Published: Feb 24, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.318483
Volume 12
Meshwa Rameshbhai Savalia, Jaiprakash Vinodkumar Verma
Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus...
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Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.
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MLA
Savalia, Meshwa Rameshbhai, and Jaiprakash Vinodkumar Verma. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques." IJRQEH vol.12, no.1 2023: pp.1-19. http://doi.org/10.4018/IJRQEH.318483
APA
Savalia, M. R. & Verma, J. V. (2023). Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(1), 1-19. http://doi.org/10.4018/IJRQEH.318483
Chicago
Savalia, Meshwa Rameshbhai, and Jaiprakash Vinodkumar Verma. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.1: 1-19. http://doi.org/10.4018/IJRQEH.318483
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Published: May 19, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.323570
Volume 12
Bipin Kumar Rai, Pranjal Sharma, Sagar Singhal, Basavaraj S. Paruti
In recent years, there have been many attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity. In this paper, the...
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In recent years, there have been many attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity. In this paper, the authors have reviewed in-depth existing blockchain-based identity management papers and patents published online. Based on that analysis of the literature, a system will be implemented which will come up with the current issues and try to minimize them. Being transparent, immutable, and decentralized in nature, blockchain mechanism is found to be a better technology which can reduce the corruption in the experimental scenario. The objective is to develop a decentralized system which can be used for the verification of the employees in an organization. This is done to stop or reduce the cases of identity theft and data leakage in recent time. This system will be using Ethereum blockchain platform for monitoring the information and smart contract for authentication.
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Rai, Bipin Kumar, et al. "Decentralized Blockchain-Enabled Employee Authentication System." IJRQEH vol.12, no.1 2023: pp.1-13. http://doi.org/10.4018/IJRQEH.323570
APA
Rai, B. K., Sharma, P., Singhal, S., & Paruti, B. S. (2023). Decentralized Blockchain-Enabled Employee Authentication System. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(1), 1-13. http://doi.org/10.4018/IJRQEH.323570
Chicago
Rai, Bipin Kumar, et al. "Decentralized Blockchain-Enabled Employee Authentication System," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.1: 1-13. http://doi.org/10.4018/IJRQEH.323570
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Published: Jul 11, 2023
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DOI: 10.4018/IJRQEH.325354
Volume 12
Chanemougavally J., Shruthy K. M., Selvaraj Sudhakar, M. Sasirekha
Medical education is experimenting with different tools to make teaching-learning more compatible with the medical curriculum. One such addition is blended learning, which combines traditional...
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Medical education is experimenting with different tools to make teaching-learning more compatible with the medical curriculum. One such addition is blended learning, which combines traditional teaching with e-learning. The study aims to assess the effectiveness of combining e-learning and traditional face-to-face gross anatomy teaching in undergraduate medical students. This collaborative study was done in the Department of Anatomy, A.C.S Medical College and Hospital, Dr. M.G.R. Educational and Research Institute (Deemed to be University). One hundred fourteen students volunteered to participate in the study. Six topics from the gross anatomy of the abdomen were chosen for the study. An overall pre-test questionnaire was delivered with the didactic lectures. Another pre-test questionnaire was given about the selected topic before sharing the online learning materials. A post-test questionnaire in Google form was collected at the end of the day. Feedback was collected from all study participants.
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Chanemougavally J., et al. "The Effect of E-Learning and Traditional Teaching Done Hand-in-Hand for First-Year M.B.B.S. Students." IJRQEH vol.12, no.1 2023: pp.1-10. http://doi.org/10.4018/IJRQEH.325354
APA
Chanemougavally J., Shruthy K. M., Sudhakar, S., & Sasirekha, M. (2023). The Effect of E-Learning and Traditional Teaching Done Hand-in-Hand for First-Year M.B.B.S. Students. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(1), 1-10. http://doi.org/10.4018/IJRQEH.325354
Chicago
Chanemougavally J., et al. "The Effect of E-Learning and Traditional Teaching Done Hand-in-Hand for First-Year M.B.B.S. Students," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.1: 1-10. http://doi.org/10.4018/IJRQEH.325354
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Published: Jul 24, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.326765
Volume 12
Jalal Rabbah, Mohammed Ridouani, Larbi Hassouni
Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune...
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Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune to new strains. They underscore the need to introduce increasingly effective techniques to deal with such a spread and help identify new infections more quickly, at a reasonable cost and with a minimum error rate. Machine learning models constitute a new approach, used increasingly in this field. In this proposed work, the authors built a classification model named CovStacknet based on StackNet metamodeling methodology combined with the deep convolutional neural network as the basis for feature extraction from x-ray images. Firstly, the proposed model used VGG16 as a transfer learning of deep convolutional neural networks and achieved an accuracy score of 98%. Secondly, the proposed model is extended to evaluate four other deep convolutional neural networks, ResNet-50, Inception-V3, MobileNet-V2 and DenseNet, and ResNet-50, has achieved the best performance.
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Rabbah, Jalal, et al. "A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images." IJRQEH vol.12, no.1 2023: pp.1-23. http://doi.org/10.4018/IJRQEH.326765
APA
Rabbah, J., Ridouani, M., & Hassouni, L. (2023). A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(1), 1-23. http://doi.org/10.4018/IJRQEH.326765
Chicago
Rabbah, Jalal, Mohammed Ridouani, and Larbi Hassouni. "A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.1: 1-23. http://doi.org/10.4018/IJRQEH.326765
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