainable urban environment.

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A Newton-like Method based on Model Reduction Techniques for Implicit Numerical Methods

A Newton-like Method based on Model Reduction Techniques for...

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European Control Conference (ECC)

作者： Tobias K. S. Ritschel Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

In this paper, we present a Newton-like method based on model reduction techniques, which can be used in implicit numerical methods for approximating the solution to ordinary differential equations. In each iteration, the Newton-like method solves a reduced order linear system in order to compute the Newton step. This reduced system is derived using a projection matrix, obtained using proper orthogonal decomposition, which is updated in each time step of the numerical method. We demonstrate that the method can be used together with Euler’s implicit method to simulate CO 2 injection into an oil reservoir, and we compare with using Newton’s method. The Newton-like method achieves a speedup of between 39% and 84% for systems with between 4,800 and 52,800 state variables.

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QT-GWAS:A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

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Molecular Plant 2023年第7期16卷 1212-1227页

作者： Marlies Brouckaert Meng Peng RenéHofer lias El Houari Chiarina Darrah Veronique Storme Yvan Saeys Ruben Vanholme Geert Goeminne Vitaliy I.Timokhin John Ralph Kris Morreel Wout Boerjan Ghent University Department of Plant Biotechnology and Bioinformatics9000 GhentBelgium VIB Center for Plant Systems Biology 9052 GhentBelgium Ghent University Department of Applied MathematicsComputer Science and Statistics9000 GhentBelgium VIB Center for Inflammation Research 9052 GhentBelgium vIB Metabolomics Core 9052 GhentBelgium Department of Biochemistry and US Department of Energy Great Lakes Bioenergy Research CenterWisconsin Energy InstituteUniversity of Wisconsin-MadisonMadisonWI53706USA Ghent University Department of Applied MathematicsComputer Science and StatisticsGhentBelgium ViB Center for Inflammation Research GhentBelgium BioNTech SE MainzRhineland-PalatinateGermany Eunomia Research&Consulting BristolUK VIB Agro-incubator NeveleBelgium Research Institute for Chromatography KortrijkBelgium

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind,a large fraction of these metabolites and their biosynthetic pathways remain *** metabolite structures and their biosynthetic pathways is key to gaining biological understanding andto allow metabolic *** orderto retrieve novel biosynthetic genes involved in specialized metabolism,we developed a novel untargeted method designated as qualitative trait GWAs(QT-GWAS)that subjects qualitative metabolic traits to a genome-wide association study,while the conventional metabolite GWAS(mGWAS)mainly considers the quantitative variation of *** a proof of the validity of QT-GWAS,23 and 15of the retrieved associations identified in Arabidopsis thaliana by QTGWAS and mGWAS,respectively,were supported by previous ***,seven genemetabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme *** such,we established that CYTOCHROME P450706A5(CYP706A5)is involved in the biosynthesis of chroman derivatives,UDP-GLYCOSYLTRANSFERASE 76C3(UGT76C3)is able tohexosylate guanine in vitro and in planta,and SULFOTRANSFERASE 202B1(SULT202B1)catalyzes the sulfation of neolignans in ***,our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes,even new associations that cannot be found by the conventional mGwAs,providing a new approach for dissecting qualitative metabolic traits.

关键词： qualitative method proof

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Multi-District School Choice: Playing on Several Fields

arXiv

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arXiv 2024年

作者： Gonczarowski, Yannai A. Yin, Michael Zhang, Shirley Department of Economics Department of Computer Science Harvard University United States Department of Applied Mathematics Harvard University United States Department of Computer Science Harvard University United States

We extend the seminal model of Pathak and Sönmez (2008) to a setting with multiple school districts, each running its own separate centralized match, and focus on the case of two districts. In our setting, in addition to each student being either sincere or sophisticated, she is also either constrained-able to apply only to schools within her own district of residence-or unconstrained-able to choose any single district within which to apply. We show that several key results from Pathak and Sönmez (2008) qualitatively flip: A sophisticated student may prefer for a sincere student to become sophisticated, and a sophisticated student may prefer for her own district to use Deferred Acceptance over the Boston Mechanism, irrespective of the mechanism used by the other district. We furthermore investigate the preferences of students over the constraint levels of other students. Many of these phenomena appear abundantly in large random markets. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering

arXiv

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arXiv 2024年

作者： Gohari, Rasoul Jafari Aliahmadipour, Laya Valipour, Ezat Department of Computer Science Shahid Bahonar University of Kerman Kerman Iran Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Computer Science Faculty of Mathematical Sciences and Statistics Malayer University Malayer Iran Department of Applied Mathematics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran

The world of Machine Learning (ML) has witnessed rapid changes in terms of new models and ways to process users' data. The majority of work that has been done is focused on Deep Learning (DL) based approaches. However, with the emergence of new algorithms such as the Tsetlin Machine (TM) algorithm, there is growing interest in exploring alternative approaches that may offer unique advantages in certain domains or applications. One of these domains is Federated Learning (FL), in which users' privacy is of utmost importance. Due to its novelty, FL has seen a surge in the incorporation of personalization techniques to enhance model accuracy while maintaining user privacy under personalized conditions. In this work, we propose a novel approach called TPFL: Tsetlin-Personalized Federated Learning, in which models are grouped into clusters based on their confidence towards a specific class. In this way, clustering can benefit from two key advantages. Firstly, clients share only what they are confident about, resulting in the elimination of wrongful weight aggregation among clients whose data for a specific class may not have been enough during the training. This phenomenon is prevalent when the data are non-Independent and Identically Distributed (non-IID). Secondly, by sharing only weights towards a specific class, communication cost is substantially reduced, making TPLF efficient in terms of both accuracy and communication cost. The TPFL results were compared with 5 other baseline methods;namely FedAvg, FedProx, FLIS, IFCA and FedTM. The results demonstrated that TPFL performance was better than baseline methods with 98.94% accuracy on MNIST, 98.52% accuracy on FashionMNIST and 91.16% accuracy on FEMNIST dataset. © 2024, CC BY.

关键词： Federated learning

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Evaluating and Addressing Interoperability Constraints in Health Information Systems for Enhanced Healthcare Delivery in Zimbabwe

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Journal of computer and Communications 2024年第7期12卷 133-154页

作者： Prosper Tafadzwa Denhere Nelson Matanana Munyaradzi Zhou Caleb Manjeese Talent Mawere Department of Marketing and Information Sciences Midlands State University Gweru Zimbabwe Department of Computer Science and Information Systems Manicaland State University of Applied Sciences Mutare Zimbabwe Department of Mathematics and Computer Science Great Zimbabwe University Masvingo Zimbabwe

Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.

关键词： Health Information System Interoperability Interoperability Constraints

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Optimization Analysis of Bus Operation Scheduling Considering Passenger and Cargo Sharing under Uncertain Demand

Optimization Analysis of Bus Operation Scheduling Considerin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiacheng Li Masato Noto Yang Zhang Department of Applied Systems and Mathematics Kanagawa University Yokohama Japan Department of Computer Science Kanagawa University Yokohama Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In view of the problems faced during the ongoing development of public transport, such as departure intervals that are too long, low occupancy rates, difficulties making a profit, and high express delivery costs, bus operation and scheduling models that consider passenger and cargo sharing have been attracting attention. In this work, considering the interests of both bus companies and passengers, we propose a bus operation scheduling optimization model that ensures the largest revenue for urban and rural bus companies and the shortest travel time for passengers under uncertain demand. We also design a particle group genetic algorithm for use with this model. The results of a case study set in China demonstrate the feasibility and effectiveness of the model and algorithm.

关键词： Costs Optimization models Companies Scheduling Optimization Cybernetics Genetic algorithms

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Maximizing the Lifetime of WSN Using Hybrid Energy Harvesting Approach 9

Maximizing the Lifetime of WSN Using Hybrid Energy Harvestin...

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9th IEEE International Conference on sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Dalila, Boulerial Bouabdellah, Kechar Ali, Benzerbadj Department of Computer Sciences Faculty of Exact and Applied Sciences Oran Algeria Ain Temouchent University Department of Mathematics and Computer Science Ain TemouchentBP 284 Algeria Oran Algeria

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

The 21st century is marked by a remarkable expansion of IoT. This technology that allows us to connect with our objects permanently and thus monitor each interaction that may be crucial in our daily life. And among the most important bronchi of this technology we are interested in wireless sensor networks with mobile sink (WSN-MS1 for short). These networks, while being very useful and efficient in the world of internet of things (IoT)[33],[34], they face serious challenges, particularly that of the battery which ensures them a very limited lifetime, as well as the problem of the broken links between the transmitting nodes and the mobile sink. In this paper, we have proposed a new approach EH-HXMAC that solves the first problem by collecting ambient energy from the two most powerful sources (the sun and the wind), and by acting on the duty cycle of the emitting nodes. To remedy the second problem, we have resorted to the Handover technique which ensures data transmission integrity between the sensor and the mobile sink. The results of the simulation carried out by the Cooja Contiki simulator show that the combination of these three techniques provides an improvement in terms of network lifetime , throughput and received packets number. © 2022 IEEE.

关键词： Energy harvesting

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in Thailand 9

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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