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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and Applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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MediGuard:A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications

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computers, Materials & Continua 2025年第6期83卷 3975-4029页

作者： Shrabani Sutradhar Rajesh Bose Sudipta Majumder Arfat Ahmad Khan Sandip Roy Fasee Ullah Deepak Prashar Institute of Engineering and Technology Dibrugarh UniversityDibrugarh786004AssamIndia Departments of Computational Sciences Brainware UniversityKolkata700125West BengalIndia Department of Computer Science&Engineering JIS UniversityKolkata700109West BengalIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610Perak Darul RidzuanMalaysia Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411PunjabIndia Jadara University Research Center Jadara UniversityIrbid21110Jordan

Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare *** digitization has major advantages,mainly enhancing the security barriers of the green tree *** this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system *** addition,we considered several solutions proposed by thousands of researchers *** results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and ***,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power *** propose a multi-layered architecture for the future healthcare ***,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these *** thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional *** conducted in-depth comparative studies on different interoperability parameters in the blockchain *** research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and *** the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.

关键词： Blockchain internet of medical things cloud infrastructure cyber-attacks privacy issues

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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Width Helps and Hinders Splitting Flows

Width Helps and Hinders Splitting Flows

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作者： Cáceres, Manuel Cairo, Massimo Grigorjew, Andreas Khan, Shahbaz Mumey, Brendan Rizzi, Romeo Tomescu, Alexandru I. Williams, Lucia Department of Computer Science University of Helsinki Helsinki60002952 Finland Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee60031818 India School of Computing Montana State University Bozeman60022522 United States Department of Computer Science University of Verona Verona60032256 Italy Department of Computer Science University of Montana Missoula60022355 United States

Minimum flow decomposition (MFD) is the NP-hard problem of finding a smallest decomposition of a network flow/circulation X on a directed graph G into weighted source-to-sink paths whose weighted sum equals X. We show that, for acyclic graphs, considering the width of the graph (the minimum number of paths needed to cover all of its edges) yields advances in our understanding of its approximability. For the version of the problem that uses only non-negative weights, we identify and characterise a new class of width-stable graphs, for which a popular heuristic is a O(log Val (X))-approximation (Val(X) being the total flow of X), and strengthen its worst-case approximation ratio from to ω (m/log m) for sparse graphs, where m is the number of edges in the graph. We also study a new problem on graphs with cycles, Minimum Cost Circulation Decomposition (MCCD), and show that it generalises MFD through a simple reduction. For the version allowing also negative weights, we give a (glog g-X g-g+1)-approximation (g-X g-being the maximum absolute value of X on any edge) using a power-of-two approach, combined with parity fixing arguments and a decomposition of unitary circulations (g-X g-≤ 1), using a generalised notion of width for this problem. Finally, we disprove a conjecture about the linear independence of minimum (non-negative) flow decompositions posed by Kloster et al. [2018], but show that its useful implication (polynomial-time assignments of weights to a given set of paths to decompose a flow) holds for the negative version. © 2024 Copyright held by the owner/author(s).

关键词： Polynomial approximation

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Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

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computers, Materials & Continua 2022年第8期72卷 2683-2696页

作者： M.Saravanan J.Jayanthi U.Sakthi R.Rajkumar Gyanendra Prasad Joshi L.Minh Dang Hyeonjoon Moon Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegePerundurai638060India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing *** the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing ***,image compression techniques can be utilized to process remote sensing *** this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image *** process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving *** this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing *** goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of ***,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is *** design of SBO algorithm for remote sensing image compression depicts the novelty of the *** showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

关键词： Remote sensing images image compression vector quantization sand bowerbird optimizer metaheuristics space savings

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Fault Secured JPEG-Codec Hardware Accelerator with Piracy Detective Control using Secure Fingerprint Template 36

Fault Secured JPEG-Codec Hardware Accelerator with Piracy De...

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36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Chaurasia, Rahul Reddy Asireddy, Abhinav Sengupta, Anirban Indian Institute of Technology Computer Science and Engineering Indore India Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India

ISBN: (纸本)9798350315004

This paper presents a novel methodology to design fault secured JPEG-compression-decompression (codec) hardware accelerator with piracy detective control using secure fingerprint template of intellectual property (IP) vendor. In the proposed approach, firstly, the fault secured schedule for JPEG-codec design is generated from its behavioral description. Subsequently, in order to enable detective control against piracy, secure digital template corresponding to fingerprint biometric of IP vendor is generated and implanted into the design. In order to do so, firstly, fingerprint features are transformed based on IP vendor specified key-set. Subsequently, the generated secure fingerprint template in the form of encoded hardware security constraints is implanted into the design during behavioral/ high-level synthesis phase of integrated circuit (IC) design. The implanted hardware security constraints thereby ensure the seamless detection and isolation of fake/pirated design versions during piracy detection process, before their conjunction into system on chip (SoC) of electronic systems. The proposed approach achieves the following: i) fault secured hardware accelerator for JPEG-codec with integrated piracy detective control at negligible design cost overhead ii) higher resiliency against tampering attack and iii) lower probability of coincidence (indicating definitive proof of ownership for genuine IP vendor). © 2023 IEEE.

关键词： Fault detection

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Integrating the Bigdata and Deep Learning Analysis Human Movement to Improve the Sports 3

Integrating the Bigdata and Deep Learning Analysis Human Mov...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Usha, R. Devi, G. Vasundhara Basi Reddy, A. Divya, B. Selvan, R. Senthamil Mits Madanapalle Department of Computer Science and Engineering Madanapalle India School of Computing Mohan Babu University Department of Computer Science and Engineering Tirupati India Gurunanak Institute of Technical Campus Department of Computer Science and Engineering Ibrahimpatnam India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350330724

Key technological tools driving the fast growth of integrated manufacturing may be found in information technologies including big data, deep learning, cloud computing, and the Iot. Recent years have seen significant progress in the application of deep learning to the analysis of large datasets. This work focuses on the automated comprehension of human motions in free gymnastics movies as part of the research of a high-precision classification model for sports footage. This work uses deep learning and large-data-based computer imagery to identify and represent human motions in videos. Sports footage is classified using an independent narrative based on long- and short-term memory associations. Short-term and long-term memory networks learn the map relationship between word sequences and video frame sequences in S2VT. We recommend stressing freestyle gymnastics' building blocks. This study creates a competitive freestyle gymnastics routine database. Using predicted sampling and data and custom-built dataset evaluation, we can narrow training decoder-prediction decoder disparities. This research found rethinking sports footage categorization better. Deep learning video categorization with big data enhances user experience by classifying videos more correctly. Feature extraction networks automate free gymnastics movement descriptions and compare feature extraction results to classify lifting sports models. © 2023 IEEE.

关键词： Decoding

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A Review on Deep Learning Approaches for Histopathology Breast Cancer Classification 4th

A Review on Deep Learning Approaches for Histopathology Brea...

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4th International Conference on computer and Communication Technologies, IC3T 2022

作者： Kalavathi, Rathlavath Swamy Das, M. Research Scholar Department of Computer Science and Engineering Osmania University Hyderabad India Department of Computer Science and Engineering Chaitanya Bharati Institute of Technology Hyderabad India

ISBN: (纸本)9789811985621

Deep learning (DL) is the most rapidly expanding in the current scenario. For image analysis and categorization, deep neural networks (DNNs) are presently the most extensively utilized technology. DNN designs include GoogleNet, residual networks, and AlexNet, among others. Breast cancer is seen as a major problem that endangers the lives and health of women. Ultrasonography or MRI scanning methods are used to diagnose breast cancer disease. Imaging methods used for diagnosis include digital mammography, ultrasonography, magnetic resonance imaging, and infrared thermography. The primary objective is to investigate different deep learning algorithms for recognizing breast cancer-affected imageries. The best models provide accuracy for the 2, 4, and classifications on cancer datasets. No previous research is carried out for the current model investigation. Early detection and screening are critical for effective therapy. The following is a synopsis of recent progress in mammograms and identification, as well as a discussion of technological advancements. An effective test result should meet the following requirements: performance, sensitivity, specificity, precision, recall, and low cost. The experimental settings for every study on breast cancer histopathology images are thoroughly reviewed and deliberated in this article. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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QRCODE: Quasi-Residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-15页

作者： Lin, Chia-Hsiang Hsu, Chih-Chung Young, Si-Sheng Hsieh, Cheng-Ying Tai, Shen-Chieh National Cheng Kung University Department of Electrical Engineering Miin Wu School of Computing Tainan70101 Taiwan Institute of Data Science Miin Wu School of Computing National Cheng Kung University Tainan70101 Taiwan National Cheng Kung University Institute of Computer and Communication Engineering Department of Electrical Engineering Tainan70101 Taiwan Institute of Data Science National Cheng Kung University Tainan70101 Taiwan

Considering that hyperspectral image (HSI) is often of lower spatial resolution when compared to multispectral image (MSI), an economical approach for obtaining a high-spatial-resolution (HSR) HSI is to fuse the acquired HSI and MSI, thereby greatly facilitating the subsequent material identification and classification in satellite remote sensing. As satellite-acquired HSI and MSI are often misaligned, the proposed deep neural network does not require the input HSI/MSI to be spatially co-registered, making the challenging fusion network design even more difficult. In this study, we propose a streamlined and efficient convex model integrated into the subnetwork, which obviates the need for complex network structures in learning spatial-spectral relationships, effectively guiding the quasi-residual learning task in our alignment-free fusion network. The convex subnetwork is a low-rank model that leverages the convex geometric structure implicitly embedded in the hyperspectral signature space. To address the misalignment between HSI and MSI effectively, we introduce a novel shifted window attention module (SWAM) that exploits the neighboring correlation in the feature domain, significantly enhancing the performance and stability of the fusion task. Capitalizing on the redundancy among spectrums, we employ grouped convolution to decrease the computational complexity without causing additional performance degradation. The proposed quasi-residual convex deep (QRCODE) network demonstrates the state-of-the-art performance in alignment-free HSI/MSI fusion tasks. © 1980-2012 IEEE.

关键词： Deep neural networks

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