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A Blockchain Based Vehicular Ad-hoc System 11

A Blockchain Based Vehicular Ad-hoc System

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： thomas, Samuel Sabu Anjana, S. Parameswaran, Viswesh Sidharth, J.A. Paul, Arya Vyshnav, J.C. Jayesh, P.T. Zenyze Labs Ernakulam India Growing Stars Infotech Ernakulam India Riafy Technologies Ernakulam India Vector India Pvt Ltd Bangalore India Adi Shankara Institute of Engineering and Technology Electronics and Communication Engineering Ernakulam India Trivandrum India Jawaharlal College of Engineering and Technology Electronics and Communication Engineering Palakkad India

ISBN: (纸本)9798350379136

As connected vehicles become more prevalent, vehicular ad-hoc networks (VANETs) are being used to support vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications for advanced driver-assistance systems (ADAS) and other intelligent services. However, secure communication is crucial in VANETs to prevent traffic accidents and ensure the safety of drivers and passengers. Many studies have been conducted on V2I authentication protocols, but most do not consider handover situations and can cause unnecessary computations, which can overload vehicles with limited computing resources. To address these issues, researchers have proposed a blockchain-based handover authentication protocol for VANETs. this protocol leverages the strength of blockchain technology to provide decentralization, data integrity, and transparency. It also ensures efficiency by only requiring lightweight computations during handover situations. the proposed protocol was subjected to formal analysis using Burrows-Abadi-Needham (BAN) logic, Real-Or-Random (ROR) oracle model, and automated Validation of Internet Security Protocols and Applications (AVISPA) simulation. the researchers also conducted simulations using Network Simulator 3 (NS-3) to verify the practicality of the proposed protocol. Computational cost and security features of the proposed protocol were compared with existing protocols, demonstrating that the proposed protocol is more secure and efficient. Overall, the blockchain-based handover authentication protocol has the potential to improve the security and efficiency of VANETs, enabling safe and reliable communication between vehicles and infrastructure. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Semantic Segmentation of Building Models with Deep learning in CityGML 19

Semantic Segmentation of Building Models with Deep Learning ...

引用

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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Privacy-Preserving Energy Recommendations Using Federated learning and Local LLMs on the Edge 11

Privacy-Preserving Energy Recommendations Using Federated Le...

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11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Chronis, Christos Varlamis, Iraklis Dimitrakopoulos, George Bensaali, Faycal Papadopoulos, Georgios th. Harokopio University of Athens Dept. of Informatics and Telematics Athens Greece Qatar University Dept. of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350367300

Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. this study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. the approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. the key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. the models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. this approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns. © 2024 IEEE.

关键词： Federated learning

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data fusion and machine learning for bridge damage detection 11th

Data fusion and machine learning for bridge damage detection

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Wang, Hao Barone, Giorgio Smith, Alister School of Architecture Building and Civil Engineering Loughborough University United Kingdom

ISBN: (纸本)9781032345314

this study presents a new approach for bridge damage detection using multi-level data fusion and anomaly detection techniques. the approach utilises as input data accelerations, deflections and bending moments, measured at multiple sensor locations on a bridge subjected to a moving vehicle. A damage sensitive feature is constructed, coupling principal component analysis and mahalanobis distance, allowing for initial data dimensionality reduction and information integration. Anomaly detection using a convolutional autoencoder is performed to identify the presence of damage on the bridge. the proposed approach is independent of the mass and speed of the moving vehicles. the performance of the proposed approach is demonstrated using synthetic data generated from surrogate numerical models, showing applications for a variety of damage scenarios. the accuracy of damage identification via anomaly detection is shown to be consistently greater than 99%. © 2022 the Author(s).

关键词： Anomaly detection

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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...

引用

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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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 ...

引用

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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Machine learning methods for condition assessment of transportation infrastructure 11

Machine learning methods for condition assessment of transpo...

引用

11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Almheiri, Zainab Meguid, Mohamed Department of Civil Engineering McGill University MontréalQC Canada Peter Guo-Hua Fu School of Architecture Faculty of Engineering McGill University MontréalQC Canada

the process of damage identification methods for civil structures and infrastructures is defined as structural health monitoring (SHM). SHM examines the health condition of structures/infrastructures using advanced sensor technologies to collect periodically sampled data. these data can then be fed into intelligent algorithms to identify changes to the structural material and geometric properties of structures. this paper reviews the state-of-the-art research on machine learning methods to assess transportation infrastructure performance, focusing on journal articles. Machine learning can leverage valuable information from real data, thus avoiding environmental and economic catastrophes. therefore, it is necessary to define efficient and accurate machine learning techniques for researchers and decision-makers to assess the performance of critical infrastructures. this review proves limited studies relied on machine learning methods for condition assessment of the selected transportation infrastructure. In addition, this review shows that research is still ongoing to provide a robust approach in this field. Challenges and future directions for decision-makers are also discussed in this paper. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： condition assessment energy pipelines Machine learning roadways transportation infrastructure water

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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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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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