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Skin lesion segmentation using convolutional neural networks with improved U-Net architecture

Skin lesion segmentation using convolutional neural networks...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Rasool Iranpoor Amir Soltany Mahboob Shakiba Shahbandegan Nasrin Baniasadi School of Electrical Engineering University of Birjand Birjand Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

The location of skin lesions is of particular importance in the diagnosis and monitoring of skin disease. For this purpose, image segmentation could be used, for which various methods, algorithms and approaches have been proposed. Lately, many convolutional neural networks (CNN) with different architectures have been effectively employed for semantic image segmentation. In this paper, a CNN with improved U-Net architecture is introduced; this architecture is used for applying image segmentation to a dermatology image dataset including images of three different skin damage types. In the proposed method, the efficiency of the architecture is significantly improved by employing a pre-trained architecture in the encoding section and replacing some of the pooling layers. Various factors affecting the network such as the function of layers and their effects on network performance are investigated. Compared to existing CNN architectures, the proposed method attains higher stability and efficiency for the given dataset. For training data, %92 accuracy and for testing data %89 accuracy has been achieved.

关键词： Image segmentation Training data Skin Lesions Convolutional neural networks Task analysis Testing

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Input data Clustering for the Efficient Operation of Renewable Energy Sources in a Distributed Information System

Input Data Clustering for the Efficient Operation of Renewab...

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international Computer Sciences and Information Technologies (CSIT)

作者： Nikolay Kiktev Volodymyr Osypenko Nataliia Shkurpela Anastasiia Balaniuk National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Institute of Computer Engineering and Information Technologies Kyiv National University of Technologies and Design Kyiv Ukraine Faculty of Information Technology Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9781728174433

ISBN: (纸本)9781728174440

In this work a local database of the Microsoft SQL Server type to store meteorological data for any region received from the Internet was designed and implemented. Each of the tables has fields describing the weather conditions necessary to solve the problem determining the source of electricity, using of which is cost-effective in a given period of the year, time of day, geographical location and weather conditions. A database application in C # using the Windows Forms Application template was developed. The distribution of temperature indicators depending on the time of the studies for a certain period using cluster analysis was implemented.

关键词： Meteorology data models Servers Microgrids Distributed databases Clustering methods Clustering algorithms

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Cybersecurity and data Privacy in the Cloudlet for Preliminary Healthcare Big data Analytics

Cybersecurity and Data Privacy in the Cloudlet for Prelimina...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Tariq Javid Muhammad Faris Hira Beenish Muhammad Fahad Faculty of Engg. Sci. & Tech. Hamdard University Karachi Pakistan Hamdard University Karachi Pakistan College of Computing & Info. Sci. PAF – Karachi Inst. of Engg. & Tech. Karachi Pakistan

ISBN: (数字)9781728126807

ISBN: (纸本)9781728126814

In cyber physical systems, cybersecurity and data privacy are among most critical considerations when dealing with communications, processing, and storage of data. Geospatial data and medical data are examples of big data that require seamless integration with computational algorithms as outlined in Industry 4.0 towards adoption of fourth industrial revolution. Healthcare Industry 4.0 is an application of the design principles of Industry 4.0 to the medical domain. Mobile applications are now widely used to accomplish important business functions in almost all industries. These mobile devices, however, are resource poor and proved insufficient for many important medical applications. Resource rich cloud services are used to augment poor mobile device resources for data and compute intensive applications in the mobile cloud computing paradigm. However, the performance of cloud services is undesirable for data-intensive, latency-sensitive mobile applications due increased hop count between the mobile device and the cloud server. Cloudlets are virtual machines hosted in server placed nearby the mobile device and offer an attractive alternative to the mobile cloud computing in the form of mobile edge computing. This paper outlines cybersecurity and data privacy aspects for communications of measured patient data from wearable wireless biosensors to nearby cloudlet host server in order to facilitate the cloudlet based preliminary and essential complex analytics for the medical big data.

关键词： Cloud computing Industries Big data Computer security data privacy Medical services Mobile handsets

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Adaptive and Efficient Streaming Time Series Forecasting with Lambda Architecture and Spark

Adaptive and Efficient Streaming Time Series Forecasting wit...

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IEEE international conference on Big data

作者： Arjun Pandya Oluwatobiloba Odunsi Chen Liu Alfredo Cuzzocrea Jianwu Wang University of Maryland Baltimore County Baltimore USA North China University of Technology Beijing China iDEA Lab University of Calabria Italy

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The rise of the Internet of Things (IoT) devices and the streaming platform has tremendously increased the data in motion or streaming data. It incorporates a wide variety of data, for example, social media posts, online gamers in-game activities, mobile or web application logs, online e-commerce transactions, financial trading, or geospatial services. Accurate and efficient forecasting based on real-time data is a critical part of the operation in areas like energy & utility consumption, healthcare, industrial production, supply chain, weather forecasting, financial trading, agriculture, etc. Statistical time series forecasting methods like Autoregression (AR), Autoregressive integrated moving average (ARIMA), and Vector Autoregression (VAR), face the challenge of concept drift in the streaming data, i.e., the properties of the stream may change over time. Another challenge is the efficiency of the system to update the Machine Learning (ML) models which are based on these algorithms to tackle the concept drift. In this paper, we propose a novel framework to tackle both of these challenges. The challenge of adaptability is addressed by applying the Lambda architecture to forecast future state based on three approaches simultaneously: batch (historic) data-based prediction, streaming (real-time) data-based prediction, and hybrid prediction by combining the first two. To address the challenge of efficiency, we implement a distributed VAR algorithm on top of the Apache Spark big data platform. To evaluate our framework, we conducted experiments on streaming time series forecasting with four types of data sets of experiments: data without drift (no drift), data with gradual drift, data with abrupt drift and data with mixed drift. The experiments show the differences of our three forecasting approaches in terms of accuracy and adaptability.

关键词： Reactive power Time series analysis Big data Predictive models Real-time systems data models Forecasting

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Evaluation of Oh Model for Estimating Surface Parameter of Soil Using L-Band and C-Band SAR data 3rd

Evaluation of Oh Model for Estimating Surface Parameter of S...

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3rd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2020

作者： Yadav, Ajit Raisoddin, Momin Shafiyoddin, B. Sayyad Zeeshan, R. Mohammed Department of Computer Science Milliya Arts Science and Management Science College Beed Maharashtra431122 India

ISBN: (纸本)9789811604928

In this proposed work we have focuses on the estimation of soil moisture using L-band and C-band polirimetric (HH, HV, VH, VV) and dual band (VV, VH) SAR (synthetic aperture radar) data set. The empirical model derived by Oh for finding scattering parameter from bare soil surface was implemented. Polarimetric radar backscattering were conducted for bare soil surface for a different values of surface roughness and surface soil moisture at L-band and C-band at different incidence angle from 20° to 60°. A series of field data were collected from different areas as per the standard method given in literature. The data in this paper were taken from two days near Bardoli and Ahmedabad city during the field experiment conducted in 2017. Currently we analyze the collected data to understand the relation between field parameter (surface soil moisture, dielectric constant, surface roughness) and SAR data (radar backscattering). In addition radiative transfer model and radar backscattering model are used to simulate the L-band, C-band data observations. In this work we have simulated empirical Oh model for estimating surface soil moisture using L-band, C-band SAR data set. We have applied all the models given by Oh in different research paper between (1992–2002). Further this results were used to find dielectric constant and use Topp’s model to derived surface soil moisture. A good agreement was observed between the estimated and simulated values. Performance of Oh model is promising for L-band and quite good for C-band SAR data for surface soil moisture estimation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Surface roughness

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Effective Pitch Estimation using Canonical Correlation Analysis

Effective Pitch Estimation using Canonical Correlation Analy...

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Advanced Information and Communication Technology (ICAICT), international conference on

作者： Subrata Kumer Paul Rakhi Rani Paul University of Rajshahi Rajshahi Bangladesh

ISBN: (数字)9780738123233

ISBN: (纸本)9781665422406

Effective pitch estimation is very challenging task in recent research area. There are a large set of methods that have been developed in the speech processing area for the estimation of pitch. In this paper, focus on an effective pitch estimation method using Canonical Correlation Analysis (CCA). It is used for predicting IMF components from reference signals. The proposed pitch estimation algorithm dominant harmonic model enhanced the pitch peak through signal reshaping, then for on-linear and non-stationary data analysis method EMD uses to decompose the signal into a finite number of band limited signal called IMFs. Each IMF have specific frequency band. The Keele pitch reference database is considered for evaluation of the proposed method. Autocorrelation function to detect pitch period. Finally, Multivariate multidimensional statistics data analysis tools CCA is used for selecting IMF components. Then summing up IMF components for a partial reconstruction of the NACF signal except for noisy components. The reconstructed signal contains less noise. We estimated pitch from the reconstructed signal. The performance of the proposed pitch estimation is compared in terms of Gross Pitch Error (% GPE) with existing algorithms. Pitch is an important speech parameter for a wide range of applications such as speech coding, text to speech synthesis, speech recognition, gender identification, accent identification, accent synthesis, speaker identification, speaker verification, and so on.

关键词： Time-frequency analysis Correlation data analysis Speech coding Estimation Speech recognition Prediction algorithms

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data Factory: An Efficient data Analysis Solution in the Era of Big data

Data Factory: An Efficient Data Analysis Solution in the Era...

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IEEE international conference on Big data Analysis (ICBDA)

作者： Yaojun Wang Yangyang Li Jingyan Sui Yang Gao College of Information and Electrical Engineering China Agricultural University Beijing China School of Economics and Management Beijing University of Technology Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

For each industry in the era of big data, a steady stream of data is generated continually. Due to the enormous information contained in big data, how to maximally extract the value from big data with lower cost to provide decision-making, guide production and resource allocation for the enterprises has attracted more and more attention of most enterprises. This paper proposes a data analysis solution in the era of big data-data factory, and implements a software system to build a data factory for an enterprise promptly and efficiently. By processing, analyzing and modeling the data in a workshop-based production mode similar to the traditional factories using raw materials, our data factory will acquire the analysis results and prediction models and then realize the clustering, classification, evaluation, and prediction data analysis. Moreover, the application of deploying data factory for an enterprise reveals that data factory is an efficient solution for big data analysis, and it improves the efficiency of enterprise data analysis.

关键词： Production facilities Big data data analysis conferences Monitoring

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PrefixFPM: A Parallel Framework for General-Purpose Frequent Pattern Mining

PrefixFPM: A Parallel Framework for General-Purpose Frequent...

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international conference on data Engineering

作者： Da Yan Wenwen Qu Guimu Guo Xiaoling Wang University of Alabama at Birmingham (UAB) East China Normal University (ECNU)

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Frequent pattern mining (FPM) has been a focused theme in data mining research for decades, but there lacks a general programming framework that can be easily customized to mine different kinds of frequent patterns, and existing solutions to FPM over big transaction databases are IO-bound rendering CPU cores underutilized even though FPM is NP-hard. This paper presents, PrefixFPM, a general-purpose framework for FPM that is able to fully utilize the CPU cores in a multicore machine. PrefixFPM follows the idea of prefix projection to partition the workloads of PFM into independent tasks by divide and conquer. PrefixFPM exposes a unified programming interface to users who can customize it to mine their desired patterns, and the parallel execution engine is transparent to end-users and can be reused for mining all kinds of patterns. We have adapted the state-of-the-art serial algorithms for mining frequent patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of cores. A demo is available at https://***/PfioC0GDpsw; the code is available at https://***/yanlab19870714/PrefixFPM.

关键词： Task analysis databases Encoding Instruction sets data mining Programming Multicore processing

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Localising In Complex Scenes Using Balanced Adversarial Adaptation

Localising In Complex Scenes Using Balanced Adversarial Adap...

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international conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Gil Avraham Yan Zuo Tom Drummond ARC Centre of Excellence for Robotic Vision Monash University Australia

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Domain adaptation and generative modelling have collectively mitigated the expensive nature of data collection and labelling by leveraging the rich abundance of accurate, labelled data in simulation environments. In this work, we study the performance gap that exists between representations optimised for localisation on simulation environments and the application of such representations in a real-world setting. Our method exploits the shared geometric similarities between simulation and real-world environments whilst maintaining invariance towards visual discrepancies. This is achieved by optimising a representation extractor to project both simulated and real representations into a shared representation space. Our method uses a symmetrical adversarial approach which encourages the representation extractor to conceal the domain that features are extracted from and simultaneously preserves robust attributes between source and target domains that are beneficial for localisation. We evaluate our method by adapting representations optimised for indoor Habitat simulated environments (Matterport3D and Replica) to a real-world indoor environment (Active Vision dataset), showing that it compares favourably against fully-supervised approaches.

关键词： Task analysis Cameras Adaptation models data models Training Three-dimensional displays Feature extraction

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The Brain Memory Architecture HW/SW Co-Design Platform with Adaptive CNN Algorithm

The Brain Memory Architecture HW/SW Co-Design Platform with ...

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international Computer Symposium (ICS)

作者： Jih-Ching Chiu Yu-Yi Wang Wei-Yi Lin National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9781728192550

ISBN: (纸本)9781728192567

As the demand for machine learning, edge computing, and the Internet of Things technology increases, computing efficiency and energy consumption has become an important basis for computing choices. Although the graphics processing unit(GPU) has a high degree of parallel computing capability, its energy consumption is large, and the data transmission is limited by the system bus bandwidth. Therefore, our laboratory previously proposed the Brain Memory Architecture prototype architecture, which integrates FPGA and memory as a computing architecture, which has the advantages of high-efficiency, and low-power computing and does not require data exchange through the system bus. Based on this prototype architecture, this paper constructs the Brain Memory Architecture HW/SW Co-Design Platform (BMCD platform) to provide a good user interface so that users can easily build a hardware and software collaborative design computing environment. Through the library provided by the platform to establish the data transmission and calculation between acceleration hardware and memory to solve the bandwidth limitation of the traditional system bus. In this platform, the AXI4-stream interconnect core is provided as a standard interface for data handshaking with acceleration hardware, which reduces user design complexity and maintains the scalability of connection with other computing IP cores. In platform evaluation, design and adaptive CNN algorithm for hardware and software design platform, provide data quantization methods to reduce data bits to reduce the required data bandwidth and storage space and propose a dynamic adjustment algorithm for integer and decimal ratios to correct the accuracy and design problems that may be caused by data quantization. With this adaptive CNN algorithm architecture and BMCD platform to construct a rapid data transmission. This paper finally analyzes the comparison of the weight transmission time of different CNN models with the CPU and the GPU. The m

关键词： Heuristic algorithms Memory architecture Software algorithms Graphics processing units Bandwidth Hardware data communication

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