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Novel intelligent predictive networks for analysis of chaos in stochastic differential SIS epidemic model with vaccination impact

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Mathematics and computers in Simulation 2024年 219卷 251-283页

作者： Anwar, Nabeela Ahmad, Iftikhar Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Mathematics University of Narowal 51600 Pakistan Department of Mathematics University of Gujrat 50700 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Yuan Ze University AI Center Taoyuan320 Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock43600 Pakistan

In this paper, stochastic predictive computing networks are exploited to investigate the dynamics of the SIS with vaccination impact based epidemic model (SISV-EM) represented by nonlinear systems of stochastic differential equations (SDEs) by exploitation of artificial neural networks (ANNs) with the backpropagated Levenberg-Marquardt technique (BLMT) i.e., (ANNs-BLMT) to approximate the solution behavior. The stochastic nonlinear SISV-EM is governed with three classes: susceptible, infectious, and vaccinated populations. The referenced or target datasets for ANNs-BLMT are constructed by employing Euler-Maruyama (EM) scheme for solving stochastic differential systems in case of sufficiently various nonlinear SISV-EM scenarios by varying the percentage of vaccination for newly born, the coefficient of transmission, the natural mortality rates, the infectious rates of recovery, the rate at which vaccinated people lose their immunity, the rate of death caused by disease, the proportion of vaccinated against susceptible and the white noise in the environment. Based on arbitrary training, testing, and validation samples from the referenced dataset, the ANNs-BLMT provides an approximate solution for the stochastic nonlinear SISV-EM, with significant correlations to the referenced results. Exhaustive simulation-based results using error histograms, mean square errors, and regression analyses further demonstrate that the proposed ANNs-BLMT is efficient, consistent, and accurate for solving SISV-EM. © 2023 International Association for Mathematics and computers in Simulation (IMACS)

关键词： Mean square error

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Save the Yummy Candyland: An Asymmetric Virtual Reality Game 22

Save the Yummy Candyland: An Asymmetric Virtual Reality Game

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9th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI PLAY 2022

作者： Arendttorp, Emilie Maria Nybo Winschiers-Theophilus, Heike Itenge, Helvi Skovfoged, Milo Marsfeldt Architecture Design And Media Technology Aalborg University Denmark Computer Science Namibia University Of Science And Technology Namibia Tlu Namibia University Of Science And Technology Namibia Department Of Architecture Design And Media Technology Aalborg University Denmark

ISBN: (纸本)9781450392112

This preliminary study explored how to make otherwise passive bystanders into an integral part of a virtual reality (VR) experience using asymmetric game design. We present a fully implemented collaborative VR game, co-designed by Namibian school children. The team needs to solve puzzles, whereby one player, using a VR headset, enters the digital world of Candyland, communicating with the other teammates, who are using a printed puzzle book. Ten Namibian school children, split into three groups, evaluated the game. Both the children inside and outside of VR were excited and eager to play the game through, despite the game being too complex, with too little feedback and a number of design issues, which required much guidance from the researchers. © 2022 Owner/Author.

关键词： Game design

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Co-design of virtual reality exergames for balance rehabilitation

Co-design of virtual reality exergames for balance rehabilit...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Henriksen, Signe Toftgaard Adjorlu, Ali Serafin, Stefania Onofrei, Marius George Fontana, Federico Søndergaard, Kasper Aalborg University Department of Architecture Design and Media Technology Copenhagen Denmark University of Udine Department of Mathematics Computer Science and Physics Udine Italy University Hospital Herlev and Gentofte Department of Occupational and Physical Therapy Hellerup Denmark

ISBN: (纸本)9798331514846

Balance disorders, particularly in the elderly population, pose a significant public health challenge due to the associated risk of falls. Exercise-based therapies are effective for reducing this risk but face limitations like cost, accessibility, and sustaining patient engagement. By enabling personalized rehabilitation through engaging exergames, Virtual Reality (VR) offers a promising, cost-effective solution. This paper presents the collaborative design and preliminary evaluation of two VR-based games targeting specific balance skills. Developed in collaboration with a physiotherapist with extensive experience with patients with balance disorders, the games align with a rehabilitation protocol used by the therapist that categorizes training based on individual balance skills. Inspired by traditional physiotherapy exercises, the gameplay aims to train the same skills. Six health professionals agreed on the games' therapeutic potential, while recommending improvements in usability, difficulty adjustment and monitoring of patient progress. © 2025 IEEE.

关键词： Patient monitoring

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Machine Learning Approach to the Internet of Things Threat Detection

Machine Learning Approach to the Internet of Things Threat D...

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International Conference on Data science and Network Engineering, ICDSNE 2023

作者： Upadhyay, Alka Naaz, Sameena Thakur, Vinay Ansari, Iffat Rehman Department of Computer Science and Technology School of Engineering Sciences and Technology Jamia Hamdard New Delhi India New Delhi India Electronics Engineering Section Faculty of Engineering and Technology University Women’s Polytechnic A.M.U.UP Aligarh202002 India

ISBN: (纸本)9789819967544

The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k-means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts - A Prosumager Smart Fog Computing Perspective

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IEEE Internet of Things Journal 2023年第21期10卷 18898-18908页

作者： Vohnout, Rudolf Bukovsky, Ivo Chou, Shuo-Yan Geyer, Jakub Budik, Ondrej Sharma, Rohit Prokysek, Milos Horvath, Tomas Wyckmans, Annemie University of South Bohemia in České Budějovice Faculty of Science Department of Computer Science České Budějovice37005 Czech Republic National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan SRM Institute of Science and Technology NCR Campus Department of Electronics and Communication Engineering Ghaziabad201204 India Brno University of Technology Department of Telecommunications Brno61600 Czech Republic CESNET a.l.e Department of Optical Networks Prague16000 Czech Republic NTNU-Norwegian University of Science and Technology Department of Architecture and Planning Trondheim7491 Norway

Living lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts (PEDs), where timely data dissemination is essential for prosumager decisions and their greedy behavior. This article interconnects intelligent information exchange, supported by ultralow latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal task offloading for effective information distribution constitutes the convergence problem, we reintroduced higher order neural units. These units contribute to sustaining both computational and energy efficiency, as well as the balance of the entire system. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the data set provided by one of Europe's smart city pioneers, where the prosumager PED transition has already started. © 2023 IEEE.

关键词： Decision making

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Empower Hub: Sustainable Innovative Navigation and Smart Interior Design 3

Empower Hub: Sustainable Innovative Navigation and Smart Int...

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3rd International Conference on Sustainable Mobility Applications, Renewables and technology, SMART 2024

作者： Abdalla, Manoon Nasri Al Falasi, Mohammed El-Messiry, Haythem School of Architecture and Interior Design Canadian university Dubai United Arab Emirates School of Engineering Applied science and Technology Canadian university Dubai United Arab Emirates The Department of Computer Science Canadian University Dubai United Arab Emirates

ISBN: (纸本)9798331532925

This paper will conclude the integration between smart interior design and Artificial Intelligence including sustainability within design principles and functions. Presenting "Empower hub" a center that is specifically devoted to people with physical disabilities that concludes AI in the infrastructure. Using sustainable materials and energy-efficient lighting are the objectives. This can include solar power smart systems and adaptive technology, which greatly assist people with various types of impairments, and it is not adequately integrated in many sectors. The design focus is to address the special needs of people with visual, auditory, or mobility impairments, utilize state-of-the-art adaptive technology. Therefore, navigation assistance and motion detection systems are being proposed for the center. The smart prototypes will process data and sensors that will detect movements for smooth access. In addition to proximity sensors that can be vocal to ensure audio assistance. Most of those sensors will be powered by solar energy to ensure the sustainable factor of the project in addition to reducing energy consumption. Overall, the research will conclude the integration of AI and interior design while applying sustainable design principles in interior design with the integration of machine learning to benefit people of determination in order of accessibility and function to allow them to give back to society by giving them easy access to ensure their safety and wellbeing. © 2024 IEEE

关键词： Architectural design

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Resource Management approaches to Internet of Vehicles

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Multimedia Tools and Applications 2023年第30期82卷 46811-46844页

作者： Hosseinzadeh, Mehdi Abbasi, Shirin Rahmani, Amir Masoud Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The Internet of Vehicles (IoV) has become a significant issue in designing smart cities. Many applications and services are provided in IoV for various purposes. They need the resources for the computation and data collection. One of the open issues in IoV is resource management because of resource limitation, resource heterogeneity, and dynamic networks. To our knowledge, no systematic and thorough research in resource management methodologies was conducted in IoV, despite the critical nature of resource management challenges. This paper analyzes the recently published studies on resource management in IoV. Our analysis shows that resource allocation has the most utilization rate of resource management approaches at 30%. The most critical parameter in resource allocation is cost, which includes energy, time, price, and processing capacity. The analytical reports show those cost parameters have the most evaluation in the approaches by 19%. Furthermore, we propose a taxonomy for resource management in IoV and extract challenges and open issues in this field. It can be a start point to suggest new methods for resource management in IoV. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Resource allocation

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Radial basis kernel harmony in neural networks for the analysis of MHD Williamson nanofluid flow with thermal radiation and chemical reaction: An evolutionary approach

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Alexandria Engineering Journal 2024年 103卷 98-120页

作者： Butt, Zeeshan Ikram Raja, Muhammad Asif Zahoor Ahmad, Iftikhar Hussain, Syed Ibrar Shoaib, Muhammad Ilyas, Hira Department of Mathematics University of Gujrat Gujrat50700 Pakistan Future Technology Research Centre National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science University of Palermo Via Archirafi 34 Palermo90123 Italy Department of Mathematics University of Houston HoustonTX77204 United States Yuan Ze University AI Center Taoyuan320 Taiwan Department of Physical Sciences University of Chenab Gujrat50700 Pakistan

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze magnetohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction as well as thermal radiation in a porous medium. This newly designed technique is an amalgam of a well-known reliable global solver named genetic algorithms (GAs) and a swift convergence generated local solver named sequential quadratic programming (SQP) used in ANNs by taking RBF as a kernel function i.e. ANNs-RBF-GASQP solver. The PDEs demonstrating the current nanofluid problem flow are transformed into the system of non-linear ODEs through a relevant similarity transformation and subsequently solved using ANNs-RBF-GASQP solver to investigate thermohydraulic properties by manipulating the values of various system parameters present in the ODEs. Moreover, the simulation results show that increasing the heat source parameter leads to a significant decrease in temperature. Additionally, an increase in the porosity parameter causes a decrease in the velocity of nanofluid, as a higher value of porosity increases fluid permeability and greater resistance to flow. The efficacy of the suggested solver is scrutinized through various statistical and convergence analyses. © 2024

关键词： Chemical reactions

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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New Evolutionary Selection Operators for Snake Optimizer 14th

New Evolutionary Selection Operators for Snake Optimizer

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14th International Joint Conference on Computational Intelligence, IJCCI 2022

作者： Khurma, Ruba Abu Alazab, Moutaz Merelo, J.J. Castillo, Pedro A. Department of Computer Science Al-Ahliyya Amman University Amman Jordan Department of Artificial Intelligence Al-Balqa University Al-Salt Jordan Department of Computer Architecture and Computer Technology ETSIIT CITIC University of Granada Granada Spain

ISBN: (纸本)9789897586118

Evolutionary algorithms (EA) adopt a Darwinian theory which is known as "survival of the fittest". Snake Optimizer (SO) is a recently developed swarm algorithm that inherits the selection principle in its structure. This is applied by selecting the fittest solutions and using them in deriving new solutions for the next iterations of the algorithm. However, this makes the algorithm biased towards the highly fitted solutions in the search space, which affects the diversity of the SO algorithm. This paper proposes new selection operators to be integrated with the SO algorithm and replaces the global best operator. Four SO variations are investigated by individually integrating four different selection operators: SO-roulettewheel, SO-tournament, SO-linearrank, and SO-exponentialrank. The performance of the proposed SO variations is evaluated. The experiments show that the selection operators have a great influence on the performance of the SO algorithm. Finally, a parameter analysis of the SO variations is investigated. Copyright © 2022 by SCITEPRESS - science and technology Publications, Lda.

关键词： Evolutionary algorithms

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