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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

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Biomechanics and Modeling in Mechanobiology 2025年 1-18页

作者： Coccarelli, Alberto Polydoros, Ioannis Drysdale, Alex Harraz, Osama F. Kadapa, Chennakesava Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Mechanical Engineering Faculty of Science and Engineering Swansea University Swansea University Bay Campus Fabian Way Crymlyn Burrows Skewen Swansea SA1 8EN United Kingdom Department of Pharmacology Larner College of Medicine and Vermont Center for Cardiovascular and Brain Health University of Vermont Burlington United States School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular Ca2+). The robustness of the integrated fluid–structure interaction framework was assessed using different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. The proposed in-silico methodology aims to quantify how acute changes in upstream luminal pressure propagate and influence blood flow across a network of rat cerebral arteries. Weak coupling ensured accurate results with a lower computational cost for the vessel size and boundary conditions considered. To complete the analysis, we evaluated the effect of an upstream pressure surge on vascular network haemodynamics in the presence and absence of myogenic tone. This provided a clear quantitative picture of how pressure, flow and vascular constriction are re-distributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to

关键词： 1D blood flow dynamics Autoregulation Biologically-motivated model Cerebral arterial networks Fluid-structure interaction Myogenic response

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Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding

arXiv

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arXiv 2025年

作者： Elhenawy, Mohammed Ashqar, Huthaifa I. Rakotonirainy, Andry Alhadidi, Taqwa I. Jaber, Ahmed Tami, Mohammad Abu CARRS-Q and Centre for Data Science Queensland University of Technology Kelvin GroveQLD4059 Australia Data Science and AI Department Arab American University P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University New YorkNY10027 United States

Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (ai) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analysis on the Honda Scenes dataset, which contains a collection of about 80 hours of annotated driving videos capturing diverse real-world road and weather conditions, our study highlights the robustness of CLIP models in learning visual concepts from natural language supervision. Results also showed that fine-tuning the CLIP models, such as ViT-L/14 and ViT-B/32, significantly improved scene classification, achieving a top F1-score of 91.1%. These results demonstrate the ability of the system to deliver rapid and precise scene recognition, which can be used to meet the critical requirements of Advanced Driver Assistance Systems (ADAS). This study shows the potential of CLIP models to provide scalable and efficient frameworks for dynamic scene understanding and classification. Furthermore, this work lays the groundwork for advanced autonomous vehicle technologies by fostering a deeper understanding of driver behavior, road conditions, and safety-critical scenarios, marking a significant step toward smarter, safer, and more context-aware autonomous driving systems. © 2025, CC BY.

关键词： Advanced driver assistance systems

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Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks

arXiv

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arXiv 2025年

作者： Alhadidi, Taqwa I. Alazmi, Asmaa Jaradat, Shadi Jaber, Ahmad Ashqar, Huthaifa I. Elhenawy, Mohammed Civil Engineering Department Al-Ahliyya Amman University Amman Jordan Department of Construction Project Ministry of Public Work of Kuwait Kuwait City Kuwait CARRS-Q Centre for Data Science Queensland University of Technology Brisbane Australia Department of Transport Technology and Economics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary AI and Data Sceince Department Arab American University Ramallah Palestine

Pavement distress, such as cracks and potholes, is a significant issue affecting road safety and maintenance. In this study, we present the implementation and evaluation of Bidirectional Cascaded Neural Networks (BCNNs) for the classification of pavement crack images following image augmentation. We classified pavement cracks into three main categories: linear cracks, potholes, and fatigue cracks on an enhanced dataset utilizing U-Net 50 for image augmentation. The augmented dataset comprised 599 images. Our proposed BCNN model was designed to leverage both forward and backward information flows, with detection accuracy enhanced by its cascaded structure wherein each layer progressively refines the output of the preceding one. Our model achieved an overall accuracy of 87%, with precision, recall, and F1-score measures indicating high effectiveness across the categories. For fatigue cracks, the model recorded a precision of 0.87, recall of 0.83, and F1-score of 0.85 on 205 images. Linear cracks were detected with a precision of 0.81, recall of 0.89, and F1-score of 0.85 on 205 images, and potholes with a precision of 0.96, recall of 0.90, and F1-score of 0.93 on 189 images. The macro and weighted average of precision, recall, and F1-score were identical at 0.88, confirming the BCNN's excellent performance in classifying complex pavement crack patterns. This research demonstrates the potential of BCNNs to significantly enhance the accuracy and reliability of pavement distress classification, resulting in more effective and efficient pavement maintenance and management systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Fatigue crack

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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Machine Learning Algorithms for Cloud-Based Educational Games: Engaging Students with Adaptive Learning Systems

Machine Learning Algorithms for Cloud-Based Educational Game...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Ravindar B Mahipal Reddy Podduturi D Khalandar Basha Chinnem Rama Mohan Geetanjali Popat Rokade M. Kalyan Chakravarthi School of CS & AI SR University Warangal Telangana Solutions Architect Affiliation Penske Corporation Downingtown PA USA Institute of Aeronautical Engineering Hyderabad Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India CSE (Cyber Security and Data Science) G. H. Raisoni College of Engineering and Management (GHRCEM) Pune Maharashtra Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Oman

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The climate of contemporary instruction is changing because of the fast improvement of instructive methodologies. This review looks at a scope of new instructive innovations, including their purposes, benefits, hardships, and potential to reform regular homeroom settings. The research explores how understudy commitment, learning results, and showing approaches are impacted by innovations as well as man-made brainpower (simulated intelligence), virtual reality, augmented reality (AR), gamification, and versatile learning frameworks. The objective of the ongoing task is to decide the ideal calculation for anticipating homeroom investment from understudies. In this review, we gathered information from virtual learning environment (VLE) utilizing versatile learning and handled it utilizing a scope of information preprocessing strategies, like exception ID, standardization, encoding, and missing worth evacuation. Utilizing a scope of commonsense markers and cross-approval draws near, this paper surveyed various ai (ML) characterization frameworks on our information. The outcomes demonstrate that the CatBoost model outperforms the others in terms of accuracy. When compared to earlier studies, this suggested model performed better in every way. According to the paper's results section, the CatBoost model's accuracy was roughly 95.34%, its precision was 97.75%, and its recall was 95.4%. The aiSAR model was contrasted with when compared to the aiSAR 91% model, our model's precision of 97.75% indicates that our findings are superior.

关键词： Solid modeling Adaptation models Adaptive learning Technological innovation Analytical models Accuracy Electronic learning Reviews Predictive models Encoding

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Optimizing ai Algorithms for Accurate Remote Diagnosis in Cardiovascular Monitoring Systems

Optimizing AI Algorithms for Accurate Remote Diagnosis in Ca...

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ai-Driven Smart Healthcare for Society 5.0

作者： Geetha Manoharan Lalit Mohan Trivedi S. Suma Christal Mary Divya Nimma Malik Bader Alazzam B Kiran Bala School of Business SR University Warangal Telangana India Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Information Technology Panimalar Engineering College Chennai PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

In recent years a new trend of architectures named the hybrid deep learning models have been derived as using several architectures jointly provides better performance for the problem. Out of these models, using CNN and GRU is therefore promising for upcoming tasks that need feature extraction and predicting sequential data. The primary contribution of the present work is an attempt to combine CNN for spatial feature extraction and GRU for temporal features, making the model efficient in handling sequential input data with lesser errors. The CNN-GRU structure based on this work combines CNN layer for feature extraction from raw input data and GRU layer to process features sequentially. This hybrid architecture is then fine-tuned by applying state of the art deep learning methods achieving higher classification accuracy. Accuracy of the proposed model was 98.9%, precision was 98.5%, recall was 99.1% and F1 score was 98.8%. An analysis of the results obtained shows that these values are markedly higher than when using only CNN or GRU, Random Forest, and SVM for standalone models. The proving work also confirms that the CNN-GRU hybrid model provides better solutions than the traditional model for various complicated classification problems in views of the better accuracy, precision, and recall. Nonetheless, it has the major drawbacks related to computational efficiency because it requires the input of large labeled data. Possible improvements for future work include further improvements to the efficiency of the algorithm as well as to investigate its effectiveness in practical domains such as healthcare, finance, and autonomous vehicles.

关键词： Deep learning Support vector machines Accuracy Computational modeling Computer architecture Medical services Feature extraction data models Classification algorithms Convolutional neural networks

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EI-Drive: A Platform for Cooperative Perception With Realistic Communication Models

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IEEE Internet of Things Journal 2025年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan University of California Davis AI Robotics and Edge Laboratory DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States Csiro data61 ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive (Edge Intelligent Drive), an Edge-ai based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2014 IEEE.

关键词： Digital elevation model

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Monocular Depth Estimation for UAV Navigation Using Segmental Fully Convolutional Prairie Dog Neural Networks

Monocular Depth Estimation for UAV Navigation Using Segmenta...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Indradeep Kumar A. Elumalai G. Santha Meena Jagath Narayana Kamineni Joshuva Arockia Dhanraj M. Revathi Amity Institute of Technology Amity University Noida Uttar Pradesh India Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Mechanical Engineering Sri Venkateshwara College of Engineering Tirupati Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India Department of Artificial Intelligence and Data Science St.Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Unmanned Aerial Vehicles (UAVs) play an important role in very precise navigation for its usage in applications like disaster management, agricultural surveillance, and as delivery drones. Precise depth estimation is crucial for UAV to safely avoid obstacles and thus obtain proper paths for flying. Monocular Depth Estimation (MDE), is another method which is becoming popular due to its easy implementation as compared to stereo vision and LiDAR systems in which single camera depth information is inferred. However, found that there are still some problems in the current a few deep learning methods of MDE, including high computational cost, low prediction accuracy in dynamic conditions, and unsuitability for real-time or online applications. In order to solve these problems, the current research work introduces a Segmental Fully Convolutional Neural Network (SFCNN) tended by the Prairie Dog Optimization (PDO) for UAV flight. The block structure improves adaptability as well as computational complexity; PDO seeks to minimize network's parameters to increase the result precision of the depth forecast and lessen the computation time. tarting from this background, the proposed approach is intended to support lightweight real-time and reliable means for MDE to enhance UAV navigation in complex environments.

关键词： Grasslands Navigation Depth measurement Dogs Autonomous aerial vehicles Real-time systems Stereo vision Convolutional neural networks Vehicle dynamics Optimization

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and ai-Driven Analytics

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Divya Nimma Ch. Anudeep Tripti Sahu Nusrat Hamid Shah Lubna Hamid Shah B Kiran Bala PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Management studies Shri Vishnu Engineering college for women (A) Bhimavaram India MIMA Institute of Management Pune Maharashtra India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Department of Management and Marketing College of Business Jazan University Kingdom of Saudi Arabia Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world.

关键词： Consumer behavior Accuracy Soft sensors Predictive models data models Social factors Real-time systems Safety Sustainable development Business

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