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Proceedings of the Winter simulation conference

作者： Maziar Ghorbani Anastasia Anagnostou Nura T. Abubakar Hridyanshu Aatreya Damon Curry Simon J. E. Taylor Modelling & Simulation Group Dept. of Computer Science Brunel University London Uxbridge UK Pitch Technologies US Orlando FL USA

ISBN: (纸本)9798331534202

This paper presents an introductory tutorial based on the simulation Exploration Experience (SEE) 2024, highlighting a collaborative effort by NASA, SISO and international academic partners to model lunar facilities and habitats through the High-Level Architecture (HLA) for distributed simulations. Focused on federating simulations of lunar infrastructure, this paper outlines methodical steps for creating and executing models that incorporate communication systems and 3D visualizations to support educational and research initiatives in space exploration. Reflecting on SEE 2024's advancements, the tutorial emphasizes significant progress in using simulation technology to promote innovation and collaboration across various scientific disciplines. This contribution, intended for discussion at the Winter simulation conference (WSC) 2024, showcases the role of HLA runtime infrastructure (RTI) in enabling realistic and interoperable simulation environments, enriching the discourse on simulation education.

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research on Planting Optimisation Strategies Based on 3D Dynamic Matrix Linear Optimisation Algorithm and Monte Carlo simulation

Research on Planting Optimisation Strategies Based on 3D Dyn...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Xiyuan Wu Shuling Xie Tiantian Sun Information Engineering University Zhengzhou China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The aim of this paper is to investigate the application of three-dimensional dynamic matrix linear optimisation algorithms and Monte Carlo simulation in planting optimisation strategies. In this paper, a series of computer algorithm-based models and strategies are constructed as an example of a crop planting programme optimization problem from 2024 to 2030 in a village. Firstly, a three-dimensional dynamic matrix linear optimization algorithm is used to transform the crop planting problem into a profit-maximising optimization problem. By constructing a multidimensional matrix under the constraints and combining the CBC solver with the Big-M algorithm, the optimal planting plan that meets the requirement of maximum profit is solved. Next, Monte Carlo simulation is used to deal with the uncertainties in crop cultivation by forecasting the expected sales volume, cultivation cost, sales price, etc., and combining with the revised constraints to optimise the cultivation strategy to reduce the potential risks. The whole process is efficiently solved and optimised by computer algorithms on the data and models, and 3D spatial distribution maps and 2D heat maps are shown to visualise the optimal crop planting scenarios for each year. This study provides scientific decision support for optimal allocation of resources in complex systems through advanced algorithmic models, demonstrating the potential of computer technology to be widely applied in agricultural planning and sustainable development.

关键词： Monte Carlo methods Three-dimensional displays Uncertainty Heuristic algorithms Computational modeling Crops Planning Resource management Sustainable development Optimization

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Machine Learning Based on Antennas modeling for 5G and 6G Communication Systems: A Systematic Review

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international Journal of Communication Systems 2025年第9期38卷

作者： Muttair, Karrar Shakir Shareef, Oras Ahmed Taher, Hazeem Baqir Department of Computer Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq Nanotechnology and Advanced Materials Research Unit Faculty of Engineering University of Kufa Najaf Iraq Department of Medical Devices Technical Engineering Al-Ayen Iraqi University AUIQ Nasiriyah Iraq Department of Computer Science College of Education for Pure Sciences Thi-Qar University Nasiriyah Iraq

Artificial intelligence (AI)-aided communications have gained significant traction in recent years due to the widespread application of machine learning (ML) and deep learning (DL) machines with algorithms to solve math problems in wireless communications. This study offers an overview of the use of ML models in antenna design and optimization. This incorporates DL on ML frameworks, categories, and structure to get practical and broad insights using ML techniques for high throughput, quick data analysis, and prediction. This article also comprehensively reviews recent research papers on antenna design via ML. This includes an analysis of several ML algorithms that have been applied to produce antenna parameters such as the reflection coefficient (S-parameters), efficiency and gain values, and radiation patterns of the antennas. However, the current antenna design's structure, variables, and external factors remain complex. In addition, the expense of time and processing resources is inescapable and unacceptable to most designers. ML-based antennas have been created to increase antenna modeling efficiency and accuracy to solve these challenges. Techniques for modeling data may be used to predict the performance of an antenna for a certain set of antenna factors of design. As a result, this study highlights the most sophisticated applied ML techniques that have been presented to increase antenna modeling efficiency and accuracy. The results demonstrate that AI, ML, and DL may minimize simulation needs, predict antenna behavior, and reduce time with high accuracy. © 2025 John Wiley & Sons Ltd.

关键词： Deep learning

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031829598

The proceedings contain 104 papers. The special focus in this conference is on computer Aided Systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems;Using AutomationML for advanced simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on computer Aided Systems Theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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Accelerating Cloud Database Recovery for Providing Real-Time Service After a Cyberattack 20th

Accelerating Cloud Database Recovery for Providing Real-Time...

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on modeling, simulation and Visualization Methods, MSV 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Panda, Brajendra Buchanan, Noah Electrical Engineering and Computer Science Department University of Arkansas FayettevilleAR72701 United States

ISBN: (纸本)9783031858833

Given the many advantages offered by cloud computing, organizations are increasingly moving their databases to cloud. Some of these databases contain critical infrastructure data requiring real-time service. Unfortunately, data in critical infrastructure systems has become one of the major targets of attackers. Since such data are highly connected and interdependent, the initial damage done by an attacker spreads quickly through the system when valid transactions make any updates based on the value of a damaged object. As a result, it adversely affects the real-time service the system is designed to offer. In this work, we provide a model to perform fast damage assessment and recovery. Our approach allows all unaffected data objects to be available to users and makes the affected system recover swiftly. This work uses a modified log mechanism and a graph-based approach to perform fast damage assessment and recovery. Through simulation, we proved that the model expedites the process significantly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud security

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LoopGAN: A Novel Multi-Step Generative Architecture for Sequential Stock Forecasting

LoopGAN: A Novel Multi-Step Generative Architecture for Sequ...

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international CSI computer conference, CSICC

作者： Deniz NoorMohammadzadehMaleki Masoud Kargar Shahin Sharbaf Movassaghpour Sondos Ardebili Ehsan Samadian Robotics and Soft Technologies Research Center Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Azerbaijan Charkhe Niloofari Higher Education Institue Tabriz Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

This paper introduces “LoopGAN,” an advanced generative model architecture designed for sequential stock forecasting, leveraging the strengths of Conditional GAN (CGAN) and Least Squares GAN (LSGAN) to enhance prediction accuracy in highly volatile financial time series. LoopGAN's unique recursive prediction mechanism allows each output to feed back as input, creating dynamic, extended forecasts across multiple time steps. Extensive evaluation of the Dow J ones Industrial Average (DJIA) dataset, highlights LoopGAN's superior performance, achieving a Mean Absolute Percentage Error (MAP E) of 0.005739. This result surpasses traditional models, with LoopGAN outperforming LSTM and RNN models, which registered MAPE scores of 0.006803 and 0.008023, respectively. These findings underscore LoopGAN's robustness, offering a marked improvement in prediction accuracy and confirming its reliability for complex financial forecasting tasks.

关键词： Accuracy Computational modeling Time series analysis computer architecture Predictive models Robustness Feeds Forecasting Long short term memory

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Deep Learning for Detection and Morphological Analysis of Stomatal Complexes in Microscopic Imaging

Deep Learning for Detection and Morphological Analysis of St...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Vikas Shukla Sanjeev Kumar Shukla Kuldeep Pal Pawan Kumar Axis Institute of Higher Education Kanpur India Pranveer Singh Institute of Technology Kanpur India Krishna Institute of Technology Kanpur India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The accurate detection and classification of stomatal complexes play a critical role in understanding plant physiology, particularly in areas such as gas exchange regulation and water conservation. Leveraging advancements in computer vision and deep learning, this study introduces a robust framework for the automated identification and morphological analysis of stomata cells within microscopic images. A large annotated dataset was utilized to train and evaluate a deep learning model capable of localizing stomatal complexes with high precision. Employing advanced object detection architectures, our model achieved high performance metrics, demonstrating its efficacy in distinguishing stomatal regions from surrounding epidermal cells. The developed system not only facilitates rapid and accurate detection but also contributes to improved morphological analysis, allowing re-searchers to quantitatively assess stomatal features under various conditions. This approach promises significant contributions to plant sciences, providing a scalable tool for cellular analysis that may inform studies on plant adaptation, environmental stress response, and crop improvement. Our findings illustrate the potential of integrating computer vision with biological research, setting a precedent for future work in automated plant cell analysis.

关键词： Deep learning YOLO Measurement computer vision Analytical models Accuracy Microscopy Biological system modeling Computational modeling Water conservation

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Beyond Playlists: AI-Driven Emotion-Based Music Recommendation Systems

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： Hrishikesh Shiralaskar Sameer Mendhe Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education md Research Wardha Maharashtra India Department of Computer Science & Design Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems.

关键词： Deep learning Emotion recognition Sentiment analysis Analytical models Adaptation models Reinforcement learning Brain modeling Real-time systems Physiology Recommender systems

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