448 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faulty Node Detection Using Vertex Magic Total Labelling in Distributed System Subject The topic of the resource Adjacency matrix; Clientserver communication; Distributed system; Faulty node; Undirected graph; Vertex magic total labeling Description An account of the resource Distributed system consists of huge number of nodes that are connected to a network, which is mainly intended and predominantly used for information sharing. Large users are prone to share data through the network and the stability and reliability of the nodes are remaining as the major concern in this system. Therefore, the inconsistent message transmission causes the nodes in the network to act differently, which would not be acceptable. A rapid method of malfunctioning nodes detection can improve the QoS of distributed computing environment. In this paper, a novel algorithm is proposed based on the calculation of vertex magic total labelling (VMTL) value for each and every node in the network. Upon receiving the message from the sender node, the receiver node will quickly detect the faulty node by comparing the VMTL pivot value (Pv). Experimental results show that the proposed approach leads to high true fault rate (TFR) detection accuracy compared to the false fault rate (FFR) detection. Finally, all the information related to the faulty nodes will be sent to the server node for further investigation and action. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. Creator An entity primarily responsible for making the resource Puthussery A.; Muneeswari G. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-55, pp. 619-630. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-8677-4_50" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-8677-4_50</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101165609&doi=10.1007%2F978-981-15-8677-4_50&partnerID=40&md5=d21130333863750e399334fe94889c8c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101165609&doi=10.1007%2f978-981-15-8677-4_50&partnerID=40&md5=d21130333863750e399334fe94889c8c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Puthussery A., Department of Computational Sciences, School of Science, CHRIST (Deemed to be University), Delhi, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classifying bipolar personality disorder (bpd) using long short-term memory (lstm) Subject The topic of the resource AUC; Borderline personality disorder (BPD); Gated recurrent unit (GRU); Long short-term memory (LSTM); Recurrent neural network (RNN) Description An account of the resource With the advancement in technology, we are offered new opportunities for long-term monitoring of health conditions. There are a tremendous amount of opportunities in psychiatry where the diagnosis relies on the historical data of patients as well as the states of mood that increase the complexity of distinguishing between bipolar disorder and borderline disorder during diagnosis. This paper is inspired by prior work where the symptoms were treated as a time series phenomenon to classify disorders. This paper introduces a signature-based machine learning model to extract unique temporal pattern that can be attributed as a specific disorder. This model uses sequential nature of data as one of the key features to identify the disorder. The cases of borderline disorder that are either passed down genetically from parents or stem from exposure to intense stress and fear during childhood are discussed in this study. The model is tested with the synthetic signature dataset provided by the Alan Turing Institute in signature-psychiatry repository. The end result has 0.95 AUC which is an improvement over the last result of 0.90 AUC. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Ghosh S.; Pahuja K.; Jiji J.M.; Puthussery A.; Shukla S.; Unal A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 169-176. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_17</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090374616&doi=10.1007%2F978-981-15-5309-7_17&partnerID=40&md5=303ba5d54c6df8cfe82fb5c524005d8e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090374616&doi=10.1007%2f978-981-15-5309-7_17&partnerID=40&md5=303ba5d54c6df8cfe82fb5c524005d8e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ghosh S., CHRIST(Deemed to be University), Bengaluru, India; Pahuja K., CHRIST(Deemed to be University), Bengaluru, India; Jiji J.M., CHRIST(Deemed to be University), Bengaluru, India; Puthussery A., CHRIST(Deemed to be University), Bengaluru, India; Shukla S., CHRIST(Deemed to be University), Bengaluru, India; Unal A., Digital Monozukuri, Stanford, United States Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity Subject The topic of the resource Clustering; DBSCAN; Density-based; Document classification; Similarity; SNN; Unstructured data email documents Description An account of the resource Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Goswami M. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-57, pp. 267-276. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-9509-7_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-9509-7_23</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101969114&doi=10.1007%2F978-981-15-9509-7_23&partnerID=40&md5=64a25e6e08f9ed549a83983f6972f79d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101969114&doi=10.1007%2f978-981-15-9509-7_23&partnerID=40&md5=64a25e6e08f9ed549a83983f6972f79d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Goswami M., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Algorithms for the metric dimension of a simple graph Subject The topic of the resource Algorithms; BFS; Locating set; Metric basis; Metric dimension; Resolving set; Spanning tree Description An account of the resource Let G = (V, E) be a connected, simple graph with n vertices and m edges. Let v1, v2 $$\in$$ V, d(v1, v2) is the number of edges in the shortest path from v1 to v2. A vertex v is said to distinguish two vertices x and y if d(v, x) and d(v, y) are different. D(v) as the set of all vertex pairs which are distinguished by v. A subset of V, S is a metric generator of the graph G if every pair of vertices from V is distinguished by some element of S. Trivially, the whole vertex set V is a metric generator of G. A metric generator with minimum cardinality is called a metric basis of the graph G. The cardinality of metric basis is called the metric dimension of G. In this paper, we develop algorithms to find the metric dimension and a metric basis of a simple graph. These algorithms have the worst-case complexity of O(nm). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Chelladurai X.; Kureethara J.V. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 91-105. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_10</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090370380&doi=10.1007%2F978-981-15-5309-7_10&partnerID=40&md5=bb6607b970fa6b5c25368b687128ee53" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090370380&doi=10.1007%2f978-981-15-5309-7_10&partnerID=40&md5=bb6607b970fa6b5c25368b687128ee53</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chelladurai X., CHRIST (Deemed to be University), Bangalore, 560029, India; Kureethara J.V., CHRIST (Deemed to be University), Bangalore, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Toxic text classification Subject The topic of the resource Classification; Content moderation; Deep learning; Internet content; Social media Description An account of the resource The users of the Internet increase every moment with increasing population and accessibility of the Internet. With the increase in the number of users of the Internet, the number of controversies, arguments and abuses of all kinds increases. It becomes necessary for social media and other sites to identify toxic content amongst a large number of content being posted by the users of the sites every second. The traditional algorithms that depend on users reporting toxic content for it to be deleted and necessary actions to be taken against the users posting the content would take a long time, within which it would have gained media attention and would have lead to huge fights over the content. Thus, it becomes important for the content to be evaluated for toxicity at the time it is posted in order to stop it from being posted. Therefore, we have designed and trained a deep learning model that can be read through the textual content given through it and determine if it is toxic or not. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Ghosh S.; Kumar S.; Lepcha S.; Jain S.S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 251-260. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_27</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372547&doi=10.1007%2F978-981-15-5309-7_27&partnerID=40&md5=8c2062be624c172d4b54463181a84d0f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372547&doi=10.1007%2f978-981-15-5309-7_27&partnerID=40&md5=8c2062be624c172d4b54463181a84d0f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ghosh S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Kumar S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Lepcha S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Jain S.S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Diabetic retinopathy detection using convolutional neural networka study Subject The topic of the resource Convolutional neural network; Deep learning; Diabetic retinopathy Description An account of the resource Detection and classification of Diabetic Retinopathy (DR) is a challenging task. Automation of the detection is an active research area in image processing and machine learning. Conventional preprocessing and feature extraction methods followed by classification of a suitable classifier algorithm are the common approaches followed by DR detection. With the advancement in deep learning and the evolution of Convolutional Neural Network (CNN), conventional preprocessing and feature extraction steps are rapidly being replaced by CNN. This paper reviews some of the recent contributions in diabetic retinopathy detection using deep architectures. Further, two architectures are implemented with minor modifications. Experiments are carried out with different sample sizes, and the detection accuracies of the two architectures are compared. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Wahid F.F.; Raju G. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 127-133. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_13</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369011&doi=10.1007%2F978-981-15-5309-7_13&partnerID=40&md5=411621492f3ce8dfdfddd55c8773a769" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369011&doi=10.1007%2f978-981-15-5309-7_13&partnerID=40&md5=411621492f3ce8dfdfddd55c8773a769</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Wahid F.F., Department of Informtion Technology, Kannur University, Kerala, India; Raju G., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Tag indicator: a new predictive tool for stock trading Subject The topic of the resource Mean square error (MSE); Predictive stock market tool; Stochastic gradient descent method; TAG indicator Description An account of the resource In this paper, TAGan indicator for stock market prediction in which volume-based means for measuring potential trading and investing decision-making is introduced. This task has been in correlation of the changes in the volume with the changes in the actual trade volume. Using this, a concise trading strategy is formulated. Hoping to outperform the market and analyze the results by back testing across intraday, price data for the last 1 year, 2019, is performed. It was discovered that about 48.9% of the time, the volume-based trading strategy outperformed and the returns from market are also healthy enough to support the claim. Statistical methods like linear regression, mean square error in prediction and stochastic gradient descent are applied. Furthermore, while the scope of the study was limited to a few stocks in Nifty in order to mitigate selection bias, nonetheless, we hypothesize that numerous other assets that similarly possess a predictable correlation to volumes based on daily high and low are likely to exist. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Tom T.; Cheruparambil G.; Puthussery A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 261-266. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_28</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090368269&doi=10.1007%2F978-981-15-5309-7_28&partnerID=40&md5=8fe7f0cb0fe029a4f23a93fa9d0aa950" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090368269&doi=10.1007%2f978-981-15-5309-7_28&partnerID=40&md5=8fe7f0cb0fe029a4f23a93fa9d0aa950</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tom T., CHRIST (Deemed to be University), Bengaluru, India; Cheruparambil G., CHRIST (Deemed to be University), Bengaluru, India; Puthussery A., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Ocr system framework for modi scripts using data augmentation and convolutional neural network Subject The topic of the resource Convolutional neural network; Data augmentation; Handwritten character recognition; MODI script Description An account of the resource Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Joseph S.; Datta A.; Anto O.; Philip S.; George J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 201-209. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_21</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372247&doi=10.1007%2F978-981-15-5309-7_21&partnerID=40&md5=4bf4f8ed81f7f18403b7ca84bc753f20" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372247&doi=10.1007%2f978-981-15-5309-7_21&partnerID=40&md5=4bf4f8ed81f7f18403b7ca84bc753f20</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph S., CHRIST (Deemed to Be University), Bengaluru, India, Carmel College of Arts, Science and Commerce for Women, Nuvem, Goa, India; Datta A., CHRIST (Deemed to Be University), Bengaluru, India; Anto O., CHRIST (Deemed to Be University), Bengaluru, India; Philip S., CHRIST (Deemed to Be University), Bengaluru, India; George J., CHRIST (Deemed to Be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of attention deficit hyperactivity disorder using various classifiers Subject The topic of the resource ADHD; Bayes net; Classification; Data mining; J48 decision tree; Logistic model tree; Nae bayes; Random forest; Sequential minimal optimization Description An account of the resource Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral childhood impairment that wipes away the beauty of the individual from a very young age. Data mining classification techniques which are becoming a very important field in every sector play a vital role in the analysis and identification of these disorders. The objective of this paper is to analyze and evaluate ADHD by applying different classifiers like Nae Bayes, Bayes Net, Sequential Minimal Optimization, J48 decision tree, Random Forest, and Logistic Model Tree. The dataset employed in this paper is the first publicly obtainable dataset ADHD-200 and the instances of the dataset are classified into low, moderate, and high ADHD. The analysis of the performance metrics and therefore the results show that the Random Forest classifier offers the highest accuracy on ADHD dataset compared to alternative classifiers. With the current need to provide proper evaluation and management of this hyperactive disorder, this research would create awareness about the influence of ADHD and can help ensure the proper and timely treatment of the affected ones. Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource George H.K.; Nizar Banu P.K. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-53, pp. 283-296. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5258-8_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5258-8_28</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372646&doi=10.1007%2F978-981-15-5258-8_28&partnerID=40&md5=bf4947e42b2dd7f567f9e06db9a2121b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372646&doi=10.1007%2f978-981-15-5258-8_28&partnerID=40&md5=bf4947e42b2dd7f567f9e06db9a2121b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George H.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Body mass index implications using data analysis in the soccer sports Subject The topic of the resource BMI; Co-relation; Data analysis; Soccer; Soccer; Sports Description An account of the resource Soccer is considered among the most popular sports in the world among the last few years. At the same time, it has become a prime target in developing countries like India and other Asian countries. As science and technology grow, we can see that sports also grow with science, and hence technology being used to determine the results sometime or sometimes it is used to grow the overall effect. This paper presents the attributes and the qualities which are necessary to develop in a player in order to play for the big-time leagues called Premier League, La Liga, Serie A, German Leagues and so on. Simple correlation and dependence techniques have been used in this paper in order to get proper relationship among the attributes. This paper also examines how the body mass index plays an effect on the presentation of soccer players with respect to their speed, increasing speed, work rate, aptitude moves and stamina. The point is likewise to discover the connection of the above credits concerning body mass index. As in universal exchange, football clubs can profit more in the event that they have practical experience in what they have or can make a similar bit of room to maneuver. In a universe of rare assets, clubs need to recognize what makes them effective and contribute in like manner. Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Dasmondal A.; Nizar Banu P.K. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-53, pp. 213-227. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5258-8_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5258-8_22</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371562&doi=10.1007%2F978-981-15-5258-8_22&partnerID=40&md5=652d36c3f8adde47c546fa42ea6263e4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371562&doi=10.1007%2f978-981-15-5258-8_22&partnerID=40&md5=652d36c3f8adde47c546fa42ea6263e4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dasmondal A., Department of Computer Science CHRIST (Deemed to be University), Bangalore, Karnataka, India; Nizar Banu P.K., Department of Computer Science CHRIST (Deemed to be University), Bangalore, Karnataka, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A predictive model on post-earthquake infrastructure damage Subject The topic of the resource Decision tree; Disaster management; Earthquake; Predictive model; Random forest Description An account of the resource Disaster management initiatives are employed to mitigate the effects of catastrophic events such as earthquakes. However, post-disaster expenses raise concern for both the government and the insurance companies. The paper provides insights about the key factors that add to the building damage such as the structural and building usage properties. It also sheds light on the best model that can be adopted in terms of both accuracy and ethical principles such as transparency and accountability. From the performance perspective, random forest model has been suggested. From the perspective of models with ethical principles, the decision tree model has been highlighted. Thus, the paper fulfills to propose the best predictive model to accurately predict the building damage caused by earthquake for incorporation by the insurance companies or government agency to minimize the post-disaster expenses involved in such catastrophic event. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Paul A.; George J.P. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 267-280. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_29</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090370576&doi=10.1007%2F978-981-15-5309-7_29&partnerID=40&md5=ba2acef044ac0dd7058dcedb63bb93b1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090370576&doi=10.1007%2f978-981-15-5309-7_29&partnerID=40&md5=ba2acef044ac0dd7058dcedb63bb93b1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Paul A., CHRIST (Deemed to be University), Bengaluru, India; George J.P., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient handwritten character recognition of modi script using wavelet transform and svd Subject The topic of the resource Feature extraction method; Handwritten character recognition; Image processing; MODI script; Wavelet transform Description An account of the resource MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Joseph S.; George J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 227-233. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_24</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371826&doi=10.1007%2F978-981-15-5309-7_24&partnerID=40&md5=838f3129ad676e860f8ffb4d49a9e190" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371826&doi=10.1007%2f978-981-15-5309-7_24&partnerID=40&md5=838f3129ad676e860f8ffb4d49a9e190</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph S., CHRIST (Deemed to Be University), Bengaluru, India, Carmel College of Arts Science and Commerce for Women, Nuvem, Goa, India; George J., CHRIST (Deemed to Be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of financial news articles using machine learning algorithms Subject The topic of the resource Classifications; n-grams; Stock market; Support vector machine; Text mining Description An account of the resource The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Saxena A.; Banik A.; Saswat C.; Joan Jose M.; Bhagat V. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 193-199. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_20</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375590&doi=10.1007%2F978-981-15-5309-7_20&partnerID=40&md5=547939bc37d8157d9d15e0a7debf0c17" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375590&doi=10.1007%2f978-981-15-5309-7_20&partnerID=40&md5=547939bc37d8157d9d15e0a7debf0c17</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Saxena A., CHRIST (Deemed to be University), Bengaluru, India; Banik A., CHRIST (Deemed to be University), Bengaluru, India; Saswat C., CHRIST (Deemed to be University), Bengaluru, India; Joan Jose M., CHRIST (Deemed to be University), Bengaluru, India; Bhagat V., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An advanced machine learning framework for cybersecurity Subject The topic of the resource Anomaly detection; Cybersecurity; GAN; Machine learning; Threat Description An account of the resource The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Joshy A.P.; Natarajan K.; Naik P. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 235-241. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_25</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375074&doi=10.1007%2F978-981-15-5309-7_25&partnerID=40&md5=334bd5991beb9e7bf0163d1d9267c1bb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375074&doi=10.1007%2f978-981-15-5309-7_25&partnerID=40&md5=334bd5991beb9e7bf0163d1d9267c1bb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joshy A.P., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India; Natarajan K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India; Naik P., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementation of integer factorization algorithm with pisano period Subject The topic of the resource Cryptography; Integer factorization algorithm; Network security; Pisano period Description An account of the resource The problem of factorization of large integers into the prime factors has always been of mathematical interest for centuries. In this paper, starting with a historical overview of integer factorization algorithms, the study is extended to some recent developments in the prime factorization with Pisano period. To reduce the computational complexity of Fibonacci number modulo operation, the fast Fibonacci modulo algorithm has been used. To find the Pisano periods of large integers, a stochastic algorithm is adopted. The Pisano period factorization method has been proved slightly better than the recently developed algorithms such as quadratic sieve method and the elliptic curve method. This paper ideates new insights in the area of integer factorization problems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Tom T.; Tamang A.; Garia A.S.; Shaji A.; Sahu D. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 313-319. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_34" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_34</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090374024&doi=10.1007%2F978-981-15-5309-7_34&partnerID=40&md5=e1a664c33af20dd29ccd9ba717b82180" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090374024&doi=10.1007%2f978-981-15-5309-7_34&partnerID=40&md5=e1a664c33af20dd29ccd9ba717b82180</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tom T., CHRIST (Deemed to be University), Bengaluru, India; Tamang A., CHRIST (Deemed to be University), Bengaluru, India; Garia A.S., CHRIST (Deemed to be University), Bengaluru, India; Shaji A., CHRIST (Deemed to be University), Bengaluru, India; Sahu D., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Treexpan instantiation of xpattern framework Subject The topic of the resource PathXP; Structure-based XML clustering; TreeXP; XML; XML clustering; XPattern Description An account of the resource Most of the data generated from social media, Internet of Things, etc. are semi-structured or unstructured. XML is a leading semi-structured data commonly used over cross-platforms. XML clustering is an active research area. Because of the complexity of XML clustering, it remains a challenging area in data analytics, especially when Big Data is considered. In this paper, we focus on clustering of XML based on structure. A novel method for representing XML documents, Compressed Representation of XML Tree, is proposed following the concept of frequent pattern tree structure. From the proposed structure, clustering is carried out with a new algorithm, TreeXP, which follows the XPattern framework. The performances of the proposed representation and clustering algorithm are compared with a well-established PathXP algorithm and found to give the same performance, but require very less time. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Accottillam T.; Remya K.T.V.; Raju G. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 61-69. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_7</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369859&doi=10.1007%2F978-981-15-5309-7_7&partnerID=40&md5=6be1596fdce2344dedb4330e89b66116" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369859&doi=10.1007%2f978-981-15-5309-7_7&partnerID=40&md5=6be1596fdce2344dedb4330e89b66116</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Accottillam T., Department of Information Technology, Kannur University, Kannur, 670567, Kerala, India; Remya K.T.V., Department of Information Technology, Kannur University, Kannur, 670567, Kerala, India; Raju G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kanminike, Kumbalagod, Bengaluru, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index Subject The topic of the resource Deep learning; DiCoM images; Image enhancement; ZCA whitening. LBP Description An account of the resource Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Dominic D.; Balachandran K. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 107-114. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_11</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369574&doi=10.1007%2F978-981-15-5309-7_11&partnerID=40&md5=e3414f48e0f7d9abd5fd2705d4562da3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369574&doi=10.1007%2f978-981-15-5309-7_11&partnerID=40&md5=e3414f48e0f7d9abd5fd2705d4562da3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dominic D., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India; Balachandran K., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A comparative study of text mining algorithms for anomaly detection in online social networks Subject The topic of the resource Anomaly detection; Decision trees; Entropy and Euclidean distance; Gini index; K-Nearest neighbour (KNN) and Support Vector Machine (SVM); Kernel function; Rocchio algorithm; Social media Description An account of the resource Text mining is a process by which information and patterns are extracted from textual data. Online Social Networks, which have attracted immense attention in recent years, produces enormous text data related to the human behaviours based on their interactions with each other. This data is intrinsically unstructured and ambiguous in nature. The data involves incorrect spellings and inaccurate grammars leading to lexical, syntactic and semantic ambiguities. This causes wrong analysis and inappropriate pattern identification. Various Text Mining approaches are being used by researchers which can help in Anomaly Detection through Topic Modeling, identification of Trending Topics, Hate Speeches and evolution of the communities in Online Social Networks. In this paper, a comparative analysis of the performance of four classification algorithms, Support Vector Machine (SVM), Rocchio, Decision Trees and K-Nearest Neighbour (KNN) for a Twitter data set is presented. The experimental study revealed that SVM outperforms better than other classifiers, and also classifies the dataset into anomalous and non-anomalous users opinions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Kokatnoor S.A.; Krishnan B. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 29-37. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369987&doi=10.1007%2F978-981-15-5309-7_4&partnerID=40&md5=832032cda9a14a0f1b467866b8cb5734" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369987&doi=10.1007%2f978-981-15-5309-7_4&partnerID=40&md5=832032cda9a14a0f1b467866b8cb5734</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kokatnoor S.A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Adaptive artificial bee colony (aabc)-based malignancy pre-diagnosis Subject The topic of the resource Adaptive artificial bee colony; Feed-forward back-propagation neural network; Risk factor and symptoms and modification rate Description An account of the resource Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Kokatnoor S.A.; Krishnan B. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 149-157. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_15</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372979&doi=10.1007%2F978-981-15-5309-7_15&partnerID=40&md5=d507f04568db1956a97bf2305991bfea" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372979&doi=10.1007%2f978-981-15-5309-7_15&partnerID=40&md5=d507f04568db1956a97bf2305991bfea</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource On some classes of equitable irregular graphs Subject The topic of the resource Datasets; Edge labeling; Equitable irregular graph; k-equitable irregular edge labeling Description An account of the resource Graph labeling techniques are used by data scientists to represent data points and their relationships with each other. The segregation/sorting of similar datasets/points are easily done using labeling of vertices or edges in a graph. An equitable irregular edge labeling is a function $$f: E(G) \rightarrow N$$ (not necessarily be injective) such that the vertex sums of any two adjacent vertices of $$G$$ differ by at most one, where vertex sum of a vertex is the sum of the labels under $$f$$ of the edges incident with that vertex. A graph admitting an equitable irregular edge labeling is called an equitable irregular graph (EIG). In this paper, more classes of equitable irregular graphs are presented. We further generalize the concept of equitable irregular edge labeling to $$k$$-equitable irregular edge labeling by demanding the difference of the vertex sum of adjacent vertices to be $$k \ge 1$$. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Puthussery A.; Hamid I.S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 281-288. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_30</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372456&doi=10.1007%2F978-981-15-5309-7_30&partnerID=40&md5=19e8439f52aa1587b11019d4a26dadbb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372456&doi=10.1007%2f978-981-15-5309-7_30&partnerID=40&md5=19e8439f52aa1587b11019d4a26dadbb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Puthussery A., CHRIST Deemed to be University, Bengaluru, India; Hamid I.S., The Madura College, Madurai, 11, India