188
20
16481
-
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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Kalaiselvi, K.; Khundongbam, Alex; Steffyn, Kezya; Mangaiyarkarasi, T.
Title
A name given to the resource
Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2026
Source
A related resource from which the described resource is derived
Studies in Systems, Decision and Control;Volume;645;pp.241-258
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-3-032-10016-0_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-10016-0_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026786951?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026786951?origin=resultslist</a>
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Kalaiselvi K., Department of Computer Science, Kristu Jayanti University, Bengaluru, India; Khundongbam A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Steffyn K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Mangaiyarkarasi T., Department of Management, FOM-MBA SRMIST VDP Campus, Chennai, India
Description
An account of the resource
Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Subject
The topic of the resource
Adversarial attacks; Adversarial defense; Computational efficiency; Model Robustness
Publisher
An entity responsible for making the resource available
Springer Science and Business Media Deutschland GmbH
Relation
A related resource
ISSN: 21984182;
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Rights
Information about rights held in and over the resource
Restricted Access; Hardcopy may be available in the library
-
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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 Algorithms Used in the Study of EEG-Based Epileptic Seizure Detection
Subject
The topic of the resource
Classification; Decision Tree Classifier; EEG; Epileptic Seizures; KNN; Linear Discriminant Analysis; SVM; XGBOOST
Description
An account of the resource
Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent. 2021 IEEE.
Creator
An entity primarily responsible for making the resource
Umme Salma M.; Najmusseher
Source
A related resource from which the described resource is derived
Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021, pp. 1518-1521.
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
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/10.1109/ICOSEC51865.2021.9591824" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOSEC51865.2021.9591824</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123219227&doi=10.1109%2FICOSEC51865.2021.9591824&partnerID=40&md5=1289ffceb8a262902f5365d356bf1894" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123219227&doi=10.1109%2fICOSEC51865.2021.9591824&partnerID=40&md5=1289ffceb8a262902f5365d356bf1894</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 978-166543368-6
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Umme Salma M., CHRIST (Deemed To Be University), Department of Computer Science, Bangalore, India; Najmusseher, CHRIST (Deemed To Be University), Department of Computer Science, 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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 and analysis of Alzheimer's Disease using Deep Learning methods on MRI and PET
Subject
The topic of the resource
Alzheimer's Disease; Convolutional Neural Networks; Deep Learning; MRI; PET; ResNet50
Description
An account of the resource
Alzheimer's disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further. 2022 IEEE.
Creator
An entity primarily responsible for making the resource
Antony F.; Anita H B.
Source
A related resource from which the described resource is derived
2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/IMPACT55510.2022.10029052" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IMPACT55510.2022.10029052</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148039392&doi=10.1109%2FIMPACT55510.2022.10029052&partnerID=40&md5=661b8994ee4574dae41837aa6a6f1590" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148039392&doi=10.1109%2fIMPACT55510.2022.10029052&partnerID=40&md5=661b8994ee4574dae41837aa6a6f1590</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 978-166547647-8
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Antony F., Deemed to Be University, Department of Computer Science Christ, Bangalore, India; Anita H B., Deemed to Be University, Department of Computer Science Christ, 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
Articles
Article
Faculty Publications -Articles
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 and characterization using HCT/HFOSC spectra of carbon stars selected from the HES survey
Subject
The topic of the resource
Stars: atmospheric parameters; Stars: carbon; Stars: late-type; Stars: low-mass; Stars: metallicity; Stars: population II
Description
An account of the resource
We present results from the analysis of 88 carbon stars selected from Hamburg/ESO (HES) survey using low-resolution spectra (R ?1330 & 2190). The spectra were obtained with the Himalayan Faint Object Spectrograph Camera (HFOSC) attached to the 2-m Himalayan Chandra Telescope (HCT). Using well-defined spectral criteria based on the strength of carbon molecular bands, the stars are classified into different groups. In our sample, we have identified 53 CH stars, four C-R stars, and two C-N type stars. Twenty-nine stars could not be classified due to the absence of prominent C2 molecular bands in their spectra. We could derive the atmospheric parameters for 36 stars. The surface temperature was determined using photometric calibrations and synthesis of the H-alpha line profile. The surface gravity log g estimates are obtained using parallax estimates from the Gaia DR3 database whenever possible. Microturbulent velocity (?) was derived using calibration equation of log g & ? . We could determine metallicity for 48 objects from near-infrared Ca II triplet features using calibration equations. The derived metallicity ranges from ?0.43 ? [Fe/H] ? ?3.49. Nineteen objects were found to be metal-poor ([Fe/H] ? ?1), 14 very metal-poor ([Fe/H] ? ?2), and five extremely metal-poor ([Fe/H] ? ?3.0) stars. Eleven objects were found to have a metallicity in the range ?0.43 ? [Fe/H] ? ?0.97. We could derive the carbon abundance for 25 objects using the spectrum synthesis calculation of the C2 band around 5165 The most metal-poor objects found will make important targets for follow-up detailed chemical composition studies based on high-resolution spectroscopy, and are likely to provide insight into the Galactic chemical evolution. 2024, The Author(s), under exclusive licence to Springer Nature B.V.
Creator
An entity primarily responsible for making the resource
Purandardas M.; Goswami A.
Source
A related resource from which the described resource is derived
Astrophysics and Space Science, Vol-369, No. 1
Publisher
An entity responsible for making the resource available
Springer Science and Business Media B.V.
Date
A point or period of time associated with an event in the lifecycle of the resource
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s10509-024-04269-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10509-024-04269-8</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182444418&doi=10.1007%2Fs10509-024-04269-8&partnerID=40&md5=1f407b2f4131fceff290398ab999647b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182444418&doi=10.1007%2fs10509-024-04269-8&partnerID=40&md5=1f407b2f4131fceff290398ab999647b</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 0004640X
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Purandardas M., Indian Institute of Astrophysics, Koramangala, Karnataka, Bangalore, 560034, India, Department of Physics and Electronics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Goswami A., Indian Institute of Astrophysics, Koramangala, Karnataka, Bangalore, 560034, 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
Articles
Article
Faculty Publications -Articles
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 and correlational analysis on lower spine parameters using data mining techniques
Subject
The topic of the resource
Classification; Correlation; Data mining; Decision tree; Lower back pain; Pelvic incidence; Sacral slope; Spondylolisthesis; Support vector machines
Description
An account of the resource
The application of data mining in the field of medical science is slowly gaining popularity. This is due to the fact that enormous statistical inferences from data related to the human body and medicine was a possible with high accuracy rates which was a tedious task in the past. This had led to discoveries and breakthroughs which has saved thousands of lives. Lower back pain is one of the most common issues faced by majority of the population throughout the world. The early detection and treatment of LBP can avoid life threatening issues in the body. Objective: This study aims to create a classification model which can be used to detect an unhealthy spine using the lumbar and sacral parameters. Correlational analysis was performed between different attributes to find distinguishing factors between healthy and unhealthy spine. Method: Classification methods were used such as decision tree and SVM. Correlational analysis was performed using pearson method between each attribute. Results: After creating the model using the different classification methods it was found that Ctree produced the highest accuracy with 92.80% on average. It was also found that there were 6 attribute pairs that had high correlation coefficient to distinguish unhealthy and healthy spine observations. BEIESP.
Creator
An entity primarily responsible for making the resource
Johny R.V.; Roseline Mary R.
Source
A related resource from which the described resource is derived
International Journal of Recent Technology and Engineering, Vol-7, No. 6, pp. 1450-1456.
Publisher
An entity responsible for making the resource available
Blue Eyes Intelligence Engineering and Sciences Publication
Date
A point or period of time associated with an event in the lifecycle of the resource
2019-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067055940&partnerID=40&md5=759470d8c842a716bfcc3197e783d299" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067055940&partnerID=40&md5=759470d8c842a716bfcc3197e783d299</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 22773878
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Johny R.V., Department of Computer Science, CHRIST (Deemed To Be University), India; Roseline Mary R., Department of Computer Science, CHRIST (Deemed To Be University), India
-
https://archives.christuniversity.in/files/original/ac80541c744606dd8e266ad9201ca8de.pdf
73c3f5ec669d12a4c7c20bb0757ace71
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
MPHIL
Mphil
Mphil Thesis
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 and Retrieval of Research
Classification and Retrieval of Research Papers: A Semantic Hierarchical Approach
Creator
An entity primarily responsible for making the resource
Mirza Nazura Abdulkarim
Date
A point or period of time associated with an event in the lifecycle of the resource
2010
Source
A related resource from which the described resource is derived
Computer Science
Description
An account of the resource
"Classification and Retrieval of Research papers: A Semantic Hierarchical Approach" demonstrates an effective and efficient technique for classification of Research documents pertaining to Computer Science. The explosion in the number of documents and research publications in electronic form and the need to perform a semantic search for their retrieval has been the incentive for this research.
The popularity and the widespread use of electronic documents and publications, has necessitated the development of an efficient document archival and retrieval mechanism. Categorizing journal papers by assigning them relevant and meaningful classes, predicting the latent concept or the topic of research, based on the relevant terms and assigning the appropriate Classification labels is the objective of this thesis.
This thesis takes a semantic approach and applies the text mining techniques in a hierarchical manner in order to classify the documents.
The use of a lexicon containing domain specific terms (DSL) adds a semantic dimension to classification and document retrieval. The Concept Prediction based on Term Relevance (CPTR) technique demonstrates a semantic model for assigning concepts or topics to papers.
This Thesis proposes a conceptual framework for organizing and classifying the research papers pertaining to Computer Science. The efficacy of the proposed concepts is demonstrated with the help of Classification experiments. Classification experiments reveal that the DSL technique of training works efficiently when categorization is based on keywords. The CPTR technique, on the other hand, shows very high accuracy; when the classification is based on the contents of the document.
Both these techniques lend a semantic dimension to classification.
Narrowing down the scope of search at each level of hierarchy enables time efficient retrieval and access of the goal documents. The hierarchical interface for Document Retrieval enables retrieval of the target documents by gradually restricting the scope of search at each level of hierarchy
This work comprises of two main components.
1. The Framework for Hierarchical Classification.
2. The Hierarchical Interface for Document Retrieval.
Two distinct techniques for classification are proposed in this thesis. These include
1. The use of Domain Specific Lexicon (DSL) which is comparable to a Domain Specific Ontology.
2. The Concept Prediction Based on Term Relevance (CPTR) technique
These techniques lend a semantic dimension to classification.
Keywords: Text Mining, Classification, Document Retrieval, Hierarchical, Domain specific lexicon (DSL), Probabilistic Latent Semantic Analysis (PLSA) , Concept Prediction based on Term frequency (CPTR)
-
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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 Framework for Fraud Detection Using Hidden Markov Model
Subject
The topic of the resource
Credit card; Emission probability; Fraud detection; Hidden Markov method; Initial probability; Machine learning; Supervised learning
Description
An account of the resource
Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Creator
An entity primarily responsible for making the resource
Hegde D.S.; Samanta D.; Dutta S.
Source
A related resource from which the described resource is derived
Lecture Notes in Networks and Systems, Vol-291, pp. 29-36.
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
2022-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-16-4284-5_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4284-5_3</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116791878&doi=10.1007%2F978-981-16-4284-5_3&partnerID=40&md5=1db508346aa18c79dcafa8a2203d2adc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116791878&doi=10.1007%2f978-981-16-4284-5_3&partnerID=40&md5=1db508346aa18c79dcafa8a2203d2adc</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 23673370; ISBN: 978-981164283-8
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Hegde D.S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Dutta S., Institute of Engineering and Management, Kolkata, 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
Articles
Article
Faculty Publications -Articles
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 a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
Subject
The topic of the resource
artificial intelligence; binary spring search algorithm; neonatal hyperbilirubinemia; new-born jaundice detection; XGBoost classifier
Description
An account of the resource
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved.
Creator
An entity primarily responsible for making the resource
Inamanamelluri H.V.S.L.; Pulipati V.R.; Pradhan N.C.; Chintamaneni P.; Manur M.; Vatambeti R.
Source
A related resource from which the described resource is derived
Revue d'Intelligence Artificielle, Vol-37, No. 2, pp. 257-265.
Publisher
An entity responsible for making the resource available
International Information and Engineering Technology Association
Date
A point or period of time associated with an event in the lifecycle of the resource
2023-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.18280/ria.370202" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/ria.370202</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163445008&doi=10.18280%2Fria.370202&partnerID=40&md5=a7d3e3f3e6c36aa372875ec220a7144a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163445008&doi=10.18280%2fria.370202&partnerID=40&md5=a7d3e3f3e6c36aa372875ec220a7144a</a>
Rights
Information about rights held in and over the resource
All Open Access; Bronze Open Access
Relation
A related resource
ISSN: 0992499X
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Inamanamelluri H.V.S.L., Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Pulipati V.R., Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, 500090, India; Pradhan N.C., Department of Electronics, Reykjavik University, Reykjavik, 101, Iceland; Chintamaneni P., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 520002, India; Manur M., Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, 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
Articles
Article
Faculty Publications -Articles
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 adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
Subject
The topic of the resource
(ABPNN-ANFIS); Chronic kidney disease (CKD); DL algorithms; MATLAB; UCI CKD Dataset
Description
An account of the resource
A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. 2024
Creator
An entity primarily responsible for making the resource
KR V.; Maharajan M.S.; K B.; Sivakumar N.
Source
A related resource from which the described resource is derived
e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-7
Publisher
An entity responsible for making the resource available
Elsevier Ltd
Date
A point or period of time associated with an event in the lifecycle of the resource
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1016/j.prime.2024.100463" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.prime.2024.100463</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186496923&doi=10.1016%2Fj.prime.2024.100463&partnerID=40&md5=a0bb55f1bc8a8412e96b3d50ccd79b42" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186496923&doi=10.1016%2fj.prime.2024.100463&partnerID=40&md5=a0bb55f1bc8a8412e96b3d50ccd79b42</a>
Rights
Information about rights held in and over the resource
All Open Access; Gold Open Access
Relation
A related resource
ISSN: 27726711
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
KR V., Computer Science, Christ Deemed to be University, Karnataka, Bangalore, 560029, India; Maharajan M.S., Department of ECE, SRM Institute of Science and Technology, Chennai, Kattankulathur, India; K B., Electronics and Communication Engineering, Alva's Institute of Engineering and Technology, Mijar, Moodbidri, Karnataka, 74225, India; Sivakumar N., Artificial Intelligence & Data Science, Panimalar Engineering College, Tamilnadu, Chennai, 600123, India
-
https://archives.christuniversity.in/files/original/76236278bb3ea37729b7da82038ab898.pdf
3f5ed18eed874f390413b4cd5e4462a0
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
Thesis
PhD
PhD Thesis
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 alzheimer s disease stages using machine learning techniques /
Subject
The topic of the resource
Computer Science
Creator
An entity primarily responsible for making the resource
Febin Antony - 1942042
Publisher
An entity responsible for making the resource available
CHRIST (Deemed to be University)
Date
A point or period of time associated with an event in the lifecycle of the resource
2023-07-08
Language
A language of the resource
English
Type
The nature or genre of the resource
PhD
-
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
PHD
PhD
PhD Thesis
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/.
Relation
A related resource
61000290
Title
A name given to the resource
Classification of Alzheimer's Disease Stages Using Machine Learning Techniques
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Alzheimer s disease (AD) is a type of mental disorder which deteriorates the normal functioning of human brain by reducing the memory capacity of an individual. Age is the most common factor for AD and this disease cannot be reversed or stopped. Doctors can only treat the symptoms of AD which include personality changes and brain structural changes. Analyzing neuro-degenerative disorders, neuroimaging plays an important role in diagnosing subjects with AD and other stages of AD. The proposed research identified this gap and using MRI and PET newlineimages for recognizing AD in its early occurrences by the professionals. This helps in tailoring an appropriate treatment procedure for treating AD. As per literature survey, many researchers have worked with convolutional methods like inbuilt skull stripping with two or more conversions and classified with different CNN architectures. The proposed research experimented advanced skull stripping method and classified using deep learning architectures. This research emphasizes the implementation of ResNet50 architecture with T1 weighted MRI and Amyloid PET images for detecting the abnormalities in the brain patterns based on the image attributes. For the proposed experiment, a total of 5000 T1 weighted MRI data and 3000 newlineAmyloid PET data were used. The collected images were pre-processed with noise removal newlinetechniques and skull stripping method. The ResNet50 is used to classify AD from the data newlineobtained from the ADNI dataset. Pre-processed images /data were fed to the tuned for three class classification on ADNI image data at 200 Epochs shows the accuracy of 97.3% for T1 weighted MRI data and 98% for Amyloid PET data. The experimental results of the proposed model prove that it classifies the images according to various stages with better accuracy than the other existing models by achieving excellent results.
Creator
An entity primarily responsible for making the resource
Febin, Antony
Source
A related resource from which the described resource is derived
Author's Submission
Publisher
An entity responsible for making the resource available
Christ(Deemed to be University)
Date
A point or period of time associated with an event in the lifecycle of the resource
2024-01-01
Contributor
An entity responsible for making contributions to the resource
H B, Anita
Rights
Information about rights held in and over the resource
Open Access
Format
The file format, physical medium, or dimensions of the resource
PDF
Language
A language of the resource
English
Type
The nature or genre of the resource
PhD
Identifier
An unambiguous reference to the resource within a given context
<a href="http://hdl.handle.net/10603/547684" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/547684</a>
-
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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques
Subject
The topic of the resource
Breast cancer; Deep learning; Histology microscopy image; Invasive ductal cancer; Pattern recognition
Description
An account of the resource
Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs).A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Creator
An entity primarily responsible for making the resource
Jaisingh W.; Preethi N.; Murali S.
Source
A related resource from which the described resource is derived
Lecture Notes in Electrical Engineering, Vol-990 LNEE, pp. 261-271.
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
2023-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-19-9090-8_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-9090-8_23</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161198501&doi=10.1007%2F978-981-19-9090-8_23&partnerID=40&md5=a47d82c0d2fba20961ded8c593186e00" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161198501&doi=10.1007%2f978-981-19-9090-8_23&partnerID=40&md5=a47d82c0d2fba20961ded8c593186e00</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 18761100; ISBN: 978-981199089-2
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Jaisingh W., School of Information Science, Presidency University, Karnataka, Bangalore, 560064, India; Preethi N., Department of Data Science, Christ University, Bengaluru, India; Murali S., Department of Mathematics, Coimbatore Institute of Technology, Coimbatore, 641014, 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
Articles
Article
Faculty Publications -Articles
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 countries based on development indices by using K-means and grey relational analysis
Subject
The topic of the resource
Development; Grey relational analysis; K-means clustering; Principal component analysis
Description
An account of the resource
Clustering countries based on their development profile is important, as it helps in the efficient allocation and use of resources for institutions like the World Bank, IMF and many others. However, measuring the status of development in each country is challenging, as development encompasses several facets such as economic, social, environmental and institutional aspects. These dimensions should be captured and aggregated appropriately before attempting to classify countries based on development. In this context, this paper attempts to measure various dimensions of development through four indices namely, Economic Index (EI), Social Index (SI), Sustainability Index (SUI) and Institutional Index (II) for the period between 1996 through 2015 for 102 countries. And then we categorize the countries based on these development indices using the grey relational analysis and K-means clustering method. Our study classifies countries into four clusters with twelve countries in the first cluster, fifty in second, twenty-seven and thirteen countries in third and fourth clusters respectively. Having taken each of the dimensions of development independently, our results show that no cluster has performed poorly in all four aspects. 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Creator
An entity primarily responsible for making the resource
Basel S.; Gopakumar K.U.; Rao R.P.
Source
A related resource from which the described resource is derived
GeoJournal, Vol-87, No. 5, pp. 3915-3933.
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
2022-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s10708-021-10479-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10708-021-10479-2</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111681763&doi=10.1007%2Fs10708-021-10479-2&partnerID=40&md5=be02e6284f17693d55669bae5af2f69a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111681763&doi=10.1007%2fs10708-021-10479-2&partnerID=40&md5=be02e6284f17693d55669bae5af2f69a</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 3432521; CODEN: GEOJD
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Basel S., Department of Economics, Christ University, Karnataka, Bengaluru, 560029, India; Gopakumar K.U., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, Prasanthi Nilayam, 515134, India; Rao R.P., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, Prasanthi Nilayam, 515134, 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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 Disaster Tweets using Machine Learning and Deep Learning Techniques
Subject
The topic of the resource
deep learning; disaster management; disaster tweets; machine learning; natural language processing; Twitter
Description
An account of the resource
Social networks provide a plethora of information for gathering extra data on people's behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful. 2022 IEEE.
Creator
An entity primarily responsible for making the resource
Asinthara K.; Jayan M.; Jacob L.
Source
A related resource from which the described resource is derived
2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Date
A point or period of time associated with an event in the lifecycle of the resource
2022-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/TQCEBT54229.2022.10041629" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT54229.2022.10041629</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149176827&doi=10.1109%2FTQCEBT54229.2022.10041629&partnerID=40&md5=9a15cd7ae61a9f15b2085cfad0420a2e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149176827&doi=10.1109%2fTQCEBT54229.2022.10041629&partnerID=40&md5=9a15cd7ae61a9f15b2085cfad0420a2e</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 978-166545361-5
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Asinthara K., Christ University, Bangalore, India; Jayan M., Christ University, Bangalore, India; Jacob L., Christ 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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 Diseased Leaves in Plants Using Convolutional Neural Networks
Subject
The topic of the resource
Convolutional neural network; Diseased leaf
Description
An account of the resource
The article focuses on the classification of diseased leaves using a machine learning algorithm. The main focus in agriculture is controlling pests and weeds, for which farmers spray chemical pesticides to get a good yield. The issue here is over-usage and under-usage of pesticides, which might harm the end consumer. To achieve the goal of reducing pesticide use and detecting pests in the crop early, the machine learning algorithm is deployed on the leaf image. The image data of the leaf of the cauliflower plant is collected for 40days. The data was collected from the day the plant was seeded in a pot until the day it was ready to be planted in the soil. From this data, the pest attack on the plants is tracked without the application of pesticides. To achieve this, the CNN algorithm is used on the collected image data. The outcome of the study would be to classify the diseased leaves based on the pest attack and know the right time to spray the pesticides to reduce the damage to the plant. This also reduces the use of pesticides and costs to the farmer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Creator
An entity primarily responsible for making the resource
Reddy B.D.K.; Iyer L.S.
Source
A related resource from which the described resource is derived
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 139-149.
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
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-97-0975-5_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_12</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197267405&doi=10.1007%2F978-981-97-0975-5_12&partnerID=40&md5=2de148875332e07e14e95707beb2f4a8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197267405&doi=10.1007%2f978-981-97-0975-5_12&partnerID=40&md5=2de148875332e07e14e95707beb2f4a8</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 23673370; ISBN: 978-981970974-8
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Reddy B.D.K., School of Business and Management, Christ University, Bangalore, India; Iyer L.S., School of Business and Management, Christ 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
Articles
Article
Faculty Publications -Articles
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 epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
Subject
The topic of the resource
Electroencephalogram; Entropy; Log energy entropy; Norm entropy; Recurrent Elman neural network; Wavelet packets
Description
An account of the resource
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70% for normal-pre-ictal, 99.70% for normal-epileptic and 99.85% for pre-ictal-epileptic. 2016, Springer Science+Business Media Dordrecht.
Creator
An entity primarily responsible for making the resource
Raghu S.; Sriraam N.; Kumar G.P.
Source
A related resource from which the described resource is derived
Cognitive Neurodynamics, Vol-11, No. 1, pp. 51-66.
Publisher
An entity responsible for making the resource available
Springer Science and Business Media B.V.
Date
A point or period of time associated with an event in the lifecycle of the resource
2017-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s11571-016-9408-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11571-016-9408-y</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987657596&doi=10.1007%2Fs11571-016-9408-y&partnerID=40&md5=365282d410d68653faa8d75a47eca1a5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987657596&doi=10.1007%2fs11571-016-9408-y&partnerID=40&md5=365282d410d68653faa8d75a47eca1a5</a>
Rights
Information about rights held in and over the resource
All Open Access; Green Open Access
Relation
A related resource
ISSN: 18714080
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
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Raghu S., Center for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology (An Autonomous Institution Affiliated to VTU Belgaum), Bangalore, India; Sriraam N., Center for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology (An Autonomous Institution Affiliated to VTU Belgaum), Bangalore, India; Kumar G.P., Department of ECE, Christ University, Bangalore, India
-
https://archives.christuniversity.in/files/original/5fc2a39bb9a7cc6d6953d3160a556f00.pdf
a7c0514f016011f3c930c4423b524e07
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
Thesis
PhD
PhD Thesis
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 extragalactic point sources and flux variability characteristics of blazars
Subject
The topic of the resource
Physics
Description
An account of the resource
Classification of different types of astronomical objects in large surveys usually done through spectroscopy requires enormous amounts of time. Hence, many attempts have been made using broad band photometric magnitudes and spectroscopic observations to classify the sources, particularly extragalactic sources such as active galactic nuclei (AGNs), starburst galaxies and normal galaxies. However, a method which does not involve spectroscopic data would be ideal.
Creator
An entity primarily responsible for making the resource
Anjum, Ayesha - 1445203
Publisher
An entity responsible for making the resource available
CHRIST (Deemed to be University)
Language
A language of the resource
English
Type
The nature or genre of the resource
PhD
-
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
PHD
PhD
PhD Thesis
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/.
Relation
A related resource
61000146
Title
A name given to the resource
Classification of extragalactic point sources and flux variability characteristics of blazars
Subject
The topic of the resource
Physics and Electronics
Description
An account of the resource
Classification of different types of astronomical objects in large surveys usually done through spectroscopy requires enormous amounts of time. Hence, many attempts have been made using broad band photometric magnitudes and spectroscopic observations to classify the sources, particularly extragalactic sources such as active galactic nuclei (AGNs), starburst galaxies and newlinenormal galaxies. However, a method which does not involve spectroscopic data would be ideal. With this in view, in this work we have made an effort to classify a sample of 37,492 point sources into Quasi-Stellar Objects (QSOs), galaxies and stars using template fitting technique and multiwavelength photometric magnitudes from the Sloan Digital Sky Survey (SDSS) and newlinethe Galaxy Evolution Explorer (GALEX) with coverage from the optical (z: 8931 to the far ultraviolet (FUV: 1516 . Templates for QSOs, galaxies and stars were used to fit the data of the objects to the seven photometric bands of SDSS and GALEX. The results were compared with SDSS spectroscopic classification. Two UV bands (NUV and FUV) were included to remove the possible degeneracies in the classification based only on optical bands or in color-color method. UV bands play a crucial role in the classification and characterization of astronomical objects that emit over a wide range of wavelengths, especially for those that are bright at UV. Classification using template fitting method is consistent with spectroscopic methods, provided UV information of the objects is available. UV bands are particularly important for separating quasars and stars, as well as spiral and starburst galaxies. We have achieved the efficiency of 89% for QSOs, 63% for galaxies and 84% for stars. Objects for which spectroscopic data is not available can also be classified using this method which does not require spectroscopic information.
Creator
An entity primarily responsible for making the resource
Anjum, Ayesha.
Source
A related resource from which the described resource is derived
Author's Submission
Publisher
An entity responsible for making the resource available
Christ(Deemed to be University)
Date
A point or period of time associated with an event in the lifecycle of the resource
2021-01-01
Contributor
An entity responsible for making contributions to the resource
Gudennavar, Shivappa B
Rights
Information about rights held in and over the resource
Open Access
Format
The file format, physical medium, or dimensions of the resource
PDF
Language
A language of the resource
English
Type
The nature or genre of the resource
PhD
Identifier
An unambiguous reference to the resource within a given context
<a href="http://hdl.handle.net/10603/338926" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/338926</a>
-
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
Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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 fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
Description
An account of the resource
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s).
Creator
An entity primarily responsible for making the resource
Devi M.R.; Sivakumar V.; Sindhu V.; Nataraj C.; Kanna R.R.; Karthikeswaran D.
Source
A related resource from which the described resource is derived
AIP Conference Proceedings, Vol-3161, No. 1
Publisher
An entity responsible for making the resource available
American Institute of Physics
Date
A point or period of time associated with an event in the lifecycle of the resource
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1063/5.0229501" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0229501</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203995853&doi=10.1063%2F5.0229501&partnerID=40&md5=c3b806d92b5d574bc14e228d2a7ec245" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203995853&doi=10.1063%2f5.0229501&partnerID=40&md5=c3b806d92b5d574bc14e228d2a7ec245</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 0094243X
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
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Devi M.R., School of Information Science, Presidency University, Bangalore, India; Sivakumar V., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Sindhu V., Department of Computer Science, CHRIST University, Bangalore, India; Nataraj C., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Kanna R.R., Department of Computer Science, CHRIST University, Bangalore, India; Karthikeswaran D., Department of Information Technology, Nehru Institute of Technology, Coimbatore, 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>
<br /><br /><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