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Detection of Partially Occluded Area in Images Using Image Segmentation Technique 1

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4th Congress on Intelligent systems (CIS)

作者： Cherapanamjeri, Jyothsna Rao, B. Narendra Kumar Jawaharlal Technol Univ Anantapur Andhra Pradesh India Sri Vidyanikethan Engn Coll Tirupati India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Computer vision is subfield of artificial intelligence. Detection of occluded area in images is one of the challenging tasks in the computer vision. In this paper, we concentrate on occluded area detection in images using image segmentation techniques. Occlusion is nothing but one object in image is hidden by another object. Detection of occluded area in images using image segmentation techniques. Image segmentation is the process of dividing the images into different regions based on the characteristics of the pixels in the original image and reduces the complexity of analysis. When determining the number of objects in a scene, instance segmentation is an excellent choice. But in semantic segmentation algorithms if all that is required is to group items that belong to the same class. Image segmentation has many applications in a number of fields, including gaming, robotics, autonomous vehicles, agriculture, object detection, and pedestrian detection. In this research work, we concentrate on detection of occluded area in images using Mask R-CNN. The experiment will show better segmentation results corresponding to the manually labeled occluded area.

关键词： Artificial intelligence Machine learning Deep learning Computer vision Image segmentation Occluded images

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Requirements Modeling and Automatic Transformations Towards Autonomous Driving Scenarios Description

Requirements Modeling and Automatic Transformations Towards ...

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Intelligent systems Conference (IntelliSys)

作者： Hu, Yanlin Wang, Tiexin Shi, Yize Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

ISBN: (纸本)9783031477171;9783031477188

Autonomous Driving systems (ADSs) have been developed quickly in recent years, robust and complete system requirements are essential for high-quality ADSs. Requirements modeling is a process of describing and analyzing system requirements using various techniques, which is a critical step for ensuring high-reliability requirements. Restricted Use Case Modeling (RUCM) is a use case modeling method to reduce the ambiguity of requirements written in natural language, which has been extended to diverse applications. By extending RUCM with autonomous driving concepts, RUCM for Autonomous Driving systems (RUCM4ADS) can specify autonomous driving scenarios in the form of use case specifications and diagrams. However, due to the diversity and complexity of autonomous driving scenarios, the constructed requirement models require multiple manifestations to be compared and analyzed. To this end, in this paper, we extend the metamodel of the Unified Modeling Language (UML) class and activity diagrams according to the Wise Drive framework for accurately expressing autonomous driving concepts and behaviors. Further, we realize modeling autonomous driving scenarios with extended UML meta-models, which enrich the manifestation of autonomous driving requirements. Meanwhile, we propose automatic model transformation approaches to promote the automatic analysis of autonomous driving scenario requirements, which generates UML extended models from RUCM4ADS models based on proposed extended metamodels. Finally, two real-world autonomous driving scenarios are used to verify the feasibility and effectiveness of the proposed model transformation approach. Results show that our proposed approach is effective, and can generate high semantic and syntactic accuracy class diagrams and activity diagrams with autonomous driving concepts and behaviors.

关键词： Autonomous driving Metamodel extension Scenario modeling Model transformation

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Development and Validation of a Training Framework Towards the Integration of Smart Micro-grids into Manufacturing: A Case from the Lifts' Industry 14th

Development and Validation of a Training Framework Towards t...

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14th International Conference on Learning Factories (CLF)

作者： Manettas, Christos Mastos, Theofilos Hellbach, Robert Kanellos, Dimitrios Bampoula, Xanthi Papadopoulos, Angelos Alexopoulos, Kosmas Univ Patras Dept Mech Engn & Aeronaut Lab Mfg Syst & Automat LMS Patras 26504 Greece Univ Bremen BIBA Bremer Inst Prod & Logist GmbH Bremen Germany KLEEMANN HELLAS SA Kilkis Ind Area Kilkis 61100 Greece

ISBN: (纸本)9783031653995;9783031654008

The overarching aim for sustainable manufacturing systems requires new energy sources, with minimum or zero CO2-footprint. Under this spectrum, Smart MicroGrid systems have emerged as retrofitting sets of Industry 4.0 technologies combining renewable energy and continuous energy monitoring towards improving energy efficiency, reduce operational costs, and enhance resiliency to external energetic crises. However, their integration has been challenged by the employees' lack of required technological skills and technology acceptance. This work presents a three-phase training framework for Smart MicroGrid integration aiming at increasing the skills and competencies of personnel on managing smart micro-grid systems in manufacturing. The first phase involves preliminary learning activities in the form of online learning paths and hands-on training offered as Teaching and testbed assisted Learning Factory sessions. The second phase addresses the enhanced, daily duties that employees are requested to follow in a Smart MicroGrid policy depending on their organizational position. The third phase refers to the overall evaluation of the learning and technological penetration of the framework. The proposed framework has been validated by a training pilot for Smart MicroGrid adoption by a lifts' manufacturer, presenting high learning penetration, an improved readiness level for technology integration and a more positive attitude towards everyday duty changes.

关键词： Smart MicroGrids Training framework Renewable energy Teaching Factory Learning Factory

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Escape Rooms as a Tool for Sustainable Knowledge Transfer in Learning Factories on the Topic of Lean Management 14th

Escape Rooms as a Tool for Sustainable Knowledge Transfer in...

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14th International Conference on Learning Factories (CLF)

作者： Potthoff, Leonie Naussedat, Rolf Gunnemann, Lisa Univ Appl Sci & Arts Dortmund Sonnenstr 96 D-44139 Dortmund Germany

ISBN: (纸本)9783031653995;9783031654008

Especially in the age of digitalization lean management continues to be of great importance for the manufacturing industry. The consistent realization and implementation as well as the long-term success of lean management require the involvement of all process participants from the management level to the shopfloor. For a solid basic knowledge of lean management methods and concepts, it is important to train students as future specialists extensively as well as to conduct regular trainings for employees in companies. Classical training formats often prove to be ineffective, as the transfer of knowledge is not sustainable. The use of gamification has recently established itself as an effective tool to increase motivation and engagement in various contexts. Gamification refers to the incorporation of game elements and principles into situations and environments not normally associated with games. The aim of this paper is to give an overview of the background on gamification and to present an application example in teaching context. For this purpose, the theoretical background of gamification will be discussed extensively and the concept of Escape Rooms for university teaching will be analyzed. This is followed by the presentation of an application example, called the FHDO Lean Escape Room, developed for the use of gamification for knowledge transfer in the field of lean management at the University of Applied Sciences and Arts Dortmund.

关键词： Gamification Lean Management Escape Rooms

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Unlocking Education Together: A Meta-model Emphasizing Usability and Reuse of Open Educational Resources 12th

Unlocking Education Together: A Meta-model Emphasizing Usabi...

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

作者： Bucchiarone, Antonio Vazquez-Ingelmo, Andrea Garcia-Holgado, Alicia Jose Garcia-Penalvo, Francisco Schiavo, Gianluca Theron, Roberto Fdn Bruno Kessler FBK Trento Italy Univ Salamanca GRIAL Res Grp Salamanca Spain

ISBN: (纸本)9783031602207;9783031602214

Open Educational Resources (OERs) represent a paradigm shift in education, offering diverse, freely accessible materials to democratize knowledge. However, challenges in usability and integration hinder their practical application. This paper addresses these issues within the innovative ENCORE (ENriching Circular use of OeR for Education) project, dedicated to optimizing OER utilization. Focusing on the direct usability challenges faced during ENCORE's development, an extension to the ENCORE meta-model is proposed. This enhancement ensures uniform representation, empowering educators to seamlessly integrate and utilize OERs in personalized learning paths and assessments. Beyond conventional classrooms, the extended meta-model fosters community awareness through collaborative material sharing. This paper serves as a call to action, emphasizing the pivotal role of OERs in reshaping education. Additionally, the paper introduces a research roadmap addressing key research areas such as personalized learning paths, generative AI applications, and community building, among others.

关键词： Education Open Educational Resources Meta-Modeling Research Roadmap

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Using Resizing Layer in U-Net to Improve Memory Efficiency 1

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19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Denes-Fazakas, Lehel Csaholczi, Szabolcs Eigner, Gyorgy Kovacs, Levente Szilagyi, Laszlo Obuda Univ Univ Res & Innovat Ctr Physiol Controls Res Ctr Budapest Hungary Obuda Univ John von Neumann Fac Informat Biomat & Appl Artificial Intelligence Inst Budapest Hungary Obuda Univ Appl Informat & Appl Math Doctoral Sch Budapest Hungary Sapientia Hungarian Sci Univ Transylvania Cluj Napoca Romania

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

The segmentation of medical images is becoming increasingly important in the daily clinical practice, particularly with regard to tumor segmentation. Artificial intelligence, which is becoming more and more widespread, is already able to achieve good results in segmentation, making the task of medical staff easier. The U-net is a widely popular convolutional neural network designed to be involved in image segmentation. However, when training the U-net, we may encounter difficulty not having enough memory due to the large size of the images. The main goal of this paper is to investigate the effects of reducing the resolution of the image data using a resizing layer on the input of the U-net and then convert it back at the output also using a resizing layer. The main question is whether we are able to achieve fine segmentation quality. Also it is investigated, what resolution is needed to achieve acceptable segmentation on the dataset. As a result, we managed to achieve an F1-score of 96.9%. Furthermore, we have demonstrated that it is possible to segment the images well with U-net network at lower resolution. Also, it is shown that neither too large nor too small resolution supports good-quality segmentation.

关键词： Segmentation U-net resizing layer optimal resolution memory

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Pertinent Issues in Artificial Intelligence systems Adoption 1

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

作者： Akobe, David Roodt, Sumarie Mulaji, Sarah Univ Cape Town Dept Informat Syst Cape Town South Africa

ISBN: (数字)9789819735594

ISBN: (纸本)9789819735587;9789819735594

Management support, lack of skills, data privacy, data security and ethics have proven to be persisting issues faced by managers and information systems professionals in adopting artificial intelligence systems. These issues hinder the adoption and successful implementation of artificial intelligence systems in organizations. This is especially crucial in African organizations where more understanding of emerging technology is critical to their adaptation to the context for successful adoption. This paper analyzes the aforementioned issues to enlighten managers about the importance of their role in equipping for successful adoption. It concludes that continuous research on the developing nature of these issues is key in providing managers with the necessary information needed to continue to navigate them in the future and adopt these systems smoothly.

关键词： Artificial intelligence Management support Data privacy Data security Skills Ethical issues

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A Deep Learning Approach to Potato Late Blight Detection: Developing and Evaluating a Lightweight Single-Stage Detection Model 4th

A Deep Learning Approach to Potato Late Blight Detection: De...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Zarrouk, Yassine Bourhaleb, Mohammed Rahmoune, Mohammed Hachami, Khalid Hamdaoui, Hajar Al Kaddouri, Hanae Mohammed First Univ ENSAO LaRSA Oujda Morocco Mohammed First Univ FSO LAPABE Oujda Morocco Mohammed First Univ ENSAO SmartICT Oujda Morocco

ISBN: (纸本)9783031686740;9783031686757

Detecting and identifying crop diseases in their early stages remains a challenge in modern agricultural practices, affecting yield and food security worldwide. Potato late blight, caused by Phytophthora infestans is a serious threat to potato crops requiring fast and accurate detection methods to minimize losses. A new approach using deep learning was suggested in this study to spot this disease at an early stage. The study focused on developing and training a YOLOv8n model with a dataset of 14,150 images and annotations from Berkane, Morocco. This dataset was carefully chosen to cover a diverse range of symptoms and stages of potato blight under different environmental conditions to ensure the model's reliability, robustness, and generalizability. The model is known for its suitable design for real-time decisions in agricultural settings due to its lightweight size. However, this experiment shows that YOLOv8n has a modest detection performance on complex and diverse datasets, making it an interesting opportunity for enhancing research towards early intervention against potato late blight using lightweight models - the potential of deep learning to improve farming methods.

关键词： Deep learning Crop disease detection Computer vision Precision Agriculture

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Measuring the Effectiveness of the Online Education System Through DEA 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Emec, Seyma Erzurum Tech Univ Erzurum Turkiye

ISBN: (数字)9783031671951

ISBN: (纸本)9783031671944;9783031671951

One of the most important needs of our time is for individuals to have basic skills, access to information and the ability to build healthy social relationships. This need can be satisfied by a correct and efficient education system. However, due to some negative situations, a transition to online education may occur to prevent the education system from being disrupted. While some precautions were taken during the coronavirus pandemic that first appeared in Turkey in 2020, after the closure of educational institutions, most of them stopped face-to-face classes and switched to online education at all possible levels. At the same time, in Turkey, the online education system was converted again due to the effects of the February 6, 2023 earthquake, which was described as the disaster of the century. Accordingly, in this study, in order to measure the effectiveness of the online education system, annual education data starting from the 2014-2015 academic year were considered. The data examined were compared with the data of the 2020-2021 and 2022-2023 academic period. The effectiveness of the online education system was examined based on the differences between the data using the Data Envelopment Analysis method. The results show that the efficiency of the education system during the pandemic and the disaster of the century was less effective than during the periods of face-to-face teaching.

关键词： Covid-19 Data Envelopment Analysis Earthquake Online Education

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Reinforcement Learning Applied to PSO for Multidimensional Knapsack Problem 13th

Reinforcement Learning Applied to PSO for Multidimensional K...

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13th Computer Science Online Conference

作者： Olivares, Rodrigo Rios, Victor Olivares, Pablo Serrano, Benjamin Univ Valparaiso Escuela Ingn Informat Valparaiso Chile

ISBN: (纸本)9783031705946;9783031705953

The present work delves into the field of bio-inspired algorithms, focusing on enhancing the Particle Swarm Optimization (PSO) algorithm using QLearning. The main challenge these algorithms face is getting trapped in local optima, leading to inefficient use of computational resources. To mitigate this, we propose a solution that integrates QLearning with entropic diversity as a movement component in PSO. This integration aims to effectively detect and overcome stagnation in local optima. Our study conducts exhaustive tests on 30 instances of binary optimization problems from the OR-Library, specifically the Multidimensional Knapsack Problem. The results demonstrate a marginal but consistent improvement in the performance of the PSO-QL algorithm over standard PSO, particularly in instances with previously unknown solutions and in scenarios of more complex problems. Additionally, suggestions for future research directions are added, including comparing various movement techniques and adapting PSO to different forms of entropy, to further validate and refine the proposed approach.

关键词： Reinforcement Learning QLearning PSO Entropy

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