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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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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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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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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Kirubhakaran D. Karthikeswaran M. Arun R. Arun Chakravarthy J. Akshya M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.

关键词： Industries Adaptation models Heuristic algorithms Real-time systems Path planning Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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Comparative Analysis of Deep Learning Models for Multilingual Language Translation 8

Comparative Analysis of Deep Learning Models for Multilingua...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Nimma, Divya Somasekhar Srinivas, V.K. Gupta, Santoshi Sen Nair, Hari Devi, R. Latha Bala, B. Kiran University of Southern Mississippi Data Analyst in UMMC Department Computational Science United States Mohan Babu University Tirupati Department of Mathematics Rangampeta India Graphic Era Hill University Bhimtal India Graphic Era Deemed to Be University School of Management Uttarakhand Dehradun India Department of English A.P. Bhimavaram India Department of English Nagercoil India K.Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331509170

Multilingual language translation is a crucial undertaking in herbal language processing (NLP), facilitating pass-linguistic verbal exchange and know-how. Existing deep getting to know models, including Transformers, Multilingual BERT (mBERT), and mBART, have proven sizable advancements in translation satisfactory;however, they come with boundaries. Standard Transformers and mBERT models regularly struggle with low-resource languages and 0-shot translation situations because of their reliance on substantial parallel corpora or confined language representation. To cope with those drawbacks, a singular method is proposed that mixes the strengths of the mBART and M2M-100 architectures. This method leverages a hybrid version structure incorporating a chain-to-sequence Transformer framework more suitable with go-lingual switch mastering and information augmentation techniques. The approach allows higher performance in low-aid language settings and extra correct translations across non-English language pairs. The proposed methodology entails pre-education on a diverse set of multilingual datasets accompanied by using pleasant-tuning with augmented low-useful resource language records. Experimental consequences display that this model outperforms current fashions like mBART and M2M-100 via 12% in BLEU ratings for low-resource languages and achieves trendy performance in many-to-many translation duties. Additionally, the model well-known shows more suitable efficiency and scalability, making it suitable for real-world multilingual applications. This research contributes to the development of extra sturdy and efficient multilingual translation systems, paving the way for stepped forward movelingual verbal exchange in numerous international contexts. © 2024 IEEE.

关键词： Contrastive Learning

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On the Robustness of Cross-Concentrated Sampling for Matrix Completion

On the Robustness of Cross-Concentrated Sampling for Matrix ...

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Annual Conference on Information sciences and Systems (CISS)

作者： HanQin Cai Longxiu Huang Chandra Kundu Bowen Su Department of Statistics and Data Science Department of Computer Science University of Central Florida Orlando FL USA Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East Lansing MI USA Department of Statistics and Data Science University of Central Florida Orlando FL USA Department of Mathematics Michigan State University East Lansing MI USA

Matrix completion is one of the crucial tools in modern data science research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has caught much attention. However, the robustness of the CCS model against sparse outliers remains unclear in the existing studies. In this paper, we aim to answer this question by exploring a novel Robust CCS Completion problem. A highly efficient non-convex iterative algorithm, dubbed Robust CUR Completion (RCURC), is proposed. The empirical performance of the proposed algorithm, in terms of both efficiency and robustness, is verified in synthetic and real datasets.

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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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