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arXiv 2025年

作者： Sulle, Methusela Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Gyimah, Nana Kankam Department of Engineering South Carolina State University OrangeburgSC29117 United States Department of Computational Data Science and Engineering North Carolina A&T State University GreensboroNC27411 United States

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018–2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques—including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost—to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98%, accuracy of 90%, precision of 92%, recall of 90%, and an F1 score of 91%. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety. © 2025, CC BY.

关键词： Pedestrian safety

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Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application

arXiv

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arXiv 2025年

作者： Sulle, Methusela Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Gyimah, Nana Kankam Roberts, Jaylen Ruganuza, Denis Department of Engineering South Carolina State University OrangeburgSC29117 United States Department of Computational Engineering and Data Science North Carolina A&T State University GreensboroNC27411 United States

Pedestrian safety is a critical public health priority, with pedestrian fatalities accounting for 18% of all U.S. traffic deaths in 2022. The rising prevalence of distracted walking, exacerbated by mobile device use, poses significant risks at signalized intersections. This study utilized an immersive virtual reality (VR) environment to simulate real-world traffic scenarios and assess pedestrian behavior under three conditions: undistracted crossing, crossing while using a mobile device, and crossing with Light-emitting diode (LED) safety interventions. Analysis using ANOVA models identified speed and mobile-focused eye-tracking as significant predictors of crossing duration, revealing how distractions impair situational awareness and response times. While LED measures reduced delays, their limited effectiveness highlights the need for integrated strategies addressing both behavioral and physical factors. This study showcases VR’s potential to analyze complex pedestrian behaviors, offering actionable insights for urban planners and policymakers aiming to enhance pedestrian safety. © 2025, CC BY-NC-SA.

关键词： Pedestrian safety

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HVAC Intelligence: Energy Conservation Based on Occupancy

HVAC Intelligence: Energy Conservation Based on Occupancy

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IEEE Southeastcon

作者： Hiba Darwish Raymond Tesiero Marwan Bikdash Balakrishna Gokaraju Computational Data Science and Engineering Department North Carolina A&T State University Greensboro USA Civil Architectural and Environmental Engineering North Carolina A&T State University Greensboro USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This research investigates the effectiveness of implementing an occupancy-based control strategy for Heating, Ventilation, and Air Conditioning (HVAC) systems to minimize energy consumption and maintain thermal comfort. The proposed system utilizes an infrared camera to detect room occupancy. When occupancy is detected, the HVAC system adjusts the indoor temperature based on the threshold (changeover) temperature. Conversely, in unoccupied conditions, the system maintains default temperature settings designed to prevent extreme conditions (not excessively warm or overly cold). The HVAC system operation is further influenced by outdoor temperature conditions: cooling is activated when the outdoor temperature exceed the defined changeover, and heating is initiated when the outdoor temperatures fall below this threshold. The study's findings demonstrate that occupancy-based control can result in significant energy savings, quantified as 8,632 kWh/year for our small scale project, corresponding to a cost reduction of $627.06. These results highlight the potential for such systems to enhance energy efficiency while maintaining occupant thermal comfort.

关键词： Heating systems Energy consumption HVAC Costs Systems operation Energy conservation Cameras Ventilation Energy efficiency Software

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Exploring Image-Based Approaches for Effective Synthetic Time-Series data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Samgwon Oh Seungmin Oh Junghoon Lee Doyeon Kim Minsoo Hahn Jinsul Kim Department of Intelligence Electronics Computer Engineering Chonnam National University Gwangju Korea Department of Intelligence Information Convergence Chonnam National University Gwangju Korea Department of Computational and Data Science Astana IT University Astana Kazakhastan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications.

关键词： Measurement Visualization Scalability data collection Generative adversarial networks data models Forecasting Monitoring Anomaly detection Synthetic data

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A GNN-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin

arXiv

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arXiv 2025年

作者： Isah, Abubakar Aliyu, Ibrahim Rashid, Sulaiman Muhammad Park, Jaehyung Hahn, Minsoo Kim, Jinsul Department of Intelligent Electronics and Computer Engineering Chonnam National University Buk-gu Gwangju61186 Korea Republic of Department of Computer Science Ahmadu Bello University Zaria810241 Nigeria Department of Computational and Data Science Astana IT University Astana Kazakhstan

Graph Neural Networks are gaining attention in Fifth-Generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classification in multiclass settings. Digital twins of 5G networks increasingly employ graph classification as the main method for identifying failure types. However, the skewed distribution of failure occurrences is a major class imbalance issue that prevents effective graph data mining. Previous studies have not sufficiently tackled this complex problem. In this paper, we propose Class-Fourier Graph Neural Network (CF-GNN) introduces a class-oriented spectral filtering mechanism that ensures precise classification by estimating a unique spectral filter for each class. We employ eigenvalue and eigenvector spectral filtering to capture and adapt to variations in the minority classes, ensuring accurate class-specific feature discrimination, and adept at graph representation learning for complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed CF-GNN could help with both the creation of new techniques for enhancing classifiers and the investigation of the characteristics of the multi-class imbalanced data in network digital twin Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Reliable Vertical Ground Reaction Force Estimation with Smart Insole During Walking

arXiv

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arXiv 2025年

作者： Olugbon, Femi Ghoreishi, Nozhan Huang, Ming-Chun Xu, Wenyao Chen, Diliang Department of Electrical and Computer Engineering University of New Hampshire DurhamNH03824 United States Department of Data and Computational Science Duke Kunshan University Suzhou China Department of Computer Science & Engineering University at Buffalo The State University of New York BuffaloNY14260 United States

The vertical ground reaction force (vGRF) and its characteristic weight acceptance and push-off peaks measured during walking are important for gait and biomechanical analysis. Current wearable vGRF estimation methods suffer from drifting errors or low generalization performances, limiting their practical application. This paper proposes a novel method for reliably estimating vGRF and its characteristic peaks using data collected from the smart insole, including inertial measurement unit data and the newly introduced center of the pressed sensor data. These data were fused with machine learning algorithms including artificial neural networks, random forest regression, and bi-directional long-short-term memory. The proposed method outperformed the state-of-the-art methods with the root mean squared error, normalized root mean squared error, and correlation coefficient of 0.024 body weight (BW), 1.79% BW, and 0.997 in intra-participant testing, and 0.044 BW, 3.22% BW, and 0.991 in inter-participant testing, respectively. The difference between the reference and estimated weight acceptance and push-off peak values are 0.022 BW and 0.017 BW with a delay of 1.4% and 1.8% of the gait cycle for the intra-participant testing and 0.044 BW and 0.025 BW with a delay of 1.5% and 2.3% of the gait cycle for the inter-participant testing. The results indicate that the proposed vGRF estimation method has the potential to achieve accurate vGRF measurement during walking in free-living environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training Computer vision Accuracy Perturbation methods Neural networks Closed box Computer architecture Robustness Ensemble learning Standards

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Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

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IEEE Conference on Wireless Communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

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Enhanced Bitcoin Price Prediction Using RNN-GRU Algorithm with Optimized Parameters: Overcoming LSTM Ambiguities for Improved Accuracy and Efficiency

Enhanced Bitcoin Price Prediction Using RNN-GRU Algorithm wi...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： M. Kathiravan Meenakshi M Kaviya M S. Sreesubha Sathyadurga. V V. Neela Gandhi Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of CSE Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Artificial Intelligence And Data Science Panimalar Engineering College Chennai Department of AIML Rajalakshmi Engineering College Thandalam Chennai Department of CSE Hindustan Institute of Technology and Science padur Chennai Department of CSE Panimalar Engineering college Chennai

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The “mining” process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as “nodes” or “miners.” The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly; hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods.

关键词： Accuracy Time series analysis Bitcoin Blockchains Convolutional neural networks Probes Long short term memory Standards Random forests Investment

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