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Achievable rate-exponent region for parallel distributed detection via binary symmetric channels 6

Achievable rate-exponent region for parallel distributed det...

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6th IEEE international conference on Intelligent computing and Signal Processing, ICSP 2021

作者： Liu, Xiangyang Wang, Gang Wang, Qiang Li, Xian Ma, Simin National University of Defense Technology College of Information and Communication Xi'an Shaanxi Province China

ISBN: (纸本)9780738143705

distributed detection with multiple sensors and a fusion center over binary symmetric channels has been investigated. The fusion center receives noisy observations of the local binary decisions. A fundamental tradeoff between achievable information rate and the error exponent of the missing probability exists. The tradeoff is well characterized by the rate-exponent region. The paper first derived a closed-form analytic expression for the error exponent and information rate by means of the theory of relative entropy typical set. Second, the properties of the optimal error exponent and information rate are discussed in detail. It was demonstrated that they are monotonically decreasing in the bit error probability of the binary symmetric channel. Simulation results validated the conclusions. © 2021 IEEE.

关键词： Wireless sensor networks

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Comparative Study on Energy-Efficiency for Wireless Body Area Network using Machine Learning Approach

Comparative Study on Energy-Efficiency for Wireless Body Are...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Mitu Sehgal Sandip Goyal Sunil Kumar Department of Computer Science & Engineering MMEC Ambala India School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781665454025

Awareness of one's own health needs is a quickly spreading revolution in contemporary living. Wireless Body Area Network (WBAN), that uses smart IoT devices for device evaluation, enables affordable healthcare services. WBANs offer a wide range of applications in telemedicine, the military, sports, entertainment, and other areas that call for ongoing Quality of Service (QoS) optimization with relation to dependability, energy use, delay and ease. The communication phase of WBANs presents energy optimization problems for low-powered battery devices. The prediction models for energy consumption were properly designed in conjunction with machine learning (ML) techniques. Improvements in QoS parameters are seen in ML models, along with accuracy, resilience, and precision. Artificial Neural networks (ANN), Support Vector Regression (SVR), Deep Neural networks (DNN), K-nearest neighbours (KNN), among other existing ML approaches, are acknowledged as the best method for preserving energy usage and performance. The objective of this paper is to further the understanding of WBAN with ML and its practical application among readers and academics. By methodically examining the results and limitations of previous research, this review paper seeks to provide an overview of the present practical challenges associated with using machine-learning models to improve building energy efficiency.

关键词： Wireless communication Support vector machines Telemedicine Quality of service Artificial neural networks Body area networks Energy efficiency

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6th international Symposium on Algorithmic Aspects of Cloud computing, ALGOCLOUD 2021

6th International Symposium on Algorithmic Aspects of Cloud ...

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6th international Symposium on Algorithmic Aspects of Cloud computing, ALGOCLOUD 2021

ISBN: (纸本)9783030930424

The proceedings contain 5 papers. The special focus in this conference is on Algorithmic Aspects of Cloud computing. The topics include: New Results on Test-Cost Minimization in Database Migration;privately Querying Privacy: Privacy Estimation with Guaranteed Privacy of User and Database Party;brief Announcement: On the distributed Construction of Stable networks in Polylogarithmic parallel Time.

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TABot – A distributed Deep Learning Framework for Classifying Price Chart Images 10th

TABot – A Distributed Deep Learning Framework for Classifyi...

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10th international Advanced computing conference, IACC 2020

作者： Siper, Matthew Makinen, Kyle Kanan, Raman CS6513 CSE NYU Tandon School of Engineering New York University BrooklynNY11201 United States

ISBN: (纸本)9789811604003

In this work, we propose a framework called TABot, a distributed deep learning (DL) framework for classifying price chart images. The deep learning engine we outline consists of an ensemble of convolutional neural networks employed for classifying price chart images. We present three workflows that compose our framework. The first is the data sourcing workflow: a distributed asynchronous pipeline to collect price data and programmatically generate candlestick charts. We measure the processing times of our distributed solution relative to a synchronous analog to identify a nominal processing time differential between the two solutions. The second is the model training workflow. We again leverage a distributed asynchronous pipeline to train each convolutional neural network in a parallel fashion. We measure the processing time of our parallel solution and compare to a synchronous analog. The third is the prediction workflow. We introduce a simple scheme for collecting the prediction output of each component model in an ensemble network model. Our results support the viability of convolutional neural networks to classify price chart images. The TAbot architecture additionally highlights the benefit of utilizing elastic computing environments to manage computational and data persistence costs incurred by deep learning frameworks. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired

Indian Currency Recognition:A Lightweight Transfer Learning ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Poonam Saini Ankit Goyal Bishwash Pokhrel Jaspreet Singh Neetish Kumar Dept. of Computer Science and Engg. Punjab Engineering College Chandigarh India

ISBN: (纸本)9781665454025

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application.

关键词： Training Face recognition Transfer learning Neural networks Computer architecture Real-time systems Convolutional neural networks

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Security Issues in IoT: A Review

Security Issues in IoT: A Review

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international conference on parallel distributed and Grid computing (PDGC)

作者： Monika Sharma Dr. Amit Kumar Bindal Department of Computer Science & Engineering M M Engineering College Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India

ISBN: (纸本)9781665454025

Internet of Things (IoT) has only recently come into existence. Since the inception of IoT, the majority of IoT studies, have been on a strategic level, including planning, architecture, standardization, and the most recent technology. Risk management strategies studies on IoT are still sparse. In this paper, the author covered the difficulties with privacy and security encountered when constructing IoT networks. Security recommendations by various researchers are also emphasized. Over the past many years, healthcare monitoring systems in hospitals and other health facilities have experienced enormous expansion, and wireless healthcare monitoring devices with a variety of technologies have attracted significant interest in many countries across the world. While the Internet of Things (IoT) has played a crucial role in the transmission of healthcare data, it also poses security issues to the patient-specific health information needed for remote medical treatment. The majority of currently published security solutions for healthcare data are not concentrated on data flow from IoT sensor devices implanted on a patient's body through network routers to doctor’s offices.

关键词： Wireless communication Wireless sensor networks Privacy Hospitals Standardization Planning Security

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A Comparative Review on State-of-the-art Clustering techniques for Vehicular Ad-Hoc networks

A Comparative Review on State-of-the-art Clustering techniqu...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Geetanjali Goyal Shubham Goel Department of Computer Science Punjabi University Patiala India Department of CSE/IT Jaypee University of Information Technology Solan India

ISBN: (纸本)9781665454025

Advancements in wireless technologies have given start to the exposure of vehicular ad-hoc networks (VANETs). It is a special kind of Mobile ad-hoc network that has a capability to provide communication among vehicles in a close proximal space. Moreover, VANETs even have capability to support the communication between the vehicles and some special kind to nearby equipment’s that are fixed on roadsides commonly named as Road Side Units (RSU). The main goal of VANETs is to reduce vehicular traffic and to provide comfortable and safe journey to passengers. In VANETs there are certain limitations i.e. dynamic topology changes, high mobility of nodes, packet loss, rapid pattern movement and network fragmentation. There is a great risk involved during communication in VANETs. To provide safe and reliable communication among nodes, clustering techniques are designed. With the dynamic nature of VANETs, stability of clusters is a very key challenging task. Several clustering algorithms has been presented by the researchers based on the stability of cluster formation. This paper surveys and analyzes the state-of-the-art clustering techniques designed for stability of clusters and safe communication.

关键词： Wireless communication Space vehicles Heuristic algorithms Road side unit Clustering algorithms Power system stability Stability analysis

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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international conference on parallel and distributed Systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors Data mining Computational complexity Remote sensing

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Machine learning based data classification methods in cloud security using cloudlightning framework

Machine learning based data classification methods in cloud ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Tridiv Swain Awantika Singh Khushali Verma Abhaya Kumar Sahoo Shefalika Ghosh Samaddar Rabindra Kumar Barik Kalinga Institute Of Industrial Technology Bhubaneswar India Dr. Sudhir Chandra Sur Institute of Technology & Sports Complex India

ISBN: (纸本)9781665454025

With the increase in the usage of Cloud services and applications, securing the data naturally becomes a major concern. There are numerous methods in which cloud data can be secured and numerous more ways that are being currently researched. In this paper, we present the approach of data classification and encryption to secure the data. It is incorrect to use security methods without understanding the requirements of that data and hence we use the K-NN classification method to divide the data with respect to their needs followed by encrypting the sensitive data to prevent any security hazards. We used the CloudLightning simulator to test the framework. The framework is broad in scope, but it also aims to enable cloud services for high-performance computing. Infrastructure-as-a-service. The core use case is service provision. However, we believe that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.

关键词： Oils High performance computing Neural networks Genomics distributed databases Machine learning Ray tracing

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A Comparison of Various Machine Learning Algorithms for Automated Fever Detection

A Comparison of Various Machine Learning Algorithms for Auto...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Kumari Monika Himanshu Jindal CSE/IT Jaypee University of Information Technology Solan Himachal Pradesh India

ISBN: (纸本)9781665454025

Various diseases causing fever-like germinal infection, viral infection, mononucleosis, epidemic catarrh, acute inflammatory disease is a neurodegenerative illness that manifests itself in a range of motor and non-motor symptoms. Patients with various diseases frequently experience difficulties in the initial stages of the condition. Patients with other diseases have often symptoms like cough, cold, headache, and fever. Research provides machine learning algorithms for classifying various diseases utilizing data that are built on machine learning algorithm. This paper describes the diverse technique of supervised machine learning classification that determines the best magnificence algorithm based on statistics set, quantity of cases, and variability (features). To implement the algorithms, Fever data set was used for the classification with no of instances with attributes as independent variable and one as dependent variable for the analysis. Therefore, ML algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning.

关键词： Support vector machines Training Measurement Machine learning algorithms Neural networks Prediction algorithms Feature extraction

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