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international Symposium on Artificial Intelligence and signal processing (AISP)

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Secure data Sharing and Mining in Distributed Environments 3

Secure Data Sharing and Mining in Distributed Environments

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dari, Sukhvinder Singh Alawadi, Ahmed Hussein Verma, Ankit Sheela, M. Sahaya Jebaseeli, S. Kanmani Symbiosis Law School Nagpur Symbiosis International Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Electronics & Communication Engineering Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology India

ISBN: (纸本)9798350342338

In today's interconnected world, distributed systems have emerged as the backbone for facilitating global collaborations and real-time data exchanges. As more organizations migrate to distributed architectures, the ability to share and mine data securely across disparate networks becomes paramount. Secure data sharing ensures that information is accessible to only authorized entities, while secure data mining aims to extract meaningful patterns from vast datasets without compromising privacy or integrity. This paper delves into the intricacies of implementing robust security protocols in distributed environments, emphasizing the Role-Based Access Control (RBAC) and transaction processing mechanisms. Through a comprehensive exploration, we aim to highlight the importance of marrying security with functionality to harness the full potential of distributed systems without sacrificing confidentiality and trust. © 2023 IEEE.

关键词： data mining Distributed systems Role-Based Access Control (RBAC) secure data sharing transaction processing

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Design of an Intelligent processing System for Business data Analysis Based on Improved Clustering Algorithm 3

Design of an Intelligent Processing System for Business Data...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wang, Ning Central South University Hunan Changsha410083 China

In order to further optimize the processing of business data and increase the effectiveness and accuracy of business data analysis, this research investigated the design of an intelligent processing system for business data analysis based on improved clustering algorithms. Some issues with business data analysis can be efficiently resolved by enhancing clustering algorithms, resulting in the provision of more intelligent, thorough, and precise business data analysis services for businesses and organizations. The improved clustering algorithm is compared with the traditional clustering algorithm to analyze the experimental data, the results showed that the maximum clustering effect of the improved clustering algorithm was above 80%, the highest robustness was 91.65%, and the highest point in algorithm performance was 90.97%. From these three aspects, it can be seen that improved clustering algorithms have more advantages than traditional clustering algorithms. Therefore, improving clustering algorithms can make business data processing more efficient, accurate, and reliable, providing enterprises and organizations with higher quality business data analysis services. © 2023 Elsevier B.V.. All rights reserved.

关键词： Clustering algorithms

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From GPS to Maps: Path Reconstruction Techniques for Understanding People Mobility with Low-Sampling-Rate data 3

From GPS to Maps: Path Reconstruction Techniques for Underst...

引用

3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Rakhman, Arkham Zahri Nugroho, Lukito Edi Widyawan, Widyawan Utami, Nurul Khairiza Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Urban mobility is a critical aspect of city planning and transportation management, with the Global Positioning System (GPS) playing an essential role in analyzing and understanding patterns of human movement. Particularly using smartphones, GPS provides highly detailed digital trace data, enabling in-depth analysis of human movement behavior. However, there are significant challenges when working with GPS datasets that have a low frequency, which often occurs due to storage capacity limitations or data transmission costs. This study explores methodologies for reconstructing movement paths from low-frequency GPS datasets. The process includes several key steps: data pre-processing involving cleaning and normalizing data;aligning GPS data with existing road networks using the nearest-neighbor KD-Tree algorithm;and accurate path reconstruction. In addition, the study develops a method for time interpolation estimation to determine movement times between the aligned coordinate points. The findings of this research indicate that with the proposed approach, movement paths can be reconstructed effectively and accurately, even from data with low sampling frequencies. This approach promises a wide potential application, ranging from transportation infrastructure planning to the development of more sophisticated navigation systems. With ongoing research, the integration of additional sensors and machine learning techniques is anticipated to further enhance the accuracy and utility of this methodology. © 2023 IEEE.

关键词： Global positioning system

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Object-level data Augmentation for Visual 3D Object Detection in Autonomous Driving

Object-level Data Augmentation for Visual 3D Object Detectio...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cheng, Haoran Xu, Junkai Peng, Liang Yang, Zheng He, Xiaofei Wu, Boxi Zhejiang University Hangzhou310027 China FABU Inc. Hangzhou310030 China

ISBN: (纸本)9798350368741

data augmentation plays an important role in visual-based 3D object detection. Existing detectors typically employ image/BEV-level data augmentation techniques, failing to utilize flexible object-level augmentations because of 2D-3D inconsistencies. This limitation hinders us from increasing the diversity of training data. To alleviate this issue, we propose an object-level data augmentation approach that incorporates scene reconstruction and neural scene rendering. Specifically, we reconstruct the scene and objects by extracting image features from sequences and aligning them with associated LiDAR point clouds. This approach is intended to conduct the editing process within a 3D space, allowing for flexible object manipulation. Additionally, we introduce a neural scene renderer to project the edited 3D scene onto a specified camera plane and render it onto a 2D image. Combined with scene reconstruction, it overcomes the challenges stemming from 2D/3D inconsistencies, enabling the generation of object-level augmented images with corresponding labels for model training. To validate the proposed method, we apply our method to various multi-camera 3D object detectors, consistently boosting the performance. © 2025 IEEE.

关键词： data augmentation Visual 3D object detection

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Enhancing Colonoscopy Image Quality with CLAHE in the GASTROLAB dataset 3

Enhancing Colonoscopy Image Quality with CLAHE in the GASTRO...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Karthikha, R. Najumnissa Jamal, D. B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Electrical and Electronics Engineering Chennai India B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798350343632

This research focuses on improving image quality in the context of colonoscopy, a critical procedure for diagnosing gastrointestinal conditions such as colorectal polyps, which can lead to cancer. The study highlights the challenges posed by the large volume of frames generated during colonoscopy procedures, which necessitates the use of automated systems to detect anomalies. Despite the fact that some datasets contain low-quality images, it emphasizes the importance of various datasets in gastrointestinal disease research and machine learning. The primary goal of this work is to improve the image quality of the GASTROLAB dataset, emphasizing the significance of accurate results derived from high-quality data. The study looks into various image enhancement techniques, with a focus on Contrast Limited Adaptive Histogram Equalization (CLAHE), which produces superior results in image quality metrics like Peak signal to Noise Ratio (PSNR) and), Structural Similarity Index (SSIM), which are 32.26 and 0.912, respectively. The findings demonstrate the utility of PSNR and SSIM as image quality assessment tools, while also emphasizing the importance of clinical validation and expert judgement in medical image evaluation. The study concludes with findings that support CLAHE as a preferred method for improving image quality, particularly when visualizing small polyps during colonoscopy. © 2023 IEEE.

关键词： Colonoscopy Contrast Limited Adaptive Histogram Equalization (CLAHE) GASTROLAB dataset Image Enhancement Peak signal to Noise Ratio (PSNR) Structural Similarity Index (SSIM)

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High-precision measurement of satellite-borne SAR signal bandwidth based on channelized waterfall diagrams 3

High-precision measurement of satellite-borne SAR signal ban...

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3rd international conference on Optics and Communication Technology, ICOCT 2023

作者： Liu, Guoman Zhang, Jiazhen Feng, Yi Peng, Rui Beijing Institute of Technology Beijing100081 China Jiuquan Satellite Launch Center Jiuquan732750 China

ISBN: (纸本)9781510672529

Spaceborne SAR has a vast scope of operation that is not restricted by national borders or airspace, so it has been extensively utilized in diverse fields. Due to its non-cooperation, it is difficult for the reconnaissance antenna to obtain the main lobe signal for parameter analysis and working mode identification of the SAR signal, especially when the spaceborne SAR imaging area is far from the location of the reconnaissance antenna. The reconnaissance receiving antenna can only receive a weak SAR signal with a low signal-to-noise ratio, resulting in a significant error in signal parameter measurement and making it unable to fulfill the analysis of its intended operation. Aiming to address the issue of significant measurement errors in spaceborne SAR signal bandwidth parameters under low signal-to-noise ratio conditions, a high-precision measurement method for spaceborne SAR signal bandwidth is proposed. This method is based on a channelized waterfall diagram, which involves accumulating the channelized signal twice to create the chart. The chirp signal on the waterfall chart is then extracted using an image processing technique to accurately measure the signal bandwidth. Additionally, scene simulation under low signal-to-noise ratio conditions is conducted. The simulation results show that even when the signal-to-noise ratio is as low as -10dB, the error in signal bandwidth measurement does not exceed 17%, indicating stable measurement accuracy. © 2023 SPIE.

关键词： Synthetic aperture radar

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Estimation of Respiratory Displacements Using a Nonlinear Observer 3

Estimation of Respiratory Displacements Using a Nonlinear Ob...

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3rd Modeling, Estimation and Control conference (MECC)

作者： Ba, M. Pianosi, Paolo Rajamani, R. Univ Minnesota Twin Cities Minneapolis MN 55455 USA

ISBN: (纸本)9781713872344

This paper develops and evaluates a wearable sensor-based system for the estimation of three-dimensional thoracoabdominal displacements. Such estimation is useful for a number of respiratory diagnosis applications, including detection of paradoxical breathing, quantification of tidal volume, and determining efficiency of mechanical ventilation. The use of inertial sensors on the body to estimate respiratory displacements is challenging due to the tilting of the body that occurs with breathing, causing continuous changes in the gravity component at the same frequency as the breathing frequency. A method to estimate the front-to-back and side-to-side tilt angles is developed using a nonlinear observer. The global stability of the observer is analyzed using Lyapunov analysis. Example experimental results are presented using data from a supine subject at various breathing frequencies and tidal volumes. Displacement estimates are found to be typically within +/- 1 mm compared to a gold standard reference for most data sets. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Biomedical signal processing respiration monitoring nonlinear observer inertial measurement units wearable sensors

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Knowledge Graph-Based Approach to Unstructured Multi-Source data Fusion 3

Knowledge Graph-Based Approach to Unstructured Multi-Source ...

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3rd international conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2023

作者： Mao, Zhengxiong Chuan, Tao Zhou, Jing Su, Wenwei He, Yingjun Mei, Donghui Li, Chenglin Information Center of Yunnan Power Grid Co. Ltd Kunming650011 China

ISBN: (纸本)9781643684444

The current unstructured multi-source data fusion has insufficient application of data attributes, which leads to unsatisfactory data fusion results. To this end, an unstructured multi-source data fusion method based on knowledge graph is proposed. Building a data tracking model to integrate unstructured multi-source datasets. processing unstructured multi-source datasets. Associating data based on knowledge graphs. Alignment and complementary processing of data attributes to correlate unstructured multi-source data attribute fusion. The experiments show that the average fusion of the method reaches 0.92152. The fusion of unstructured multi-source data is substantially improved and has high practical application value. © 2023 The Authors.

关键词： data fusion

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Early Prediction and Grading of Diabetic Retinopathy using Fusion Based Learning System 3

Early Prediction and Grading of Diabetic Retinopathy using F...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Dhanalakshmi, V. Baskar, K. Ayyadurai, Sathiya Suchithra, B. Kongunadu College of Engineering and Technology Department of Cse Thottiyam Trichy India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Thottiam Trichy India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India Sri Eshwar College of Engineering Department of Cse Coimbatore India

ISBN: (纸本)9798350361186

Diabetic retinopathy (DR), a visual impairment caused by long-term diabetes mellitus, is a type of diabetes that destroys the retinal blood vessels. Determining and quantifying this form of diabetes from retinal pictures takes time and experience. The classification of DR necessitates the grading of multiple aspects. Finding the severity is a process that ophthalmologists must perform with great care and attention, and it may be error-prone. Convolutional neural networks (CNNs) with a transfer learning technique have been proposed to automatically diagnose diabetic retinopathy by gauging its severity. The enhanced version of VGG16 performed in a very admirable way. The rate of training and validation accuracy increased steadily with each subsequent frame. Overall, a VGG-16 model trained on the DIARETDB1 dataset achieved strong performance of 0.89 quadratic weighted kappa on the task of detecting diabetic retinopathy from fundus images. This suggests the approach is effective for this medical imaging application. © 2024 IEEE.

关键词： Grading

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