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Enhancing Healthcare Data Security in Cloud computing systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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Recent Advances in Science and Engineering technology (ICRASET), International Conference on

作者： I. Sudha P.S. Ramesh A. Ponmalar J. Jagannathan Rajkumar Pandiarajan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient’s data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems.

关键词： Cloud computing Machine learning algorithms Metaheuristics Distributed databases Medical services Organizations Machine learning Homomorphic encryption Protection Electronic medical records

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A Vein Segmentation Method Based on Improved U-Net

A Vein Segmentation Method Based on Improved U-Net

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Information and Communication technology (ICTech), International Conference of

作者： Hongliang Wang Liang Zhu Changyi Deng Pengfei Xiu Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China China Industrial Control Systems Cyber Emergency Response Team Institute of Software Beijing China

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making.

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Smart Harvesting based on Image Processing

Smart Harvesting based on Image Processing

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2020 International Computer Symposium, ICS 2020

作者： Joseph, S.P Wijerathna, L.L.M.C. Epa, K.G.R.D. Egalla, E.K.W.A.P.K. Abeygunawardhana, P.W.K. De Silva, R Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Faculty of Computing Malabe10115 Sri Lanka Sri Lanka Institute of Information Technology Dep. of Software Engineering Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9781728192550

Vision device is a critical component of fruit harvesting mobile robot which is designed for recognizing and harvesting of tomato fruits. The system of vision device of the moving robot, for a dedicated path identification with the use of colour detection algorithms and contour detection algorithms could be stated as an additional usage of the image-based technology. When the robot operates in real life, varying environmental parameters would not affect its activity, since the robot will be functioning mainly indoors, specifically in a greenhouse. Robust fruit segmentation algorithms for the visual system and the fruit plucking mechanism using a readymade robot arm with a soft gripper where it operates with basic hardware components like motors, controlled by a microcontroller with the intervention of kinematic theories, is being used with the goal of collecting fruit objects efficiently in the natural world. Plucking only the ripe and healthy object fruit without damaging both the fruit and the tree is the intended task of the system. The aim is to reduce human effort by promoting automation concepts in agriculture in developing countries like Sri Lanka with great potential, where agriculture plays a vital role in the development of the economy while indoor plantation and harvesting mechanism are also being developing to cater to the economy of the country as well. The way that this system is implemented will be discussed in this paper. © 2020 IEEE.

关键词： Fruits

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Distributed authentication scheme for Industry Internet Platform application based on consortium blockchain

Distributed authentication scheme for Industry Internet Plat...

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2021 International Conference on Computer Network Security and software Engineering, CNSSE 2021

作者： Yan, Min Huang, Haibo Yang, Shuaifeng Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijng100190 China Department of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China Department of Security Technology China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

With the rapid development of industrial internet services, the integration of IT/OT has enabled the continuous expansion of network security boundaries and put forward higher requirements for platform application cross-domain identity authentication. Aiming at the insufficient security of existing user-centric identity authentication schemes, this paper proposes a distributed authentication scheme based on the consortium blockchain in industrial Internet platform. The solution analyzes its applicability to the Industry Internet Platform from three perspectives: network architecture, industrial Internet platform user identity control, and user privacy. It defines the identity composed of consortium ID and consortium digital identity credentials, and builds distributed authentication architecture. The consortium blockchain is used to record the digital identity information of the industrial Internet platform users, and the consortium ID and the consortium digital identity credential are designed to realize the industrial Internet platform business securely access. © Published under licence by IOP Publishing Ltd.

关键词： Blockchain

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Application of Uncertainty-Aware Sensor Fusion in Physical Sensor Networks

Application of Uncertainty-Aware Sensor Fusion in Physical S...

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IEEE Instrumentation and Measurement technology Conference

作者： Maximilian Gruber Wenzel Pilar von Pilchau Varun Gowtham Nikolaos-Stefanos Koutrakis Nicolas Schö nborn Sascha Eichstä dt Jö rg Hä hner Marius-Iulian Corici Thomas Magedanz Julian Polte Eckart Uhlmann Metrology for Digital Transformation Physikalisch-Technische Bundesanstalt Berlin Germany Organic Computing Group University of Augsburg Augsburg Germany Software-Based Networks Fraunhofer FOKUS Berlin Germany Production Machines and System Management Fraunhofer Institute for Production Systems and Design Technology IPK Berlin Germany TTD Technology Development Endress+Hauser Level+Pressure Maulburg Germany Organic Computing Group University of Augsburg Berlin Germany Production Systems Fraunhofer Institute for Production Systems and Design Technology IPK Berlin Germany

Modern industrial processes often collect redundant information from multiple sensors. It is of interest to leverage this information to form a more accurate or robust estimate of an observed quantity utilizing a sensor fusion operation. To be able to comply with quality requirements on the fused value, preservation of traceability is required. Moreover, the sensor fusion needs to be implemented within suitable digital architectures for Industrial Internet of Things (IIoT) environments. In a continuation of previous work, this is achieved by the usage of digital twins that represent the entities of two IIoT testbeds and the adoption of a method for uncertainty-aware homogeneous sensor fusion, which is presented in full detail. Metrological traceability of the fused value is established by propagating the measurement uncertainty of the input sensor according to metrological standards. The method is implemented as a modular service connected to an existing IIoT architecture. The flexibility of the method is shown by application to two different scenarios with only minimal adaption efforts. The fused sensor values are (as indicated by earlier work) robust to outliers and perform well in practical scenarios within the chosen IIoT architecture.

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Short-time simulation of quantum dynamics by Pauli measurements

arXiv

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arXiv 2024年

作者： Faehrmann, Paul K. Eisert, Jens Kieferová, Mária Kueng, Richard Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Integrated Circuits and Quantum Computing Johannes Kepler University Linz Austria Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 Berlin14109 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia

Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here, we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical post-processing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth, time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary time evolution, or the application of intractable operations to sub-universal quantum simulators in classical post-processing. © 2024, CC BY.

关键词： Hamiltonians

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Infrastructure and city ontologies

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2022年第2期176卷 43-52页

作者： Varga, Liz McMillan, Lauren Hallett, Stephen Russell, Tom Smith, Luke Truckell, Ian Postnikov, Andrey Rodger, Sunil Vizcaino, Noel Perkins, Bethan Matthews, Brian Lomax, Nik Department of Civil Environmental and Geomatic Engineering Infrastructure Systems Institute University College London London United Kingdom School of Water Energy and Environment Cranfield University Cranfield United Kingdom Environmental Change Institute Oxford University Oxford United Kingdom Software Engineering Department De la Rue Team Valley Trading Estate Gateshead United Kingdom Lincoln Agri-Robotics University of Lincoln Lincoln United Kingdom Zentrum Klinische Studien Universitätsklinikum Freiburg Freiburg Germany Scientific Computing Department Science and Technology Facilities Council Uk Research and Innovation Rutherford Appleton Laboratory Harwell Campus Didcot United Kingdom School of Geography University of Leeds Leeds United Kingdom

The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use of cases that rely on real-world integration of disparate systems, the need for semantic congruence across boundaries and the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems, but they are particularly apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated, undermining the potential for integration to solve societal challenges such as net zero, resilience to climate change, equity and affordability. This paper reports on findings of a literature review on infrastructure and city ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to the literature and provide avenues for further research on (a) belief systems that underpin non-top-level ontologies and the potential for interference from them, (b) the need for a small number of top-level ontologies and translation mechanisms between them and (c) clarity on the role of standards and information systems in the adaptability and quality of data sets using ontologies. A gap is also identified in the extent that ontologies can support more complex automated coupling and data transformation when dealing with different scales. © 2023 ICE Publishing: All rights reserved.

关键词： Ontology

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

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Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning

Explainable Trust-aware Selection of Autonomous Vehicles Usi...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Gaith Rjoub Jamal Bentahar Omar Abdel Wahab King Hussein School of Computing Sciences Princess Sumaya University for Technology (PSUT) Jordan Amman CIISE Concordia University Montreal Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal Canada EECS Khalifa University Abu Dhabi UAE Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada

Autonomous driving has been gaining a lot of attention in the field of transportation technology in recent years. The use of autonomous vehicles has the potential to reduce the number of road accidents caused by human error, improve traffic flow, increase fuel efficiency and save time for travelers. In federated learning systems, selecting trustworthy autonomous vehicles (AVs) to participate in training is critical for ensuring system performance and reliability. In this work, we propose a trust-aware approach to AV selection that incorporates the performance of each AV using the Local Interpretable Model-Agnostic Explanations (LIME) method and One-Shot Federated Learning. We modify the XAI LIME Deep Q-learning-based AV selection model to include the trust metric, resulting in the Trust-Aware XAI LIME Deep Q-learning-based AV selection model. Our experiments show that the trust-aware approach outperforms the standard approach in terms of both accuracy and reliability, demonstrating the effectiveness of incorporating trust metrics in AV selection.

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