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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Mohammad Khaja Shaik D Kalpana U Sesadri Saptarshi Mukherjee C Dastagiraiah Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Departmet of CSE-Data Science Malla Reddy Engineering College (Autonomous) India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telanagana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computer Science and Engineering Anurag University Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (MRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of MRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(ML) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of MRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Magnetic resonance imaging Computational modeling Artificial neural networks

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Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

arXiv

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arXiv 2023年

作者： Mondal, Joyanta Jyoti Islam, Md. Farhadul Islam, Raima Rhidi, Nowsin Kabir Newaz, Sarfaraz Manab, Meem Arafat Al Islam, A.B.M. Alim Noor, Jannatun Department of Computer Science College of Arts and Sciences University of Alabama Birmingham United States Research Group School of Data and Sciences BRAC University Dhaka Bangladesh Research Group Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh School of Law and Government Dublin City University Dublin Ireland

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://***/lepotatoguy/aqi. © 2023, CC BY-NC-SA.

关键词： Smartphones

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Constant Scaling Asymptotics of Communication Bounds in Covert Channels Against Selective Adversary

Constant Scaling Asymptotics of Communication Bounds in Cove...

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MILCOM, Military Communications Conference

作者： Xinchun Yu Shuangqin Wei Chenhao Ying Xiao-Ping Zhang Information and Data Science Institute Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA School of Electronic Information and Electrical Engnieering Shanghai Jiao Tong University Shanghai China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

In the existing works on asymptotics of throughput of covert communications, an adversary is typically assumed to run a single binary hypothesis testing to determine the presence of active transmission, which is equivalent to distinguish between an induced mixture distribution by all codewords and the distribution with an inactive transmitter. In this paper, we show that both of the first and second order asymptotics are O(1) when the adversary can afford running parallel simple binary hypothesis testing against detection of each single codeword. The covert constraint in this case is given as an upper-bound δ on the total variation distance (TVD) between the simple distribution induced by each codeword and the one of pure noise. The result is drastically different in a negative way than the commonly known Square Root Law for covert communication over AWGN channels with a more benign adversary.

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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An Exploratory data Analysis and Visualizations of Underprivileged Communities Diabetes dataset for Public Good

An Exploratory Data Analysis and Visualizations of Underpriv...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Majdi Owda Amani Yousef Owda Maria Fasli Faculty of Data Science UNESCO Chair in Data Science for Sustainable Development Arab American University Ramallah West Bank Palestine Deprtment of Natural Engineering and Technology Sciences Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine University of Essex School of Computer Science and Electronic Engineering Institute for Analytics and Data Science Colchester UK

Exploratory data analysis and visualization are essential tools for discovering patterns, highlighting anomalies, and discovering insights from datasets. In addition, visualizations tend to provide information in a way that is more accessible for the audience to understand and comprehend. Underprivileged communities will need an easier way to comprehend issues, challenges, and threats such as avoiding diseases. The use of exploratory data analysis and visualizations of diabetes datasets can be used as a means to make this topic easier to understand for underprivileged communities. A methodology has been developed to explore a dataset collection of diabetes patients. The dataset was obtained from the Palestinian community medical center (first-hand dataset), then the dataset was prepared and cleaned, and all rows with missing values or wrong values were dropped and removed, then an exploratory data analysis with a large number of data visualizations was produced, and last, a sample of the produced visualizations have been used to conduct a social acceptance experiment to educate an underprivileged population in the Palestinian community about the risks of diabetes. The result of the experiment was excellent and very promising in which participants understood the contents in a much easier way and provided very positive feedback on utilizing visualizations of diabetes datasets for public awareness and good.

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Fu, Huazhu PCA Laboratory The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Computer Science and Engineering Southeast University Nanjing211189 China The Medical School Nanjing University Nanjing210023 China The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To further exploit the scale information, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales to help produce more accurate segmentation maps. Extensive experimental results on multiple medical segmentation tasks (polyp, skin lesion, and brain glioma) demonstrate the effectiveness of our DECSeg against other state-of-the-art semi-supervised segmentation approaches. The implementation code will be released at https://***/taozh2017/DECSeg. Copyright © 2023, The Authors. All rights reserved.

关键词： Self-supervised learning

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DAAP: privacy-preserving model accuracy estimation on unlabeled datasets through distribution-aware adversarial perturbation 24

DAAP: privacy-preserving model accuracy estimation on unlabe...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Guodong Cao Zhibo Wang Yunhe Feng Xiaowei Dong School of Cyber Science and Engineering Wuhan University P. R. China The State Key Laboratory of Blockchain and Data Security Zhejiang University P. R. China and School of Cyber Science and Technology Zhejiang University P. R. China Department of Computer Science and Engineering University of North Texas

ISBN: (纸本)9781939133441

In the dynamic field of deep learning, accurately estimating model performance while ensuring data privacy against diverse and unlabeled test datasets presents a critical challenge. This is primarily due to the significant distributional shifts between training and test datasets, which complicates model evaluation. Traditional methods for assessing model accuracy often require direct access to the entire test dataset, posing significant risks of data leakage and model theft. To address these issues, we propose a novel approach: Distribution-Aware Adversarial Perturbation (DAAP). This method is designed to estimate the accuracy of deep learning models on unlabeled test datasets without compromising privacy. Specifically, DAAP leverages a publicly available dataset as an intermediary to bridge the gap between the model and the test data, effectively circumventing direct interaction and mitigating privacy concerns. By strategically applying adversarial perturbations, DAAP minimizes the distributional discrepancies between datasets, enabling precise estimation of model performance on unseen test data. We present two specialized strategies for white-box and black-box model contexts: the former focuses on reducing output entropy disparities, while the latter manipulates distribution discriminators. Overall, the DAAP introduces a novel framework for privacy-preserving accuracy estimation in model evaluation. This novel approach not only addresses critical challenges related to data privacy and distributional shifts but also enhances the reliability and integrity of model performance assessments. Our extensive evaluation on the CIFAR-10-C, CIFAR-100-C, and CelebA datasets demonstrates the effectiveness of DAAP in accurately estimating performance while safeguarding both data and model privacy.

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An Application of the Partially Observed Markov Process in the Analysis of Transmission Dynamics of COVID-19 via Wastewater

An Application of the Partially Observed Markov Process in t...

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2021 IEEE Systems and Information engineering Design Symposium, SIEDS 2021

作者： Jiang, Sihang Maggard, Kristen Shakeri, Heman Porter, Michael D. University of Virginia Department of Engineering Systems and Environment CharlottesvilleVA United States University of Virginia Department of Computer Science CharlottesvilleVA United States University of Virginia School of Data Science CharlottesvilleVA United States University of Virginia School of Data Science Department of Engineering Systems and Environment CharlottesvilleVA United States

ISBN: (纸本)9781665412506

As the ongoing outbreak of Coronavirus Disease 2019 (COVID-19) is severely affecting all over the world, analysis of the transmission of COVID-19 is of more and more interest. We focus on the application of compartmental models in the analysis of transmission of COVID-19 based on the detected viral load in wastewater and the reported number of cases. The measurement of COVID-19 RNA concentrations in primary sludge gives us information about the virus prevalence on a population level. Since the transmission of COVID-19 is a partially observed Markov process including different states, we consider a likelihood-based approach to our statistical inference to understand the inner relationship between different states and how COVID-19 actually transmits. Understanding the transmission dynamics of COVID-19 could give suggestions on public policies. © 2021 IEEE.

关键词： COVID-19

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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