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Semantic Segmentation of Building Models with Deep learning in CityGML 19

Semantic Segmentation of Building Models with Deep Learning ...

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19th 3D GeoInfo conference / 31st EG-ICE international Workshop on intelligent Computing in engineering

作者： Rashidan, Hanis Musliman, Ivin Amri Rahman, Alias Abdul Coors, Volker Buyuksalih, Gurcan Univ Teknol Malaysia Fac Built Environm & Surveying 3D GIS Res Lab Johor Baharu Malaysia Stuttgart Univ Appl Sci HFT Stuttgart Ctr Geodesy & Geoinformat Stuttgart Germany Istanbul Univ Inst Marine Sci & Management Istanbul Turkiye

Semantic segmentation of 3D urban environments plays an important role in urban planning, management, and analysis. this paper presents an exploration of leveraging BuildingGNN, a deep learning framework for semantic segmentation of 3D building models, and the subsequent conversion of semantic labels into CityGML, the standardized format for 3D city models. the study begins with a methodology outlining the acquisition of a labelled dataset from BuildingNet and the necessary preprocessing steps for compatibility with BuildingGNN's architecture. the training process involves deep learning techniques tailored for 3D building structures, yielding insights into model performance metrics such as Intersection over Union (IoU) for several architectural components. Evaluation of the trained model highlights its accuracy and reliability, albeit with challenges observed, particularly in segmenting certain classes like doors. Moreover, the conversion of semantic labels into CityGML format is discussed, emphasizing the importance of data quality and meticulous annotation practices. the experiment as described in the methodology shows that outputs from the BuildingGNN for semantic segmentation can be utilized for the generation of CityGML building elements with some percentage of success. this particular work reveals several challenges such as the identification of individual architectural elements based on geometry groups. We believe that the improvement of the segmentation process could be further investigated in our near future work.

关键词： 3D Buildings Models Semantic Segmentation CityGML

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Efficient Parcel Damage Detection via Faster R-CNN: A Deep learning Approach for Logistical Parcels' automated Inspection 20th

Efficient Parcel Damage Detection via Faster R-CNN: A Deep L...

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20th EAI international conference on Mobile and Ubiquitous Systems - Computing Networking and Services (EAI MobiQuitous)

作者： Chen, Zhi Du, Cuifeng Guan, Quanlong Zhou, Yuyu Hoo, Vichen Huang, Xiujie Li, Zhefu Lv, Shuanghuan Wu, Xiaofeng Zhuang, Xiaotian Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Cetc Potevio Sci & Technol Co Ltd Guangzhou Peoples R China Guangzhou Polytech Sports Guangzhou Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Guangzhou Peoples R China Beijing JD Zhenshi Informat Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9783031639913;9783031639920

Parcel damage detection poses a significant challenge in warehouse automation and conveyor belt transportation, with a particular focus on leveraging the potential of Internet of things (IoT) technologies. With this purpose in mind, we introduce RPA R-CNN, an improved deep learning-based object detection method that seamlessly integrates IoT capabilities. Our proposed approach enhances the feature extraction network of the Faster R-CNN model and utilizes advanced post-processing techniques to further refine the extracted features. By employing a meticulously curated dataset specifically designed for parcel detection, we train our model and conduct rigorous experimental verification. Encouragingly, the outcomes convincingly demonstrate that the RPA R-CNN method surpasses the performance of the original model in various metrics in domestic or cross-border logistics parcel detection. these promising results not only offer a practical solution to the challenging problem of identifying parcel damage but also underscore the immense potential of integrating IoT principles to optimize automation and enhance operational efficiency within the domestic or cross-border logistics industry.

关键词： Faster R-CNN Parcel Damage Detection IoT

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Event-Triggered Adaptive Neural Network Asymptotic Control for Non-Triangular Stochastic Nonlinear Systems 11

Event-Triggered Adaptive Neural Network Asymptotic Control f...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Liu, Yongchao Zeng, Bowen Institute of Complexity Science School of Automation Qingdao University Qingdao China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper designs an event-triggered adaptive neural network asymptotic control (ANNAC) method for non-triangular stochastic nonlinear systems (SNS). In contrast to published neural network schemes where the tracking errors converge bounded regions, a bound estimation scheme is given for every subsystem based on backstepping technique. the presented event-triggered ANNAC method can make the considering closed-loop systems to be asymptotically stable. © 2022 IEEE.

关键词： Backstepping

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An Intrusion Detection System for Smart Autonomous E-Bikes 11

An Intrusion Detection System for Smart Autonomous E-Bikes

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Maged, Roba Sabry, Mohamed Ali, Ahmed Mesabah, Islam Mazhr, Ahmed Soubra, Hassan Cairo Egypt Madrid Spain Ecole Centrale d'Élctronique Computer Engineering ECE Lyon France

ISBN: (纸本)9798350322101

As the world continues to face environmental challenges, cost-effective ecological mobility solutions are sought. In this context, E-bikes have been gaining popularity. In this context, a Smart Autonomous E-Bike prototype has been implemented at our institution. this Bike can be seen as an IoT (Internet of things) network of synchronized embedded devices and sensors/actuators that communicate and exchange data. With multiple connected modules that must function safely, securing a Smart Bike becomes crucial to avoid breaches that can cause severe consequences, such as accidents or disclosure of sensitive information. Hence, this paper presents an effective Smart Autonomous Bike Intrusion Detection System (IDS) specifically designed for smart autonomous bikes to address the aforementioned concerns. this IDS is capable of detecting and mitigating various types of cyber threats targeting different modules within the smart bike architecture, including path planning, platooning and Advanced Rider Assistance Systems (ARAS) mechanisms. the proposed system is capable of detecting both network attacks and physical tampering on the hardware components of the bike. An open-source Network-Based IDS (Snort) is first used to detect network intrusions, and an anomaly detection system is used for physical attacks. As an enhancement, the proposed IDS employs an additional hybrid Machine learning (ML) techniques for anomaly and signature classification to analyse incoming external network traffic to detect intrusion attempts, anomalous activities, and potential security breaches. the enhanced module of the proposed IDS is trained using a comprehensive dataset (Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS)-2017) and assessed using different evaluation parameters. © 2023 IEEE.

关键词： Internet of things

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A Medical Chatbot for Diabetic Patients Using Artificial Intelligence 11

A Medical Chatbot for Diabetic Patients Using Artificial Int...

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11th international conference on Bio Signals, Images, and Instrumentation, ICBSII 2025

作者： Mangai, S. Supriya, A. Kavitha, V. Viveka, S. Maheswari, S. Govindaraj, S. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India SRM Institute of Science and Technology Department of Data Science and Business systems Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331513863

the quick rise in diabetes cases globally underscores the essential need for technology-driven, innovative management solutions. the project gives the introduction about the cutting-edge in AI-based chatbot tailored for diabetic patients. the combination of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), results the chatbot to provide highly accurate, personalized recommendations to improve self-management. the added advantage is the inclusion of voice-based interaction which offers enhanced accessibility, especially for the elders and visually impaired persons. this study demonstrates about the ability of the chatbot to interpret the complex health data, delivers the real-time insights, and promote adherence to the treatment plans. the advancement in the fusion of machine learning algorithms and voice interaction secures a dynamic and user-friendly experience for diabetes management. © 2025 IEEE.

关键词： Artificial Intelligence Chatbot Diabetes Management KNN Machine learning Personalized Healthcare SVM Voice Interaction

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Ensemble learning for Modeling Luminance in Plasmonic Nanoellipse for Tunable Spaser 11

Ensemble Learning for Modeling Luminance in Plasmonic Nanoel...

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11th IEEE international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Jamil, Saqib Jamil, Saima Farooq, Waqas Ardabili, Sina Khan, Adnan Daud Khalil, Usman Khan Mosavi, Amir Sarhad University of Science and Information Technology Department of Electrical Engineering Peshawar Pakistan Virtual University of Pakistan Pakistan University of Mohaghegh Ardabili Department of Biosystem Engineering Ardabil Iran US-Pak Center for Advanced Studies in Energy Uet Peshawar Peshawar Pakistan Cecos University Department of Electrical Engineering Peshawar Pakistan Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350350142

in a recent study we provided a theoretical framework for plasmonic coupled elliptical nanostructure capable of producing high-intensity spasers or nanolasers. the nanostructure which consists of a gold film substrate, underwent testing for various light polarizations where we selected a simple elliptical shape and then modified it to explore different geometries. the structure exhibited strong coupled resonance modes, particularly localized surface plasmon (LSP). through finite-element method (FEM) simulations, we investigated the localized surface plasmon resonance (LSPR) of the system. Results revealed that introducing symmetry-breaking techniques, such as creating a symmetry-broken nano-ellipse (SBE), significantly increased both LSPR peaks and local field intensity (LFI). By selecting appropriate gain media, the nanostructure could support various plasmonic resonance modes across different wavelengths. Moreover, it demonstrated potential for developing a compact nanolaser with a broad tunability range. Adjusting light polarization and making structural modifications led to the observation of Surface Plasmon Amplification by Stimulated Emission of Radiation (SPASER) phenomena. the proposed model, with its substantial LSPR values, shows promise for applications like surface-enhanced Raman spectroscopy and sensing. the application of the data-driven methods in modeling the above phenomena has not been explored yet. Due to the complex nature of the phenomena, it is expected that the data-driven models can provide promising results. Consequently, in this article the primary results of proposing ensemble machine learning in modeling the above phenomenon are presented. In this work random forest (RF) and support vector machine (SVM) were employed for modeling and analysis of outcomes. © 2024 IEEE.

关键词： Surface plasmon resonance

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A Machine learning Approach for Network Slice Selection 59

A Machine Learning Approach for Network Slice Selection

引用

59th international Scientific conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Georgieva, Polya Vlahov, Atanas Poulkov, Vladimir Manolova, Agata Tech Univ Sofia Fac Telecommun Studentski KompleksUl Prof Georgi Bradistilov 11 Sofia 1756 Bulgaria ICI Lab R&D&I Consortium Blvd Tsarigradsko Shose 111 Sofia 1784 Bulgaria

ISBN: (纸本)9798350386202;9798350386196

the surge in diverse and demanding applications in 5G networks requires advanced management techniques, such as network slicing, to ensure optimal service quality across varied requirements. this paper introduces a machine learning-based approach for optimizing network slice selection in 5G networks, focusing on the AdaBoostClassifier due to its robust predictive capabilities. Key performance indicators critical for slice differentiation such as packet loss rate and technology support are processed through extensive data preprocessing and feature engineering to improve the model's accuracy and efficiency. Furthermore, a data balancing method was implemented to mitigate the model's bias towards the dominant class. this approach significantly enhanced the model's overall performance, resulting in more accurate and reliable outcomes

关键词： Network Slice Selection Machine learning 5G Networks Network Management Predictive Analytics

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ON ACCELERATED PERCEPTRONS AND BEYOND 11

ON ACCELERATED PERCEPTRONS AND BEYOND

引用

11th international conference on learning Representations, ICLR 2023

作者： Wang, Guanghui Hanashiro, Rafael Guha, Etash Abernethy, Jacob College of Computing Georgia Tech AtlantaGA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Google Research AtlantaGA30309 United States

the classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify n linearly separable data points, assuming the classes are separated by some margin γ > 0. A foundational result is that Perceptron converges after O(1/γ2) iterations. there have been several recent works that managed to improve this rate by a quadratic factor, to O(√log n/γ), with more sophisticated algorithms. In this paper, we unify these existing results under one framework by showing that they can all be described through the lens of solving min-max problems using modern acceleration techniques, mainly through optimistic online learning. We then show that the proposed framework also leads to improved results for a series of problems beyond the standard Perceptron setting. Specifically, a) for the margin maximization problem, we improve the state-of-the-art result from O(log t/t2) to O(1/t2), where t is the number of iterations;b) we provide the first result on identifying the implicit bias property of the classical Nesterov's accelerated gradient descent (NAG) algorithm, and show NAG can maximize the margin with an O(1/t2) rate;c) for the classical p-norm Perceptron problem, we provide an algorithm with (equation presented) convergence rate, while existing algorithms suffer the (equation presented) convergence rate. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Gradient methods

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Hybrid Deep learning Model for Arch of Aorta Classification Based on Slime Mould Algorithm 11

Hybrid Deep Learning Model for Arch of Aorta Classification ...

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11th international conference on Bioinformatics and Computational Biology (ICBCB)

作者： Cheng, Shi-Hang Cao, Jun-Zhe Dalian Univ Technol Sch Control Sci & Engn Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian Peoples R China

ISBN: (纸本)9798350397871

As an important anatomical structure, the mediastinum can develop a variety of lesions. Endoscopic ultrasonography-guided fine needle aspiration is a novel auxiliary diagnostic method for these diseases at present. Since the images presented by endoscopic ultrasonography are not direct views, even experienced doctors may have certain difficulties in identifying the exact location of lesions in clinical diagnosis. therefore, deep learning can be used to classify endoscopic images to assist doctors in the positioning of endoscopic ultrasound (EUS). In this paper, a hybrid model combining the pre-trained deep learning networkResNet and the metaheuristic algorithm called Slime Mould Algorithm (SMA) is proposed to identify the structure of the aortic arch by distinguishing the aortic arch from non-aortic arch to assist doctors in positioning rapidly. the hybrid model utilizes ResNet50 for a raw feature extraction, adopts SMA for a further feature selection based on the extracted features, and finally uses the Support Vector Machine classifier to classify the filtered features. the hybrid model takes advantage of the excellent feature extraction ability of the deep network and the good search characteristics in the solution space of the SMA. this model achieves an average accuracy of 92.21% for classification on practical data of 14 patients' EUS images. Experimental results show that the proposed model outperforms other deep networks and metaheuristic algorithms. It shows that this model has achieved good results in the aortic arch classification problem.

关键词： Endoscopic Ultrasound (EUS) aortic arch Resnet50 Slime Mould Algorithm (SMA)

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A Smart Blind Stick with Object Detection, Obstacle Avoidance, and IoT Monitoring for Enhanced Navigation and Safety 11

A Smart Blind Stick with Object Detection, Obstacle Avoidanc...

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11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Abdel-Rahman, Adel B. Allam, Ahmed Abdelwahab, Moataz Elsharkawy, Mostafa Kobasy, Bassam Eltabakh, Habib Essam, Omar Egypt-Japan University of Science and Technology Department of Electronics and Communications Engineering New Borg El-Arab City Alexandria21934 Egypt

ISBN: (纸本)9798350369328

Visual impairment affects millions of people globally, presenting challenges in everyday tasks. this research paper introduces a Smart Blind Stick, equipped with deep learning-based object recognition, health, and location monitoring through IoT capabilities and ultrasonic sensors-based obstacle avoidance. the system enhances independence and safety for visually impaired individuals, providing real-time feedback and remote monitoring. Results demonstrate high accuracy and user satisfaction, paving the way for further advancements in assistive technology. Future work includes optimizing data transfer, enhancing sensors, and expanding features for a comprehensive solution. © 2023 IEEE.

关键词： Internet of things

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