222 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Efficiency of Indian Banks with Non-Performing Assets as Undesirable Outputs Subject The topic of the resource Data Envelopment Analysis; efficiency; Indian Banks; Non-performing assets; Undesirable output Description An account of the resource The performance evaluation of any banks is of utmost importance for bank management, investors, and policymakers. Due to globalization, all the banks are working in a competitive environment. Several risk factors affect the operational efficiency of banking system. This study aims to evaluate the efficiency of Indian banks with NPAs as uncontrolled variables. Due to the nature of NPAs, these are assumed as undesirable outputs in the DEA modelling. The results reveal that public sector banks experienced more input losses due to NPAs compared to private banks. The private banks experienced more loss in inputs due to the scale of operation. The Wilcoxon Signed-Rank test shown that the impact of NPAs and scale of operation are statistically significant at 0.05 level. 2023 American Institute of Physics Inc.. All rights reserved. Creator An entity primarily responsible for making the resource Subramanyam T.; Zalki M.; Satish Kumar V.; Amalanathan S. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2649 Publisher An entity responsible for making the resource available American Institute of Physics Inc. 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/10.1063/5.0114283" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0114283</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166400116&doi=10.1063%2F5.0114283&partnerID=40&md5=a0de9486772e33fe186b966845741884" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166400116&doi=10.1063%2f5.0114283&partnerID=40&md5=a0de9486772e33fe186b966845741884</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X; ISBN: 978-073544553-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Subramanyam T., Department of Mathematics and Statistics, M.S. Ramaiah University of Applied Sciences, Bangalore, India; Zalki M., Department of Mathematics, REVA University, Bangalore, India; Satish Kumar V., Department of Computer Science, East Point College of Higher Education, Bangalore, India; Amalanathan S., Department of Business and Management, Christ University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Efficiency study of coconut producer companies in India-A DEA approach Subject The topic of the resource DMU; EBITDA; FPC Model; Technical Efficiency Description An account of the resource The concept of the Farmer Producer Company (FPC) model has been a hot issue, especially during the 2020-21 Indian farmers' protest. Considering the pioneering initiatives of the Coconut Development Board (CDB) in setting up CPCs, we compare the technical efficiencies of CPCs that focus on coconut and its byproducts in Rural India for two consecutive financial years (2018-19 and 2019-20). Coconut Producer Companies' efficiency scores are estimated using Data Envelopment Analysis (DEA), a mathematical technique to assess technical efficiencies across homogeneous units. The results reveal that 35.11 percent of the sampled CPCs for FY 2018-19 are overall technical efficient, and approximately 76 percent are purely technical efficient. It is found that technical inefficiency is reported for a few CPCs due to scale inefficiency. The overall technical and pure technical efficiency have improved in FY 2019-20 compared to the previous period. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. Creator An entity primarily responsible for making the resource Kappil S.R.; Sahoo A.K.; Joseph A. Source A related resource from which the described resource is derived Emerging Trends and Innovations in Industries of the Developing World: A Multidisciplinary Approach, pp. 7-13. Publisher An entity responsible for making the resource available CRC Press 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174142350&partnerID=40&md5=85f1ab376973624bf85ed2598151ecba" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174142350&partnerID=40&md5=85f1ab376973624bf85ed2598151ecba</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100381954-7; 978-103260103-8 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 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 Kappil S.R., Central University of Haryana, India; Sahoo A.K., Central University of Haryana, India; Joseph A., CHRIST (Deemed to be University), 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 Efficiency Wage and Productivity in the Indian Microfinance Industry: A Panel Evidence Subject The topic of the resource Efficiency wage; financial sustainability; India; microfinance; microfinance institutions; mission drift; outreach Description An account of the resource Enhanced productivity remains a crucial agenda for firms to attain cost and competitive advantages in the market. Hence, the main purpose of this study is to investigate the effects of efficiency wage (EW) on the productivity of microfinance institutions (MFIs) with respect to their dual objectives, namely, outreach (depth and breadth) and financial sustainability. Unbalanced panel data of 179 Indian MFIs were collected over the period 20102018 from the Microfinance Information Exchange (MIX) market platform (now obtainable from the World Bank catalogue). Under a static model setting (fixed effects model), the observed relationship between EW and MFIs productivity is mixed. On the one hand, EW exhibits a strong and statistically significant positive relationship with the breadth of outreach, even after considering various control variables and alternative proxies of EW. On the other hand, EW shows no positive influence on the MFIs depth of outreach; rather, it results in a mission drift of MFIs, with the poorest of the poor being neglected (weak and insignificant for proxy of EW). Concerning the financial sustainability of MFIs, EW exhibits a positive and statistically significant effect, except for the profitability dimension when an alternative proxy of EW is used. A two-step system generalized method of moments (GMM) performed to limit endogeneity problems also validates most of our findings. The outcomes of this study could help MFIs managers in designing appropriate financial packages to enhance MFIs productivity and subsequently attain the dual objective of outreach and sustainability. 2022 SAGE Publications. Creator An entity primarily responsible for making the resource Mia M.A.; Pellegrina L.D.; Zhang C.; Sangwan S. Source A related resource from which the described resource is derived IIM Kozhikode Society and Management Review, Vol-11, No. 2, pp. 235-252. Publisher An entity responsible for making the resource available Sage Publications India Pvt. Ltd 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/10.1177/22779752211061203" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/22779752211061203</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142279142&doi=10.1177%2F22779752211061203&partnerID=40&md5=e3e1d09b13b30a93f87fce40850de1ce" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142279142&doi=10.1177%2f22779752211061203&partnerID=40&md5=e3e1d09b13b30a93f87fce40850de1ce</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22779752 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 Mia M.A., School of Management, Universiti Sains Malaysia, Penang, Malaysia; Pellegrina L.D., Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Piazza Ateneo Nuovo, Milan, Italy; Zhang C., School of Accounting, Nanjing University of Finance and Economics, Nanjing, China; Sangwan S., Department of Commerce, Christ University, Maharashtra, Pune, 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 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 No Thesisi Title A name given to the resource Efficient algorithms for enhanced performance in distributed systems Subject The topic of the resource Computer Science Description An account of the resource NA Creator An entity primarily responsible for making the resource Beaulah, Soundarabai P 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 2014-01-01 Contributor An entity responsible for making contributions to the resource Thriveni, J 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/10603/73526" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/73526</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 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 Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification Subject The topic of the resource brain tumor; classification; feature selection; Machine learning; MRI image processing; neural network; stochastic spin model Description An account of the resource Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. Creator An entity primarily responsible for making the resource Veneeswari J.; Sankar Ganesh S.; Krishnasamy L.; Rengaraj T.; Suseela D.; Kumaran N. Source A related resource from which the described resource is derived International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 11, pp. 177-187. Publisher An entity responsible for making the resource available Ismail Saritas 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184883200&partnerID=40&md5=039aea9fd306cb13023733840b8e4ec5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184883200&partnerID=40&md5=039aea9fd306cb13023733840b8e4ec5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21476799 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 Veneeswari J., Department of Information Technology, iNurture Education Solutions Private Limited, Vels University, Tamil Nadu, Chennai, 600117, India; Sankar Ganesh S., Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Medchal, Telangana, Hyderabad, 501301, India; Krishnasamy L., Department of CSE, School of Engineering and Technology, Christ (Deemed to be) University, Bengaluru, 560074, India; Rengaraj T., Department of Electrical and Electronics Engineering, P. S. R Engineering College, Sevalpatti, Tamil Nadu, Sivakasi, 626140, India; Suseela D., Department of Artificial Intelligence and Machine Learning, Bannari Amman Institute of Technology, Tamil Nadu, Sathyamangalam, 638401, India; Kumaran N., Department of Mathematics, Veltech Rangarajan Dr.Sagunthala R&D institute of science and technology, Avadi, Tamil Nadu, Chennai, 600062, 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 Efficient cationic dye removal from water through Arachis hypogaea skin-derived carbon nanospheres: a rapid and sustainable approach Description An account of the resource The present study investigates the potential of Arachis hypogaea skin-derived carbon nanospheres (CNSs) as an efficient adsorbent for the rapid removal of cationic dyes from aqueous solutions. The CNSs were synthesized through a facile, cost-effective, catalyst-free and environmentally friendly process, utilizing Arachis hypogaea skin waste as a precursor. This is the first reported study on the synthesis of mesoporous carbon nanospheres from Arachis hypogaea skin. The structural and morphological characteristics of the CNSs were confirmed by different nano-characterization techniques. The adsorption performance of the carbon nanospheres was evaluated through batch adsorption experiments using two cationic dyes-methylene blue (MB) and malachite green (MG). The effects of the initial dye concentration, contact time, adsorbent dosage, and pH were investigated to determine the optimal conditions for dye removal. The results revealed that the obtained CNSs exhibited remarkable adsorption capacity and rapid adsorption kinetics. Up to ?98% removal efficiency was noted for both dyes in as little as 2 min for a 5 mg L?1 dye concentration, and the CNSs maintained their structural morphology even after adsorption. The adsorption data were fitted to various kinetic and isotherm models to gain insights into the adsorption mechanism and behaviour. The pseudo-second-order kinetic model and Redlich-Peterson model best described the experimental data, indicating multi-layer adsorption and chemisorption as the predominant adsorption mechanism. The maximum adsorption capacity was determined to be 1128.46 mg g?1 for MB and 387.6 mg g?1 for MG, highlighting the high affinity of the carbon nanospheres towards cationic dyes. Moreover, CNS reusability and stability were examined through desorption and regeneration experiments, which revealed sustained efficiency over 7 cycles. CNSs were immobilised in a membrane matrix and examined for adsorption, which demonstrated acceptable efficiency values and opened the door for further improvement. 2024 RSC. Creator An entity primarily responsible for making the resource Sharma A.; Shivanna J.M.; Alodhayb A.N.; Hegde G. Source A related resource from which the described resource is derived Nanoscale Advances, Vol-6, No. 12, pp. 3199-3210. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1039/d4na00254g" target="_blank" rel="noreferrer noopener">https://doi.org/10.1039/d4na00254g</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193331463&doi=10.1039%2Fd4na00254g&partnerID=40&md5=c8856f21b264a8dd12ce8e97fc3b7e72" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193331463&doi=10.1039%2fd4na00254g&partnerID=40&md5=c8856f21b264a8dd12ce8e97fc3b7e72</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 25160230 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 Sharma A., Department of Chemistry, School of Sciences, CHRIST, Deemed to be University, Hosur Road, Bengaluru, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST, Deemed to be University, Hosur Road, Bengaluru, 560029, India; Shivanna J.M., Department of Chemistry, AMC Engineering College, Bannerghatta Road, Bengaluru, 560083, India; Alodhayb A.N., Department of Physics and Astronomy, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Hegde G., Department of Chemistry, School of Sciences, CHRIST, Deemed to be University, Hosur Road, Bengaluru, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST, Deemed to be University, Hosur Road, 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 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 Efficient chemical fixation of CO2from direct air under environment-friendly co-catalyst and solvent-free ambient conditions Description An account of the resource The capture and conversion of CO2from direct air into value-added products under mild conditions represents a promising step towards environmental remediation and energy sustainability. Consequently, herein, we report the first example of a Mg(ii)-based MOF exhibiting highly efficient fixation of CO2from direct air into value-added cyclic carbonates under eco-friendly co-catalyst and solvent-free mild conditions. The bifunctional MOF catalyst was rationally constructed by utilizing an eco-friendly Lewis acidic metal ion, Mg(ii), and a nitrogen-rich tripodal linker, TATAB. The MOF possesses a high BET surface area of 2606.13 m2g?1and highly polar 1D channels decorated with a high density of CO2-philic sites which promote a remarkably high CO2uptake of 50.2 wt% at 273 K with a high heat of adsorption value of 55.13 kJ mol?1. The high CO2-affinity combined with the presence of a high density of nucleophilic and Lewis acidic sites conferred efficient catalytic properties to the Mg-MOF for chemical fixation of CO2from direct air under environment-friendly mild conditions. The remarkable performance of the Mg-MOF for the fixation of CO2from direct air was further supported by in-depth theoretical calculations. Moreover, the computational studies provided an insight into the mechanistic details of the catalytic process in the absence of any co-catalyst and solvent. Overall, this work represents a rare demonstration of carbon capture and utilization (CCU) from direct air under eco-friendly mild conditions. The Royal Society of Chemistry 2021. Creator An entity primarily responsible for making the resource Das R.; Ezhil T.; Palakkal A.S.; Muthukumar D.; Pillai R.S.; Nagaraja C.M. Source A related resource from which the described resource is derived Journal of Materials Chemistry A, Vol-9, No. 40, pp. 23127-23139. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1039/d1ta05138e" target="_blank" rel="noreferrer noopener">https://doi.org/10.1039/d1ta05138e</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117506707&doi=10.1039%2Fd1ta05138e&partnerID=40&md5=2852ca4d73d04a577a594ac9d5c4b7f8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117506707&doi=10.1039%2fd1ta05138e&partnerID=40&md5=2852ca4d73d04a577a594ac9d5c4b7f8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 20507488; CODEN: JMCAE 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 Das R., Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India; Ezhil T., Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India; Palakkal A.S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India; Muthukumar D., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India; Pillai R.S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India, Department of Chemistry, Christ University, Bangalore, 560023, Karnataka, India; Nagaraja C.M., Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, 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 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 61000095 Title A name given to the resource Efficient data mining techniques for medical data Subject The topic of the resource Computer Science Description An account of the resource Healthy decision making for the well being is a challenge in the current era with abundant information everywhere. Data mining, machine newlinelearning and computational statistics are the leading fields of study that are supporting the empowered individual to take valuable decisions to optimize the outcome of any working domain. High demand for data newlinehandling exists in healthcare, as the rate of increase in patients is proportional to the rate of population growth and life style changes. Techniques for early diagnosis and prognosis prediction of diseases are the need of the hour to provide better treatment for the human community. Data mining techniques are a boon for building a quality and newlineefficient model for health prediction applications. As cancer explodes everywhere in recent years, the data sets from cancer newlineregistries have been focused as the medical data in this research. The main aim of thesis is to build a constructive and efficient classifier model for cancer prognosis prediction. Most of the existing system develops a diagnosis prediction models from the screening or survey data, as the data newlineset is widely available and are easy to collect due the insensitive nature of newlinethe factors involved in such research. Whereas the prognosis prediction requires a sensitive details of the patients those who are under treatment for a diagnosed disease. Hospitals and the community registries newlinemaintained by the government are the main source for data collection. Well maintained electronic hospital records with histopathology information is not public in India for the researchers. Hence cancer data newlinefrom a US based open access data center has been used in this research for all experimentation. This research work is a progressive model that gradually improves the newlineprediction accuracy by selecting appropriate data mining techniques in each phase. Creator An entity primarily responsible for making the resource Saleema, J S. 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 2018-01-01 Contributor An entity responsible for making contributions to the resource Shenoy, P Deepa 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/10603/252043" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/252043</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 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 Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter Subject The topic of the resource False data injection attack; Kalman filter; Random attack; Reconfigurable Euclidean detector; Smart grid Description An account of the resource The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reconfigurable Euclidean detector (RED) evaluator. The MATLAB function randn() has been used to produce sequence distribution channel noise with mean value zero to analysed the amplitude variation with respect to evolution state variable. The detector noise rate is analysed with respect to threshold. The detection rate of various attacks such as DDOS, Random and false data injection attacks is also analysed. The proposed mathematical model is effectively reconstructed to frame the original sinusoidal signal from the evaluator state variable using reconfigurable Euclidean detectors. 2022, Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Dayananda P.; Srikantaswamy M.; Nagaraju S.; Velluri R.; Kumar D.M. Source A related resource from which the described resource is derived International Journal of Power Electronics and Drive Systems, Vol-13, No. 4, pp. 2086-2097. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science 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/10.11591/ijpeds.v13.i4.pp2086-2097" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijpeds.v13.i4.pp2086-2097</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139948832&doi=10.11591%2Fijpeds.v13.i4.pp2086-2097&partnerID=40&md5=fb190e305d9e228144980183b77de5da" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139948832&doi=10.11591%2fijpeds.v13.i4.pp2086-2097&partnerID=40&md5=fb190e305d9e228144980183b77de5da</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20888694 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 Dayananda P., Department of Electrical and Electronics Engineering, SJB Institute of Technology, Bangalore, India; Srikantaswamy M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bangalore, India; Nagaraju S., Department of Electrical and Electronics Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India; Velluri R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Kumar D.M., Department of Electronics and Instrumentation Engineering, JSS Academy of Technical Education, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient discrimination by MIRU-VNTRs of Mycobacterium tuberculosis clinical isolates belonging to the predominant SIT11/EAI3-IND ancestral genotypic lineage in Kerala, India Subject The topic of the resource India; IS6110-RFLP; Kerala; MIRU-VNTR; Mycobacterium tuberculosis; Spoligotyping Description An account of the resource The present study evaluated the ability of MIRU-VNTRs to discriminate Mycobacterium tuberculosis (MTB) clinical isolates belonging to the SIT11/EAI3-IND ancestral genotypic lineage, which is highly prevalent in Kerala, India. Starting from 168 MTB clinical isolates, spoligotyping (discriminatory index of 0.9113) differentiated the strains into 68 distinct patterns, the biggest cluster being SIT11/48 SIT11 ( n= 48). The present study shows that 12-loci MIRUs and 3 ETRs allowed an efficient discrimination of these isolates (discriminatory indexes of 0.7819 and 0.5523, respectively). 2013 Asian-African Society for Mycobacteriology. Creator An entity primarily responsible for making the resource Joseph B.V.; Soman S.; Hill V.; Kumar R.A.; Rastogi N.; Mundayoor S. Source A related resource from which the described resource is derived International Journal of Mycobacteriology, Vol-2, No. 4, pp. 244-247. Date A point or period of time associated with an event in the lifecycle of the resource 2013-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ijmyco.2013.10.005" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijmyco.2013.10.005</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888408580&doi=10.1016%2Fj.ijmyco.2013.10.005&partnerID=40&md5=c7a2777906f263da5eb77ca06e57ab1a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888408580&doi=10.1016%2fj.ijmyco.2013.10.005&partnerID=40&md5=c7a2777906f263da5eb77ca06e57ab1a</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22125531 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 Joseph B.V., Department of Biotechnology, Christ University, Bangalore 560 029, Karnataka, Hosur Road, India; Soman S., Mycobacteria Research Group, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram 695 014, Kerala, Thycaud Post, India; Hill V., WHO Supranational TB Reference Laboratory, TB and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, Abymes, France; Kumar R.A., Mycobacteria Research Group, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram 695 014, Kerala, Thycaud Post, India; Rastogi N., WHO Supranational TB Reference Laboratory, TB and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, Abymes, France; Mundayoor S., Mycobacteria Research Group, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram 695 014, Kerala, Thycaud Post, 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 Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution Subject The topic of the resource Decision Tree; Deep Learning; Disease Magnitude; Multi-classification; Wheat Diseases; Yellow Rust Description An account of the resource Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security. 2023 IEEE. Creator An entity primarily responsible for making the resource Konidena A.; Shanbhog M.; Singh S.; Sharma V.; Jain A.K.; Sharma N. Source A related resource from which the described resource is derived Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 648-654. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTACS59847.2023.10389846" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTACS59847.2023.10389846</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185374849&doi=10.1109%2FICTACS59847.2023.10389846&partnerID=40&md5=91d61b110abae6d16ce9423ee3d252e7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185374849&doi=10.1109%2fICTACS59847.2023.10389846&partnerID=40&md5=91d61b110abae6d16ce9423ee3d252e7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034233-8 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 Konidena A., Iilm University, Department of Cse, Uttar Pradesh, Greater Noida, India; Shanbhog M., Christ University, Department of Computer Science, Uttar Pradesh, Ghaziabad, India; Singh S., School of Computing MIT-ADTU, Department of Information Technology, Pune, India; Sharma V., Graphic Era Hill University, Uttarakhand, Dehradun, India; Jain A.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sharma N., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 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 Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification Subject The topic of the resource Enhanced sunflower optimisation; Gait recognition; GAIT-IT; GAIT-ITS; Improved convolutional neural network; Median filter; Modified bidirectional LSTM; Parkinsons disease Description An account of the resource The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. Creator An entity primarily responsible for making the resource Reshma S.; Chennakesavulu M.; Patil S.S.; Lamani M.R. Source A related resource from which the described resource is derived International Journal of Information Technology (Singapore), Vol-16, No. 6, pp. 3963-3971. Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s41870-024-01886-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-024-01886-y</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191988973&doi=10.1007%2Fs41870-024-01886-y&partnerID=40&md5=bf0d81b093125d57da7290dcd2d81d77" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191988973&doi=10.1007%2fs41870-024-01886-y&partnerID=40&md5=bf0d81b093125d57da7290dcd2d81d77</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25112104 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 Reshma S., Dept of Artificial Intelligence and Machine Learning, Dayananda Sagar College of Engineering, Bangalore, India; Chennakesavulu M., Department of Electronics and Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous), Andhra Pradesh, Nandyal, 518501, India; Patil S.S., Rajarambapu Institute of Technology, Rajaramnagar, Uran Islampur, India; Lamani M.R., CHRIST(Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient handwritten character recognition of modi script using wavelet transform and svd Subject The topic of the resource Feature extraction method; Handwritten character recognition; Image processing; MODI script; Wavelet transform Description An account of the resource MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Creator An entity primarily responsible for making the resource Joseph S.; George J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-132, pp. 227-233. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-5309-7_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_24</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371826&doi=10.1007%2F978-981-15-5309-7_24&partnerID=40&md5=838f3129ad676e860f8ffb4d49a9e190" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090371826&doi=10.1007%2f978-981-15-5309-7_24&partnerID=40&md5=838f3129ad676e860f8ffb4d49a9e190</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph S., CHRIST (Deemed to Be University), Bengaluru, India, Carmel College of Arts Science and Commerce for Women, Nuvem, Goa, India; George J., CHRIST (Deemed to Be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Efficient hydrogen evolution reaction performance of Ni substituted WS2 nanoflakes Subject The topic of the resource Hydrogen evolution reaction; Transition metal dichalcogenides; Two dimensional materials; WS2 Description An account of the resource We have investigated the structural, optical and electrocatalytic hydrogen evolution reaction (HER) performance of pristine, Co and Ni substituted WS2 nanoflakes synthesised by facile hydrothermal method. The XRD pattern confirms the formation of hexagonal WS2 for both pristine and substituted WS2 nanoflakes. The FESEM images validate the flake-like structure for both pristine and substituted WS2. In addition, we have also analysed the Raman and UV-Vis absorbance spectra of the samples. The electrocatalytic studies reveal that the nickel-substituted WS2 (Ni-WS2) nanoflakes show superior hydrogen evolution (HER) performance compared to cobalt-substituted WS2 (Co-WS2) nanoflakes. Hence, we have varied the Ni concentration and investigated the dependence of Ni content on the electrocatalytic performance. It is found that the electrocatalytic performance of the Ni-WS2 nanoflakes increases with an increase in Ni content owing to the modified edge structures. Thus, our studies suggest Ni substitution in WS2 nanostructures can boost electrocatalytic HER performance. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Joseph A.; Chacko L.; Sanal K.C.; Pineda-Aguilar N.; Jasna M.; Antony A.; Aneesh P.M. Source A related resource from which the described resource is derived Applied Physics A: Materials Science and Processing, Vol-130, No. 12 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/s00339-024-08050-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00339-024-08050-0</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209774084&doi=10.1007%2Fs00339-024-08050-0&partnerID=40&md5=24b45119c6f3ff82cd90e79cff36b662" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209774084&doi=10.1007%2fs00339-024-08050-0&partnerID=40&md5=24b45119c6f3ff82cd90e79cff36b662</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9478396; CODEN: APAMF 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 Joseph A., Department of Physics, Central University of Kerala, Tejaswini Hills, Kerala, Periye, Kasaragod, 671320, India; Chacko L., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, India; Sanal K.C., Facultad de Ciencias Quicas, Universidad Automa de Nuevo Leon, San Nicolas de los Garza, Mexico; Pineda-Aguilar N., Centro de Investigaci en Materiales Avanzados, S.C. (CIMAV), Subsede Monterrey, Parque de Investigaci e Innovaci on Tecnolica, Apodaca, Mexico; Jasna M., Department of Physics, Cochin University of Science and Technology, Kerala, Kochi, 682022, India, Microelectronics Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, FI-90014, Oulu, Finland; Antony A., Department of Physics, Cochin University of Science and Technology, Kerala, Kochi, 682022, India; Aneesh P.M., Department of Physics, Central University of Kerala, Tejaswini Hills, Kerala, Periye, Kasaragod, 671320, 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 Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting Subject The topic of the resource Battery; Efficiency; Multiport converter; PV Cell Description An account of the resource This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE. Creator An entity primarily responsible for making the resource Parimalasundar E.; Jayakumar S.; Chandrika V.S.; Muthukaruppasamy S.; Devaraju T.; Suresh K. Source A related resource from which the described resource is derived Proceedings of the 5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023, pp. 1590-1595. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIRCA57980.2023.10220754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIRCA57980.2023.10220754</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172199813&doi=10.1109%2FICIRCA57980.2023.10220754&partnerID=40&md5=7d5683e30695eb92e61d42eb3610efdd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172199813&doi=10.1109%2fICIRCA57980.2023.10220754&partnerID=40&md5=7d5683e30695eb92e61d42eb3610efdd</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835032142-5 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 Parimalasundar E., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, India; Devaraju T., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient lipophilicity prediction of molecules employing deep-learning models Subject The topic of the resource Convolutional neural network; Deep learning; Drug design; Lipophilicity; Long-short term memory; Multi-layer perceptron Description An account of the resource Lipophilicity, expressed as logP, is a significant physiochemical property and is an indicator of absorption, distribution, metabolism and elimination characteristics of drugs used in medication. It is one of the major deciding factors of the fate of a molecule to be a successful drug. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. The work described here aims to simplify prediction of logP values with high-degree of accuracy by using Deep Learning (DL) models paired with Mol2vec. The work described in this paper empirically demonstrates that by using the described DL models paired with Mol2vec, one can achieve results which are much better than the conventional ML techniques as well as more complex and recent algorithms like Message-passing Neural Networks (MPNN), Graph Convolution (GC) and Spatial Graph embedding (C-SGEN). Our RMSE (Root Mean Square Error) scores from the ensemble model is one of the best reported so far in literature. The methods elaborated in this paper are simple, yet effective in predicting logP values to a great degree of accuracy due to the use of Mol2vec and standard TensorFlow operators. The models employed here can be coded and maintained with much more ease compared to the techniques of MPNN, C-SGEN or GC. 2021 Elsevier B.V. Creator An entity primarily responsible for making the resource Datta R.; Das D.; Das S. Source A related resource from which the described resource is derived Chemometrics and Intelligent Laboratory Systems, Vol-213 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.chemolab.2021.104309" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.chemolab.2021.104309</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104961606&doi=10.1016%2Fj.chemolab.2021.104309&partnerID=40&md5=f4c3a6a54a3a7c8a1d49ff40cdab3da5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104961606&doi=10.1016%2fj.chemolab.2021.104309&partnerID=40&md5=f4c3a6a54a3a7c8a1d49ff40cdab3da5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1697439; CODEN: CILSE 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 Datta R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Das D., Intel, Bengaluru, 560103, India; Das S., Sprinklr India Pvt Ltd, Yemalur, Bengaluru, 560037, 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 Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images Subject The topic of the resource Cancer Detection; CNN; computer aided diagnosis; Deep Learning; Histopathological Images; image Classification; machine learning Description An account of the resource Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE. Creator An entity primarily responsible for making the resource Ramesh M.; Maheswaran S.; Theivanayaki S.; Kodeeswari K.; Krishnasamy L.; Sriram N. Source A related resource from which the described resource is derived 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCNT56998.2023.10307852" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT56998.2023.10307852</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&doi=10.1109%2FICCCNT56998.2023.10307852&partnerID=40&md5=e0adb3b1e5b898cf056da480adc4a970" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&doi=10.1109%2fICCCNT56998.2023.10307852&partnerID=40&md5=e0adb3b1e5b898cf056da480adc4a970</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033509-5 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 Ramesh M., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Theivanayaki S., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Kodeeswari K., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Krishnasamy L., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Sriram N., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India https://archives.christuniversity.in/files/original/688454fd7457d084629343d5c4e9c8d1.pdf b84c0f348bfa2e673bc0da9da7eb105f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MPHIL Mphil Mphil Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient management of feed resources using data mining techniques / Creator An entity primarily responsible for making the resource Rani, V Uma. - 0935015 Date A point or period of time associated with an event in the lifecycle of the resource 2022-05-19 Description An account of the resource Feed is the largest input in any livestock enterprise and the rapid increase in feed prices and shortage of feed resources has been one of the major constraints for farmers, livestock industries, planners and the policy makers. This calls for prudent management of available resources and application of computing techniques can be one of the possible potential approaches. India is endowed with a wide range of feed resources varying widely in their composition and utility for different livestock species. Clustering of feed resource into different groups based on the composition can help in better feed management. To evaluate and to suggest a best technique for clustering feed resources, we have evaluated three clustering techniques viz. K-means, spectral k-means and auto spectral on two different data sets containing 236 and 106 feed resources with major constituents like crude protein, crude fiber ash, fat etc., . Subject The topic of the resource Computer Science Publisher An entity responsible for making the resource available CHRIST (Deemed To Be University) 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 MPhil 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 Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM Subject The topic of the resource Correlation Based Feature Subset; Personality Prediction; Social Network Description An account of the resource Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE. Creator An entity primarily responsible for making the resource Sujatha N.; Pramod S.; Bhatla S.; Thulasimani T.; Kant R.; Chauhan A. Source A related resource from which the described resource is derived 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 959-964. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICESC57686.2023.10193058" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICESC57686.2023.10193058</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168311387&doi=10.1109%2FICESC57686.2023.10193058&partnerID=40&md5=b0f31995b5aa6d68f05631a1d4c73ec3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168311387&doi=10.1109%2fICESC57686.2023.10193058&partnerID=40&md5=b0f31995b5aa6d68f05631a1d4c73ec3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030009-3 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 Sujatha N., B. M. S. College of Engineering, Department of Mathematics, Karnataka, Bengaluru, India; Pramod S., Nitte Meenakshi Institute of Technology Yelahanka Bangalore, Department of Mathematics, Karnataka, India; Bhatla S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Thulasimani T., Bannari Amman Institute of Technology Sathyamangalam, Department of Mathematics, Tamilnadu, Erode, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, 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 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 Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine Subject The topic of the resource Extreme Learning Machine (ELM); Grey Level Co-occurence Matrix; K-Means Algorithm Description An account of the resource Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE. Creator An entity primarily responsible for making the resource Chaturvedi A.; Yagnasree S.; Chhabra G.; Chidambara Rajan P.; Chauhan A.; Maranan R. Source A related resource from which the described resource is derived 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 1179-1184. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICESC57686.2023.10193102" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICESC57686.2023.10193102</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168308909&doi=10.1109%2FICESC57686.2023.10193102&partnerID=40&md5=08c0d015be371abbdda4a5eb115cac80" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168308909&doi=10.1109%2fICESC57686.2023.10193102&partnerID=40&md5=08c0d015be371abbdda4a5eb115cac80</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030009-3 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 Chaturvedi A., Gla University, Department of Electronics and Communication Engineering, Uttar Pradesh, Mathura, India; Yagnasree S., Lovely Professional University, Department of Electronics & Communication Engineering, Punjab, Phagwara, India; Chhabra G., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Chidambara Rajan P., Dr. N. G. P. Arts and Science College, Department of Biotechnology, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Maranan R., Saveetha School of Engineering, Simats, Department of Research and Innovation, Tamil Nadu, Chennai, India