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Book•DOI•

Knowledge Computing and Its Applications

S. Margret Anouncia1, Uffe Kock Wiil2•
VIT University1, Maersk2
01 Jan 2018-
TL;DR: A Gateway-based Strategic CDS (GWS-CDS) is constructed based on strategy and communication range, which aims to construct a CDS in heterogeneous networks and showed that the proposed algorithm was better when compared to the existing algorithms.
Abstract: Wireless sensor networks (WSNs) have a worldwide attraction because of its increasing popularity. The key enablers for the Internet of Things (IoT) are WSN, which plays an important role in future by collecting information through the cloud. Fog Computing, the latest innovations, connects sensor-based IoT devices to the cloud. Fog Computing is a decentralized computing infrastructure in which the data, compute, storage, and applications are distributed efficiently between the data source and the cloud. The main aim of Fog Computing is to reduce the amount of data transported to the cloud and hence increase the efficiency. The knowledge-upgraded IoT devices will be embedded with a piece of software into it, which can able to understand the Distributed Denial of Service (DDoS). Such attacks are not forwarded to the cloud and thus the cloud server down problem is avoided. The IoT devices enabled with such knowledge is connected together to form a Connected Dominating Set (CDS). The data routed through only such IoT devices will be directly connected to the cloud. The CDS-based approach reduces the search for a minimum group of IoT devices called nodes, thus forming the backbone network. Various CDS algorithms have been developed for constructing CDSs with minimum number of nodes. However, most of the research work does not focus on developing a CDS based on application and requirement. In this chapter, a Gateway-based Strategic CDS (GWS-CDS) is constructed based on strategy and communication range. Here, any node in the network assigned a critical communication range, which is in a strong neighbourhood and which is within the communication range of more than one network, will be selected as the starting node, instead of the node with maximum connectivity. If a node is not within a critical communication range, then the following factors will be increased: V. Ceronmani Sharmila (&) Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India e-mail: ceronvlsi@gmail.com A. George Department of Mathematics, Periyar Maniammai University, Thanjavur, Tamilnadu, India e-mail: amalanathangeorge@gmail.com © Springer Nature Singapore Pte Ltd. 2018 S. Margret Anouncia and U. K. Wiil (eds.), Knowledge Computing and Its Applications, https://doi.org/10.1007/978-981-10-6680-1_1 3 the number of nodes that locally compete over a shared channel, access delay, network throughput and network partitioning. The other nodes for CDS construction are selected based on density and velocity. The focus of this research work was to construct a CDS in heterogeneous networks. The algorithm was tested with respect to three metrics—average CDS node size, average CDS Edge Size and average hop count per path. Simulation results showed that the proposed algorithm was better when compared to the existing algorithms.
Citations
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Journal Article•
A Methodological Framework for Data Warehouse Design

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Zhang Hengxi
01 Jan 2003-Computer Engineering
TL;DR: This paper outlines a general methodological framework for DW design and discusses the relationships between different steps and how to carry them out based on the difference between data warehouse and operational system.
Abstract: This paper outlines a general methodological framework for DW design and discusses the relationships between different steps and how to carry them out based on the difference between data warehouse and operational system.

65 citations

Journal Article•DOI•
Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine

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L. J. Sankpal1, S. H. Patil1•
Bharati Vidyapeeth University1
19 Oct 2020-The Computer Journal
TL;DR: An effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA) to solve the webpage re-ranking problem.
Abstract: The webpage re-ranking is a challenging task while retrieving the webpages based on the query of the user. Even though the webpages in the search engines are ordered depends on the importance of the content, retrieving the necessary documents based on the input query is quite difficult. Hence, it is required to re-rank the webpages available in the websites based on the features of the pages in the search engines, like Google and Bing. Thus, an effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA). The input queries are forwarded to different search engines, and the webpages generated from the search engines with respect to the input query are gathered. Initially, the keywords are generated for the webpages. Then, the top keyword is selected, and the features are extracted from the top keyword using factor-based, text-based and rank-based features of the webpage. Finally, the webpages are re-ranked using the Rider-Rank algorithm. The performance of the proposed approach is analyzed based on the metrics, such as F-measure, recall and precision. From the analysis, it can be shown that the proposed algorithm obtains the F-measure, recall and precision of 0.90, 0.98 and 0.84, respectively.

3 citations

Book Chapter•DOI•
Invasive Ductal Carcinoma (IDC) Classification Based on Breast Histopathology Images Using Convolutional Neural Network

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Nor Kumalasari Caecar Pratiwi1, Yunendah Nur Fuadah1, Nur Ibrahim1, Syamsul Rizal1, Sofia Saidah1 •
Telkom University1
01 Jan 2021
TL;DR: Invasive Ductal Carcinoma (IDC) is the most common sub-type of all breast cancers that affected adult women worldwide as mentioned in this paper and it can spread to other areas of the body such as liver, lungs and even bones.
Abstract: Invasive Ductal Carcinoma (IDC) is the most common sub-type of all breast cancers that affected adult women worldwide. IDC can spread to other areas of the body such as liver, lungs and even bones. The process of identifying and categorizing breast cancer sub-types accurately is a very important clinical task. IDC Diagnosis requires extremely serious measures, such as surgery and radiation therapy. Diagnosis based on pathological imagery is no less difficult, requiring a microscope and manual learning to classify it as positive or negative cancer. This process is very time consuming and conveys many errors due to human cognitive limitations. The existence of a system which can automatically perform such work, is expected to save time and reduce the error rate diagnose. This study proposed IDC and Non IDC classification by analyzing the Breast Histopathology Images using Convolutional Neural Network (CNN) method. The dataset consisted of 1020 IDC images, the same number is also used for Non-IDC. The model was composed of CNN with three hidden layers plus one fully connected layer with sigmoid activation. An evaluation is carried out to see the performance of the proposed method by using a matrix of precision, recall, F1, and accuracy. The experimental results show that the proposed method provides precision, recall, F1-score of 0.93 and 93% accuracy. This study is expected to be validated for later use in assisting medical authorities for conducting clinical diagnoses.
References
More filters
Journal Article•DOI•
Social Network Analysis: Methods and Applications.

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Christopher Winship, Stanley Wasserman, Katherine Faust
01 Sep 1996-Journal of the American Statistical Association
TL;DR: This work characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links that connect them.
Abstract: Social Network Analysis Methods And Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network ...

12,634 citations

"Knowledge Computing and Its Applica..." refers background in this paper

  • ...Therefore, identification and handling concept drift in ‘Big Data’ streams is a current area of interest [5]....

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  • ...Twitter is one of the most well-known social media platforms, being characterized by providing a micro-blogging service where users can post text-based messages of up to 140 characters, known as tweets, mimicking the SMS (Short Message Service) messages [5, 6]....

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  • ...GL Grunwald–Letnikov GLCM Gray Level Co-occurrence Matrix GRNN General Regression Neural Network GRR Google Rapid Response GUI Graphical User Interface GWS GateWay-based Strategic GWS-CDS GateWay-based Strategic Connected Dominating Set HAS Harmony Search Algorithm HCI Human–Computer Interaction HDFS Hadoop Distributed File System HI Huberts Index HOG Histogram of Oriented Gradients xviii Abbreviations HTTP Hypertext Transfer Protocol IBE Identity-Based Encryption ICA Independent Component Analysis ID Intrusion Detection IDS Intrusion Detection System II Intensity of Interaction IMDB Internet Movie Database IoT Internet of Things IPS Internet Protocol Security ISODATA Interactive Self-Organizing Data Analysis Technique JT/RM Job Trackers/Resource Manager K-ANMI K-means and Mutual Information KB Knowledge Base KNN K-Nearest Neighbor LDA Latent Dirichlet Allocation LDA Linear Discriminant Analysis LET Link Expiration Time LHS Left-Hand Side MAC Mandatory Access Control MAE Mean Absolute Error MANET Mobile Ad hoc Network MaxD Maximum Density MB Measure of Belief MCDA Multicriteria Decision Analysis MCDS Minimum Connected Dominating Set MD Measure of Disbelief MDD Major Depressive Disorder MDL Minimum Description Length MDM Mobile Device Management MDP Missed Detection Rate MGR Mean Gain Ratio MLP Multilayer Perceptron MLR Multinomial Logistic Regression MM Mammographic Mass MMHCI Multimodal Human–Computer Interaction MPANN Memetic Pareto Artificial Neural Network MRI Magnetic Resonance Imaging MTFA Mean Time between False Alarms MTP Mean Time Detection MWMCDS Maximal Weight Minimum Connected Dominating Set NB Naive Bayes NBP Neighbor-based Binary Pattern NECAS National Epidemiological Catchment Area Survey NIDS Network Intrusion Detection System NIMH National Institute of Mental Health Abbreviations xix NLP Natural Language Processing NMI Normalized Mutual Information Matrix...

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  • ...Several open source tools [5] are available in order to counter such threats and to protect our big data stores from risk....

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  • ...An ad hoc wireless network can be symbolized by a unit disk graph [5], where each node is connected with a disk focused at this node with the same communication range (also called radius)....

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Evolution of the social network of scientific collaborations

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Albert-László Barabási1, Hawoong Jeong1, Zoltán Néda1, Zoltán Néda2, Erzsébet Ravasz1, András Schubert3, Tamás Vicsek4 •
University of Notre Dame1, Babeș-Bolyai University2, Hungarian Academy of Sciences3, Eötvös Loránd University4
01 Mar 2002
TL;DR: The results indicate that the co-authorship network of scientists is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links, and a simple model is proposed that captures the network's time evolution.
Abstract: The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it o8ers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991–98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, a8ecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network’s time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks. c

2,277 citations

"Knowledge Computing and Its Applica..." refers background or methods in this paper

  • ...Fog Computing and Internet of Things (IoT) are the two evolving highest technological prototypes that to date have a larger influence in the recent world [1]....

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  • ...In this approach, a large training set of labeled tweets is used to train the classifier and achieve sentiment polarity detection [1]....

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  • ...[1] present a big data lifecycle model consisting four phases: data collection, data storage, data processing, and knowledge creation....

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  • ...Here the user can post a single message up to 140 characters only, and these messages are well known as tweets [1]....

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Journal Article•DOI•
Anomaly-based network intrusion detection: Techniques, systems and challenges

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Pedro García-Teodoro1, Jesús E. Díaz-Verdejo1, Gabriel Maciá-Fernández1, E. Vázquez2•
University of Granada1, Technical University of Madrid2
01 Feb 2009-Computers & Security
TL;DR: The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.

1,712 citations

"Knowledge Computing and Its Applica..." refers methods in this paper

  • ...For detecting both local and global changes in the transactional data streams, Koh [39] presented an algorithm where transactions falling in a given window are represented as a tree....

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  • ...The Wu and Palmer [39] similarity metric is used to measure the depth of the given concepts in the Word Net taxonomy, the least common subsumer (LCS) depth and combines these figures into a similarity score....

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Journal Article•DOI•
Unit disk graphs

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Brent N. Clark1, Brent N. Clark2, Charles J. Colbourn2, Charles J. Colbourn1, David S. Johnson2, David S. Johnson1 •
AT&T1, University of Waterloo2
02 Jan 1991-Discrete Mathematics
TL;DR: It is shown that many standard graph theoretic problems remain NP-complete on unit disks, including coloring, independent set, domination, independent domination, and connected domination; NP-completeness for the domination problem is shown to hold even for grid graphs, a subclass of unit disk graphs.

1,525 citations

Journal Article•DOI•
Some unique properties of eigenvector centrality

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Phillip Bonacich1•
University of California, Los Angeles1
01 Oct 2007-Social Networks
TL;DR: Eigenvectors, and the related centrality measure Bonacich's c(β), have advantages over graph-theoretic measures like degree, betweenness, and closeness centrality: they can be used in signed and valued graphs and the beta parameter in c( β) permits the calculation of power measures for a wider variety of types of exchange.

1,122 citations

"Knowledge Computing and Its Applica..." refers background in this paper

  • ...But due to the growth in the number of Internet-connected devices, the improved request of low-latency, real-time services is proving to be a challenging task for the outdated cloud computing context [4]....

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  • ...Recently, social networking sites have also been found to be utilized by the government for agenda setting, policy making, and to communicate new initiatives [4]....

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  • ...Researchers experience several restrictions in e-Science work, including genomics [4], meteorology, complex simulations, connectomics, and biological and ecological research [21]....

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  • ...Another major issue with streaming data is that the concepts behind the data evolve with time; therefore, discovering hidden concepts in data streams with time imposes an enormous challenge to cluster analysis [3, 4]....

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