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Effects of Lighting and Barricades in Crowd Management

Effects of Lighting and Barricades in Crowd Management

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Intelligent systems Conference

作者： Fast, Elisha Park, Andrew J. Ficocelli, Ryan Patterson, Lee Dodich, Frank Spicer, Valerie Trinity Western Univ Langley BC V2Y 1Y1 Canada Thompson Rivers Univ Kamloops BC V2C 0C8 Canada Justice Inst British Columbia New Westminster BC V3L 5T4 Canada Simon Fraser Univ Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9783031664274;9783031664281

A large crowd gathers for various purposes: to celebrate holidays, cheer on their sports teams, or protest against their government. For mass gathering events, law enforcement agencies must prepare pre-, during-, and post-event planning, risk assessment, rapid emergency response, and crowd management strategies. Without adequate preparations, disasters can happen, resulting in injuries and deaths due to crowd surges and/or stampedes. This paper focuses on crowd management strategies, describing the systematic studies of the effects of lighting on a landmark, barricades, and guarding law enforcement officers in crowd management. Based on the Honda Celebration of Light event, realistic crowd simulations were conducted to measure the crowd dispersal rates in various configurations with/without lighting on the landmark, barricades of different sizes, and whether barricades are guarded or unguarded. Experimental results show a faster dispersal rate with porous unguarded barricades and without lights. However, use of the guarded barricades is recommended for safety reasons. This study can be applied to better manage a crowd by setting up proper barricades and controlling lights on landmarks.

关键词： Crowd management Guarded/unguarded barricades Lights on landmarks Light attraction Agent-based modelling and simulation Social force model

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Modelling the Drift of Social Media Posts 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Czyz, Henryka Schumann, Andrew Gawel, Arkadiusz Univ Informat Technol & Management Rzeszow Dept Cognit Sci & Math Modelling Sucharskieho 2 PL-35225 Rzeszow Poland Univ Informat Technol & Management Rzeszow Rzeszow Poland

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

We have developed a theory that allows us to accurately characterize information waves in social networks such as Twitter. This theory is an extrapolation of a mathematical theory that studies particle drifts in the acoustic field. We have noticed that each post on Twitter can be considered as a particle, and its radius is the number of followers of the author of the post. Then the number of likes and retweets of this entry can be considered as a drift of this particle. We have found that our hypothesis is correct, and in this interpretation, Twitter posts behave like particles in the acoustic field. In particular, if the author of the entry has no more than 15,000 followers, then this behavior is characterized as a radiative drift in the acoustic field, and if more than 15,000, then as a viscous drift. Based on this behavior of particles (posts on Twitter), we can mathematically reconstruct an appropriate information wave.

关键词： Twitter radiation drift viscous drift particles information wave

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Exploring Transparency in Decisions of Artificial Neural networks for Regression 12th

Exploring Transparency in Decisions of Artificial Neural Net...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Ribeiro, Jose Santos, Ricardo Analide, Cesar Silva, Fabio Politecn Porto CIICESI ESTG Felgueiras Portugal Univ Minho ALGORITMI Res Ctr LASI Braga Portugal

ISBN: (纸本)9783031602207;9783031602214

The interpretation and explanation of machine learning models are important tasks to ensure transparency and credibility. Various methods have been developed to achieve this goal, including Shap, Lime, and Explanatory Dashboards. The application of these explainability methods aims not only at interpreting the machine learning model but also at incorporating transparency into the implementation of a system that uses artificial intelligence algorithms. By leveraging part of a an initial manufactoring project, we implemented techniques to enhance the understanding of the model's time prediction through explainability methods. This improves trust among the involved entities and contributes to the overall effectiveness of the system in various application contexts. Our objective is to apply those strategies to other projects with in industry 4.0 and manufactoring to increase the adoption of new and innovative understandable intelligent systems.

关键词： Machine Learning Shap Lime Explainer Dashboard

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The Development an IoT-Based Virtual Model of Power Grid System with Renewable Energy Sources for a Laboratory Practicum of Control systems 21st

The Development an IoT-Based Virtual Model of Power Grid Sys...

引用

21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Stupak, Maryna Telychko, Hanna Stupak, Hlib Potsepaiev, Valerii Donetsk Natl Tech Univ 2 Shybankova Sq UA-85300 Donetsk Ukraine

ISBN: (纸本)9783031618901;9783031618918

Today, Ukraine faces challenges that significantly impact the educational process in higher education institutions. Initially, the COVID-19 pandemic and later the war have hindered students' ability to develop hard skills effectively. Approximately 50% of students in technical universities in Ukraine lack access to laboratories for practical training. To compensate for educational losses and enhance the quality of practical training, it is essential to focus efforts on virtualizing laboratories and ensuring remote continuous access to them. Modern technologies and cloud services enable the creation of digital twins of technological processes that can replace physical equipment, conduct simulation modeling of various systems, create interactive systems, and allow students to research and test solutions they have developed in a safe environment. This realization method ensures uninterrupted access to educational content for learners, regardless of their actual location and at a convenient time in secure conditions. The result of this work is a prototype IoT system created using databases, Node-RED, and protocols such as MQTT, HTTP/HTTPS, among others. The developed IoT system familiarizes education seekers with examples of building geographically distributed computer-integrated networks for automation and monitoring of control object statuses using Industry 4.0 and IoT. The virtual model of the IoT system represents a power grid consisting of consumers, power plants, and renewable energy sources. All model components interact in a unified information space, allowing students to gain practical experience in virtual environments with networking technologies, programming, data analysis, and control algorithm development. This work was supported by the European Commission [grant number ENI/2019/413-664 "EDUTIP"] and Edunet World Association (EWA).

关键词： Industry 4.0 Virtual Laboratory Green Transition Cloud Technologies IoT/IIoT

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The Implementation of Quantum Annealing for Ensemble Pruning 1

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Yulianti, Lenny Putri Trisetyarso, Agung Santoso, Judhi Surendro, Kridanto Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia Bina Nusantara Univ Sch Comp Sci Dept Math Jakarta 11480 Indonesia

ISBN: (数字)9789819733057

ISBN: (纸本)9789819733040;9789819733057

Ensemble pruning stands as a pivotal strategy in simplifying ensemble complexity while maintaining its accuracy. Although optimization-based methods have historically dominated this field due to their comprehensive solutions, issues with local optima and time-intensive processes persist. Consequently, this study proposes quantum annealing as a quantum-based solution to address these limitations. The approach involves four key stages: formulating pruning problems, embedding formulations into the D-Wave solver, annealing, and reading out the optimal solution. The pruning process aims to maximize ensemble accuracy while ensuring the inclusion of at least one trained learner. The evaluation was conducted across five datasets, focusing on ensemble size and accuracy metrics. A comparative analysis was performed against bagging, AdaBoost, hybrid QA, hybrid QA pruned using particle swarm optimization, and proposed method. The results demonstrate that while the proposed method may not significantly reduce ensemble size compared to particle swarm optimization, it exhibits exceptional performance in all datasets, highlighting its potential to enhance accuracy.

关键词： Quantum annealing Pruning Ensemble Accuracy

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An Internet of Medical Things for Covid-19 Health Indicators Monitoring 9th

An Internet of Medical Things for Covid-19 Health Indicators...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Khoa Tran Thi-Minh Dung Nguyen Quang Han Nguyen Ngoc Phuc Dang Thi Ind Univ Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (纸本)9789819735587;9789819735594

Although the Covid-19 pandemic has passed, it does not mean that it has disappeared forever. It still exists and is still happening somewhere. It is necessary to have a system that can monitor the health indicators of Covid-19 patients during the period of social distance, as well as patients who cannot visit the hospital regularly. In this paper, we propose to develop an Internet of Medical Things (IoMT) system that can real-time monitor the health indicators of patients such as heartbeat, body temperature, and oxygen in the blood. Patient information is stored regularly in a cloud database. The system includes a website that doctors can access to check the information of their patients and then make an online recommendation if necessary. Additionally, the system schedules to monitor and immediately warn the patient if any indicator is exceeding the threshold.

关键词： Internet of Medical Things technology Cloud platform Communication protocols Smart healthcare web application Database management system IoT wearable device

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Creating Feature Model for YAML Generator in CI/CD Pipelines with React Web Application 9th

Creating Feature Model for YAML Generator in CI/CD Pipelines...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Cak, Filip Dakic, Pavle Slovak Univ Technol Bratislava Fac Informat & Informat Technol Inst Informat Informat Syst & Software Engn Bratislava Slovakia Singidunum Univ Fac Informat & Comp Belgrade Serbia

ISBN: (纸本)9789819733040;9789819733057

Continuous integration and continuous deployment (CI/CD) pipelines must be used effectively to deliver reliable software in the business environment. Configuring pipelines for React web applications can be a time-consuming and complex operation. The pipeline technique alleviates the problems encountered during the development and delivery of the final software product while also improving the delivery time. It also standardizes the techniques utilized in CI/CD. Pipelines are used for automated application deployment, including testing, building, and deploying. This study attempts to recognize the gaps since there is no widely available solution to create YAML files with a structure that is often utilized in CI/CD workflows. This research paper proposes a tool that uses Python as the back-end for YAML file generation, with JavaScript and the React framework on the front end to offer a good user experience. Using the concept of a feature model, the tool proposal attempts to simplify and standardize the process of configuring CI/CD pipelines in React projects. Essential considerations within the feature model include defining the features and addressing their dependencies within the model.

关键词： Feature model YAML code Pipelines scripts CI/CD React Testing

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Risk Prediction of Diabetic Disease Using Machine Learning Techniques 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Tamanna Kumari, Ritika Bansal, Poonam Dev, Amita Indira Gandhi Delhi Tech Univ Women Delhi India Guru Gobind Singh Indraprastha Univ USICT New Delhi India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

The type of nutrition we are receiving today, collectively with our inconsistent dietary habits and schedules, are important contributors to the rising prevalence of diabetes. The main causes of diabetes are obesity, high blood sugar, and other parameters. With a focus on the Pima Indian Diabetes dataset, this research study gives a thorough investigation of predictive modeling for diabetes using machine learning approaches. We thoroughly assess different classification algorithms, such as logistic regression, K-nearest neighbors (KNN), random forest, decision trees, Naive Bayes, and support vector machine (SVM), for effectiveness in early diabetes detection with data. Best practices for feature selection, data preprocessing, and model evaluation guide our methodical approach. Results show that machine learning has the potential to improve healthcare decision-making by giving physicians trustworthy tools for identifying people at risk for diabetes. This research advances the utilization of machine learning in healthcare.

关键词： Diabetes prediction Machine learning Decision tree SVM Logistic regression

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A Data Management Concept for Learning Factories to Support Scenario-Based Learning of Advanced Manufacturing Data Analytics for SMEs 14th

A Data Management Concept for Learning Factories to Support ...

引用

14th International Conference on Learning Factories (CLF)

作者： Grano, Alice Prince, Gabor Erol, Selim Horbe, Roman Univ Appl Sci Wiener Neustadt A-2700 Wiener Neustadt Austria

ISBN: (纸本)9783031653995;9783031654008

Increasing digitalization has led to vast amounts of data being generated in small- and medium-sized manufacturing companies. Despite the potential for gaining deeper insights in operational performance and improving efficiency, many small- and medium-sized enterprises (SMEs) struggle to capitalize on data-driven insights, largely due to poor data analytics (DA) skills and scarcely documented use-cases. Despite initial efforts, i.a. in a learning factory (LF) context, the transition to advanced analytics, particularly machine learning techniques, is often hampered by a lack of hands-on learning offerings. Exploring the role that LFs can play in bridging this gap, this study illustrates potential benefits of merging process and machine data from LFs with different production settings. After selecting SME-specific use-cases through literature research and interviews with SMEs in the region, data management (DM) requirements to support teaching activities of advanced DA for SMEs in a scenario-based learning setting were assessed and relevant data from the LFs were identified. On this basis, a DM concept for our LFs to support teaching with data from different production systems was developed. We could show that our DM approach covers a wide range of SME-relevant DA tasks. Furthermore, we highlighted the contribution of collecting data from more production settings, considering also the prototyping phase of the product lifecycle and different manufacturing areas.

关键词： data management advanced data analytics cross-factory data SMEs scenario-based learning

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Spatial Co-location Pattern Mining-A Survey of Recent Trends 1

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4th Congress on Intelligent systems (CIS)

作者： Meshram, Swati Wagh, Kishor P. Govt Coll Engn Dept Comp Sci & Engn Amravati India Govt Coll Engn Dept Informat Technol Amravati India

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

Spatial co-location pattern mining conception was introduced to examine the existence of objects often found together as patterns of interest. Detecting patterns from spatial data is a task of importance as co-location pattern mining has various applications such as in criminology, ecology, epidemiology, recommendation systems, understanding urban functions and so on. However, due to the absence of notion of transactions and the complexity in spatial data have led to discovery of useful spatial patterns with techniques other than association rules. For spatial co-location pattern mining study, which is not well explored, we provide in this paper a general framework, highlight distinct types of co-location patterns, and present an overview of the state-of-the-art methods in the field of co-location pattern mining. Finally, we conclude our survey by discussing the unresolved challenges in this field.

关键词： Neighbourhood Prevalent patterns Cliques Measure of interest

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