637
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
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
Real hypersurfaces of complex space forms satisfying FischerMarsden equation
Subject
The topic of the resource
Complex Euclidean space; FischerMarsden equation; Hopf hypersurface; Non-flat complex space form
Description
An account of the resource
Let M be a real hypersurface of a complex space form of constant curvature c. In this paper, we study the hypersurface M which admits a nontrivial solution to FischerMarsden equation, that is, the induce metric g of M satisfies Hessg(?) = (?g?) g+ ?Sg, where ? is a nontrivial function. We prove that there does not exist a complete Hopf real hypersurface in a non-flat complex space form satisfying FischerMarsden equation. Finally, we show that a complete real hypersurface with A?= ??, ?? 0 , of a complex Euclidean space Cn satisfying FischerMarsden equation is locally congruent to a sphere or S1R2n-2. 2021, Universitdegli Studi di Ferrara.
Creator
An entity primarily responsible for making the resource
Venkatesha V.; Naik D.M.; Kumara H.A.
Source
A related resource from which the described resource is derived
Annali dell'Universita di Ferrara, Vol-67, No. 1, pp. 203-216.
Publisher
An entity responsible for making the resource available
Springer-Verlag Italia s.r.l.
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/s11565-021-00361-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11565-021-00361-x</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104106753&doi=10.1007%2Fs11565-021-00361-x&partnerID=40&md5=be30b4c7f068761e9ec1a66b18f7a588" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104106753&doi=10.1007%2fs11565-021-00361-x&partnerID=40&md5=be30b4c7f068761e9ec1a66b18f7a588</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 4303202
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
Venkatesha V., Department of Mathematics, Kuvempu University, Shivamogga, Karnataka, India; Naik D.M., Department of Mathematics, Kuvempu University, Shivamogga, Karnataka, India, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Kumara H.A., Department of Mathematics, Kuvempu University, Shivamogga, 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
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
Real time conversion of sign language to speech and prediction of gestures using artificial neural network
Subject
The topic of the resource
Artificial Neural Network; Global System for Mobile; GSM; Mute people; Sign Languages
Description
An account of the resource
Sign language is generally used by the people who are unable to speak, for communication. Most people will not be able to understand the Universal Sign Language (unless they have learnt it) and due to this lack of knowledge about the language, it is very difficult for them to communicate with mute people. A device that helps to bridge a gap between mute persons and other people forms the crux of this paper. This device makes use of an Arduino Uno board, a few flex sensors and an Android application to enable effective communication amongst the users. Using the flex sensors, gestures made by the wearer is detected and then according to various pre-defined conditions for the numerous values generated by the flex sensors, corresponding messages are sent using a Global System for Mobile(GSM) module to the wearer?s android device, which houses the application that has been designed to convert text messages into speech. The GSM module is also used to send the sensor inputs to a cloud server and these values are taken as input parameters into the neural network for a time series based prediction of gestures. The system is designed to be a continually learning device and improve reliability by monitoring every individual?s behaviour at all times. 2018 The Authors. Published by Elsevier B.V.
Creator
An entity primarily responsible for making the resource
Abraham A.; Rohini V.
Source
A related resource from which the described resource is derived
Procedia Computer Science, Vol-143, pp. 587-594.
Publisher
An entity responsible for making the resource available
Elsevier B.V.
Date
A point or period of time associated with an event in the lifecycle of the resource
2018-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1016/j.procs.2018.10.435" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2018.10.435</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058273796&doi=10.1016%2Fj.procs.2018.10.435&partnerID=40&md5=3c6bb6be4eb6474bcca80416e94662ba" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058273796&doi=10.1016%2fj.procs.2018.10.435&partnerID=40&md5=3c6bb6be4eb6474bcca80416e94662ba</a>
Rights
Information about rights held in and over the resource
All Open Access; Gold Open Access
Relation
A related resource
ISSN: 18770509
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
Abraham A., Deemed to Be University, Hosur Road, Karnataka, Bengaluru, 560029, India; Rohini V., Deemed to Be University, Hosur Road, Karnataka, Bengaluru, 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
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
Parmal, Varun; Banerjee, Snigdha; Agrawal, Swati
Title
A name given to the resource
Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems;pp.284-306
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1201/9781003612858-11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003612858-11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012012823?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012012823?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
Parmal V., Chameli Devi Group of Institutions, Indore, India; Banerjee S., School of Statistics, Devi Ahilya University, Indore, India; Agrawal S., Christ University, Bengaluru, India
Description
An account of the resource
We study the inventory model of a product having demand affected by its freshness and selling price in the context of supply chains, freshness, and price-dependent demand, where the supplier is dominated, as is usually the case with producers of agri-based products. The product when received exhibits heterogeneous quality. The retailer subdivides the product into quality-dependent segments, which he sells simultaneously during the selling season at prices commensurate with the quality. The sizes of the segments are random variables. The supplier can get a partial advance payment from the dominant retailer by providing a discount on the partial advance with the proportion of partial payment as well as the epoch of partial payment chosen by the supplier. The retailer can, at times, choose the advance proportion to be paid, and the discounted price which we call the endogenous case but takes a loan for the advance payment from a financer, whom he repays with interest when a delayed payment period permitted by the supplier gets over. The retailer in turn gets some time before he can pay his remaining dues and pays the supplier a fraction of the cost price commensurate with the quality of the product. Lost sales shortages are considered for fresh items. The model is aimed at obtaining optimal values of ordering amount, selling price, and discounted selling prices for the various segments. It is also aimed to obtain advance proportion and the discount on advance payment for the endogenous case. Real-coded genetic algorithm (RCGA) and Hybrid RCGA have been used to obtain the optimal solutions for numerical examples and the results are compared. Finally, sensitivity analysis to evaluate the effects of changes in some parameter values has also been presented. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors.
Publisher
An entity responsible for making the resource available
CRC Press
Relation
A related resource
ISBN: 978-104034487-3; 978-103277087-1;
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
Real-Time Application of Document Classification Based on Machine Learning
Subject
The topic of the resource
Balanced accuracy; Document classification; OCR; SVM; t-SNE; Text analytics; tf-idf
Description
An account of the resource
This research has been performed, keeping a real-time application of document (multi-page, varying length, scanned image-based) classification in mind. History of property title is captured in various documents, recorded against the said property in all the countries across the world. Information of the property, starting from ownership to the conveyance, mortgage, refinance etc. are buried under these documents. This is by far a human driven process to manage these digitized documents. Categorization of the documents is the primary step to automate the management of these documents and intelligent retrieval of information without or minimal human intervention. In this research, we have examined a popular, supervised machine learning technique called, SVM (support vector machine) with a heterogeneous data set of six categories of documents related to property. The model obtained an accuracy of 88.06% in classifying over 988 test documents. 2020, Springer Nature Switzerland AG.
Creator
An entity primarily responsible for making the resource
Guha A.; Samanta D.
Source
A related resource from which the described resource is derived
Learning and Analytics in Intelligent Systems, Vol-9, pp. 366-379.
Publisher
An entity responsible for making the resource available
Springer Nature
Date
A point or period of time associated with an event in the lifecycle of the resource
2020-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-3-030-38501-9_37" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-38501-9_37</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179861266&doi=10.1007%2F978-3-030-38501-9_37&partnerID=40&md5=589c7b4db3449494601bb73ef9d093c1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179861266&doi=10.1007%2f978-3-030-38501-9_37&partnerID=40&md5=589c7b4db3449494601bb73ef9d093c1</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 26623447
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
Guha A., Eagle Labs, First American India Private LTD., Bangalore, India; Samanta D., Department of Computer Science, Christ University (Deemed to be), 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
Real-Time Application with Data Mining and Machine Learning
Description
An account of the resource
Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are manyrelevantfields, like data analysis in finance,retail, telecommunications sector, analyzing biological data, otherscientific uses, and intrusiondetection.The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few exampleswhere it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Creator
An entity primarily responsible for making the resource
Chakraborty S.; Islam S.H.; Samanta D.
Source
A related resource from which the described resource is derived
EAI/Springer Innovations in Communication and Computing, pp. 129-157.
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/10.1007/978-3-030-93088-2_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-93088-2_6</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130560223&doi=10.1007%2F978-3-030-93088-2_6&partnerID=40&md5=3b0c28a471b58e2641a83d6105222acb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130560223&doi=10.1007%2f978-3-030-93088-2_6&partnerID=40&md5=3b0c28a471b58e2641a83d6105222acb</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 25228595
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
Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
-
https://archives.christuniversity.in/files/original/bf229c47f3ff4d43c5e1e823f75d7aab.pdf
4869e78b52e07430c9600118693a3961
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
Real-time architectural efforts in building a social network using NOSQL databases
Subject
The topic of the resource
Computer Science
Creator
An entity primarily responsible for making the resource
Tauro, Clarence J M - 0945005
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
Description
An account of the resource
Relational database management systems (RDBMS) today are the predominant technology for storing structured data in web and business applications. Along with the increasing size of the datasets, the number of accesses and operations performed increases. This growth, enhanced by the proliferation of social networks, led to a depletion of traditional relational databases that were commonly used to solve a wide range of problems.
-
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
61000028
Title
A name given to the resource
Real-time architectural efforts in building a social network using NOSQL databases
Subject
The topic of the resource
Computer Science
Description
An account of the resource
NA
Creator
An entity primarily responsible for making the resource
Tauro,Clarence J M.
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
2015-01-01
Contributor
An entity responsible for making contributions to the resource
Ganesan, N
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/10603/341926" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/341926</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
Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies
Subject
The topic of the resource
Accuracy; IoT security; Machine learning; Performance metrics; Precision
Description
An account of the resource
Conducting an in-depth study on algorithms addressing the interaction problem in the fields of machine learning and IoT security involves a meticulous evaluation of performance measures to ensure global reliability. The study examines key metrics such as accuracy, precision, recall, and F1 scores across ten scenarios. The highly competitive algorithms showcase accuracy rates ranging from 95.5 to 98.2%, demonstrating their ability to perform accurately in various situations. Precision and recall measurements yield similar information about the model's capabilities. The achieved balance between accuracy and recovery, as determined by the F1 tests ranging from 95.2 to 98.0%, emphasizes the practical importance of data transfer in the proposed method. Numerical evaluation, in addition to an analysis of overall performance metrics, provides a comprehensive understanding of the algorithm's performance and identifies potential areas for improvement. This research leads to advancements in the theoretical vision of machine learning for IoT protection. It offers real-world insights into the practical use of robust models in dynamically changing situations. As the Internet of Things environment continues to evolve, the study's results serve as crucial guides, laying the foundation for developing strong and effective security systems in the realm of interaction between virtual and material reality. The Author(s) 2024.
Creator
An entity primarily responsible for making the resource
Poonia R.C.; Upreti K.; Alapatt B.P.; Jafri S.
Source
A related resource from which the described resource is derived
Lecture Notes in Networks and Systems, Vol-1011 LNNS, pp. 339-350.
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/10.1007/978-981-97-4581-4_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-4581-4_25</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202602687&doi=10.1007%2F978-981-97-4581-4_25&partnerID=40&md5=8a02527ce7f77d3a15d8c905270c80e8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202602687&doi=10.1007%2f978-981-97-4581-4_25&partnerID=40&md5=8a02527ce7f77d3a15d8c905270c80e8</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 23673370; ISBN: 978-981974580-7
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
Poonia R.C., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Alapatt B.P., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Jafri S., Administrative Science Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
-
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
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/.
Creator
An entity primarily responsible for making the resource
Mary, S. A. Sahaaya Arul; Prasad, K.D.V.; Raj, S. Dinakar; Sathyapriya, S.; Lalitha, R.V.S.; Rao, G. Siva Nageswara
Title
A name given to the resource
Real-Time Data Fusion Algorithm for Multi-Modal Environmental Sensor Networks Using Kalman Filtering and IoT Integration
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
Analytical Letters;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1080/00032719.2026.2615824" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/00032719.2026.2615824</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029404632?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029404632?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
Mary S.A.S.A., Department of AIML & Data Science, School of Engineering and Technology, CHRIST University, Bangalore, India; Prasad K.D.V., Faculty (Research), Symbiosis Institute of Business Management, Hyderabad, Symbiosis International (Deemed University), Pune, India; Raj S.D., Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India; Sathyapriya S., Assistant Professor in Information Technology, K. Ramakrishnan College of Engineering, Trichy, India; Lalitha R.V.S., Department of CSE, Aditya University, Surampalem, India; Rao G.S.N., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
Description
An account of the resource
Fusion of heterogeneous, noisy, and asynchronous multimodal data streams is essential to environmental sensor networks, given the computational, memory, and energy constraints of IoT devices. This paper introduces a real-time data fusion framework integrating hybrid adaptive Kalman filtering, distributed edge computing, and seamless IoT connectivity. The proposed framework incorporates three key innovations. First, a hybrid adaptive Kalman filtering mechanism employs the Unscented Kalman Filter (UKF) sigma-point technique, augmented with Long Short-Term Memory (LSTM) neural networks and fuzzy logic, for dynamic noise correction and robust nonlinear state estimation. Second, a three-tier distributed fusion architecture employs edge computing for local data processing, reducing network latency, communication overhead, and energy consumption. Third, a modular Service-Oriented Architecture enables seamless IoT integration, remote data access, and adaptive system reconfiguration. The framework also incorporates multi-criteria fault detection that combines chi-square tests, sequential probability ratio tests, and LSTM-based predictive compensation during sensor failures. Experimental validation employed 150 sensors for urban air-quality monitoring, industrial facility surveillance, and water-quality measurement. Sensor nodes utilized ESP32-S3 microcontrollers with LoRa communication, while Raspberry Pi 4 devices served as edge gateways connected to AWS IoT infrastructure. Compared to standard Kalman filtering, the proposed method achieved: (i) 25.2% reduction in root mean square estimation error, (ii) 41% energy reduction driven by 70% communication savings through predictive transmission and edge compression, (iii) sub-100 ms end-to-end latency representing 54% improvement, and (iv) robust performance maintaining below 10% degradation at 15% sensor failure rates. 2026 Taylor & Francis Group, LLC.
Subject
The topic of the resource
Adaptive Kalman filtering; distributed architectures; edge computing; environmental monitoring; fault-tolerant systems; IoT integration; multi-sensor data fusion
Publisher
An entity responsible for making the resource available
Taylor and Francis Ltd.
Relation
A related resource
ISSN: 32719; CODEN: ANALB
Language
A language of the resource
English
Type
The nature or genre of the resource
Article
Rights
Information about rights held in and over the resource
Restricted Access; Hardcopy may be available in the library
Format
The file format, physical medium, or dimensions of the resource
online
-
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
Das, Tamish
Title
A name given to the resource
Real-time detection and response: How AI is shaping the future of hate speech
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Ethical AI Solutions for Addressing Social Media Influence and Hate Speech;pp.293-331
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.4018/979-8-3693-9904-0.ch013" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9904-0.ch013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000227556?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000227556?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
Das T., Christ University, India
Description
An account of the resource
This chapter traverse how artificial intelligence is transforming hate speech detection by facilitating real-time detection and response. It focuses on the technical aspects of using machine learning. Natural language processing and deep learning models to identify and mark spiteful content. This chapter also discusses the advantages, obstacles, and ethical considerations associated with using AI to moderate online speech. The ultimate objective is to provide insights into how AI is changing the landscape of content moderation on platforms around the world. 2025, IGI Global Scientific Publishing. All rights reserved.
Publisher
An entity responsible for making the resource available
IGI Global
Relation
A related resource
ISBN: 979-836939906-4; 979-836939904-0;
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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Abhiram, S.; Gupta, Heena; James, C.K.
Title
A name given to the resource
Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/DECoN67170.2025.11448065" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/DECoN67170.2025.11448065</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037313746?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037313746?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
Abhiram S., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India; Gupta H., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India; James C.K., Christ (Deemed to be University), Dept of Statistics and Data Science, Bengaluru, India
Description
An account of the resource
The detection of defects in fabric is a critical process for maintaining quality standards and reducing economic losses in the textile industry. Traditional inspection methods, which rely on human operators, are often slow, inconsistent, and susceptible to error. This research introduces an innovative solution that harnesses Edge AI and deep learning to facilitate real-time, on-site defect detection. We developed a highly efficient and lightweight model based on the YOLOv8 architecture, specifically tailored for deployment on resource-constrained edge devices like NVIDIA Jetson Nano or Raspberry Pi. Through a process of comprehensive literature analysis and domain expertise, a compact, high-precision model was trained on diverse fabric defect datasets. To ensure optimal performance on edge hardware, we employed advanced optimization techniques like quantization and pruning. The primary offering of the work are threefold: the making of a streamlined YOLOv8-based model for fabric defect detection, a comparative analysis of various edge inference strategies, and a proposed system architecture for real-time embedded deployment. This study effectively demonstrates the practical application of advanced AI to solve longstanding challenges in textile quality control. Future efforts will be directed towards extensive real-world operational testing and exploring localized Model Training with Federated Learning enhancement. 2025 IEEE.
Subject
The topic of the resource
deep learning; Edge AI; embedded systems; fabric defect detection; quality control; real-time systems; YOLOv8
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833159442-8;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
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
Real-Time Fire Detection Through the Analysis of Surveillance Videos
Subject
The topic of the resource
Email alerts; Forest fire detection; Real-time; Speaker alarm; YOLOv5
Description
An account of the resource
The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. 2024 IEEE.
Creator
An entity primarily responsible for making the resource
Vigneshwaran T.; Velammal B.L.
Source
A related resource from which the described resource is derived
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
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
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545050" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545050</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204464352&doi=10.1109%2FTQCEBT59414.2024.10545050&partnerID=40&md5=71da541239e0b2d4b30033626116522d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204464352&doi=10.1109%2fTQCEBT59414.2024.10545050&partnerID=40&md5=71da541239e0b2d4b30033626116522d</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 979-835038427-7
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
Vigneshwaran T., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Velammal B.L., Anna University, College of Engineering Guindy, Department of Cse, Chennai, 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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Goswami, Jai; Shoran, Preety; Shanbhog, Manjula
Title
A name given to the resource
Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/IC2E365635.2025.11167431" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2E365635.2025.11167431</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018462669?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018462669?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
Goswami J., CHRIST University, Computer Science, Bengaluru, India; Shoran P., CHRIST University, Computer Science, Bengaluru, India; Shanbhog M., CHRIST University, Computer Science, Bengaluru, India
Description
An account of the resource
This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE.
Subject
The topic of the resource
ball detection; ByteTrack; Frames; Full frame detection; Homography; Multi object tracking; Yolov8
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833152439-5;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
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
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
Real-time human action prediction using pose estimation with attention-based LSTM network
Subject
The topic of the resource
Attention mechanism; LSTM; Pose estimation; Skeleton key joints
Description
An account of the resource
Human action prediction in a live-streaming videos is a popular task in computer vision and pattern recognition. This attempts to identify activities in an image or video performed by a human. Artificial intelligence(AI)-based technologies are now required for the security and human behaviour analysis. Intricate motion patterns are involved in these actions. For the visual representation of video frames, conventional action identification approaches mostly rely on pre-trained weights of various AI architectures. This paper proposes a deep neural network called Attention-based long short-term memory (LSTM) network for skeletal based activity prediction from a video. The proposed model has been evaluated on the BerkeleyMHAD dataset having 11 action classes. Our experimental results are compared against the performance of the LSTM and Attention-based LSTM network for 6 action classes such as Jumping, Clapping, Stand-up, Sit-down, Waving one hand (Right) and Waving two hands. Also, the proposed method has been tested in a real-time environment unaffected by the pose, camera facing, and apparel. The proposed system has attained an accuracy of 95.94% on BerkeleyMHAD dataset. Hence, the proposed method is useful in an intelligent vision computing system for automatically identifying human activity in unpremeditated behaviour. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Creator
An entity primarily responsible for making the resource
Bharathi A.; Sanku R.; Sridevi M.; Manusubramanian S.; Chandar S.K.
Source
A related resource from which the described resource is derived
Signal, Image and Video Processing, Vol-18, No. 4, pp. 3255-3264.
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/s11760-023-02987-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11760-023-02987-0</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182997614&doi=10.1007%2Fs11760-023-02987-0&partnerID=40&md5=15553d99ffc824b1ef08a4399a1de36a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182997614&doi=10.1007%2fs11760-023-02987-0&partnerID=40&md5=15553d99ffc824b1ef08a4399a1de36a</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 18631703
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
Bharathi A., National Institute of Technology, Tiruchirappalli, India; Sanku R., National Institute of Technology, Tiruchirappalli, India; Sridevi M., National Institute of Technology, Tiruchirappalli, India; Manusubramanian S., Liquid Propulsion Systems Centre, ISRO, Thiruvananthapuram, India; Chandar S.K., 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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Singhal, Prateek; Yadav, Rakesh Kumar
Title
A name given to the resource
Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.23919/INDIACom66777.2025.11115652" target="_blank" rel="noreferrer noopener">https://doi.org/10.23919/INDIACom66777.2025.11115652</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016230225?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016230225?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
Singhal P., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India, School of Sciences, Christ (Deemed to be University), Department of Computer Science, Delhi-NCR, India; Yadav R.K., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India
Description
An account of the resource
Real-time deep learning models for polyp identification and segmentation in medical imaging. Recognising the limits of current database systems for real-time applications, the research focusses on creating a deep learning model capable of recognising crucial picture components to aid in precise polyp categorisation. The suggested methodology is intended for realtime, practical healthcare and diagnostic applications that need quick polyp detection via preliminary colonoscopy testing. Performance investigation demonstrates that ResNet50 and EfficientNet B2 outperform other models, implying that they are suitable for real-world application and optimal outcomes. 2025 Bharati Vidyapeeth, New Delhi.
Subject
The topic of the resource
colonoscopy; Deep learning models; medical imaging; polyp classification; polyp detection; polyp segmentation; preliminary tests
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 978-938054460-1;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
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
Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm
Subject
The topic of the resource
fully connected layer; litter; object detection; prediction time; YOLOv4-Tiny
Description
An account of the resource
Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster. 2022 IEEE.
Creator
An entity primarily responsible for making the resource
Shalini V.; Tangade S.; Prajna P.K.; Sangeetha J.P.; Azam F.; Anoop G.L.
Source
A related resource from which the described resource is derived
MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference
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/MysuruCon55714.2022.9972356" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MysuruCon55714.2022.9972356</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145347420&doi=10.1109%2FMysuruCon55714.2022.9972356&partnerID=40&md5=770727b3bd0f9a0ea00f103ad5a04248" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145347420&doi=10.1109%2fMysuruCon55714.2022.9972356&partnerID=40&md5=770727b3bd0f9a0ea00f103ad5a04248</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 978-166549790-9
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
Shalini V., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Tangade S., Christ (Deemed to Be University), Dept. of Cse, Bangalore, India; Prajna P.K., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Sangeetha J.P., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Azam F., School of Comp. Science & Engineering, Reva University, Bangalore, India; Anoop G.L., Christ (Deemed to Be University), Dept. of Cse, 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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Sharma, Pradeep; Logeshwaran, J.
Title
A name given to the resource
Real-Time Monitoring and Anomaly Detection in Cloud-Based IoT Networks
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
2025 International Conference on Networks and Cryptology, NETCRYPT 2025;pp.448-452
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/NETCRYPT65877.2025.11102267" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NETCRYPT65877.2025.11102267</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015531970?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015531970?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
Sharma P., Service Now, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India
Description
An account of the resource
IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors. 2025 IEEE.
Subject
The topic of the resource
considered; cybersecurity; explosive; identification; implementation; impossible; management; monitoring
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833152605-4;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Jomon, Ashvin Antony; Poonia, Ramesh Chandra
Title
A name given to the resource
Real-Time Network Monitoring: Integrating Machine Learning and Custom Packet Sniffer Using Python
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
Lecture Notes in Networks and Systems;Volume;1579 LNNS;pp.1-15
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-95-0626-2_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-0626-2_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028357876?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028357876?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
Jomon A.A., Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Poonia R.C., Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India
Description
An account of the resource
The growth in network traffic and the increasing complexity of cyber threats necessitate robust systems for detecting anomalies that indicate security breaches. This research presents a methodology for finding anomalies in packets sent when the connection is established. It uses a machine learning model and a packet sniffer. It captures Transmission Control Protocol (TCP), User Datagram Protocol (UDP), IPv4, and Internet Control Message Protocol (ICMP) segments to predict if any anomalies are present (Sanders in Practical packet analysis: using wireshark to solve real-world network problems, No Starch Press, San Francisco, 2017). An unsupervised learning model is utilized. The presence of unlabeled data to enhance the real-time prediction using isolation forest model. The data collected by packet sniffer undergoes avoiding null values and encoding addresses, and thus an isolation forest is used so that it predicts if anomalies are present using binary trees. The performance is evaluated on the basis of metrics like accuracy, precision, and F1-score (Goutte and Gaussier in European conference on information retrieval, Springer, New York, 2005). The result illustrates the model is accurate in predicting whether anomalies are present. Future work is focused on enhancing the models capabilities with more protocols and an active defense mechanism. The study addresses real-world deployment challenges especially in heterogeneous environments like IoT-based networks. While isolation forest is getting high accuracy, future research could explore hybrid approaches combining traditional statistical methods with deep learning techniques for enhanced industry applications (Ahmed et al. in J Netw Comput Appl 60:1931, 2016). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Subject
The topic of the resource
Isolation forest classification; Machine learning; Network anomaly detection; Packet sniffer; Real-time processing; Unsupervised learning
Publisher
An entity responsible for making the resource available
Springer Science and Business Media Deutschland GmbH
Relation
A related resource
ISSN: 23673370; ISBN: 978-981950625-5;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
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
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
Nijhawan, Pratham; Ahmad, Faraz; Upreti, Kamal; Upreti, Shitiz; Poonia, Ramesh Chandra
Title
A name given to the resource
Real-time player performance analytics: Leveraging smart cyber-physical systems and AI in sports
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Smart Cyber-Physical Systems: Innovations and Real-World Implications;pp.236-261
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1201/9781003542513-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003542513-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017495928?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017495928?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
Nijhawan P., CHRIST (Deemed to be University), Delhi NCR, India; Ahmad F., CHRIST (Deemed to be University), Delhi NCR, India; Upreti K., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Delhi NCR, India; Upreti S., Lloyd Business School, Lloyd Group of Institutions, Greater Noida, Uttar Pradesh, India; Poonia R.C., CHRIST (Deemed to be University), Delhi NCR, India
Description
An account of the resource
[No abstract available]
Publisher
An entity responsible for making the resource available
CRC Press
Relation
A related resource
ISBN: 978-100354251-3; 978-103289293-1;
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
Faculty Publications
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/.
Creator
An entity primarily responsible for making the resource
Thaleeparambil, Noeal Rajeev; Biju, Abiya; Krishna, Deva; Prathap, Boppuru Rudra; Rekha, V.; Reddy, Vandana
Title
A name given to the resource
Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
2025 1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1109/ICAET63349.2025.10932179" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAET63349.2025.10932179</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002474353?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002474353?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
Thaleeparambil N.R., Christ (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, India; Biju A., Christ (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, India; Krishna D., Christ (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., M. S. Ramaiah University of Applied Sciences, Department of Computer Science and Engineering, Bengaluru, India; Rekha V., Christ (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, India; Reddy V., Christ (Deemed to be University), Department of Computer Science and Engineering, Bengaluru, India
Description
An account of the resource
The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols. 2025 IEEE.
Subject
The topic of the resource
Automated System; Construction Sites; RFID; Safety Monitoring; Worker Safety
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-835035561-1;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
Rights
Information about rights held in and over the resource
Restricted Access; Hardcopy may be available in the library