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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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
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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
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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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Published: Feb 16, 2023
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DOI: 10.4018/IJRQEH.318090
Volume 12
Shivani Sharma, Bipin Kumar Rai, Mahak Gupta, Muskan Dinkar
An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by...
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An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by improvising support vector machine is a platform that predicts diabetes based on the data entered into the system and offers reliable results based on that data. Earlier, the dataset consisted of a smaller number of features comprising the patients' medical details that were useful in determining the patient's health condition and was mainly focused on gestational diabetes, which only deals with pregnant women. In this work, the authors build a system that is more efficient than the previous system because of these reasons. It provides more accurate results by improvising the support vector machine, which includes more datasets and can predict the possibility of diabetes disease in both males and females.
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Sharma, Shivani, et al. "DDPIS: Diabetes Disease Prediction by Improvising SVM." IJRQEH vol.12, no.2 2023: pp.1-11. http://doi.org/10.4018/IJRQEH.318090
APA
Sharma, S., Rai, B. K., Gupta, M., & Dinkar, M. (2023). DDPIS: Diabetes Disease Prediction by Improvising SVM. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-11. http://doi.org/10.4018/IJRQEH.318090
Chicago
Sharma, Shivani, et al. "DDPIS: Diabetes Disease Prediction by Improvising SVM," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-11. http://doi.org/10.4018/IJRQEH.318090
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Published: Feb 10, 2023
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DOI: 10.4018/IJRQEH.318129
Volume 12
Bipin Kumar Rai, Shivya Srivastava, Shruti Arora
In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare...
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In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare business, counterfeit pills are one of the primary reasons for the harmful impact on human health and financial loss. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, the authors propose blockchain-based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. They offer a solution to fully decentralize the tracking by storing BBTCD on IPFS (inter planetary file system) to provide transparency and cost-effectiveness.
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Rai, Bipin Kumar, et al. "Blockchain-Based Traceability of Counterfeited Drugs." IJRQEH vol.12, no.2 2023: pp.1-12. http://doi.org/10.4018/IJRQEH.318129
APA
Rai, B. K., Srivastava, S., & Arora, S. (2023). Blockchain-Based Traceability of Counterfeited Drugs. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-12. http://doi.org/10.4018/IJRQEH.318129
Chicago
Rai, Bipin Kumar, Shivya Srivastava, and Shruti Arora. "Blockchain-Based Traceability of Counterfeited Drugs," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-12. http://doi.org/10.4018/IJRQEH.318129
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Published: Mar 24, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.320480
Volume 12
Arvind Yadav, Vinod Kumar, Devendra Joshi, Dharmendra Singh Rajput, Haripriya Mishra, Basavaraj S. Paruti
COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates...
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COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.
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Yadav, Arvind, et al. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission." IJRQEH vol.12, no.2 2023: pp.1-15. http://doi.org/10.4018/IJRQEH.320480
APA
Yadav, A., Kumar, V., Joshi, D., Rajput, D. S., Mishra, H., & Paruti, B. S. (2023). Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-15. http://doi.org/10.4018/IJRQEH.320480
Chicago
Yadav, Arvind, et al. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-15. http://doi.org/10.4018/IJRQEH.320480
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