The proceedings contain 169 papers. The topics discussed include: neurodynamic mapping for cybersickness prediction in 3D game immersion;carcinoma detection using deep learning;emerging trends in software engineering:...
ISBN:
(纸本)9798331522100
The proceedings contain 169 papers. The topics discussed include: neurodynamic mapping for cybersickness prediction in 3D game immersion;carcinoma detection using deep learning;emerging trends in software engineering: implications for development and efficiency;comparative study of brain tumor types interaction with various edge detection operators;design and implementation of open loop application of two-stage operational amplifier;an overview and investigational survey of existing electric vehicles;emergency alert system for visually impaired and loss of hearing people;gastrointestinal lung cancer disease classification via hybrid deep architecture trained with improved pattern features;enhancing summarization of legal text documents using pre-trained models;and an empirical literature review on multi class data stream learning with class imbalance problem in real world data sources.
Liver tumors present a significant challenge in the realm of public health, requiring early identification and precise prognostication to enhance patient outcomes. The current investigation delves into the utilization...
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The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residu...
ISBN:
(纸本)9781643685762
The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residual learning for fruits of ceremonial plants recognition;PiVisionSort: integrating image processing and machine learning for material recognition on conveyor belts;intelligent detection of potholes using SSD algorithm and auto-alert notification system for user;the use of motion and gaze features to detect speaking intention in VR-mediated communication environments;optimized retinal vessel segmentation using IS-Net and high-resolution dataset;a framework for adopting machine learning in the clinical domain;and a study on a domain BERT-based named entity recognition method for faulty text.
Taking underwater robots project as an example, this paper discusses the innovation of the teaching mode of engineering drawing courses under the guidance of a project. The traditional engineering drawing course often...
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This paper presents a robust theoretical framework to integrate blockchain-based security and privacy mechanisms with quantum machine learning (QML) in the edge computing domain within 6G networks. The burgeoning depl...
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ISBN:
(纸本)9783031762727;9783031762734
This paper presents a robust theoretical framework to integrate blockchain-based security and privacy mechanisms with quantum machine learning (QML) in the edge computing domain within 6G networks. The burgeoning deployment of edge devices necessitates secure, privacy-preserving, and trustworthy infrastructures to support collaborative QML tasks while upholding data confidentiality at the network periphery. Leveraging blockchain technology's decentralized and immutable ledger capabilities, this framework manages access control, ensures data provenance, and verifies integrity in edge computing environments. Furthermore, integrating quantum-resistant cryptographic primitives is explored to fortify defenses against potential threats from quantum adversaries. In addition to these considerations, the paper incorporates the theory of quantum probability within the framework, particularly in the context of the central limit theorem, to account for the probabilistic nature of quantum systems and its implications on statistical inference in QML tasks. Detailed latency analysis reveals that blockchain processing time increases with transaction complexity, quantum processing time grows more slowly, and 6G transmission time remains constant due to high bandwidth capabilities. Incorporating machine learning components such as data preprocessing and model inference times provides a comprehensive understanding of edge computing performance. Combining blockchain-based security and privacy measures with QML techniques like federated learning and differential privacy, the envisioned framework strives to establish a secure and trusted ecosystem for collaborative QML tasks at the network edge. This theoretical endeavor, enriched by quantum probability theory and detailed latency analysis, lays a solid groundwork for further research and development in this burgeoning interdisciplinary domain, promising advancements in edge computing applications' efficiency, reliability, and security w
HEMT devices have potential to handle the fast processing in applications such as real-time diagnostics utilizing the artificial-intelligence (AI) enhanced abilities. Along-with this HEMT devices can be extensively us...
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Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and...
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Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural Networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.
Placements are of utmost significance to academic organizations and college students. A strong foundation for the professional field is built up for the student beforehand, and a positive placement report gives a scho...
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Machine learning models are optimization models that make it possible to gather data, evaluate it, and provide the experts and management with the reports they need to make the best decisions. This study uses quantita...
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