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Practical cloud storage auditing using serverless computing

science China(Information sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO College of Computer Science and Software Engineering Shenzhen University College of Computer Science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

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Predicting Chronic Kidney Disease using Machine Learning in the Early Stages

Predicting Chronic Kidney Disease using Machine Learning in ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Mamatha, B. Terdal, Sujatha P. Pda College of Engineering Department of Computer Science and Engineering Gulbarga India

ISBN: (纸本)9798350315455

chronic kidney disease (CKD) is an illness that causes a decline in kidney function. The CKD early diagnosis is tough since it does not have initial symptoms. The early disease can stop the infection advancement with a well-timed diagnosis. The research aim is to implement and compare the various algorithm's performance and identify the best combinations that can provide better detection accuracy. We have found the solution using Machine Learning (ML) approaches that can achieve risk minimization. Seven ML algorithms have been utilized in this work with Accuracy, Precision, Recall, F1-Score, and Support as model evaluation parameters. In this comparison, RF and AdaBoost algorithms provided the uppermost accuracy worth 99%. Finally, data analysis is performed such as frequency distribution, Correlation between the features, Feature distribution plots, classification, and prediction plots. The domain of predictive analysis of CKD can be significantly enhanced by ML algorithms. By collaborating with them, healthcare professionals will be able to design a computer-aided diagnosis system that can enhance their ability to treat patients effectively. © 2023 IEEE.

关键词： Ada Boost CKD Logistic Regression ML Naïve Bayes Random Forest

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis 1

Panoptic Region Slicing Segmentation and Optimized Alexnet-B...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pareddy, Sudha V. Virupakshappa PDA College of Engineering Department of Computer Science & Engineering Kalaburagi India Sharnbasva University Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9798350371802

The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slicing Segmentation (PRS2), which employs an AlexNet-based optimized convolution neural network (PRS2-OCNN). The proposed system integrates several advanced techniques to improve the efficiency and accuracy of melanoma detection. The first step is to preprocess the dermoscopic image with a 2D Fusion Filter to improve its quality and prepare it for further analysis. The Panoptic Region Slicing Segmentation (PRS2) method is then used to emphasize the boundary regions, allowing for more precise localization of melanoma-affected areas. We use Threshold Histogram Evaluation (THE) to assess color variations within segmented regions, which effectively characterizes melanoma-specific color patterns. The features were extracted using Spread Spectral Menzies' Feature Selection (SSMFS), which reduced dimensionality and increased the efficiency of the subsequent analysis. The optimized convolution neural network, derived from the influential AlexNet architecture, serves as the foundation of our proposed approach. By fine-tuning the AlexNet-OCNN on the reduced feature set, we can improve its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN categorizes melanoma classes based on their risk severity, allowing dermatologists to make informed decisions about timely and appropriate treatment. A broad and diversified dataset of dermoscopic images was used in the experimental evaluations. By thoroughly analyzing the melanoma-affected regions, the proposed system performed better than existing methods in terms of detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

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computer Modeling in engineering & sciences 2025年第4期143卷 225-254页

作者： Aishwarya Rajasekar Vetriselvi Vetrian Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India

The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.

关键词： Internet of vehicles vehicular ad-hoc networks(VANET) multiaccess edge computing task offloading graph neural networks differential privacy

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Survey on a Drone Based Insect Repellent System Using IoT and ML 5

Survey on a Drone Based Insect Repellent System Using IoT an...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Soma, Shridevi Tamkeen, Samiya Pda College of Engineering Computer Science and Engineering Kalaburagi585102 India

ISBN: (纸本)9798350321425

This research provides a thorough summary of the cutting edge of drone-based insect repellent systems. It provides a synopsis of many studies and ideas that examine various methods of combining electronics and ultrasonic technology to keep unwanted pests at bay. Review topics include design constraints, testing procedures, and a system's ability to deter pests. It also emphasizes the need of creating generally acceptable and environmentally friendly solutions, as well as discussing the difficulties and debates related to electronic pest control equipment. The article also briefly discusses deep learning and AI's use in agricultural data analysis gleaned from drones. This article seeks to summarize the current state of insect repellent systems that use unmanned aerial vehicles. This study has proposed a novel bug detection method by using drones, Internet of Things (IoT), and machine learning. The drone's adaptability in terms of treatment area and specificity makes it a valuable tool for pest control. © 2023 IEEE.

关键词： Drones

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Machine Learning Based Integrated Audio and Text Modalities for Enhanced Emotional Analysis 5

Machine Learning Based Integrated Audio and Text Modalities ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Agarkhed, Jayashree Vishalakshmi Pda College of Engineering Computer Science and Engineering Kalaburagi Karnataka585102 India

ISBN: (纸本)9798350321425

Emotions play a crucial role in human communication, and the ability to automatically recognize them has broad applications in various domains, including human-computer interaction, affective computing, and virtual reality. Bimodal speech emotion recognition which aims to accurately recognize and interpret emotions expressed in speech signals. This article reviews the state-of-the-art techniques and methodologies employed in multimodal speech emotion recognition, highlighting the advancements in feature extraction, fusion strategies, and machine learning algorithms. Additionally, emerging trends and future directions in this field are discussed, focusing on the challenges and opportunities for improving the performance and robustness of multimodal speech emotion recognition systems. © 2023 IEEE.

关键词： Feature extraction

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Performance evaluation of the classifiers based on features from cotton leaf images

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Multimedia Tools and Applications 2025年 1-24页

作者： Patil, Bhagya M. Burkpalli, Vishwanath School of Engineering Math & Technology Navajo Technical University CrownpointNM United States Department of Information Science & Engineering PDA College of Engineering Karnataka Kalaburagi India

Cotton disease classification is required because it affects agricultural yield. Leaf disease was classified using machine learning and deep learning classifiers. The performance of the machine learning classifiers is evaluated based on the extracted features. Disease classification is possible after preprocessing, segmentation, and feature extraction stages. Preprocessing was achieved by bilateral filtering and segmentation using the modified Chan Vese method;color moment and texture features were extracted. Finally, multiple classifiers were used for disease classification. Classifier's support vector machine, random forest, K-nearest neighbor, Naïve Bayes, and multilayer perceptron were used for classification. We evaluated the classifier’s performance based on the extracted features. We can conclude that the performance of the classifiers improves when we combine the color and texture features. The combined color moments, gray-level co-occurrence matrix, and local binary pattern features provide a higher classification accuracy than stand-alone features. In the experiment, multilayer perceptron achieved 98% accuracy compared to SVM, K-NN, Naïve Bayes, and random forest. Further, the multilayer perceptron is compared with other well known deep learning models: AlexNet, VGG 16, and ResNet. In comparison, we could observe that the ResNet model achieved an accuracy of 98.92, higher than the other models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cotton

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Prediction and analysis of Covid-19 using the Deep Learning Models

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Research on Biomedical engineering 2024年第1期40卷 183-197页

作者： Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers

关键词： COVID-19

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