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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

Dual-Band Planar Microstrip Monopole Antenna Design Using Mu...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Hosseini, V. Shapour, F. Pinho, P. Farhang, Y. Majidzadeh, K. Ghobadi, Ch. Nourinia, J. Barshandeh, S. Shokri, M. Amiri, Zh. Jalilirad, M. Kaboutari, K. Payame Noor University Department of Computer Engineering and Information Technology Tehran Iran Islamic Azad University Urmia Branch Computer Engineering Department Urmia Iran Instituto de Telecomunicações Aveiro Portugal University of Aveiro Department of Electronics Telecommunications and Informatics Aveiro Portugal Islamic Azad University Khoy Branch Department of Computer Engineering Khoy Iran Urmia University Department of Electrical Engineering Urmia Iran School of Engineering Department of Computer Science Afagh Higher Education Institute Urmia Iran

ISBN: (纸本)9798350312843

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized S11 be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using computer Simulation technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables. © 2023 IEEE.

关键词： Monopole antennas

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Towards Accountable E-Health Policies in the Nordic Countries

Towards Accountable E-Health Policies in the Nordic Countrie...

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34th Medical informatics Europe Conference, MIE 2024

作者： Faxvaag, Arild Reponen, Jarmo Hardardottir, Gudrun Audur Vehko, Tuulikki Viitanen, Johanna Eriksen, Jeppe Koch, Sabine Nøhr, Christian Department of Neuromedicine and Movement Science Norwegian University of Science and Technology Norway Research Unit of Health Sciences and Technology University of Oulu Finland Directorate of Health Iceland Finnish Institute for Health and Welfare Finland Department of Sustainability and Planning Aalborg University Denmark Department of Computer Science Aalto University Espoo Finland Department of Learning Informatics Management and Ethics Health Informatics Centre Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9781643685335

The Nordic Countries are seen as forerunners in the field of digital health technologies and national implementation has been guided by sector specific strategies for many years. In the context of new European legislation such as the European Health Data Space (EHDS), a review of the existing strategies is indicated. The objective of this policy analysis is to assess and compare the scope, ambitions and extent of accountability in national-level digital health policies in the Nordic countries. The scope of the policies from the five countries were largely centred around a) empowering and activating citizens;b) a shift towards prevention and digital first;c) supporting health operations;d) doing the groundwork;e) making health data more available in research and innovation workflows and f) supporting health personnel. Finland comes out as the most ambitious country with the aim to transform their health system by means of digitalisation. Both Finland and Iceland work towards prevention and the digital first ambition due to large populations in rural areas. These two countries also present the most accountable policies, meaning that their policy documents are the most transparent as to how they arrived at the conclusions and how they are to evaluate the achievements. © 2024 The Authors.

关键词： Electronic health record

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Acoustic cavitation bubble under influence of frequency ultrasound: Numerical analysis 3

Acoustic cavitation bubble under influence of frequency ultr...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Abu-Nab, A.K. Abu-Bakr, A.F. Abushaeer, Zain Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow141700 Russia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Higher Institute of Engineering and Technology Department of Basic Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350323511

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles. © 2023 IEEE.

关键词： Numerical methods

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data Science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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Cost-sensitive Computational Adequacy of Higher-order Recursion in Synthetic Domain Theory

arXiv

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arXiv 2024年

作者： Niu, Yue Sterling, Jonathan Harper, Robert National Institute of Informatics Tokyo Japan Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

We study a cost-aware programming language for higher-order recursion dubbed PCFcost in the setting of synthetic domain theory (SDT). Our main contribution relates the denotational cost semantics of PCFcost to its computational cost semantics, a new kind of dynamic semantics for program execution that serves as a mathematically natural alternative to operational semantics in SDT. In particular we prove an internal, cost-sensitive version of Plotkin’s computational adequacy theorem, giving a precise correspondence between the denotational and computational semantics for complete programs at base type. The constructions and proofs of this paper take place in the internal dependent type theory of an SDT topos extended by a phase distinction in the sense of Sterling and Harper. By controlling the interpretation of cost structure via the phase distinction in the denotational semantics, we show that PCFcost programs also evince a noninterference property of cost and behavior. We verify the axioms of the type theory by means of a model construction based on relative sheaf models of SDT. © 2024, CC BY.

关键词： Semantics

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Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters 26

Non-Destructive Egg Fertility Detection in Incubation Using ...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Saifullah, Shoffan Drezewski, Rafal Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta Indonesia Institute of Computer Science AGH University of Science and Technology Cracow Poland

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results. © 2022 The Authors. Published by Elsevier B.V.

关键词： Image enhancement

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KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm 1

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Dhanvardini, R. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Healthcare Informatics Division Optum UnitedHealth Groups Hyderabad India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India Department of Data Science Manipal Institute of Technology Bengaluru Bengaluru India Department of Data Science Manipal Academy of Higher Education Manipal India Bangalore India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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