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Cloud based Acute Lymphoblastic Leukemia Detection Using Deep Convolutional Neural Networks 2

Cloud based Acute Lymphoblastic Leukemia Detection Using Dee...

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2nd international conference on Inventive Research in computing Applications, ICIRCA 2020

作者： Sankara Nayaki, K. Denny, Jincy Rubeena, M.M. Denny, Jees K Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering India Adi Shankara Institute of Engineering and Technology Computer Science and Engineering Kalady Ernakulam Kerala India MET's School of Engineering Computer Science and Engineering Mala Thrissur Kerala India

ISBN: (纸本)9781728153742

Acute lymphoblastic leukemia is a condition that occurs in the bone marrow. Most cases of Acute Lymphoblastic Leukemia (ALL) develop from cells that would turn into white blood cells (other than lymphocytes), but some cases of ALL develop in blood-forming cells of various kinds. Anything begins in the bone marrow (the soft inner part of some bones, where new blood cells are made), yet it moves rapidly into the blood by and large. It may spread to various parts of the body once in a while, including the lymph nodes, liver, spleen, central nervous system (cerebrum and spinal cord), and testicles. The traditional method used by doctors to detect ALL is tedious and time consuming. It consists of several methods such as blood testing, aspiration and biopsy to the bone marrow, image testing using CT scanning and MRI. These methods take time and are expensive. The proposed approach uses imaging processing techniques to detect irregular blood cells in the microscopic images of the blood. Using the training data collection, the program is trained using DCNN and is uploaded into HDFS Hadoop framework. The learned method classifies when a new image is inserted into the program whether the collected picture includes cancerous leukemia cells or irregular cells. This way the process of detection of leukemia is automated and can be accessed by any of the hospitals with connected to the cloud system. © 2020 IEEE.

关键词： Cells

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Implementation of the Bidirectional Reflectance Function for Modeling the Spectra Derived from Hyperspectral Images 2nd

Implementation of the Bidirectional Reflectance Function for...

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Mohammed Zeeshan, R. Sayyad Shafiyoddin, B. Department of Computer Science Milliya Arts Science & Management Science College BeedMS431122 India

ISBN: (纸本)9789811524745

Hapke’s bidirectional reflectance function based on the theory of radiative transfer has been implemented for modeling the spectra derived from Chandrayaan-1 hyperspectral images. The parameter study was done to study the behavior and influence of each parameter like grain size, porosity, iron fraction which represents the degree of space weathering and phase function was critically assessed. The model was then tested against the four standard lunar mixtures which constitutes of major lunar minerals from RELAB, and it was observed that the artificially created model spectra were successful in reproducing the overall trend in the resultant spectra. Finally, the nine representative spectra derived from hyperspectral image of the Chandrayaan-1 HySI sensor covering part of Mare Vaporum were modeled. The mass fraction of the surface minerals along with the associated Hapke parameter was predicted. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Porosity

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Modeling the Chandrayaan-1 Hyperspectral (HySI) data for Mineral Mixing Analysis 2nd

Modeling the Chandrayaan-1 Hyperspectral (HySI) Data for Min...

引用

2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Mohammed Zeeshan, R. Sayyad Shafiyoddin, B. Department of Computer Science Milliya Arts Science and Management Science College BeedMS431122 India

ISBN: (纸本)9789811524745

The study of the lunar surface mineralogy is significant to understand the origin and its evolution. For the first time, an attempt was made to model the Chandrayaan-1 Hyperspectral (HySI) data using Hapke radiative transfer model for quantifying lunar minerals along with associated parameters like grain size, porosity and submicroscopic iron SMFe which is considered to be the main product of space weathering. The model spectra were created with selected pure end member from the RELAB database, and the model validation was done against the four standard mixtures. In all four test cases, the model reproduces the trends successfully. The RMSE and the correlation coefficient were calculated between measured and modeled spectra. After testing, the active spectral signatures from bright fresh craters derived from the data set covering the Mare Crisium were modeled. The model result shows the fresh craters from the Mare areas rich in Clinopyroxene with low agglutinates. The model gives around 10% of olivine. The spectra from highland give a high percentage of plagioclase and agglutinate. However, modeling the spectra from relatively mature soil with no significant absorption is difficult to model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feldspar

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Binary Image Steganalysis Based on Local Residual Patterns 2nd

Binary Image Steganalysis Based on Local Residual Patterns

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2nd international conference on Security with Intelligent computing and Big-data Services (SICBS)

作者： Li, Ruipeng Zeng, Lingwen Lu, Wei Chen, Junjia Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Guangdong Key Lab Informat Secur Technol Sch Data & Comp SciMinist Educ Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783030169466;9783030169459

A binary image steganalysis scheme based on the statistic model of local residual pattern (LRP) is proposed in this paper. LRP means the pattern of a local area of the residual map of the binary image, which is calculated with the XOR operation. The XOR operation is sensitive to the difference between adjacent pixels, which leads to the emphasis on edge property of the residual map. The neighbouring LRPs of the modified pixel will be affect, which makes the statistic model of LRPs change. Thus the trace of steganography can be detected according to the difference between the statistic models. Finally, the experiments we conducted show that our proposed scheme is effective on binary image steganalysis.

关键词： Steganalysis Binary image Steganography Machine learning

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A Novel Approach Using Support Vector Machine for Outlier Removal and Multilayer Perceptron in Classifying Medical datasets 2nd

A Novel Approach Using Support Vector Machine for Outlier Re...

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Padmavathi, M.S. Sumathi, C.P. Research Department of Computer Science SDNB Viashnav College for Women Chennai600044 India

ISBN: (纸本)9789811524745

Healthcare industry contains vast amount of data, where analysis is essential in an optimal manner. data mining provides viable tools and methods to extract knowledge and predict the presence or absence of disease in medical datasets. Various efficient models for disease diagnosis are presented in this paper. The proposed model includes imputation by median followed by support vector machines (SVM) for removing the noisy instances and multilayer perceptron (MLP) in classifying the datasets. For the tested datasets, the newly developed model achieved a data reduction of 3.14, 22.72 and 23.82% using SVM which is relatively less compared to K-means and genetic algorithm (GA). Among the different tested classifiers, MLP attained a high classification accuracy of 99.85, 99.34 and 100% compared with Naïve Bayes and random forest. Further, the stability of the model is also tested on Wisconsin Diagnostic Breast Cancer (WDBC) dataset with no missing values. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Twin Security Model—A Machine Learning-Based Approach for DDoS Attack Detection in SDN 1

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Singh, Sukhvinder Jayakumar, S.K.V. Department of Computer Science School of Engineering and Technology Pondicherry University Pondicherry India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

In the world of emerging network technologies, the software-defined network (SDN) architecture provides the global view of the entire network. Its agility and directly programming ability helped the administrators to dynamically adjust the network, so that whenever required the flow of the traffic can be directed to meet the changing needs of the network and also enhances the security perspective. SDN architecture actively monitors the incoming and outgoing traffic and identifies the threats to further enhance modification or addition of security policies by the administrator. Decoupling of control plane and data plane in SDN increased the attack perspective for the attackers (i.e., DDoS attack, replay attack, man-in-the-middle attack, overloading, etc.). This paper proposes a "twin security model" based on machine learning which amalgamates the two techniques so that the security performance with respect to detection rate will be improved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network security

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Label Assignment and Sentimental Analysis for a Product Review on Twitter data 1

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Sengar, Chandra Prakash Singh Nirmala, S. Jaya Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

The most popular microblogging social media is Twitter, where people express their point of view toward different things. They bought different products and give their unbiased opinion on Twitter. In this paper, a novel method is proposed to get the opinions of customers toward the product or company and analyze it. The product-related Tweets are gathered by Twitter with the help of the browser. This method is not using Twitter API. The proposed methods are defined for labeling Tweets in three emotions, that is, positive, negative, and neutral efficiently. Sentiment analysis models are used for classifying the opinions of the customer toward that product and toward individual features of the product. Using these results, it improves the future versions of the product. This research technique helps and improves the marketing approaches and high selling of product as well. It predicts the best and worst feature of the product with respect to customer opinion with an accuracy of 93.62%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Ontology-Driven Clinical data Analyzer of Skin Diseases Based on SVM 2nd

Ontology-Driven Clinical Data Analyzer of Skin Diseases Base...

引用

2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Hema, D. David, Vasantha Kalyani Avinashilingam Institute for Home Science and Higher Education for Women CoimbatoreTamil Nadu641043 India

ISBN: (纸本)9789811524745

This paper describes a methodology for increasing the scope and precision of a diagnostic knowledge-based system. It has been stated that medical knowledge-based systems either are highly specialized and lack accuracy or are just too simple. To resolve this problem, we propose the use of a phased approach to diagnosis. In this research, an ontology-based classification of skin disease is done based on laboratory tests, ecological elements, and other vital signs. The data points are taken into account along with support vector machine diagnosing the skin disease, thus providing the details to pinpoint the weightage of illness and the long-run onset of illness to give the degree alerts. The combination of ontology-based classification and SVM makes the best methods to work on the trained dataset to identify various characteristics of skin diseases. The outcome produced from this technique is useful for further analysis, decision support, and accurate prediction of the disease. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Reversible data Hiding in Binary Images by Symmetrical Flipping Degree Histogram Modification 2nd

Reversible Data Hiding in Binary Images by Symmetrical Flipp...

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2nd international conference on Security with Intelligent computing and Big-data Services (SICBS)

作者： Yin, Xiaolin Lu, Wei Liu, Wanteng Zhang, Junhong Sun Yat Sen Univ Sch Data & Comp Sci Guangdong Key Lab Informat Secur Technol Minist EducKey Lab Machine Intelligence & Adv Co Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783030169466;9783030169459

In recent years, reversible data hiding in binary images has become more and more important in the field of data hiding. For the binary property, hiding information in binary images can only be achieved by flipping pixels. But flipping the pixels randomly in a binary image will cause remarkable distortion. The existing methods are capable of reversible data hiding in binary images but the visual quality is not good. In this paper, we propose a novel reversible data hiding method based on symmetrical flipping degree (SFD) histogram modification. First, we define the SFD of the block, which is a visual score measured the distance between the pixels in the block. The smaller SFD is, the better the visual quality the image has. Then the histogram is generated from the SFD, and it utilizes the zero or the minimum point and modifies the pixels with high SFD for its pre-processing. Finally, the appropriate corresponding pixels are selected adaptively to flip for information embedding. Experimental results demonstrate the feasibility of our proposed method and the visual quality is satisfactory.

关键词： Reversible data hiding Symmetrical flipping degree (SFD) Histogram modification Binary image

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Innovative Secure Authentication Interface for Hadoop Cluster Using DNA Cryptography: A Practical Study 1

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Balaraju, J. Prasada Rao, P.V.R.D. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur VaddeswaramAndhra Pradesh522502 India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

Big data (BD) handling is a vital task in today’s world where huge amount of data is circulated within a distributed network. To handle BD, Hadoop is being widely used by various organizations, as Hadoop supports both parallel processing and distributed computing over wide area networks. Hadoop clusters (HC) are widely accepted in the distributed and parallel computing frameworks for storing the BD because of its good scalability, tightly coupling among commodity hardware. Though data storage and processing can be handled well by HC, it does not provide data security which is inevitable where data may contain sensitive information. In HC Yet Another Resource Negotiator (YARN) and NameNode act as masters that provide resources for users and processes without authentication. All kinds of users in HC have the same level of accessing privileges which can be a threat to data security. HC completely depends on third-party security providers for satisfying various security requirements which in turn is a computational burden. This paper proposes a proprietary new security mechanism, implemented as Secure Authentication Interface (SAI) layer over HC. SAI provides user authentication, metadata security and access control all at a time. Compared to the existing mechanisms, SAI can provide security with less computational overhead. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Metadata

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