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436 results on '"Vivekanandan S"'

1. Visualizing Benefits: Evaluating Healthcare Information System Using IS-Impact Model

Author

Bruce Davidson, M. Ali Akber Dewan, Vivekanandan S. Kumar, Maiga Chang, and Brenda Liggett

Subjects
IS-impact model, IS-success model, benefit realization, medical information systems, information visualization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Reducing costs and optimizing operations are major challenges in many large-scale organizations including healthcare authorities. Research shows that despite ongoing investments in healthcare information systems (IS), the promised benefits often are partially realized or not at all. Part of the solution is to ensure that evaluation methodologies are available to clearly identify the success of these initiatives and from here articulate and mitigate the deficiencies or move to an alternative technology. The literature asserts that few practitioners have implemented a standardized evaluation approach. Using an established model, namely Information System Impact (IS-impact) model, we propose a modified evaluation model to assess and a visualization tool to visualize the success of an information systems from a healthcare perspective. The modified IS-Impact model includes six constructs - - individual impact, organization impact, provincial alignment impact, system quality, information quality, and service quality. We applied the modified IS-impact model and the proposed visualization tool against an existing healthcare software solution. An empirical study was conducted at a healthcare authority, with responses from 150 participants who use the healthcare IS, which confirmed that the proposed model and the visualization tool are valid and reliable to measure healthcare systems success. The evaluation model and the visualization tool are found to be efficient to narrow down the scope of inquiry from the general to the specific and quickly identifying the gaps and successes within the established software solution for the healthcare authority. Healthcare or clinical informatics researchers will be benefited from this research in evaluating the ongoing or nearly established healthcare information systems.

Published
2020
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2. A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases

Author

Parthasarathy P and Vivekanandan S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

A numerical model of an amperometric-enzymatic uric acid biosensor for a non-relentless condition has been developed. This model depends on the arrangement of nonlinear reaction diffusion equations for Michaelis-Menten formalism that depicts the concentrations of substrate and product. The new rough scientific articulations for the concentration of substrate (uricase enzyme) and product and the corresponding current response have been derived for all estimations of parameters utilizing the new perturbation technique. The non-dimensional numerical model of the amperometric biosensor can be effectively used to examine the responses. Moreover, the relative impact of these parameters is chosen by the Damkohler number and the impact of current density on this number likewise contemplated. All the analytical results are compared with simulation results using MATLAB program and the numerical outcomes concur with fitting hypotheses. Notwithstanding, strikingly the model likewise proposed that the choice of substrate and product for uric acid biosensor for the application of kidney disease and GOUT arthritis diseases. Keywords: GOUT arthritis, Amperometric, Uric acid, Damkohler number, Diffusion equations

Published
2018
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4. Valproate-induced hyperammonemic encephalopathy enhanced by topiramate and phenobarbitone: A case report and an update

Author

Vivekanandan S and Nayak S

Subjects
Encephalopathy, hyperammonemia, phenobarbitone, topiramate, valproate, Neurology. Diseases of the nervous system, RC346-429
Abstract

Although sodium valproate (VPA)-induced hepatic encephalopathy is a well-recognized entity, VPA can occasionally produce encephalopathy secondary to hyperammonemia in the presence of normal hepatic function, namely valproate-induced non-hepatic hyperammonemic encephalopathy (VNHE). Known risk factors include therapy with multiple antiepileptic drugs, especially when topiramate is one of the drugs; presence of underlying inborn errors of metabolism; febrile states; and insufficient nutritional intake. We describe a 5-year-old male child who developed VNHE while on polypharmacy with topiramate and phenobarbitone; the child also had poor nutritional intake. The encephalopathy reversed with withdrawal of VPA and treatment with L-carnitine. We emphasize the need for early recognition, investigation, and treatment of this potentially life-threatening condition. We also recommend that VPA, topiramate, and phenobarbitone should not be given in combination.

Published
2010

9. Risk of Bowel Obstruction in Patients Undergoing Neoadjuvant Chemotherapy for High-risk Colon Cancer: A Nested Case-control Matched Analysis of an International, Multi-centre, Randomised Controlled Trial (FOxTROT)

Author

Glasbey, James, Glasbey, James, Beggs, Andrew, Glimelius, Bengt, Gray, Richard, Handley, Kelly, Laurberg, Søren, Magill, Laura, Murakami, Keigo, Palmer, Andy, Quirke, Philip, Seligman, Jenny, Seymour, Matt, Sinha, Yash, West, Nick, Morton, Dion, Glasbey, James, Handley, Kelly, Palmer, Andy, Morton, Dion, Crosby, T., Olliff, J., Peto (Chair), R., Brown, Gina, Ferry, David, Glimelius, Bengt, Gray, Richard, Handley, Kelly, Ismail, Tariq, Laurberg, Søren, Magill, Laura, Morton, Dion, Oliver, Alf, Quirke, Phil, Seymour, Matt, Scott, Nigel, Seligman, Jenny, Swift, Ian, Warren, Bryan, West, Nick, Northover, J., Parmar (Chair), M., Slevin, M., Magill, Laura, Gray, Richard, Handley, Kelly, Wilcockson, Adrian, Gray, Zoe, Lancaster, Dominic, Brown, James, Palmer, Andrew, Adie, Ladan, Kennedy, Georgia, Eld, M., Holt, G., Yilmaz, M., Spendler, K. Garm, Hansen, F., Laurberg, S., Rosenkilde, M., Ahlstrom, H., Glimelius, B., Abgamu, D., Day, N., Walsh, C., Bannister, J., Furniss, D., Morgan, S., Walkington, L., Yates, S., Branagan, G., Mustajab, A., O’Neil, H., Rees, C., Geh, I., Hendrickse, C., Langman, G., Pallan, A., Conn, A., Lowe, A., Ostrowski, J., Steward, M., Callaway, M., Falk, S., Thomas, M., Wong, N., Cast, J., Hartley, J., Roy, R., Tiam, R., Blunt, D., Cleator, S., Dawson, P., Goldin, R., Gujral, D., Lowdell, C., Ziprin, P., Clenton, S., Dewdney, A., Euinton, H., Furniss, D., Gupta, R., Tarapowewalla, D., Wilshaw, V., Braun, M., Chakrabarty, B., Hill, J., Laasch, H., Saunders, M., Cruickshank, N., Davies, M., Muzaffar, S., Orme, A., Punia, P., Rea, D., Campbell, F., Hughes, M., Palmer, D., Rooney, P., Abbott, G., Hamid, B., Vimalachandran, D., Berry, J., Hinson, F., Maarouf, Z., Nicoll, J., Adams, C., Denson, J., Jackson, S., Sherriff, D., Kweka, E., McAdam, G., Peters, M., Roy, R., Khaira, M., Kurien, G., Robinson, J., Wadsley, J., White, D., Young, R., Dega, R., Lamparelli, M., Orbell, J., Osborne, R., Taylor, P., Thomas, T., Gopalakrishnan, K., Jadhav, V., Scott-Brown, M., Baijal, S., Chapman, M., Glaholm, J., Nelson, C., Singh, R., Harrison, J., Last, K., Scott, D., Scullion, D., Lind, P., Milosavljevic, Z., Dent, J., Ilsley, D., Littleford, S., Roberts, C., Crabtree, M., Orrell, J., Sherwin, E., Smith, S., Soomal, R., Braun, M., De, A., Khan, A., Khan, U., Lavin, V., McBain, C., Radharkrishna, G., Sil, R., Weerasinghe, S., Hill, J., Lee, S., Wright, P., Church, R., Holland, C., Kunene, V., Thompson, A., Glynne-Jones, R., Goh, V., Livingstone, J., Richman, P., Barlow, C., Burn, P., Geraghty, J., Walther, J., Grumett, S., Mangalika, S., Qaiyum, M., Williams, G., Borgstein, R., Bridgewater, J., Melville, D., Rees, J., Coxon, F., Hainsworth, P., Needham, S., Scott, J., Asmussen, J., Hansen, T., Jensen, K., Pfeiffer, P., Alkhaldi, A., Brittenden, J., Jackson, A., Kamposioras, K., Kumaran, G., Macklin, C., Alexander, J., Harle, A., Hickish, T., Talbot, R., Tarver, D., Bridgewater, J., Partridge, W., Sundaresan, V., Vivekanandan, S., Agrawal, N., Higginson, A., Muthuramalingam, S., O’Leary, D., Devarajan, G., Gulati, M., Kerwat, R., Maisey, N., Mikhaeel, G., Ismail, T., Middleton, G., Page, A., Steven, N., Taniere, P., Gutmann, J., Huang, J., Raouf, S., Dunn, W., Escola, C. Lopez, Potter, V., Scholefield, J., Walker, G., Zaitoun, A., Eason, D., McPhail, N., Mmeka, W., Stenhouse, G., Watson, A., Fozard, B., Hickish, T., Snape, S., Ellis, R., Faux, W., Jenkins, R., Maskell, G., Kulkarni, R., Lund, J., Menon, S., Singh, R., Chandler, I., Daniels, I., Harries, S., Osborne, M., Bell, J., Krell, D., Mayer, A., Ogunbiyi, O., Watkins, J., Bronder, C., Eaton, D., Taylor, A., Brown, G., Cunningham, D., Tekkis, P., Wotherspoon, A., Dobson, M., Mitchell, P., Pitt, M., Scott, N., Susnerwala, S., Adab, F., Britton, I., Ghiridaran, S., Howitt, C., Kirby, R., Biddlestone, L., Dalton, S., De Winton, E., Phillips, A., Ferry, D., Grumett, S., Kawesha, A., Maleki, K., Momtahan, N., Burnett, H., Hayes, S., Soop, M., Branagan, G., Cook, I., Cook, S., Iveson, T., Shablak, A., Coup, A., Hamid, A., Moore, P., O’Toole, L., Pai, D., Bateman, A., Bateman, A., Blaquiere, R., Nichols, P., Chappell, M., Dworkin, M., Jain, S., Tsang, D., Hopkins, K., Loveday, E., Lyons, A., Rooney, N., Ali, N., Chatterjee, M., Chiphang, A., Dundas, S., Myint, A. Sun, Zeiderman, M., Beharry, N., Chong, H., Lofts, F., Melville, D., Finan, P., Seymour, M., Tolan, D., West, N., Anyamene, N., Burling, D., Kennedy, R., Moorghen, M., Agrawal, S., Hasan, J., Mehta, S., Saeed, M., Burgess, P., John, L., Lowndes, S., Planner, A., Campbell, F., Hughes, M., Rooney, P., Smith, D., Hochhauser, D., Obichere, A., Rodriguez-Justo, M., Shiu, K., Taylor, S., Correa, P., James, S., Shatwell, W., Williams, N., Brady, J., Lanaspre, E., Mikhaeel, G., Ahmad, M., Gill, T., Wilson, D., Adams, R., Beehen, R., Morgan, M., Lindh, B., Adams, R., Morgan, M., Ford, A., Gopal, K., Pranesh, N., Shareef, D., Tighe, M., Busby, K., Correa, P., Sanders, S., Sinha, R., Ahmad, R., Desai, S., Ramesh, S., Hilman, S., Lott, M., O’Brien, J., Radstone, D., West, D., Amin, S., Hampton, J., Hornbuckle, J., Kitsanta, P., Ali, M., Desai, A., Hadaki, M., Hall, M., Arul, D., Hochhauser, D., Leonard, P., Mukhtar, H., Murray, D., Baxter, A., Churn, M., Farrugia, D., Lake, S., Smith, G., Bansal, A., Chandran, P., Corr, C., Gollins, S., Davenport, A., Saunders, M., Sukumar, S., Bathurst, N., Beaumont, E., Cooper, E., Francis, N., Sephton, M., Sparrow, G., Clarke, A., Haselden, J., Last, K., Woodcock, N., Atkinson, M., Gollins, S., Gupta, M., Maw, A., Abdullah, N., Bale, C., and Lord, M.

Published
2023
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12. Automated Essay Scoring and the Deep Learning Black Box: How Are Rubric Scores Determined?

Author

Kumar, Vivekanandan S. and Boulanger, David

Abstract

This article investigates the feasibility of using automated scoring methods to evaluate the quality of student-written essays. In 2012, Kaggle hosted an Automated Student Assessment Prize contest to find effective solutions to automated testing and grading. This article: a) analyzes the datasets from the contest -- which contained hand-graded essays -- to measure their suitability for developing competent automated grading tools; b) evaluates the potential for deep learning in automated essay scoring (AES) to produce sophisticated testing and grading algorithms; c) advocates for thorough and transparent performance reports on AES research, which will facilitate fairer comparisons among various AES systems and permit study replication; d) uses both deep neural networks and state-of-the-art NLP tools to predict finer-grained rubric scores, to illustrate how rubric scores are determined from a linguistic perspective, and to uncover important features of an effective rubric scoring model. This study's findings first highlight the level of agreement that exists between two human raters for each rubric as captured in the investigated essay dataset, that is, 0.60 on average as measured by the quadratic weighted kappa (QWK). Only one related study has been found in the literature which also performed rubric score predictions through models trained on the same dataset. At best, the predictive models had an average agreement level (QWK) of 0.53 with the human raters, below the level of agreement among human raters. In contrast, this research's findings report an average agreement level per rubric with the two human raters' resolved scores of 0.72 (QWK), well beyond the agreement level between the two human raters. Further, the AES system proposed in this article predicts holistic essay scores through its predicted rubric scores and produces a QWK of 0.78, a competitive performance according to recent literature where cutting-edge AES tools generate agreement levels between 0.77 and 0.81, results computed as per the same procedure as in this article. This study's AES system goes one step further toward interpretability and the provision of high-level explanations to justify the predicted holistic and rubric scores. It contends that predicting rubric scores is essential to automated essay scoring, because it reveals the reasoning behind AIED-based AES systems. Will building AIED accountability improve the trustworthiness of the formative feedback generated by AES? Will AIED-empowered AES systems thoroughly mimic, or even outperform, a competent human rater? Will such machine-grading systems be subjected to verification by human raters, thus paving the way for a human-in-the-loop assessment mechanism? Will trust in new generations of AES systems be improved with the addition of models that explain the inner workings of a deep learning black box? This study seeks to expand these horizons of AES to make the technique practical, explainable, and trustable.

Published
2021
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16. Development of Copper Nanoparticles from E-waste for Biomedical Applications

Author

Mathew, Ammu Anna, Parthasarathy, P., Vivekanandan, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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17. A Review of Existing Non-invasive Techniques for Glucose Monitoring

Author

Mathew, Ammu Anna, Vivekanandan, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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18. Anterior Segment OCT Image Analysis of Corneal Opacities that Guide Ophthalmologists in Making Decisions About Corneal Transplant Surgeries

Author

Solomons, Cassandra D., Vivekanandan, S., Rose, Jeyanth Suresh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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19. Smart Agriculture: A Survey on Challenges and Opportunities with Recent Advancements

Author

Thangatamilan, M., Suji Prasad, S. J., Vivekanandan, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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20. Autonomous Vehicle Spray Paint and Defect Detection Using Machine Vision

Author

Rawat, Mukul, Kashyap, Shubham, Pandey, Rishi, Vivekanandan, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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24. List of contributors

Author

Ameduri, Bruno, primary, Avanish Babu, T., additional, Balakrishnan, Raneesh, additional, Ben Zineb, Tarak, additional, Bouharras, Fatima Ezzahra, additional, Chacko, Sobi K., additional, Chandrasekhar, Arunkumar, additional, Chatbouri, Samir, additional, Ghosh, Sujoy Kumar, additional, John, Honey, additional, Joseph, Sherin, additional, Madhuri, W., additional, Mahanty, Biswajit, additional, Maiti, Pralay, additional, Mathew, Ammu Anna, additional, Mayeen, Anshida, additional, Muslum, Guven, additional, Pai, Avinash R., additional, Persano, Luana, additional, Rahul, M.T., additional, Raihane, Mustapha, additional, Ram, Farsa, additional, Rouxel, Didier, additional, Shanmuganathan, Kadhiravan, additional, Sukumaran, Sunija, additional, Thiyagarajan, K., additional, Thomas, Sabu, additional, Thomas, Sharin Maria, additional, Tiwari, Shivam, additional, and Vivekanandan, S., additional

Published
2022
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32. Annotation Recommendation for Online Reading Activities

Author

Chang, Miao-Han, Chang, Maiga, Kuo, Rita, Essalmi, Fathi, Kumar, Vivekanandan S., Kung, Hsu-Yang, Huang, Ronghuai, Series Editor, Kinshuk, Series Editor, Jemni, Mohamed, Series Editor, Chen, Nian-Shing, Series Editor, Spector, J. Michael, Series Editor, Chang, Maiga, editor, and Popescu, Elvira, editor

Published
2018
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42. Association of vitamin D levels with severity and outcome of COVID-19 infection among inward patients at a tertiary care unit in Sri Lanka

Author

Mayurathan, P., primary, Francis, G. R., additional, Francis, V. R., additional, Majitha, S. I., additional, Sanjeev, R., additional, Vivekanandan, S., additional, Santharooban, S., additional, Nishantha, W. M. U., additional, Peries, M. M. N., additional, Safran, A. S. M., additional, and Shiyananth, S., additional

Published
2023
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46. Skin Cancer Classification using Tensorflow and Keras

Author

null Saranya S, null Vivekanandan S J, null Vignesh K, null Sai Anand K, and null Surya Prakash R

Abstract

Skin cancer is an abnormal growth of skin cells. It generally develops in areas that are exposed to the sun, but it can also form in places that don’t normally get sun exposure. Skin cancers aren’t all identical, and they may not cause many symptoms. Still, unusual changes to your skin can be a warning sign for the different types of cancer. Being alert for changes to your skin may help you get a diagnosis earlier. Accurate and precise diagnosis of diseases has been a significant challenge and he recent advances in computer vision made possible by deep learning has paved the way for disease diagnosis for skin cancer. It described the innovative solution that provides efficient disease detection and deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various skin cancer diseases. A variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available skin cancer disease given image dataset. So, it observed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. And this model to deploy Django web framework.

Published
2022

47. Loan Fraud Detection using Machine Learning Algorithm

Author

null Reni Hena Helan R, null Abirami G, null Sultan Saleem A, null Vivekanandan S J, and null Tejaswini P R

Abstract

The widespread usage of the Web has had a massive effect on the growth of online card transactions, especially at the beginning of the previous few years. Because of the increase in internet transactions, the global banking system has been forced to deal with or confront an unexpected number of fraudulent operations. As a reason, rule-based algorithms were created to detect high-risk transactions and allow specialists to authenticate whether or not they were fraudulent. The present intruders used the static nature of rule-based systems as a defensive measure to avoid detection. As a result, researchers set out to develop adaptive fraud detection systems based mostly on machine learning techniques, including deep learning, which is a relatively new application. The widespread use of the Internet, notably at the start of the previous decade, had a considerable impact on the rise in online card transactions. The rise of internet transactions has forced the worldwide banking system to deal with or confront an unexpected amount of fraudulent operations. As a result, rule-based algorithms were developed to identify high-risk transactions and allow specialists to confirm whether they were genuine or not. To avoid detection, the current attackers used the static nature of rule-based systems as a countermeasure. As a result, researchers set out to develop adaptive fraud detection systems based mostly on machine learning techniques, including deep learning, which is a relatively new application.

Published
2022

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