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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Sripelli Jagadish Ramy Riad Al-Fatlawy Ankam Sreejyothsna Syed Salim Sowmya M School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India Department of Computer Science and Design School of Engineering Mysore University Manasa Gangothiri Mysuru India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cognitive Radio Networks (CRNs) have been introduced as an empowering advancement which enables the dynamically unutilized spectrum to enhance the spectral effectiveness. Energy efficiency and the throughput are the apprehensions for the energy-harvesting CRN. Nevertheless, obtaining a greatest maximum level of these metrices needs the optimization of sensing duration, transmission time and the energy harvesting. To tackle these problems, this articles purposes to propose the Local Search based Dingo Optimization Algorithm (LS-DOA) approach for energy harvesting in CRN. The LS-DOA enhances an energy harvesting procedure through balancing an energy collected and obtained. This flexibility results in effective energy management as well as make sures that energy is significantly harvested from the obtainable ambient playforms. LS-DOA is robust in dynamic environments, regulating to changeable spectrum obtainability and different energy harvesting environments. This designs it optimal for CRNs, where network conditions are highly variable. The multi-objective functions such as energy efficiency and throughput are considered to estimate the model effectiveness. The experimental results shows that the proposed LS-DOA approach attains the average throughput and energy efficiency of 0.241 and 2.938 as compared to the existing approaches like DOA and Pelican Optimization Algorithm (POA).

关键词： Wireless networks Heuristic algorithms Metaheuristics Throughput Search problems Energy efficiency Real-time systems Sensors Energy harvesting Cognitive radio

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Predicting Disease Outbreaks with ai: An In-depth Analysis of Infectious Diseases Surveillance

Predicting Disease Outbreaks with AI: An In-depth Analysis o...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Ankit Kumar Dubey Khushbu Gupta Venkatesan S Abhilasha Sankari Siva Sankar Namani Raavi Hemalatha Department of Computer Science and Engineering Baderia Global Institute of Engineering and Management Jabalpur MP Institute - St. Aloysius College (Autonomous) Jabalpur MP Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai Tamilnadu India Department of Computer Science Engineering Shriram Group of Institutions Jabalpur MP Department of CSE (AI & ML) G Narayanamma Institute of Technology and Science -(GNITS) Shaikpet Hyderabad Department: Computer Science & Engineering Vishnu Institute of Technology Bhimavaram

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Public health coordinators require precise and swift disease outbreak predictions to execute their functions efficiently. This study investigates the application of Reinforcement Learning (RL) in prediction of infectious disease outbreaks in order to improve such surveillance systems through provision of dynamic, adaptive forecasting models. This method applies RL computer algorithms to disease development through Deep Q-Learning and Policy Gradient Methods. The evaluation of live disease transmission patterns helps these algorithms create effective outbreak prediction methods. Reinforcement Learning models receive their training data from historical illness information together with demographic statistics and geographic data. All environmental variables together with population movement and healthcare accessibility are included in the analysis. This technique enables responses to fresh epidemics through adjustments of prediction strategies that adapt to changing conditions. Performance criteria include accuracy, precision and adaptability-that is to say how well RL captures non-linear [tic] disease processes and generate accurate and timely forecasts. A thorough analysis of the results shows how RL can potentially change the infectious disease surveillance dramatically by making substantial gains in predicting precision, resource allocation and prompt response. Finally, the study offers suggestions for RL based models to be included in the current public health infrastructure to enable their incorporation in the management of disease outbreaks in support of more proactive and data driven decision making.

关键词： Adaptation models Accuracy Infectious diseases Surveillance Heuristic algorithms Computational modeling Training data Prediction algorithms Data models Public healthcare

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IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-15页

作者： Ye, Luyao Zhou, Zikang Wang, Jianping Li, Yung-Hui Jan, Nien-Yi Lin, Yi-Rong Lin, Yen-Cheng National Engineering Research Center of Educational Big Data and the Faculty of Artificial Intelligence in Education Central China Normal University China Department of Computer Science City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute China AI Research Center of Hon Hai Research Institute China

Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety and efficiency of autonomous driving. However, existing learning-based trajectory prediction models struggle with poor generalization to out-of-distribution (OOD) scenes, potentially leading to failed predictions in real-world applications. To tackle this challenge, we introduce IMGTP, a unified framework for systematically evaluating, improving, and measuring the generalization performance of trajectory prediction models in OOD scenes. IMGTP consists of three primary components: the Argoverse-Shift dataset, the FrenÃ©t+ strategy, and an approach for OOD measurement. First, the Argoverse-Shift is a cross-domain dataset that highlights the significant differences in data distributions across domains, serving as a testing platform for models' generalization ability. Second, the FrenÃ©t+ strategy leverages map centerlines and the FrenÃ©t Frame to enhance the generalizability of baseline models by mitigating the domain gap resulting from road geometry variations. Third, we provide a comprehensive evaluation methodology for OOD measurement, quantifying the range of data that can be effectively handled by prediction models. This measurement approach employs the accuracy rate of the correctly predicted samples and their corresponding mean distance as well as standard deviation to the training set center as indicators of the model's generalization ability. The mean distance also serves as a valid criterion for OOD detection and uncertainty quantification. Experimental results demonstrate that our IMGTP framework enhances the generalization capability of mainstream trajectory prediction models by substantially improving their prediction performance in unseen domains. This advancement contributes to developing more robust and safer prediction models for autonomous driving in the real world. The code can be found at https://***/XIAOYEJIAYOU/Frenet-Strategy. IEEE

关键词： Autonomous vehicles

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DyFormer: A CNN-Transformer Network Combining Local and Global Attention for Optical Remote Sensing Object Detection 4

DyFormer: A CNN-Transformer Network Combining Local and Glob...

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4th International Conference on Artificial Intelligence, Virtual Reality and Visualization, aiVRV 2024

作者： Huang, Youxiang Wang, Qin Jiao, Donglai Wang, Shufei Wang, Yu Lu, Zhiyi Ohtsuki, Tomoaki Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Ai Research Center Nanjing Great Information Technology Co. Ltd Nanjing China Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798331528744

Remote sensing image (RSI) object detection is crucial for natural disaster management, urban planning, and resource exploration. However, due to significant differences between RSIs and natural images (NIs), existing object detectors designed for NIs are not directly applicable to RSIs. CNN-based models typically require specialized attention modules to relate small targets to global contexts, while Transformer-based models often add modules to enhance detail extraction, increasing computational costs for edge deployment. To address these challenges, we propose DyFormer, a hybrid CNN-Transformer model that combines local and global attention mechanisms for effective feature fusion. Additionally, a feature residual pyramid network (FRPN) is introduced to enhance multi-scale feature integration. We conduct experiments on the open source NWPU VHR-10 dataset to evaluate the proposed DyFormer, achieving a mAP@0.5 of 61.7%, outperforms existing sort-of-the-art methods, demonstrating its effectiveness for RSI object detection tasks. © 2024 IEEE.

关键词： Optical remote sensing

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Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective 41

Is Inverse Reinforcement Learning Harder than Standard Reinf...

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41st International Conference on Machine Learning, ICml 2024

作者： Zhao, Lei Wang, Mengdi Bai, Yu School of Mathematical Sciences University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States Salesforce AI Research Palo AltoCA United States

Inverse Reinforcement Learning (IRL)-the problem of learning reward functions from demonstrations of an expert policy-plays a critical role in developing intelligent *** widely used in applications, theoretical understandings of IRL present unique challenges and remain less developed compared with standard *** example, it remains open how to do IRL efficiently in standard offline settings with pre-collected data, where states are obtained from a behavior policy (which could be the expert policy itself), and actions are sampled from the expert *** paper provides the first line of results for efficient IRL in vanilla offline and online settings using polynomial samples and *** algorithms and analyses seamlessly adapt the pessimism principle commonly used in offline RL, and achieve IRL guarantees in stronger metrics than considered in existing *** provide lower bounds showing that our sample complexities are nearly *** an application, we also show that the learned rewards can transfer to another target MDP with suitable guarantees when the target MDP satisfies certain similarity assumptions with the original (source) MDP. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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INTERPGNN: UNDERSTAND AND IMPROVE GENERALIZATION ABILITY OF TRANSDUCTIVE GNNS THROUGH THE LENS OF INTERPLAY BETWEEN TRaiN AND TEST NODES 12

INTERPGNN: UNDERSTAND AND IMPROVE GENERALIZATION ABILITY OF ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Sun, Jiawei Li, Kailai Chen, Ruoxin Li, Jie Wu, Chentao Ding, Yue Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of AI Shanghai Jiao Tong University China Yancheng Blockchain Research Institute China Youtu Lab Tencent China

Transductive node prediction has been a popular learning setting in Graph Neural Networks (GNNs). It has been widely observed that the shortage of information flow between the distant nodes and out-of-batch nodes (for large-scale graphs) often hurt the generalization of GNNs which overwhelmingly adopt message-passing. Yet there is still no formal and direct theoretical results to quantitatively capture the underlying mechanism, despite the recent advance in both theoretical and empirical studies for GNN's generalization ability. In this paper, the L-hop interplay (i.e., message passing capability with training nodes) for an L-layer GNN is successfully incorporated in our derived PAC-Bayesian bound for GNNs in the semi-supervised transductive setting. In other words, we quantitatively show how the interplay between training and testing sets influence the generalization ability which also partly explains the effectiveness of some existing empirical methods for enhancing generalization. Based on this result, we further design a plug-and-play Graph Global Workspace module for GNNs (InterpGNN-GW) to enhance the interplay, utilizing the key-value attention mechanism to summarize crucial nodes' embeddings into memory and broadcast the memory to all nodes, in contrast to the pairwise attention scheme in previous graph transformers. Extensive experiments on both small-scale and large-scale graph datasets validate the effectiveness of our theory and approaches. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Flow graphs

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Innovative Approaches in Network Architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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Wavelet Transform based Bidirectional Long Short-Term Memory for Predicting Water Demand 1

Wavelet Transform based Bidirectional Long Short-Term Memory...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Habelalmateen, Mohammed I. Palanivel, R. Rana Veer Samara Sihman Bharattej, R. Vijayashaarathi, S. Poonguzhali, I. The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Saint Leo University St Leo Department of Computer Science United States Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Poonamallee Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

The prediction of urban water demand is essential in rational allocation of water and developing smart city. Influenced through multiple factors, the water demand huge frequency noise and difficult patterns, that is due to nostationary of daily data has huge influence on generalization capability of methods. In this research, Wavelet Transform (WT) and Hodrick-Prescott (HP) techniques are utilized to decompose of daily data for resolving an issue of non-stationary. Bidirectional Long Short-Term Memory (BiLSTM), Seasonal Auto Regressive Integrated Moving Average (SARIMA) and Gaussian Radial Basis Function Neural Network (GRBFNN) are introduced to predicting various subseries. Ensemble learning is implemented for enhancing a generalization capability of methods and interval prediction is produced depended on t-test of students is handled along difference of laws in water supply. The coupling and WT techniques correctly predicted the water demand and give optimum 0.19% of Mean Square Error (MSE), 3.37% of Mean Absolute Error (MAE), 0.2 of Mean Relative Error (MRE), 97.25% of Nash-Sutcliffe Efficiency (NSE) and 0.99 of Correlation coefficient. © 2024 IEEE.

关键词： Radial basis function networks

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Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

arXiv

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arXiv 2025年

作者： Mauricio, Fausto Suarez, Lagos Saradagi, Akshit Sumathy, Vidya Kotpalliwar, Shruti Nikolakopoulos, George Robotics and AI group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This article introduces a curriculum learning approach to develop a reinforcement learning-based robust stabilizing controller for a Quadrotor that meets predefined performance criteria. The learning objective is to achieve desired positions from random initial conditions while adhering to both transient and steady-state performance specifications. This objective is challenging for conventional one-stage end-to-end reinforcement learning, due to the strong coupling between position and orientation dynamics, the complexity in designing and tuning the reward function, and poor sample efficiency, which necessitates substantial computational resources and leads to extended convergence times. To address these challenges, this work decomposes the learning objective into a three-stage curriculum that incrementally increases task complexity. The curriculum begins with learning to achieve stable hovering from a fixed initial condition, followed by progressively introducing randomization in initial positions, orientations and velocities. A novel additive reward function is proposed, to incorporate transient and steady-state performance specifications. The results demonstrate that the Proximal Policy Optimization (PPO)based curriculum learning approach, coupled with the proposed reward structure, achieves superior performance compared to a single-stage PPO-trained policy with the same reward function, while significantly reducing computational resource requirements and convergence time. The curriculum-trained policy’s performance and robustness are thoroughly validated under random initial conditions and in the presence of disturbances. © 2025, CC BY.

关键词： Curricula

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A Gaussian Multivariate Mixture Model and VGG19 Model for Classification and Detection of Motor Vehicle

A Gaussian Multivariate Mixture Model and VGG19 Model for Cl...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： N. Deepa Bura Vijay Kumar M Sowmya Hassan M. Al-Jawahry Yannam Bharath Bhushan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In daily life, the moving vehicle plays a significant part in effective system of transportation and traffic visual surveillance systems. Most critical task is to detect the vehicle image and most of the existing method, concentrated on maximizing the prediction percentage only. Also it lacks in classifying the imbalance data. To overcome this kind of problem, the Gaussian Multivariate Mixture Model (G-MMM) and VGG19 model is implemented to detect and classify the vehicle types. The proposed method contained four process namely, preprocessing performed by Flexible Histogram Equalization method (FHE), vehicle detection using G-MMM, feature extraction using Steerable Pyramid Transform (SPT) and Weber Local Descriptor (WLD) and classification using VGG19 ensemble deep learning model. G-MMM significantly improves detection sensitivity and it transformed the correlation between attributes through the training process. VGG19 is pretrained model, so it allows the fast learning recognition on new images of vehicle. The proposed method tested on Beijing Institute of Technology (BIT) dataset and MIO vision traffic camera dataset. When compared to the others methods like modified AlexNet, Deep Convolution Neural Network (DCNN), this proposed G-MMM-VGG19 gives 99.47 % of output rate in classification phase.

关键词： Deep learning Histograms Visualization Vehicle detection Computational modeling Transportation Mixture models Feature extraction Automobiles Tuning

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