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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 IEEE.

关键词： Natural language processing systems

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Automated Configuration of Behavior Models in Digital Twins based on a Knowledge-Graph

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Procedia CIRP 2024年 130卷 683-688页

作者： Daniel Dittler Frederike Bodenstein Gary Hildebrandt Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 70569 Stuttgart Germany Institute of Smart Systems and Services Pforzheim University of Applied Sciences Pforzheim Germany

A modular production plant can be simulated with different behavior models with varying modeling depths, thus enabling a wide variety of applications. However, the plant operator faces the challenge of linking the right models for his application. This contribution describes the need to adapt model configurations and associated challenges. Related approaches from the literature and standards are discussed. Subsequently, a novel modeling approach is presented that combines and extends the information from the analyzed approaches to enable automatic linking of model configurations. The approach is realized with the help of a knowledge-graph and evaluated using a material flow simulation scenario.

关键词： model adaption behavior models knowledge-graph digital twin simulation

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Circular Economy Driven Communities - Sustainable Behavior Driven by Mobile Applications 29

Circular Economy Driven Communities - Sustainable Behavior D...

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29th CIRP Conference on Life Cycle engineering, LCE 2022

作者： Cudok, Anja Lawrenz, Sebastian Rausch, Andreas Vietor, Thomas Technische Universität Braunschweig Institute for Engineering Design Braunschweig Germany Sense4Future GmbH Goslar Germany Technische Universität Clausthal Institute for Software and Systems Engineering Clausthal-Zellerfeld Germany

The Circular Economy (CE) aims at keeping products in circles to reduce their environmental impact. To implement CE, the products themselves need to be sustainable, and they need to be used and handled sustainably, e.g., by reuse, sharing, and circulation. Mobile apps can help users understand the environmental impacts of the use and consumption of products and adjust their behavior accordingly. This paper aims to present an approach to address both sustainable use and circulation of products within the example of e-cargo bikes. The development and evaluation of an app prototype is shown, integrating the concept of eco-profile and ecopoints as an incentive system for sustainable actions. The app is found to be an excellent vehicle to intensify and elongate the use of products. © 2022 Elsevier B.V.. All rights reserved.

关键词： Environmental impact

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A high-fidelity simulation test-bed for fault-tolerant octo-rotor control using reinforcement learning 41

A high-fidelity simulation test-bed for fault-tolerant octo-...

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41st IEEE/AIAA Digital Avionics systems Conference, DASC 2022

作者： Ahmed, Ibrahim Quinones-Grueiro, Marcos Biswas, Gautam Vanderbilt University Electrical Engineering NashvilleTN United States Vanderbilt University Institute for Software Integrated Systems NashvilleTN United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

A framework for fault-tolerant control of an octo-rotor unmanned aerial vehicle (UAV) is presented. UAVs have seen a sharp rise in use in recreational and industrial communities, and governmental organizations. Octo-rotors provide additional redundancy with motors and have the capacity to lift larger loads lending themselves to many performance-critical applications. Recent work on autonomous control of UAVs has primarily focused on quad-rotors. This work contributes to the body of research by presenting a simulator to test adaptive control under faults for an octo-rotor test-bed using reinforcement learning approaches. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Generalized Likelyhood Ratio based Detection on Cyber-Attacks 37

Generalized Likelyhood Ratio based Detection on Cyber-Attack...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Wu, Hao Zhang, Deyu Dept. of Software Henan University of Engineering Zhengzhou China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665465366

In this paper, a generalized likelihood ratio based detection algorithm with a statistical method for threshold setting is proposed to detect cyber-attacks on a unified remote control architecture. Based on an encoder and a decoder embedded on plant side and remote controller side, respectively, not only additive interrupted signal (only effecting output or input signal), but also multiplicative attack signal could be detected easily. Finally, three numerical examples are provided to validate our proposed scheme. © 2022 IEEE.

关键词： Remote control

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Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Stefan Rilling Jens Henningsen Katharina Milde Daniel Martini Lorenz Wickert Patricia Kelbert Adaptive Reflective Teams Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Augustin Germany Data Science Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany Team Digital Technologies Advisory Board for Technology and Construction in Agriculture KTBL Darmstadt Germany

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

This article looks at the sustainability of various artificial intelligence (AI) methods. In recent months, AI methods have received a great deal of public attention and the benefits for sustainability have been emphasised. However, the ecological impacts of AI methods must also be considered in this debate. This article presents an overview of various approaches to sustainability assessment and addresses issues relating to energy consumption and the associated Carbon dioxide (CO2) emissions, and a Life Cycle Assessment (LCA) calculation for a concrete agricultural AI-use case.

关键词： Life cycle assessment Energy consumption Conferences Forestry Carbon dioxide Metrology Agriculture Artificial intelligence Sustainable development Carbon emissions

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Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

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2024 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2024

作者： Rilling, Stefan Henningsen, Jens Milde, Katharina Martini, Daniel Wickert, Lorenz Kelbert, Patricia Fraunhofer Institute for Intelligent Analysis and Information Systems Iais Adaptive Reflective Teams Sankt Augustin Germany Fraunhofer Institute for Experimental Software Engineering Iese Data Science Kaiserslautern Germany Advisory Board for Technology and Construction in Agriculture Ktbl Team Digital Technologies Darmstadt Germany

ISBN: (纸本)9798350355444

关键词： Crops

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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A knowledge-driven framework for synthesizing designs from modular components 34

A knowledge-driven framework for synthesizing designs from m...

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34th CIRP Design Conference, CIRP 2024

作者： Chaumet, Constantin Rehof, Jakob Schuster, Thomas Software Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Joseph-von-Fraunhofer-Straße 25 Dortmund44227 Germany

Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third step entails many repetitive and menial tasks, such as inserting parts and creating joints between them. Especially when comparing and implementing design alternatives, this issue is compounded. We propose a use-case agnostic knowledge-driven framework to automate the implementation step. In particular, the framework catalogues the acquired knowledge and the design concept, as well as utilizes Combinatory Logic Synthesis to synthesize concrete design alternatives. This minimizes the effort required to create designs, allowing the design space to be thoroughly explored. We implemented the framework as a plugin for the CAD software Autodesk Fusion 360. We conducted a case study in which robotic arms were synthesized from a set of 28 modular components. Based on the case study, the applicability of the framework is analyzed and discussed. © 2024 Elsevier B.V.. All rights reserved.

关键词： Computer circuits

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