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A Comparative Study of YOLO Models for Enhanced Vehicle Detection in Complex Aerial Scenarios 19

A Comparative Study of YOLO Models for Enhanced Vehicle Dete...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Azis, Huzain Nasrullah Abdullah, Munaisyah Ismail, Suriana Purnawansyah Syafie, Lukman Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

the use of Unmanned Aerial Vehicles (UAVs) in aerial imaging is expanding rapidly, particularly in traffic monitoring and intelligent transportation systems. Detecting small and occluded vehicles in aerial images poses significant challenges due to varying resolutions and obstructions like buildings or trees. this study seeks to enhance vehicle detection accuracy by improving You Only Look Once (YOLO) models, with a focus on small and occluded object detection. Utilizing the COWC-M dataset and advanced data augmentation techniques such as Mosaic Augmentation, this research evaluates multiple YOLO variants. the YOLOv8-L model achieved the highest mAP50 score of 0.9899, demonstrating superior detection accuracy for small objects. Additionally, the YOLOv10-L model outperformed others with the best mAP50-95 score of 0.8715, indicating strong results across different intersection-over-union (IoU) ranges. Compared to YOLO-RTUAV, which achieved an mAP50 of 0.9353, the newer YOLO models provide significant improvements in both precision and recall. these findings contribute to the development of highly efficient, real-time vehicle detection systems suitable for large-scale surveillance applications in complex environments. © 2025 IEEE.

关键词： Aerial photography

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Leveraging Large Language Models for Smart Pharmacy systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. the system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. this transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. the proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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DynKDD: Dynamic Graph-Based Network Intrusion Detection Using Graph Neural Networks 19

DynKDD: Dynamic Graph-Based Network Intrusion Detection Usin...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Mir, Aabid A. Zuhairi, Megat F. Musa, Shahrulniza Namoun, Addallah Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia

ISBN: (纸本)9798331507817

the inability to capture the temporal dynamics of network interactions limits traditional intrusion detection systems (IDSs) in detecting sophisticated threats that evolve over time. this research introduces DynKDD, a dynamic graph-based version of the NSL-KDD dataset, designed to capture the temporal relationships between network entities and their interactions. the proposed solution leverages Graph Neural Networks (GNNs) to detect network intrusions by analysing both the spatial and temporal evolution of network traffic. the methodology involves transforming the static NSL-KDD dataset into a series of time-ordered graph snapshots by assigning synthetic timestamps and constructing dynamic graphs. the snapshots represent the evolution of network interactions, enabling advanced dynamic graph learning models to process and detect intrusions. Five state-of-the-art GNN models, Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregation (GraphSAGE), Graph Isomorphism Networks (GIN) and Graph Auto-Encoders (GAE), were trained and tested on the DynKDD dataset, and their performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Evaluation results demonstrate that GraphSAGE consistently outperforms other models, achieving an accuracy of 95.80% on the DynKDD dataset, a notable improvement over the static NSL-KDD dataset. Other models follow a similar trend with a significant improvement on the constructed DynKDD dataset. the results validate the superiority of dynamic graph-based intrusion detection systems in identifying evolving network threats. With its ability to model evolving cyberattacks, DynKDD holds immense potential for real-world applications in protecting critical infrastructure from advanced and persistent threats, offering a transformative approach to network intrusion detection. © 2025 IEEE.

关键词： Network intrusion

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Best-Practices-Based Training for Improving Cybersecurity in Power Grids 19th

Best-Practices-Based Training for Improving Cybersecurity i...

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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer Security, ESORICS 2024

作者： Kavallieratos, Georgios Amro, Ahmed Gkioulos, Vasileios Stamatescu, Grigore Rantos, Kostantinos Lagkas, thomas Demertzis, Konstantinos Paterakis, Fotis Lekidis, Alexios Dalamagkas, Christos Kotsiuba, Igor Katsikas, Sokratis Norwegian University of Science and Technology Gjøvik Norway TÜV Austria Romania 139B Calea Plevnei Bucharest060011 Romania Department of Automation and Industrial Informatics University Politehnica of Bucharest 313 Splaiul Independentei Bucharest060042 Romania Department of Informatics Democritus University of Thrace Kavala65404 Greece School of Science Department of Computer Science Kavala Campus International Hellenic University Kavala65404 Greece Athens Greece Innovation Hub of Public Power Corporation S.A. Attica Greece Durham University Durham United Kingdom

ISBN: (纸本)9783031823619

the increased financial value of the power grids and the multitude of potential attackers, including such with advanced capabilities, makes the enhancement of cybersecurity in power grids very important. In order to strengthen the cybersecurity posture of the power grids, it is necessary to foster the development of cybersecurity skills and knowledge of the personnel working in them. To this end, this work proposes a systematic process that leverages cybersecurity best practices to define training content for personnel employed in power grids. To illustrate the workings of the proposed approach, best practices against phishing, spoofing, denial of service, and unauthorized activities are presented. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Phishing

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19th international Conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

19th International Conference on Artificial Intelligence: Me...

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19th international Conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

ISBN: (纸本)9783031815416

the proceedings contain 18 papers. the special focus in this conference is on Artificial Intelligence: Methodology, systems, and Applications. the topics include: Cross-Lingual Style Transfer TTS for High-Quality Machine Dubbing;an Approach to Discovering, Tracking Over Time, and Summarizing Publicly Available Information on a Given Topic;reinforcement Learning Control of Cart Pole System with Spike Timing Neural Network Actor-Critic Architecture;Predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture;deep Learning for Multi-class Diagnosis of thyroid Disorders Using Selective Features;advanced CNN-SVM Machine Learning Techniques for Facial Skin Ultrasound Image Analysis;Testing the NEAT Algorithm on a PSPACE-Complete Problem;investigating the Regularization of Deep Neural Networks for Affect Recognition with Relevance-Guided Local Explanations;Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation;Combining Graph NN and LLM for Improved Text-Based Emotion Recognition;a Novel Study on Modelling and Adaptive Optimal Control of a Tubular Reactor Based on Gaussian Processes;converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion;enhancing Question Answering in Lecture Videos with a Multimodal Retrieval-Augmented Generation Framework;agent-Based Simulation Leveraging Declarative Modeling for Efficient Resource Allocation in Emergency Scenarios;enhancing Security in Federated Learning: Detection of Synchronized Data Poisoning Attacks;Clinical and Acquisition Data for Optimizing MGMT Methylation Status Prediction: A Comprehensive Ensemble Strategy Emphasizing Non-invasive Approaches.

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Grid-Based Decompositions for Spatial Data Under Local Differential Privacy 19th

Grid-Based Decompositions for Spatial Data Under Local Diff...

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作者： Balioglu, Berkay Kemal Khodaie, Alireza Taweel, Ameer Gursoy, M.Emre Department of Computer Engineering Koç University Istanbul Turkey

ISBN: (纸本)9783031823480

Local differential privacy (LDP) has recently emerged as a popular privacy standard. With the growing popularity of LDP, recent works have applied LDP to spatial data, and grid-based decompositions have been a common building block in DP and LDP. In this paper, we study three grid-based decomposition methods for spatial data under LDP: Uniform Grid (UG), PrivAG, and AAG. UG is a static approach that consists of equal-sized cells. To enable data-dependent decomposition, PrivAG was proposed by Yang et al. (2022). To advance the state-of-the-art in adaptive grids, this paper proposes the advanced Adaptive Grid (AAG) method. For each grid cell, following the intuition that the cell’s intra-cell density distribution will be affected by its neighbors, AAG performs uneven cell divisions depending on the neighboring cells’ densities. We experimentally compare UG, PrivAG, and AAG using three real-world location datasets, varying privacy budgets, and query sizes. Results show that AAG provides higher utility than PrivAG, demonstrating the superiority of our proposed approach. Furthermore, when the grid size is chosen optimally in UG, AAG still beats UG for small queries, but UG beats AAG for large (coarse-grained) queries. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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computer Vision Based Quality Control for Molding Injection Machines 19th

Computer Vision Based Quality Control for Molding Injection...

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19th international Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Moreno, Ramón García, Oscar Del Río Cristobal, Miguel Shankar Muthuselvam, Revanth María Sanjuan, José Vallejo, Andrés Wang, Ting Department of Advanced Manufacturing 4.0 Antolin Engineering Burgos Spain Instituto Tecnológico Catilla y Leon Burgos Spain College of Electrical Engineering and Control Science Nanjing Tech University and Southeast University Nanjing China

ISBN: (纸本)9783031750090

this paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. the study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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19th international Conference on computer Aided systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international Conference on computer Aided systems theory, EUROCAST 2024

ISBN: (纸本)9783031829598

the proceedings contain 104 papers. the special focus in this conference is on computer Aided systems theory. the topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production systems;Using AutomationML for advanced Simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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19th international Conference on computer Aided systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international Conference on computer Aided systems theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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19th international Conference on computer Aided systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international Conference on computer Aided systems theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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