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Ensuring the data Security and Integrity over Cloud Computing Environment using Novel Cipher Strategy

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Ensuring the Data Security and Integrity over Cloud Computin...

IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Praveen Kumar E B. Yasotha Narmadha PG P. Chandrakala C. Ushapriya G Chamundeeswari Department of CSE (IoT) Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Data science and Business systems SRMIST kattankulathur Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning St. Martin's Engineering College Telangana India Department of Electronics and Communication Engineering Saveetha Engineering college Chennai Tamil Nadu India

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach.

关键词：

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases

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CBAM_SAUNet: A novel attention U-Net for effective segmentat...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Srividya Tirunellai Rajamani Kumar Rajamani Angeline J Karthika R Björn W. Schuller Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Rajkot Gujarat India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India CHI - Chair of Health Informatics MRI Technische Universität München (TUM) Germany Munich Data Science Institute Germany Munich Center for Machine Learning and GLAM - the Group on Language Audio & Music Imperial College London London UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)’s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

关键词： Deep learning Image segmentation Attention mechanisms Shape Magnetic resonance imaging Myocardium Decoding Medical diagnostic imaging

Ultrasound-Based Kidney Disease Detection: Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Network

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Ultrasound-Based Kidney Disease Detection: Resampling Cubatu...

International Conference on Inventive Computation Technologies (ICICT)

作者： Padmanayana N.S.L. Kumar Kurumeti Veena S Ch. Raja Vijay Jagdish Upadhye Ramya Maranan Department of Artificial Intelligence and Data Science Engineering AJ Institute of Engineering and Technology Mangaluru Karnataka India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Andhra Pradesh India Department of Electronics and Communication Engineering SJC Institute of Technology Chikkaballapur Karnataka India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Hyderabad Telangana India Department of Microbiology Dept of Microbiology Research and Development Cell (RDC) Parul Institute of Applied Sciences (PIAS) Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Limitations of ultrasound-based identification of kidney disease include poor image resolution, operator reliance, and lack of detection of small tissue anomalies. Diagnostic accuracy may be affected by artefacts or noise, or changes in patient anatomy. It may also have difficulty detecting microscopic structural changes or renal disease in its incipiency. The limitations are overcome by the analysis of the Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Networks (RCKF-PG-VGG-CCNet) for kidney disease detection from ultrasound images based on the Kidney Ultrasound Image dataset. The initial use of the Resampling Cubature Kalman Filter and the first use of Physics-Guided VGG Cross-Contextual Networks is to enhance image quality. Through identifying regions through deviations, the Scale-Aware Meet Transformer (SAMT) performs segmentation. While the Synergistic Fibroblast Optimization (SFO) enhances classification performance through optimization of the search for diseased regions, the Physics-Guided VGG Cross-Contextual Networks (PG-VGG-CCNet) classify kidney disease and identify features. Experimental output from a Python-based experiment demonstrates that RCKF-PG-VGG-CCNet is more efficient than other methods with 99.9% efficiency and 99.8% sensitivity and may enhance the accuracy of kidney disease detection.

关键词： Ultrasonic imaging Accuracy Sensitivity Noise Fibroblasts Transformers Kalman filters Kidney Optimization Diseases

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

Advanced Process Control in Manufacturing Using IoT Devices and Artificial Neural Networks

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Advanced Process Control in Manufacturing Using IoT Devices ...

Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sumathi Ramesh S Chethan Chandra S Basavaraddi Visumathi J Ishwarya M.V S. Srinivasan Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems.

关键词： Performance evaluation Adaptation models Process control Artificial neural networks Predictive models Real-time systems Manufacturing Sensors Internet of Things Monitoring

TNCR: Table net detection and classification dataset

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arXiv 2021年

作者： Abdallah, Abdelrahman Berendeyev, Alexander Nuradin, Islam Nurseitov, Daniyar Department of Machine Learning & Data Science Satbayev University Almaty Almaty050013 Kazakhstan National Open Research Laboratory for Information and Space Technologies Satbayev University Almaty Almaty050013 Kazakhstan

We present TNCR, a new table dataset with varying image quality collected from free websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 high-quality labeled images. In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone Network achieves the highest performance compared to other methods with a precision of 79.7%, recall of 89.8%, and f1 score of 84.4% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. The dataset and trained model checkpoints are available at https://***/abdoelsayed2016/TNCR_dataset © 2021, CC BY.

关键词： Convolutional neural networks

APPROXIMATION THEORY, COMPUTING, AND DEEP learning ON THE WASSERSTEIN SPACE

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arXiv 2023年

作者： Fornasier, Massimo Heid, Pascal Sodini, Giacomo Enrico TUM School of Computation Information Technlogy Department of Mathematics Boltzmannstrasse 3 Garching bei München85748 Germany TUM Institute for Advanced Studies Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany Institut für Mathematik Fakultät für Mathematik Universität Wien Oskar-Morgenstern-Platz 1 Wien1090 Austria

The challenge of approximating functions in infinite-dimensional spaces from finite samples is widely regarded as formidable. In this study, we delve into the challenging problem of the numerical approximation of Sobolev-smooth functions defined on probability spaces. Our particular focus centers on the Wasserstein distance function, which serves as a relevant example. In contrast to the existing body of literature focused on approximating efficiently pointwise evaluations, we chart a new course to define functional approximants by adopting three machine learning-based approaches: 1. Solving a finite number of optimal transport problems and computing the corresponding Wasserstein potentials. 2. Employing empirical risk minimization with Tikhonov regularization in Wasserstein Sobolev spaces. 3. Addressing the problem through the saddle point formulation that characterizes the weak form of the Tikhonov functional’s Euler-Lagrange equation. As a theoretical contribution, we furnish explicit and quantitative bounds on generalization errors for each of these solutions. In the proofs, we leverage the theory of metric Sobolev spaces and we combine it with techniques of optimal transport, variational calculus, and large deviation bounds. In our numerical implementation, we harness appropriately designed neural networks to serve as basis functions. These networks undergo training using diverse methodologies. This approach allows us to obtain approximating functions that can be rapidly evaluated after training. Consequently, our constructive solutions significantly enhance at equal accuracy the evaluation speed, surpassing that of state-of-the-art methods by several orders of magnitude. This allows evaluations over large datasets several times faster, including training, than traditional optimal transport algorithms. Moreover, our analytically designed deep learning architecture slightly outperforms the test error of state-of-the-art CNN architectures on datasets of ***

关键词： Sobolev spaces

Dynamic algorithms for online multiple testing

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arXiv 2020年

作者： Xu, Ziyu Ramdas, Aaditya Machine Learning Department Carnegie Mellon University Department of Statistics and Data Science Carnegie Mellon University

We derive new algorithms for online multiple testing that provably control false discovery exceedance (FDX) while achieving orders of magnitude more power than previous methods. This statistical advance is enabled by the development of new algorithmic ideas: earlier algorithms are more "static" while our new ones allow for the dynamical adjustment of testing levels based on the amount of wealth the algorithm has accumulated. We demonstrate that our algorithms achieve higher power in a variety of synthetic experiments. We also prove that SupLORD can provide error control for both FDR and FDX, and controls FDR at stopping times. Stopping times are particularly important as they permit the experimenter to end the experiment arbitrarily early while maintaining desired control of the FDR. SupLORD is the first non-trivial algorithm, to our knowledge, that can control FDR at stopping times in the online setting. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

Land instability Compounds the risk of sea level rise in Alexandria, Egypt

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International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Thomas, Rejoice Zouriq, Sara Fazli, Shahryar Fawzy, Amr Todorov, Nikolay Grisel Maharjan, Surendra Li, Wenzhao Linstead, Erik Struppa, Daniele El-Askary, Hesham Earth Systems Science and Data Solutions Lab Chapman University Orange 92866 CA United States Schmid College of Science and Technology Chapman University Orange 92866 CA United States Ministry of Water Resources and Irrigation Cairo Egypt Machine Learning and Assistive Technology Lab (MLAT) Chapman University Orange 92866 CA United States Fowler School of Engineering Chapman University Orange 92866 CA United States Department of Environmental Sciences Faculty of Science Alexandria University Moharem Bek Alexandria 21522 Egypt

The coastal region of Alexandria Governorate in Egypt holds significant strategic importance for trade while being susceptible to extreme weather events. It confronts a dual challenge of the rising sea levels and, as found in this study, land instability. While much attention has been rightly directed towards sea level rise (SLR), the stability of the land warrants equal consideration. Here, a comprehensive analysis of land stability is conducted in Alexandria by measuring Line of Sight (LOS) displacements and assessing their topographical, hydrological, and coastal impacts. Persistent Scatterer Interferometry technique is used to measure the LOS displacements in association with land cover classification and elevation data. Moreover, in the absence of a recent, openly accessible Digital Elevation Model (DEM) with satisfactory resolution, we developed a novel approach to update DEM using the Small Baseline Subset method. This approach enabled us to generate a projected DEM for the year 2040, facilitating the creation of a stream network based on this “future DEM”. Our observations revealed displacements ranging from −14.0 to 6.7 mm/year, predominantly occurring in low-lying and built-up areas, suggesting anthropogenic activities as a likely cause. The current and future stream networks show stream shifting and widening. Stream shifting indicates dynamic changes in topography and hydrological conditions, while stream widening suggests alterations in the hydrological network due to increased urban runoff or changes in the natural watershed. Furthermore, we found that sea surface temperature and SLR are projected to increase until the end of the century in the Mediterranean Sea near Alexandria. This escalation raises the likelihood of intensified tropical-like hurricane events in the region, commonly referred to as “medicanes”. The importance of this research is in offering a framework for comprehensive land stability assessments, along with demonstrating the scalabili

关键词： Digital Elevation Model Medicanes Persistent Scatterer Interferometry Sea Level Rise Sea Surface Temperature Small Baseline Subset