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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Jin, Xiaodan Han, Xuefei Nan, Hai Xue, Zhijian School Of Computer Science And Engineering Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350361445

Recently, 3D Gaussian Splatting (3D-GS) has gained popularity in novel-view scene synthesis, achieving high fidelity and efficiency. However, the rendering quality of 3D-GS is still not up to the quality of the best methods in NeRF, and serious artifacts will occur when changing the sampling rate and changing the focal length or camera distance. Another significant problem is that interactive real-time rendering of complex scenes also has great limitations. To address this challenge, we have come up with a new rendering system, UE-GS, that blends 3D-GS with traditional rendering techniques in Unreal Engine 5 (UE5) to achieve higher quality visuals. Our approach draws inspiration from UE5's Level of Detail (LOD) technology. When changing the focal length or camera distance, we dynamically render LOD for different viewing levels, and replace the dilatation filter in 3D-GS with 2D EWA filtering, effectively alleviating the problems of aliasing and dilatation artifacts. Use octree to optimize spatial point data in complex scenes (40 million Gaussian +) and customize rendering components so that they are not limited by particle counts in UE5. We validated our approach on a variety of data sets and scenes that retain visual quality, achieving better rendering quality and faster training and reasoning speed than 3D-GS, while retaining the benefits of real-time performance. In addition, rendering in UE5 helps with scene editing in subsequent stages. © 2024 IEEE.

关键词： Engines

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Hierarchical Access Control for Bilateral Security in Medical Privacy Data Retrieval 22

Hierarchical Access Control for Bilateral Security in Medica...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Jianshe Xu, Guangping Wen, Yanyan Hao, Wei Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798331509712

The rapid growth of medical data in cloud servers offers a rich resource for medical research and analysis. However, the sensitive nature of this data necessitates robust access controls to ensure privacy and confidentiality. Balancing the utilization of valuable medical information with the protection of personal data privacy poses a significant challenge. To address this challenge, this paper proposes a Hierarchical Retrieval scheme with Bilateral security based on Attribute encryption (BAHR) to facilitate multi-level access control. Based on an attribute-based encryption architecture, the hierarchical access control categorizes users into distinct access groups, allowing for precise data retrieval while maintaining encryption integrity at specified levels. Furthermore, the proposed scheme ensures bilateral security, providing robust protection of patients' private data, database, and user access patterns within untrusted domains. Through security analysis and experimental evaluation, BA-HR demonstrates good performance regarding retrieval time, decryption time and storage space. © 2024 IEEE.

关键词： Differential privacy

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Enhancing Chest X-ray Analysis using Encoder-Decoder with GRU for Report Generation 4

Enhancing Chest X-ray Analysis using Encoder-Decoder with GR...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Pandey, Shashank Saha, Piyali Sharan, Gaurav Sandosh, S. Vellore Institute of Technology School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350343670

Despite the strides made in deep learning, the development of a reliable framework tailored explicitly for generating chest X-ray reports remains a persistent challenge. The study addresses critical gap by introducing an innovative architecture crafted to extract intricate features from chest X-ray images and articulate comprehensive diagnostic information within generated reports. The approach includes the utilization of DenseNet121 as the image feature extractor, leveraging its pre-trained knowledge from ImageNet. The strategic choice confers a pivotal advantage by transferring learned feature representations to the domain of chest X-ray analysis. Fine-tuning DenseNet121 through end-to-end training not only harnesses these pre-existing features but also tailors them specifically for the nuanced task of report generation. The approach circumvents overfitting and the prolonged training typically associated with training a Convolutional Neural Network (CNN) from scratch. An embedded layer augments the architecture by fostering semantic relationships between words, complementing the two-stage encoder-decoder structure. The design ensures a clear specialization: the CNN encoder excels in image feature extraction, while the GRU decoder focuses on sequence generation. The holistic end-to-end training methodology enables simultaneous learning of optimal feature representations and the intricate mapping between images and reports, representing a significant leap towards more effective and detailed chest X-ray report generation. Most importantly, the suggested architecture scores an astounding 87.55% accuracy rate, indicating not only a good BLEU score but also remarkable precision in text generation and diagnostic knowledge. It validates the model's effectiveness and highlights its potential to significantly improve the diagnostic accuracy and text generation quality of chest X-ray reporting. © 2024 IEEE.

关键词： Chest X-ray Convolutional neural network Deep learning DenseNet121 GRU Image caption generator

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A Comprehensive Study on Skin Cancer Detection Using Deep Learning 3

A Comprehensive Study on Skin Cancer Detection Using Deep Le...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Sharmiladevi, S. Venkatraman, Shravan Malarvannan, Santhosh Shriyans, A. Swathisree, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384598

Skin cancer, a widespread and potentially fatal malignancy, originates from the abnormal proliferation of skin cells, emphasizing the significance of early detection for effective treatment. Deep learning (DL) techniques, notably convolutional neural networks (CNNs), offer promise in the analysis of skin lesion images. This research study aims to compare various Convolutional Neural Networks (CNNs) classifier models using DL for skin cancer detection. The primary objective is to evaluate the effectiveness and performance of these CNN models in accurately classifying the type of skin cancer. To address the class imbalance, the data was first augmented, after which it was normalized to scale pixel values. Throughout the research, six CNN models were evaluated under different configuration settings, and the results were presented through tabulations, providing insights into their comparative performance. These outcomes contribute to the field of skin cancer detection by shedding light on the effectiveness of DL models. The analysis conducted in this study concludes that the highest results were recorded at an accuracy of 99.73% by ResNet50 using the Adam optimizer at a learning rate of 10-3 © 2024 IEEE.

关键词： Lung cancer

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Visual SLAM System Integrating Feature Extraction Based on Vision Transformer and Cloud-Edge technology

Visual SLAM System Integrating Feature Extraction Based on V...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Fu, Bo Yao, Xulin School of Computer Science and Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350372274

Deep learning-based visual SLAM (Simultaneous Localization and Mapping) has become one of the highly researched areas in recent years. Deep learning can be integrated with various modules of visual SLAM systems, including VO (Visual Odometry), mapping, feature extraction, localization, BA (Bundle Adjustment) optimization, loop closure, dataset handling, etc. In visual SLAM, traditional feature point extraction using deep learning networks is based on Convolutional Neural Network (CNN) architectures. This paper proposes a method that combines feature extraction based on the vision Transformer network with edge-cloud technology to improve the performance of visual SLAM systems. This method addresses scenarios where neural network-based SLAM systems encounter limited computing resources, frequent environmental changes, or require realtime response. Experimental results demonstrate that the fusion of feature extraction based on vision Transformer and edge-cloud technology significantly improves the localization, mapping, and real-time performance of visual SLAM systems. It exhibits excellent robustness and accuracy in various indoor and outdoor environments, indicating promising application prospects and dissemination value. ©2024 IEEE.

关键词： Convolutional neural networks

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Flexible U-Net with Transfer Learning for Optic Disc Segmentation in Glaucoma Detection 3

Flexible U-Net with Transfer Learning for Optic Disc Segment...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kalyanasundaram, V. Keerthi, A.J. Prethija, G. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Glaucoma, a progressive optic neuropathy leading to irreversible visual field defects and heightened intraocular pressure, underscores the critical need for early detection to prevent vision loss. Automating glaucoma detection can significantly reduce diagnosis time for ophthalmologists. Optic disc segmentation is an essential step in detection and diagnosis of glaucoma. In the experimental study, a pre-trained deep learning model called Flexible U-Net is utilized. This model incorporates EfficientNetB0 and EfficientNetB1 as the encoder components within the U-Net architecture. The dataset used for the evaluation of their efficacy is ORIGA. By analyzing model performance, it is found that EfficientNetB1 demonstrates superior accuracy in glaucoma detection compared to other models, emphasizing its potential for enhancing diagnostic efficiency in clinical practice. The results indicate that EfficientNetB1 U-Net has achieved impressive segmentation performance based on several metrics, including a dice coefficient of 0.9476, a Jaccard score of 0.9005, an accuracy of 0.9984, and an AUC of 0.99 on the ORIGA dataset. © 2024 IEEE.

关键词： EfficientNet Glaucoma Detection Optic Disc Segmentation U-Net

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A dynamic Trust Management Model for Healthcare Supply Chain Based on Blockchain technology 3

A dynamic Trust Management Model for Healthcare Supply Chain...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Ma, Xihong Wang, Yongli Liu, Dongmei School of Computer Network and Space Security Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506582

With the increasing requirements for supply chain security, traceability and efficiency in the healthcare industry, existing centralized or decentralized management systems show obvious limitations in dynamic environments, especially in terms of inflexible rights management, inefficient data sharing and insufficient system transparency. In order to solve these problems, this paper proposes a multi-user rights-confirmation and responsibility assignment method based on dynamic access control and smart contracts. The method ensures the security and transparency of supply chain data by dynamically evaluating user role attributes and behavioral patterns, and recording and adj usting permissions in real time using smart contracts. The experiments are conducted in a blockchain environment, and the results show that the method significantly improves the supply chain data sharing efficiency and system transparency, enhances the responsiveness to market changes, and improves the overall security and risk management level at the same time. © 2024 IEEE.

关键词： Smart contract

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Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1725-1737页

作者： Weiqing Sun Wei Liu Jie Zhang Kunpeng Tian School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power *** energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ***,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of *** paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power *** upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the *** fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of ***,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation *** the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed *** simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road ***,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

关键词： Mobile energy storage system economic dispatch bi-level optimization model road congestion fuzzy constraint

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Interpretable Clustering of Spatio-Temporal Data by using SHAP Method 10

Interpretable Clustering of Spatio-Temporal Data by using SH...

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10th International Conference on Web Research, ICWR 2024

作者： Younesi, Sara Rahmani, Hossein Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

Nowadays, the simultaneous consideration of spatial features and temporal features in the form of spatiotemporal data has been considered in various applications. Among the most important of these applications, we can mention the classification of drivers, the personality of users, the prediction and monitoring of the route and many other cases. Due to the lack of labels for this type of data, unsupervised methods are the only analytical methods on the table. Today, machine learning or deep learning algorithms are very popular and widely used due to the many successes they have had in solving various problems, but due to the black box nature of these methods, the internal mechanism of these models is unclear to users. On the other hand, in addition to the high accuracy of the model, the interpretability and root-finding of analyzes based on spatial-temporal data are very important. In this paper, we intend to cluster spatio-temporal data by focusing on the interpretability of results and selected interpretable features. © 2024 IEEE.

关键词： Learning algorithms

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MRI Brain Tumor Segmentation and Classification using different deep learning models

MRI Brain Tumor Segmentation and Classification using differ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Modi, Dhruvin R. Senthil Kumar, A.M. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Brain tumors have significant challenges in diagnostic precision due to their complex morphological subtleties evident in MRI (Magnetic Resonance Imaging) scans. Clinical Decision Support Systems (CDSS) have become indispensable in this regard, with MRI brain tumor segmentation and classification at their forefront. This comprehensive survey deals with the profound impact of deep learning techniques on MRI brain tumor analysis, particularly within the context of CDSS. Emphasizing the models validated against the various Dataset, the exploration ranges from the intricate convolutional processes of CNNs (Convolutional Neural Networks) to the advanced self-attention mechanisms offered by recent Transformer-based methodologies. The significance of 3D datasets is underscored, given their ability to offer detailed and holistic representations of brain tumors. Metrics such as DSC (Dice Similarity Coefficient), JI (Jaccard Index), and sensitivity rates serve as pivotal benchmarks to evaluate the efficiency of these deep learning models. Moreover, this survey investigates the performance trade-offs inherent within these methodologies, pointing towards potential avenues for further enhancement and adaptability. By synthesizing this expansive body of knowledge, the goal is to harmonize cutting- edge technological strides with tangible clinical utility, thereby refining diagnostic accuracy and forging a brighter traj ectory for patient care in the realm of brain tumor studies. © 2024 IEEE.

关键词： Magnetic resonance imaging

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