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Biomedical Data Privacy Enhancement Architecture Based on Multi-Keyword Search Technique 7

Biomedical Data Privacy Enhancement Architecture Based on Mu...

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World Engineering, Science and Technology Congress (ESTCON) / 7th international conference on Intelligent and Advanced System (ICIAS)

作者： Singh, Niharika Lakhina, Upasana Elamvazuthi, I. Jangra, Ajay Singh, Ashutosh K. Univ Teknol PETRONAS Dept Elect & Elect Engn Bandar Seri Iskandar 32610 Perak Malaysia Kurukshetra Univ Univ Inst Engn & Technol Kurukshetra Haryana India NIT Kurukshetra Haryana India

ISBN: (纸本)9781538672693

the Collaborative healthcare cloud environment offers potential benefits, including cost reduction and improvising the healthcare quality delivered to patients. though, such scenarios experiences substantial challenges in case of the privacy and security of the exceptionally sensitive information contained in Electronic Health Records (EHRs). through this paper, a Hierarchical tree-based architecture is proposed. the work is based upon multi-keyword search technique which incorporates secure search using stemming and lemmatization algorithms to improve the process efficiency and privacy detailing. It helps biomedical departments and patients to connect easily and process their queries without any intruder's intervention. Furthermore, performance of the proposed solution is empirically indorsed through the simulation experiments.

关键词： Biomedical systems Cloud computing Data privacy EHRs Fog computing Healthcare collaboration Multi-keyword search thermal imaging ultrasound

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the GPU on biomedical image processing for color and phenotype analysis

The GPU on biomedical image processing for color and phenoty...

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7th IEEE international conference on Bioinformatics and Bioengineering, BIBE

作者： Ruiz, Antonio Ujaldón, Manuel Andrades, José Antonio Becerra, José Huang, Kun Pan, Tony Saltz, Joel Computer Architecture Dept. University of Malaga ETSI Informática Malaga 29071 Spain Cell Biology Genetics and Phisiology Dept. University of Malaga Faculty of Sciences Malaga 29071 Spain Biomedical Informatics Dept. Ohio State University 3197 Graves Hall 333 W. 10th Ave. Columbus OH 43210 United States

ISBN: (纸本)1424415098

the computational power and memory bandwidth of graphics processing units (GPUs) have turned them into attractive platforms for general-purpose applications. In this paper, we exploit this power in the context of biomedical image processing by establishing a cooperative environment between the CPU and the GPU. We deal with phenotype and color analysis on a wide variety of microscopic images from studies of cartilage and bone tissue regeneration using stem cells and genetics involving cancer pathology. Both processors are used in parallel to map algorithms for computing color histograms, contour detection using the Canny filter and pattern recognition based on the Hough transform. Task, data and instruction parallelism are exploited in the GPU to accomplish performance gains between 4x and 100x more than the typical CPU code. ©2007 IEEE.

关键词： Graphics processing unit

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Resource-Constrained Image Processing for Scientific CubeSats 6

Resource-Constrained Image Processing for Scientific CubeSat...

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6th international conference on algorithms, computing and systems, icacs 2022

作者： Kozaris, Ioannis Gioulekas, Fotios Katsaros, Panagiotis School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Directorate of Informatics 5th Regional Health Authority Larissa Greece

ISBN: (纸本)9781450397407

CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained nature of such systems poses significant challenges in software design, for the system payload. We focus on image compression and processing for scientific space missions, a vital functionality, due to the limited onboard storage capacity and the infrequent time periods, in which a satellite in orbit can send images to the ground, through a limited bandwidth connection. For CubeSat systems, the performance of image processing depends - even more than other space systems - on trade-offs, which are influenced by size, power and complexity constraints. In this context, we present the design challenges and the image compression/processing solution developed, for a university CubeSat built to carry on a biological experiment. All changes of the performance capability in the different parts of the payload image data chain (image resolution, onboard storage capacity, communication channel bandwidth and error characteristics) are taken into account. Moreover, any decision that increases the risk of incorrect reception of images from the experiment is weighted against the benefits of improved performance. We provide experimental results that show how our image processing solution resolves the associated trade-offs, while fulfilling the mission requirements. © 2022 ACM.

关键词： Image compression

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A framework for detailed objective comparison of non-rigid registration algorithms in neuroimaging

引用

Medical Image computing and Computer-Assisted Intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Crum, William R. Rueckert, Daniel Jenkinson, Mark Kennedy, David Smith, Stephen M. Division of Imaging Sciences Thomas Guy House Guy's Hospital London SEI 9RT United Kingdom Department of Computing Imperial College London SW7 2AZ United Kingdom Oxford Centre for Functional Magnetic Resonance Imaging of the Brain John Radcliffe Hospital Headington Oxford OX3 9DU United Kingdom Center for Morphometric Analysis Department of Neurology MGH 13th Street Charlestown MA 02129 United States

ISBN: (纸本)3540229760

Non-rigid image registration is widely used in the analysis of brain images to the extent it is provided as a standard tool in common packages such as SPM. However the performance of algorithms in specific applications remains hard to measure. In this paper a detailed comparison of the performance of an affine, B-Spline control-point and viscous fluid registration algorithm in inter-subject brain registration is presented. the comparison makes use of highly detailed expert manual labellings of a range of structures distributed in scale and in location in the brain. the overall performance is B-Spline, fluid, affine (best first) with all algorithms struggling to improve the match of smaller structures. We discuss caveats, evaluation strategies for registration and implications for future registration-based neuroimaging studies. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Medical imaging

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Automated wireless structural health monitoring and control using tuned liquid column dampers 7

Automated wireless structural health monitoring and control ...

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7th international conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2019

作者： Dragos, Kosmas Manolis, Georgios D. Smarsly, Kay Chair of Computing in Civil Engineering Bauhaus University Weimar Coudraystrasse 7 Weimar99423 Germany Laboratory of Experimental Mechanics Department of Civil Engineering Aristotle University of Thessaloniki University Campus Thessaloniki54124 Greece

ISBN: (纸本)9786188284463

Current trends in structural design and retrofit frequently dictate artificially modifying the structural behavior in an attempt to target unfavorable loading scenarios expected to compromise the fulfilment of stability and serviceability criteria. Particularly in structures where operational requirements result in adopting unconventional structural systems, usually falling outside the scope of codified regulations and guidelines, adapting the structural behavior by means of structural subsystems has been widespread. Referred to as "structural control", enhancing the ability of structures to adapt to unfavorable loading conditions encompasses attaching structural subsystems, such as dampers, to the main structure. Moreover, the increasing application of structural health monitoring (SHM) for structural assessment and maintenance has fueled research in coupling SHM with structural control, thus paving the way for developing structural systems capable of autonomously adapting structural properties. In this context, this paper describes a preliminary approach to automated structural health monitoring and control by coupling a wireless SHM system with a tuned liquid column damper (TLCD). the wireless SHM system is equipped with embedded algorithms managing SHM tasks, such as data sampling and data processing, and with actuators able to modify the properties of the TLCD based on any user-defined control algorithm. the proposed approach serves as a first step towards fully automated structural health monitoring and control and is validated through laboratory tests on a four-story shear frame structure equipped with a wireless SHM system and a TLCD on the top story. Copyright © 2019 COMPDYN Proceedings. All rights reserved.

关键词： Wireless sensor networks

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Optimizing Real-Time Medical Diagnostics with Adaptive AI Algorithm 7

Optimizing Real-Time Medical Diagnostics with Adaptive AI Al...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Ahmed, Zahid Pandey, Abha Sharma, Ashish Tandon, Anisha Teena Kumar, Sujeet Vivekananda Global University Department of Computer Science and Application Rajasthan Jaipur India Jagannath International Management School Vasant Kunj New Delhi India Jiet Department Of Technology Rajasthan Jodhpur India Uttar Pradesh Sector 110 Noida India Gl Bajaj Institute of Management Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Early diagnosis and treatment of disease are key in concentrations done for medical diagnostics. Technology is moving fast and with that, there are always new methods for diagnosing patients being created, the improvement of accuracy and speed even in medical diagnostics. Unfortunately, the abundance of data collected by these tools makes it prohibitively difficult to accurately interpret and analyse real-time results. this is where Adaptive AI (Artificial Intelligence) algorithms are useful. this flexibility of adaptive AI systems makes them well-suited to the real-time medical diagnostics that are required. In addition, through the use of machine learning techniques these algorithms are capable to process and analyse large scale datasets at a speed that is orders faster than any human expert. they can also detect patterns and feature outliers within the data that traditional diagnostic methods may overlook. Using adaptive AI algorithms is anticipated to optimize the diagnostic process of medical conditions and increase accuracy thus allowing faster diagnosis. It could mean finding diseases earlier, and therefore treating they better as well as costing healthcare less. these algorithms are able to constantly learn from new data which allow them not only to improve with time, but also adapt in order effectively combat changing disease patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved YOLOv7 UAV image small target detection algorithm 7

Improved YOLOv7 UAV image small target detection algorithm

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7th international conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Yingbo, Wang Zhi, Liu School of Software Liaoning Technical University Huludao125105 China School of Innovation Practice Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9798400708688

In order to improve the detection efficiency of small targets in UAV images in traffic systems, a small target detection algorithm for UAV images in traffic systems based on improved YOLOv7 is proposed. In the feature fusion network, the small target detection layer is added to extract feature information of different scales to improve the perception ability and positioning accuracy of small targets. Deep separable convolution ( DSConv ) is introduced into the feature extraction and feature fusion network of the model, and feature extraction is performed on each channel without introducing additional parameters, which reduces the computational complexity and memory occupation of the network and enhances the ability of the network to extract target features. the nearest upsampling module of the original model is replaced by the lightweight operator Carafe, which expands the receptive field of the model and improves the quality and accuracy of the feature map. In the feature extraction and feature fusion connection part of the model, the CBAM hybrid attention mechanism is introduced to dynamically adjust the channel and spatial information in the feature map, and improve the model 's ability to capture the important features of small targets in the traffic system under complex background. the experimental results show that compared with the original algorithm, the improved YOLOv7 algorithm improves the mAP by 4.5 percentage points and the recall rate by 5 %. Compared with the current mainstream algorithms, the improved algorithm can effectively improve the detection accuracy of small targets in UAV images in traffic systems, and significantly improve the false detection and missed detection of small target images in complex backgrounds. © 2023 ACM.

关键词： Drones

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Audio Compression Using Quantum Neural Network 1

引用

7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Aher, Rani Nandkishor Aher, Nandkishor Daulat Symbiosis Centre for Information Technology Symbiosis International University Pune India Tech Mahindra Limited Hinjawadi Phase 3 Rajiv Gandhi Infotech Park Maharashtra Pune India

ISBN: (数字)9789819908387

ISBN: (纸本)9789819908370

Data compression is a common feature of current information processing and has a broad range of uses. Audio compression is critical in database applications such as storage and transmission. the difficulty of lowering the number of bits required to represent digital audio is addressed via audio compression. It is used to reduce redundancy by reducing irrelevant and redundant data. Recent developments in deep learning have motivated researchers to investigate challenges involving highly structured data utilizing unified deep network models. Due to the necessity for discrete representations that are difficult to train, the construction and design of such models for compressing audio signals have proven difficult. the purpose of this study is to concentrate on quantum neural networks for compressing audio files. As a result, a quantum-based autoencoder capable of compressing audio data into a low-dimensional space is critical for achieving automatic audio compression, and in the decoder part, it decompresses the quantum audio signals by using deconvolution layers. Finally, the quantum representation of audio signals retrieves the original signals. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Proceedings - 2019 IEEE international conference on Space Mission Challenges for Information Technology, SMC-IT 2019

Proceedings - 2019 IEEE International Conference on Space Mi...

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7th IEEE international conference on Space Mission Challenges for Information Technology, SMC-IT 2019

ISBN: (纸本)9781728115443

the proceedings contain 16 papers. the topics discussed include: efficient contact graph routing algorithms for unicast and multicast bundles;a machine learning approach to modeling satellite behavior;generating transit light curves with variational autoencoders;autonomic apoptotic computing prototype;providing pre-programmed death of cubesats for avoiding space junk;interference-free contact plan design for wireless communication in space-terrestrial networks;quantifying the effects of gyroless flying of the mars express spacecraft with machine learning;fault-tolerant swarms;CGR-SPI: a new enhanced contact graph routing for multi-source data communication in deep space network;towards the design of space missions with optimal accessibility via relay satellites;complexity analysis for model-based fault diagnosis systems;and multi-mission, interactive 3d visualization in a web browser for robotic system and space flight mission development and operations.

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How Meta-heuristic algorithms Contribute to Deep Learning in the Hype of Big Data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th international conference on Advanced computing, Networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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