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检索条件"任意字段=21st International Conference on Intelligent Data Engineering and Automated Learning"

共 3148 条记录，以下是11-20 订阅

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Cooperative-Competitive Decision-Making in Resource Management: A Reinforcement learning Perspective 25th

Cooperative-Competitive Decision-Making in Resource Manageme...

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25th international conference on intelligent data engineering and automated learning

作者： Isakov, Artem Peregorodiev, Danil Brunko, Pavel Tomilov, Ivan Gusarova, Natalia Vatian, Alexandra ITMO Univ St Petersburg 197101 Russia Almazov Natl Med Res Ctr World Class Res Ctr Personalized Med St Petersburg 194156 Russia

ISBN: (纸本)9783031777301;9783031777318

This study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world resource allocation challenges, particularly in healthcare settings. The environment incorporates dynamic changes in agents' perceptions and utilizes heavy-tailed distributions to model unpredictable events. We adapt the Multi-Agent Deep Deterministic Policy Gradient algorithm to this environment, demonstrating its effectiveness in learning and strategy development. Our approach includes a role-based system and a two-component reward function that guides agents through the learning process. The results, evaluated using metrics such as cumulative reward, average episodic reward, and approximate Kullback-Leibler divergence, indicate successful learning and sufficent agent strategies. This research contributes to the field of multi-agent systems and resource management by providing a framework that can generalize learned policies to varying numbers of agents and resources, making it applicable to real-world scenarios with changing conditions.

关键词： Multi-agent systems Human-in-the-loop Reinforcement learning Resource allocation Cooperative-competitive games Heavy-tailed distribution

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21st international conference on Distributed Computing and intelligent Technology, ICDCIT 2025

21st International Conference on Distributed Computing and I...

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21st international conference on Distributed Computing and intelligent Technology, ICDCIT 2025

ISBN: (纸本)9783031814037

The proceedings contain 21 papers. The special focus in this conference is on Distributed Computing and intelligent Technology. The topics include: Invited Paper: Application of Physics-Based and data-Driven Approaches for Drug-Like Property Prediction;invited Paper: Distributed Computability: A Few Results Masters students Should Know;ioT-Based Service Allocation in Edge Computing Using Game Theory;Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments;a "Symbolic" Representation of Object-Nets;optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge;mobile Agents on Chordal Graphs: Maximum Independent Set and Beyond;faster Leader Election and Its Applications for Mobile Agents with Parameter Advice;a Novel Protocol for Mitigating Wormhole Attack in Multi-hop Payment Channels;unleashing Multicore strength for Efficient Execution of Blockchain Transactions;Enhancing QR Decomposition: A GPU-Based Approach to Parallelizing the Householder Algorithm with CUDA streams;source Sets in Temporal Graphs;team Formation Based on the Degree Distribution of the Social Networks;inVideo Search: Scene Description Clustering and Integrating Image and Audio Captioning for Enhanced Video Search;does data Balancing Impact stutter Detection and Classification?;deep learning-Driven Person Re-identification: Leveraging Color Space Transformations;an Ensembled Parking Space Classifier Across Diverse Weather Conditions;classification of Impacted Teeth from Panoramic Radiography Using Deep learning;HJCFL: Hashcash and Jaya-Based Communication Efficient Federated learning.

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Machine learning for Sensor-Based Handwritten Character Recognition: A Brief Survey 21st

Machine Learning for Sensor-Based Handwritten Character Reco...

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21st international conference on Distributed Computing and intelligent Technology

作者： Singh, Shashank Kumar Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783031814037;9783031814044

Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper reviews various efficient handwriting recognition models that utilize inertial sensors, visual sensors, EMG sensors, and acoustic and Wi-Fi signals. Inertial sensor-based approaches often use accelerometers and gyroscopes to collect three-dimensional data during handwriting, with models achieving high accuracy in recognizing digits and alphabets. Visual sensor-based techniques, leveraging cameras and image processing, have been fundamental in developing Optical Character Recognition (OCR), further enhanced by deep learning algorithms. EMG sensors have been explored for their potential to capture muscle activity for gesture recognition. At the same time, acoustic and WiFi signal-based methods offer innovative ways to recognize handwriting through sound and signal variation. Despite the progress, challenges such as sensor drift, data generalization, and computational requirements persist. Integrating multi-modal sensors and advanced machine learning techniques holds promise for overcoming these limitations and improving the accuracy and usability of handwriting recognition systems across diverse applications.

关键词： Inertial Sensor Machine learning Visual Sensors

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Invited Paper: Application of Physics-Based and data-Driven Approaches for Drug-Like Property Prediction 21st

Invited Paper: Application of Physics-Based and Data-Driven ...

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21st international conference on Distributed Computing and intelligent Technology

作者： Murugan, Natarajan Arul Sastry, Garikapati Narahari Indraprastha Inst Informat Technol Dept Computat Biol Okhla Phase 3 Ind Area New Delhi 110020 India Indian Inst Technol Dept Biotechnol Hyderabad 10587 Telangana India

ISBN: (纸本)9783031814037;9783031814044

Drug discovery projects involve the identification of hits or lead compounds having favorable drug-like properties. In other words, it is a multivariate optimization problem and the properties to be optimized are binding affinity, binding specificity, ADMET properties, solubility, and bioavailability. Computationally these properties can be estimated using physics-based and data-driven models. In the physics based models, the drug-like properties are computed as free energy differences. In data-driven models, the molecules are described numerically using various descriptors and finger-prints and machine learning and deep learning models can be built using dataset of descriptors/fingerprints and corresponding drug-like properties available from experiments. The physics-based models are usually computationally demanding and so are limited to smaller chemical space. However ML/DL models, can be applied to compute the drug-like properties of huge chemical libraries such as GDB17, Real Enamine DB, ZINC having more than billion compounds. The implementation, merits and demerits of these two approaches will be discussed in some detail in this paper.

关键词： Machine learning Deep learning Molecular Graph Representation Druggability prediction Physics based models

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Refining Multiple Instance learning with Attention Regularization for Whole Slide Image Classification 25th

Refining Multiple Instance Learning with Attention Regulariz...

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25th international conference on intelligent data engineering and automated learning

作者： Carretero, Ilan Meseguer, Pablo del Amor, Rock Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano HUMAN Valencia 46022 Spain Valencian Grad Sch & Res Network Artificial Intel Valencia 46022 Spain

ISBN: (纸本)9783031777301;9783031777318

Histopathological analysis of biopsy sections is crucial for the detection of cancer and the distinction between different tumor subtypes. To this end, pathologists identify certain key regions of the biopsy from which a diagnosis is derived. The classification of whole slide images (WSIs) can be addressed as a multiple instance learning (MIL) problem where only slide-level labels are available. In order to model the relevance scores of the different WSI patches, attention mechanisms are implemented within the MIL framework. However, excessive flexibility in the attention mechanisms for computing attention scores to patches may lead to nearly uniform attention distributions, potentially deteriorating the model's performance. In this paper, we introduce an unsupervised auxiliary loss function to recalibrate the attention mechanism enhancing emphasis on crucial patches and downscaling the influence of less relevant ones. The proposed MIL framework has been evaluated on invasive breast carcinoma (TCGA-BRCA) and renal cell carcinoma (TCGA-RCC) subtyping. The results obtained show that attention regularization not only improves the predictive capacity of the model but also significantly increases its interpretability by identifying regions and patterns of high diagnostic value.

关键词： Hispathology imaging Multiple Instance learning Attention mechanisms Attention regularization Explainable Deep learning

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Cross-Lingual Text Classification by using Bidirectional Encoder Representations from Transformers based Transfer learning 1

Cross-Lingual Text Classification by using Bidirectional Enc...

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1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Abbas, Haayder M. Raju, P. V. Narasimha Shanthi, T. Venkateshwaran, P. Durgadevi, G. The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Sagi Rama Krishnam Raju Engineering College Department of Information Technology Bhimavaram India Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent days, the growing demand for automated language processing had brought Cross-Lingual Text Classification (CLTC) into focus as powerful approach for categorizing text across multiple languages which enabled users to classify documents in different languages. Traditional approaches for CLTC had faced several issues which include translation errors, dependency on parallel data and language representation gaps. Therefore, this research proposes Bidirectional Encoder Representations from Transformers (BERT) based Transfer learning. Initially, data is collected from AG news, preprocessed by using Punctuation removal and Lemmatization which reduces irregularities in text data. After that, feature extraction is performed by using Term Frequency-Inverse Document Frequency (TF-IDF) which calculates word frequencies across documents by creating a high-dimensional feature vector representation. Further, features like semantic and contextual features are selected by using Mutual Information (MI). Finally, classification is done by using BERT-based transfer learning. The proposed BERT-based Transfer learning achieved better results in terms of accuracy (95.5%) when compared with existing Indian Sentence BERT (IndicSBERT) model. © 2025 IEEE.

关键词： Semantics

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21st EuroXR international conference, EuroXR 2024

21st EuroXR International Conference, EuroXR 2024

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21st EuroXR international conference, EuroXR 2024

ISBN: (纸本)9783031785924

The proceedings contain 15 papers. The special focus in this conference is on EuroXR. The topics include: Embodied Acts for Testing Mixed Reality Low-Fidelity Prototypes that Convey Intangible Cultural Heritage: Method and Case study;impact of Acceleration and Angle in the Real Environment on Cybersickness;study on the Influence of the Total Front HMI Size in intelligent Cabins on the Drivers’ Eye Movement Behavior;contrast and Hue in Depth Perception for Virtual Reality: An Experimental study;context-Based Annotation Visualisation in Virtual Reality: A Use Case in Archaeological data Exploration;3-2-3 Multi-AI Segmentation Framework: LoD-Based, Incremental Segmentation of 3D Scan data Using Any 2D AI;Enhancing Materiality in Adaptive BRDF Display with Light Ray Diffusion;a Tangible Interface for Creating Virtual Cutaways in Mixed Reality;MarkAR: Exploring the Benefits of Combining Microgestures and Mid-Air Marks to Trigger Commands in Augmented Reality;visual Search in People with Macular Degeneration: A Virtual Reality Eye-Tracking study;Engagement and Attention in XR for learning: Literature Review;exploring students’ Acceptance of Augmented Reality Technologies in Education: An Extended Technology Acceptance Model Approach;a Hybrid Collaboration Design for a Large Scale Virtual Reality Training Environment to Fulfil the Belongingness Needs of Maslow’s Theory.

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The Practical Reality of Effecting Change to Pedagogies for Global Responsibility in engineering Education 27th

The Practical Reality of Effecting Change to Pedagogies for ...

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27th international conference on Interactive Collaborative learning, ICL 2024

作者： Truslove, Jonathan Hitt, Sarah Jayne Crichton, Emma Engineers Without Borders UK London United Kingdom Hereford United Kingdom

ISBN: (纸本)9783031856488

Change is complex, uncomfortable, frustrating but importantly, worth the investment - to teach better and to make positive and long-lasting improvements within our educational system. However, navigating the complexities of educating the next generation of engineers is no easy feat. Education is key to helping engineering adapt to 21st-century challenges because it develops the people within a system that is itself complex. This paper shares practical experience and learnings from the experience of two organisations, Engineers Without Borders UK (EWB-UK) and the UK’s engineering Professors Council (EPC). Both have navigated this landscape to engage people to co-create resources that meaningfully integrate global responsibility into engineering education by changing how we teach. Firstly, through the engineering for People Design Challenge, which aims to bring to life real-world challenges through a project-based learning pedagogy. Secondly, the upskilling and guidance resources for educators through the EPC’s Ethics and Sustainability Toolkits which are designed to help engineering educators improve their own knowledge and skills to integrate this content into teaching, and EWB-UK’s Reimagined Degree Map which supports engineering departments to navigate the decisions that are urgently required to prepare students for 21st-century challenges. We analyse data, testimonials and insights from educators using the co-created resources who have made changes to their education practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： engineering education

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Modified Term Frequency and Inverse Document Frequency with Optimized Deep learning Algorithm based Fake News Detection 1

Modified Term Frequency and Inverse Document Frequency with ...

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1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Prashanth, V. Alabdeli, Haideer Thottan, Sunaina Sangeet Ananthi, S. Saint Leo University Department of Computer Science FL United States Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Artificial Intelligence and Data Science Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLstM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLstM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLstM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model. © 2025 IEEE.

关键词： Deep learning

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