535
20
16481
-
https://archives.christuniversity.in/files/original/9c5dcde622f1be943851c7cc42998ced.pdf
359540bc1cece6676682e4f659029a2c
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
Multi-component condensation mediated synthesis of bioactive heterocyclic compounds
Subject
The topic of the resource
Chemistry
Description
An account of the resource
Aromatic heterocycles constitute the most diverse family of organic compounds. Moreover, aromatic heterocycles are widely used for the synthesis of dyes and polymeric materials of high value. The development of selective reactions that utilize easily available and abundant precursors for the efficient synthesis of heterocyclic compounds is a long-standing goal of chemical research. Despite the centrality of its role in a number of important research areas, including medicinal chemistry, total synthesis,and materials science, a general, selective, step-economical, and step�efficient synthesis of heterocycles is still needed.Pyrano[2,3-c]pyrazole derivatives have been synthesised by a one-pot multicomponent condensation of different aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be retrieved using an external magnet and used repeatedly. A practical, scalable method for obtaining various pyranopyrazoles has been demonstrated.
Creator
An entity primarily responsible for making the resource
Chemistry
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
61000185
Title
A name given to the resource
Multi-component condesation mediated synthesis of bioactive heterocyclic compounds
Subject
The topic of the resource
Chemistry
Description
An account of the resource
Aromatic heterocycles constitute the most diverse family of organic compounds. Moreover, aromatic heterocycles are widely used for the synthesis of dyes and polymeric materials of high value. The development of selective reactions that utilize easily available and abundant precursors for the efficient synthesis of heterocyclic compounds is a long-standing goal of chemical research. Despite the centrality of its role in a number of important research areas, including medicinal chemistry, total synthesis, and materials science, a general, selective, step-economical, and step-efficient synthesis of heterocycles is still needed. newlinePyrano[2,3-c]pyrazole derivatives have been synthesised by a one-pot multicomponent condensation of different aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be retrieved using an external magnet and used repeatedly. A practical, scalable method for obtaining various pyranopyrazoles has been demonstrated. The extraordinary catalytic role of the various catalyst has been discovered in the processes, which reveals a possible character of enhancing reaction rates and stabilising the intermediates during the course of the reactions.
Creator
An entity primarily responsible for making the resource
Mishra, Manisha
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
Nizam, Aatika
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/10603/393394" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/393394</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
Reviews
Review
Faculty Publications- Reviews
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
Multi-criteria decision making (MCDM) in diverse domains of education: a comprehensive bibliometric analysis for research directions
Subject
The topic of the resource
Education; Multiple criteria decision making; Tableau; Vosviewer
Description
An account of the resource
Multiple Criteria Decision Making has been one of the powerful and structured approach in solving real world problems in the past. The aim is to determine the best alternative based on multiple criteria. It has shown a remarkable performance in the field of education. In order to gain insights into the existing body of research in this area, a bibliometric analysis was conducted. The study is conducted to provide a comprehensive analysis since 2000 in the field of application of MCDM in the various domains of education. The publication information was accessed from Scopus Database on 1 December 2023 and the bibliometric analysis has been done through Vosviewer, R package bibliometrics and Tableau. Initially 5185 documents were found which were reduced to 1706 after multi layered screening criteria. The analysis is performed to find the relevant documents, most valuable researchers, the major countries where the research in this area is exhaustively conducted. After extensive research it is observed that researchers belonging to China are highly involved in the domain taken for study. Also, research conducted in China is highly cited which shows its quality of work. Further, it is observed that mostly fuzzy analysis techniques are widely used for MCDM. The collaborative work done by Arunodaya Raj Mishra and Rani Pratibha research work is remarkable and highly recommended to conduct the research in the considered domain in the research paper. The conducted bibliometric analysis provides an overview of the scope and global trends of MCDM in shaping the education sector. This would help the researchers to explore the most relevant study, analysis and finding the research gaps as per their research needs. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.
Creator
An entity primarily responsible for making the resource
Srivastava S.; Tripathi A.; Arora N.
Source
A related resource from which the described resource is derived
International Journal of System Assurance Engineering and Management
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
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s13198-024-02332-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13198-024-02332-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192940949&doi=10.1007%2Fs13198-024-02332-9&partnerID=40&md5=dc14152990c99a330488ad1dfe4d012c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192940949&doi=10.1007%2fs13198-024-02332-9&partnerID=40&md5=dc14152990c99a330488ad1dfe4d012c</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 9756809
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
Review
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Srivastava S., Christ University, Bengaluru, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, 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
Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP
Subject
The topic of the resource
CODAS; FAHP; MOORA; T2DM mHealth applications; usability; usability score
Description
An account of the resource
People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed frameworks stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Creator
An entity primarily responsible for making the resource
Gupta K.; Roy S.; Poonia R.C.; Kumar R.; Nayak S.R.; Altameem A.; Saudagar A.K.J.
Source
A related resource from which the described resource is derived
Applied Sciences (Switzerland), Vol-12, No. 9
Publisher
An entity responsible for making the resource available
MDPI
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/10.3390/app12094156" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/app12094156</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129311119&doi=10.3390%2Fapp12094156&partnerID=40&md5=1b0282689918c9156d7a85df6669d18c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129311119&doi=10.3390%2fapp12094156&partnerID=40&md5=1b0282689918c9156d7a85df6669d18c</a>
Rights
Information about rights held in and over the resource
All Open Access; Gold Open Access
Relation
A related resource
ISSN: 20763417
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
Gupta K., Faculty of Computing and Information Technology, Usha Martin University, Ranchi, 835103, India; Roy S., Faculty of Computing and Information Technology, Usha Martin University, Ranchi, 835103, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumar R., Department of Computer Science and Engineering, GIET University, Rayagada, 765022, India; Nayak S.R., Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, 201303, India; Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, 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
Tiwari, Arpit; Chakravorthy, Soumi; Nanjundan, Preethi; Ranjan Kumar, Ravi; Karmakar, Ananya; Bishoyi Ratna, Satyaban
Title
A name given to the resource
Multi-dimensional changes in drought patterns across India
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
Climatic Change;Volume;179;Issue;1;Article No.;11;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s10584-025-04102-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10584-025-04102-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027188476?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027188476?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
Tiwari A., Christ University, Pune, Lavasa, 412112, India, India Meteorological Department, Pune, Shivajinagar, 411005, India; Chakravorthy S., India Meteorological Department, Pune, Shivajinagar, 411005, India; Nanjundan P., Christ University, Pune, Lavasa, 412112, India; Ranjan Kumar R., India Meteorological Department, Pune, Shivajinagar, 411005, India; Karmakar A., India Meteorological Department, Pune, Shivajinagar, 411005, India; Bishoyi Ratna S., India Meteorological Department, Pune, Shivajinagar, 411005, India
Description
An account of the resource
Indias hydroclimatic systems are undergoing unprecedented transitions in a warming climate, marked by shifts in temperature extremes, altered precipitation patterns, and increasing drought risk. This study presents a comprehensive assessment of drought trends and hydroclimatic variability across six major geographical zonesWestern, Central, Himalayan, Indo-Gangetic Plain (IGP), Peninsular, and Northeast Indiaduring the period 1971 to 2020. Using a set of advanced climate change metricsStandardized Local Anomalies (SLA), Novel Climate Scores (NCS), and changes in probability of local climate extremes alongside the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify changes in drought conditions and the emergence of non-analogue climates. Changes in climatic extreme are computed using high-resolution daily gridded temperature and rainfall datasets, comparing recent decades against a 19511980 baseline. SLA quantifies deviations from historical variability, highlighting intensified warming over the Indo-Gangetic Plain, western India, and the southern peninsula. NCS reveales the emergence of novel climatescombinations of temperature and precipitation conditions not previously observed, particularly in Southeast India and the Himalayan region. The probability of local climate extremes shows a substantial increase in extreme events across India indicating enhanced climate volatility. These metrics are then integrated with drought analysis using SPEI to incorporate both precipitation and temperature-driven evaporative demand. SPEI trends indicate increasing dryness in Northeast India, the Himalayas, and the Indo-Gangetic Plain, linked to declining monsoonal rainfall and rising temperatures. Meanwhile, Western and Peninsular regions show wetting trends, driven by increased rainfall and convective precipitation events. The rainfall is the dominant drought driver during the monsoon, while high maximum temperatures intensify drought conditions in pre- and post-monsoon seasons by enhancing evaporative demand. Minimum temperature exhibits regional effects, showing a drying influence in the IGP and Himalayas, but a slight moistening signal in Peninsular India. By combining drought indices with climatic extremes metrics, this study offers a comprehensive framework to monitor hydroclimatic shifts and their regional impacts. The findings underscore the need for region-specific adaptation strategies that incorporate early warning systems, sustainable water management, and climate-resilient agriculture to address Indias evolving drought risks. The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Subject
The topic of the resource
Drought; Hydroclimatic variability; India; Rainfall trends; SPEI; Temperature extremes
Publisher
An entity responsible for making the resource available
Springer Science and Business Media B.V.
Relation
A related resource
ISSN: 1650009; CODEN: CLCHD
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
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
Multi-dynamics and emission tailored fluoroperovskite-based down-conversion phosphors for enhancing the current density and stability of the perovskite solar cells
Description
An account of the resource
State-of-the-art and innovative research is being intensively employed on perovskite solar cells (PSCs) to expand their frontiers further. This study is a successful attempt to drive the limit of photocurrent density (Jsc) beyond conventional PSCs (which typically utilize the visible spectrum alone) through a nonlinear optical phenomenon called down-conversion (DC). The use of DC luminescence to harness the UV region from the solar spectrum is explored by utilizing Eu3+ activated RbCaF3, a fluoroperovskite-based phosphor material. It is observed that PSCs, which used RbCaF3:Eu3+ incorporated TiO2 electron transport layer (ETL), enhanced their Jsc and UV stability compared to those with pristine TiO2-oriented ETL. Such improvement in the aforementioned devices is due to the result of converting high-energy UV photons to effectively absorbable low-energy visible photons for perovskite absorbers. Overall, the DC-aided PSC offered a substantial Jsc of 23.54 mA cm?2 (9.2% superior to the conventional PSC) and boosted its power conversion efficiency (PCE) from 11.2% to 13.3%. It is evident that DC-based PSCs show a much better shelf-life when compared to conventional PSCs. This unique approach for boosting the Jsc with enhanced stability can be utilized for the potential applications of PSCs. 2023 The Royal Society of Chemistry.
Creator
An entity primarily responsible for making the resource
Kunka Ravindran A.; Ramesh J.K.; Narendhiran S.; Arumugam R.; Muthu S.P.; Perumalsamy R.
Source
A related resource from which the described resource is derived
Sustainable Energy and Fuels, Vol-7, No. 9, pp. 2288-2300.
Publisher
An entity responsible for making the resource available
Royal Society of Chemistry
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/10.1039/d3se00354j" target="_blank" rel="noreferrer noopener">https://doi.org/10.1039/d3se00354j</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153087309&doi=10.1039%2Fd3se00354j&partnerID=40&md5=96680fb7e6e598501550b8994c968ce8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153087309&doi=10.1039%2fd3se00354j&partnerID=40&md5=96680fb7e6e598501550b8994c968ce8</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 23984902
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
Kunka Ravindran A., Department of Physics, SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Tamilnadu, Chennai, 603110, India; Ramesh J.K., Department of Physics, SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Tamilnadu, Chennai, 603110, India, Department of Physics, Materials Science Research Center, Indian Institute of Technology Madras, Tamilnadu, Chennai, 600036, India; Narendhiran S., Department of Physics, SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Tamilnadu, Chennai, 603110, India, Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bangaluru, 560029, India; Arumugam R., CNR-SPIN, c/o University of Salerno, Fisciano, I-84084, Italy; Muthu S.P., Department of Physics, SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Tamilnadu, Chennai, 603110, India; Perumalsamy R., Department of Physics, SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Tamilnadu, Chennai, 603110, 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
Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos
Subject
The topic of the resource
Cumulative matching curve; Multi-frame descriptor; Person re-identification; Video surveillance
Description
An account of the resource
Automatic re-identification of people entering the camera network is an important and challenging task. Multiple frames of the same person will be easily available in surveillance videos for re-identification. Dealing with pose variations of the person in the image and partial occlusion issues is major challenge in single-frame re-identification process. The use of more frames from the surveillance videos can generate robust descriptor to tackle issues of pose variations and occlusion. In this paper, we have emphasized on using multiple frames from the same video to generate a multi-frame twin-channel descriptor. The work deals with building a spatial-temporal descriptor which takes advantage of the twin paths to extract features of the person image. Mahalanobis distance metric learning algorithms is used for matching and evaluation. Our descriptor is evaluated on two benchmark datasets and found to surpass the performance of the existing methods. 2017, Springer-Verlag London Ltd.
Creator
An entity primarily responsible for making the resource
Sathish P.K.; Balaji S.
Source
A related resource from which the described resource is derived
International Journal of Multimedia Information Retrieval, Vol-6, No. 4, pp. 289-294.
Publisher
An entity responsible for making the resource available
Springer London
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/s13735-017-0136-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13735-017-0136-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031902888&doi=10.1007%2Fs13735-017-0136-9&partnerID=40&md5=7d5a8906a27a1df5815a26b922b88981" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031902888&doi=10.1007%2fs13735-017-0136-9&partnerID=40&md5=7d5a8906a27a1df5815a26b922b88981</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 21926611
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
Sathish P.K., Department of Computer Science and Engineering, Christ University, Bengaluru, 560074, India; Balaji S., Centre for Incubation, Innovation, Research and Consultancy, Jyothi Institute of Technology, Bengaluru, India
-
https://archives.christuniversity.in/files/original/56e2fb67046ee5322a729d35eefa2e7f.png
ae8597b1fb62da5294ca14f6fc0ece85
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
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
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
Multi-functional rechargeable chalk dust collector with recycling unit /
Subject
The topic of the resource
Chemistry
Description
An account of the resource
Patent Number: 202041009633, Applicant: Pramod Kandoth Madathil.
A detachable electronic chalk dust collector consisting of a control unit having dust collecting components, a dusting unit with alternative directional suction, inbuilt collection unit and sensors. An attachable recharging and recycling unit with di-suction
unit and multifunctional ports and sensors.
Creator
An entity primarily responsible for making the resource
K M, Pramod.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1vKNARU1p08PmNZBqRtFdtwP_PcKksxua/view" target="_blank" title="Multi-functional rechargeable chalk dust collector with recycling unit /" rel="noreferrer noopener">https://drive.google.com/file/d/1vKNARU1p08PmNZBqRtFdtwP_PcKksxua/view</a>
Publisher
An entity responsible for making the resource available
Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2020-03-17
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
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
Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation in Cloud-SDN Environment
Subject
The topic of the resource
Cloud environment; DDoS attacks; Deep learning; Reinforcement learning; Software defined network
Description
An account of the resource
Cloud computing (CC) remains as a promising environment which offers scalable and cost effectual computing facilities. The combination of the SDN technique with the CC platform simplifies the complexities of cloud networking and considerably enhances the scalability, manageability, programmability, and dynamism of the cloud. This study introduces a novel Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation (MEDR-DDoSAD) technique in Cloud-SDN Environment. The major aim of the presented technique lies in the recognition of DDoS attacks from the cloud-SDN platform. The MEDR-DDoSAD technique transforms the input data into images and the features are derived via deep convolutional neural network based Xception model. 2022 IEEE.
Creator
An entity primarily responsible for making the resource
Christila S.A.; Sivakumar R.
Source
A related resource from which the described resource is derived
4th International Conference on Circuits, Control, Communication and Computing, I4C 2022, pp. 451-455.
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/I4C57141.2022.10057641" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I4C57141.2022.10057641</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150244806&doi=10.1109%2FI4C57141.2022.10057641&partnerID=40&md5=04f16986941ff2e850718bad897f5402" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150244806&doi=10.1109%2fI4C57141.2022.10057641&partnerID=40&md5=04f16986941ff2e850718bad897f5402</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 979-835039747-5
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
Christila S.A., CHRIST (Deemed to Be University), Dept. of Computer Science, Karnataka, Bengaluru, India; Sivakumar R., CHRIST (Deemed to Be University), Dept. of Computer Science, Karnataka, Bengaluru, India
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
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
Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy
Subject
The topic of the resource
Electroencephalograph (eeg); Emotion recognition; Feature selection; Linear regression based correlation coefficient; Machine learning techniques; Multi-layer stacking model
Description
An account of the resource
Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.
Creator
An entity primarily responsible for making the resource
Tahseen S.; Danti A.
Source
A related resource from which the described resource is derived
International Journal of Advanced Computer Science and Applications, Vol-13, No. 6, pp. 433-442.
Publisher
An entity responsible for making the resource available
Science and Information Organization
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/10.14569/IJACSA.2022.0130654" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2022.0130654</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133405438&doi=10.14569%2FIJACSA.2022.0130654&partnerID=40&md5=7e9025aa218d53d6f7c1110b4f8fa1e1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133405438&doi=10.14569%2fIJACSA.2022.0130654&partnerID=40&md5=7e9025aa218d53d6f7c1110b4f8fa1e1</a>
Rights
Information about rights held in and over the resource
All Open Access; Gold Open Access
Relation
A related resource
ISSN: 2158107X
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
Tahseen S., Dept. of Computer Science and Engineering, Christ University, Bangalore, India; Danti A., Dept. of Computer Science and Engineering, 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
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
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Subject
The topic of the resource
Financial distress; Machine learning; Modern models; Multi-level; Performance measures; Prediction
Description
An account of the resource
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Creator
An entity primarily responsible for making the resource
Fernandes R.L.; Tantia V.
Source
A related resource from which the described resource is derived
Studies in Systems, Decision and Control, Vol-555, pp. 265-274.
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
2025-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-3-031-67890-5_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-67890-5_25</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210150952&doi=10.1007%2F978-3-031-67890-5_25&partnerID=40&md5=eda7d05f977bb495219f6150c474fa16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210150952&doi=10.1007%2f978-3-031-67890-5_25&partnerID=40&md5=eda7d05f977bb495219f6150c474fa16</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 21984182
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
Fernandes R.L., Christ University, Hosur Road, Bangalore, 560029, India; Tantia V., Christ University, Hosur Road, Bangalore, 560029, India
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
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
Fernandes, Reynal Lavita; Tantia, Veerta
Title
A name given to the resource
Multi-level Prediction of Financial Distress of Indian Companies Using Machine 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
Studies in Systems, Decision and Control;Volume;555;pp.265-274
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-3-031-67890-5_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-67890-5_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85210150952?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85210150952?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
Fernandes R.L., Christ University, Hosur Road, Bangalore, 560029, India; Tantia V., Christ University, Hosur Road, Bangalore, 560029, India
Description
An account of the resource
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Subject
The topic of the resource
Financial distress; Machine learning; Modern models; Multi-level; Performance measures; Prediction
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
Multi-lingual Spam SMS detection using a hybrid deep learning technique
Subject
The topic of the resource
deep learning; ham; machine learning; smishing; spam; Spam detection
Description
An account of the resource
Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE.
Creator
An entity primarily responsible for making the resource
Ramanujam E.; Shankar K.; Sharma A.
Source
A related resource from which the described resource is derived
Proceedings - 2022 IEEE Silchar Subsection Conference, SILCON 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/SILCON55242.2022.10028936" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SILCON55242.2022.10028936</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147854946&doi=10.1109%2FSILCON55242.2022.10028936&partnerID=40&md5=ec29cdc2d6e81114e170bd9792238392" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147854946&doi=10.1109%2fSILCON55242.2022.10028936&partnerID=40&md5=ec29cdc2d6e81114e170bd9792238392</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISBN: 978-166547100-8
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
Ramanujam E., National Institute of Technology Silchar, Computer Science and Engineering, Assam, India; Shankar K., National Institute of Technology Silchar, Electronics and Instrumentation Engineering, Assam, India; Sharma A., Computer Science and Engineering, Christ (Deemed to Be) University, Bangalore, India
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
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
Asha Syamlal, Chandni; Sayantan, D.
Title
A name given to the resource
Multi-metal phytoremediation using Salvinia molesta: the role of EDDS and SDS in enhancing metal removal efficiency
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
International Journal of Phytoremediation;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1080/15226514.2026.2617380" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/15226514.2026.2617380</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028175103?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028175103?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
Asha Syamlal C., Department of Life Sciences, Christ (Deemed to be University), Bengaluru, India; Sayantan D., Department of Life Sciences, Christ (Deemed to be University), Bengaluru, India
Description
An account of the resource
Significant environmental damage to aquatic ecosystems is caused by heavy metals, and the situation necessitates strategies against the contaminants. The present study was intended to explore Salvinia molestas potential for the phytoremediation of contaminating water to remove three metals: chromium (Cr), nickel (Ni), and cadmium (Cd), with an emphasis on the influence of chemical amendments, ethylene diamine disuccinic acid (EDDS) and sodium dodecyl sulfate (SDS), applied independently. Plants were treated for a period of 60 days with single and combined metal solutions supplemented with EDDS (0.050.2%) and SDS (0.52%), and responses were measured through morphological factors and biochemical indicators, including bioaccumulation factor (BAF) with translocation factor (TF) used cautiously due to the floating habit of S. molesta. It was observed that S. molesta was capable of substantial heavy metal accumulation, with the highest accumulation recorded under EDDS amended and SDS amended treatments at elevated metal concentrations. EDDS treatments primarily enhanced metal bioavailability and uptake while maintaining plant growth and physiological stability under moderate metal stress, whereas SDS treatments, particularly at higher concentrations, resulted in increased metal accumulation accompanied by reductions in biomass, chlorophyll content and protein levels, indicating stress driven accumulation linked to altered membrane permeability. The application of EDDS or SDS resulted in higher metal uptake compared to untreated controls, with BAF values reaching 3.8 for Cr, 4.2 for Ni, and 3.5 for Cd; however, maximum accumulation under SDS treatments did not consistently correspond to biologically sustainable phytoremediation performance. Statistical analysis showed significant differences (p < 0.05) between treatments and control in metal bioavailability following amendment application, highlighting a dose-dependent tradeoff between metal uptake efficiency and plant health. This study represents the first integrated evaluation of EDDS and SDS under multi-metal (CrNiCd) conditions in S. molesta, addressing a major gap in chemical-assisted phytoremediation research. Future work should be aimed at determining the optimum concentrations of these chemical amendments to facilitate the scale-up of phytoremediation projects. 2026 Taylor & Francis Group, LLC.
Subject
The topic of the resource
Bioaccumulation factor; chelating agents (EDDS); chemical-assisted remediation; multi-metal stress; phytoremediation; Salvinia molesta; surfactants (SDS); translocation factor
Publisher
An entity responsible for making the resource available
Taylor and Francis Ltd.
Relation
A related resource
ISSN: 15226514; CODEN: IJPHF
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
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
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
Subject
The topic of the resource
Congestion forecasting; Graph neural networks; Multi-modal traffic forecasting; Recursive feature elimination; Smart cities; Transformer-based multi-source visual fusion
Description
An account of the resource
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024.
Creator
An entity primarily responsible for making the resource
Dhanasekaran S.; Gopal D.; Logeshwaran J.; Ramya N.; Salau A.O.
Source
A related resource from which the described resource is derived
International Journal of Intelligent Transportation Systems Research, Vol-22, No. 3, pp. 518-541.
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
2024-01-01
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s13177-024-00413-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13177-024-00413-4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201395364&doi=10.1007%2Fs13177-024-00413-4&partnerID=40&md5=dae60889095fac07bf59c2caacfff581" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201395364&doi=10.1007%2fs13177-024-00413-4&partnerID=40&md5=dae60889095fac07bf59c2caacfff581</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 13488503
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
Dhanasekaran S., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India; Gopal D., Department of Electronics and Communication Engineering, AVN Institute of Engineering and Technology, Hyderabad, India; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Ramya N., Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur, India; Salau A.O., Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, 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
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
Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing
Subject
The topic of the resource
Genetic algorithm; Matlab; Mutant test case; Regression testing; Software testing
Description
An account of the resource
The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing. 2021 Scientific Publishers. All rights reserved.
Creator
An entity primarily responsible for making the resource
Tripathi A.; Srivastava S.; Mittal H.; Sinha S.; Yadav V.
Source
A related resource from which the described resource is derived
Journal of Scientific and Industrial Research, Vol-80, No. 7, pp. 582-592.
Publisher
An entity responsible for making the resource available
National Institute of Science Communication and Information Resources
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="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116109051&partnerID=40&md5=4364c82591b63310aa68949ba64a73f1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116109051&partnerID=40&md5=4364c82591b63310aa68949ba64a73f1</a>
Rights
Information about rights held in and over the resource
Restricted Access
Relation
A related resource
ISSN: 224456
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
Tripathi A., VIT Bhopal University, Madhya Pradesh, India; Srivastava S., Christ University, India; Mittal H., Raj Kumar Goel Institute of Technology, Uttar Pradesh, Ghaziabad, India; Sinha S., JSS Academy of Technical Education, Uttar Pradesh, Noida, India; Yadav V., ABES Engineering College, Uttar Pradesh, Ghaziabad, 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
Reddy, R. Archana; Adnan, Myasar Mundher; Paramesh, S.P.; Petli, Vishwanath; Manohar, M.
Title
A name given to the resource
Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN
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
Lecture Notes in Electrical Engineering;Volume;1270;pp.127-137
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-97-7876-8_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7876-8_12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218941732?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218941732?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
Reddy R.A., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Adnan M.M., The Islamic University, Najaf, Iraq; Paramesh S.P., Department of Computer Science and Engineering, School of Engineering, Central University of Karnataka, Karnataka, Kalaburagi, India; Petli V., Electronics and Communication Engineering, H.K.E. Societys Sir M. Visvesvaraya College of Engineering, Raichur, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India
Description
An account of the resource
The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
The topic of the resource
Artificial neural network; Deep Q network; Multi-objective deep reinforcement learning; Particle swarm optimization and wireless sensor network
Publisher
An entity responsible for making the resource available
Springer Science and Business Media Deutschland GmbH
Relation
A related resource
ISSN: 18761100; ISBN: 978-981977875-1;
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
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
61000371
Title
A name given to the resource
Multi-Objective Optimization Approaches for Solar Photovoltaic Inverter Control and Energy Balance in A Smart Grid Environment
Subject
The topic of the resource
Electrical and Electronics Engineering
Description
An account of the resource
Placement of distributed generation in electrical distribution system is a critical newlineaspect of optimizing grid performance and ensuring effcient integration of renewable energy sources. Renewable based sources must be properly positioned and sized to avoid bidirectional power and#64258;ows, voltage/frequency and#64258;uctuations and performance degradation. Solar Photovoltaic Systems and Wind Turbines are potentially becoming the preferred renewable energy based, distribution generation sources. Precise control mechanisms like advanced inverter strategies and direct load control are crucial for regulating voltage, frequency and reactive power output, thereby optimizing grid operation and maximizing integration benefts from these sources. However, optimizing the allocation and operation of these systems in grid connected and islanded modes, particularly in radially confgured systems, requires addressing algorithmic challenges, problems related to nonlinear optimization, newlinevariable generations and load variations. To effectively allocate these systems in the newlineelectrical distribution system, advanced optimization techniques capable of newlinehandling multi-objective, nonlinear problems are needed. Similarly, optimizing the power factor of the distributed generation sources and optimizing the load factor in these systems demand adaptive algorithms that can manage nonlinear objectives and dynamic system conditions. In response to the above research questions, this study focuses on determining the optimal placement and sizing of the distributed generation sources in the electrical distribution system with the objective to minimize real power loss and improve voltage stability. Learning enthusiasm based teaching learning based optimization algorithm has been employed for location selection and sizing optimization. The effectiveness of the proposed approach is validated on standard IEEE 33-bus and newline69-bus test systems, demonstrating decreased distribution losses and improved voltage stability.
Creator
An entity primarily responsible for making the resource
Nirmala, John
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
Janamala, Varaprasad and Rodrigues, Joseph
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/10603/597565" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/597565</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
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
Haneesh, K.M.; Jisha, P.
Title
A name given to the resource
Multi-Objective Reinforcement Learning With Physics-Aware Vehicle Dynamics for Safe and Efficient Adaptive Cruise Control
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
International Journal of Intelligent Transportation Systems Research;
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/s13177-026-00669-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13177-026-00669-y</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039476332?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039476332?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
Haneesh K.M., Department of Electrical and Electronics Engineering, CHRIST University, Bangalore, India; Jisha P., Department of Electronics and Communication Engineering, BMS College of Engineering, Bangalore, India
Description
An account of the resource
Adaptive Cruise Control (ACC) enhances safety and comfort in autonomous vehicles by maintaining appropriate inter-vehicular distance and speed regulation. Traditional ACC systems based on PID or Model Predictive Control (MPC) often struggle to handle complex, unforeseen traffic scenarios such as sudden braking, pedestrian crossings, or lane changes. Reinforcement Learning (RL) offers an adaptive alternative by enabling policy learning through environment interaction. However, existing RL-based ACC methods frequently suffer from poor smoothness and energy inefficiency under emergency conditions. This work proposes an enhanced RL-based ACC framework that integrates a physics-informed, multi-objective reward function to jointly optimize safety, ride comfort, and energy efficiency. The reward components are normalized and dynamically weighted based on the current driving context, allowing the agent to adaptively prioritize objectives. Vehicular dynamics are explicitly incorporated into the learning process to improve real-world applicability. The system is trained using the DDPG algorithm, which supports continuous control and stable policy convergence. Extensive MATLAB-based simulations were conducted across diverse urban driving scenarios including stopgo traffic, traffic signals, lane changes, and pedestrian interactions. Comparative analysis against PID and MPC-based ACC controllers demonstrates that the proposed framework achieves superior performance in maintaining safe inter-vehicular distance, reducing jerk, and improving energy efficiency. This study validates the feasibility of deploying a computationally efficient, model-free RL-based ACC for robust and safe autonomous driving in dynamic traffic environments. The Author(s), under exclusive licence to ITS Japan 2026.
Subject
The topic of the resource
Adaptive cruise control; Deep Deterministic Policy Gradient; Multi-objective reward function; Physics-aware vehicle dynamics; Reinforcement learning
Publisher
An entity responsible for making the resource available
Springer
Relation
A related resource
ISSN: 13488503;
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
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
Manoj, Meby Joseph; Azarudheen, S.
Title
A name given to the resource
MULTI-REFERENCE SKIP-LOT SAMPLING OF TYPE 3 (MR-SkSP-3)
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
Reliability: Theory and Applications;Volume;20;Issue;3;pp.473-484
Identifier
An unambiguous reference to the resource within a given context
<a href="https://www.scopus.com/pages/publications/105018079801?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018079801?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
Manoj M.J., Department of Statistics and Data Science CHRIST (Deemed to be University), Bengaluru, India; Azarudheen S., Department of Statistics and Data Science CHRIST (Deemed to be University), Bengaluru, India
Description
An account of the resource
In the current industrial sector, the rate of defective products present in the lots has been decreasing and most of the products keeps up a good history of quality throughout the production also. Skip-lot sampling plans are the suitable acceptance sampling plan for the situations where the series of products shows a stable and excellent quality. The skip-lot sampling plans are still widely used because of its reduced sampling cost and efforts, because the plan only needs to inspect a fraction of the lots submitted after a continues series of lots with excellent quality. This approach makes the skip-lot plan more cost-effective than the other sampling plans, thus making it an economically important plan. The current study incorporated a modification on the skip-lot sampling of type 3 and designated it as multi-reference skip lot sampling of type 3. The proposed plan has the provision of having multiple reference plans in normal and skipping inspection of a skip-lot sampling plan, unlike the traditional skip-lot plans which has the same reference plan in all phases. The performance measures of the proposed plan are derived using the power series approach. A designing methodology to determine the optimal parameters for the plan using the unity value approach is also described with the help of a numerical illustration. Behaviour of the operating characteristic curves for varying set of parameters are also analysed for the plan. Comparison of the proposed plan is done between the conventional plans using performance measure values and graphical representations. This analysis shows that the new plan is able to effectively optimize the preferences of producer and consumer simultaneously, where the traditional plans fail to. The analysis is supported with the help of graphical representations and tabulated values. 2025, Gnedenko Forum. All rights reserved.
Subject
The topic of the resource
acceptance sampling plan; consumers risk; operating characteristic curve; producers risk; Quality control; skip-lot sampling plan; unity value approach
Publisher
An entity responsible for making the resource available
Gnedenko Forum
Relation
A related resource
ISSN: 19322321;
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