Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body's cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF), are compared with the proposed EL technique. Experiments reveal that the EL method performs better than the existing ML approaches in predicting diabetic illness, with the remarkable accuracy of 97.11%. The proposed ensemble learning could be useful in assisting specialists in accurately diagnosing diabetes and assisting patients in receiving the appropriate therapy.

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Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease

The paper presents a structural and functional model of information technology for forming the quality of art and technical design of book at the stage of prepress preparing. The basis of the proposed technology is a fuzzy knowledge base which allows us to build the relationships between physically separated variables during the process of art and technical design of books by the use of both the means of mathematical theory of fuzzy sets and distinguished factors for prepress preparation of books. A fuzzy knowledge matrix for linguistic variables H – "quality of art design of books" which contain the linguistic variables G – "quality of art decoration" and R – "quality of technical design" have been formed and presented as the tables. The systems of fuzzy logical equations which determine the procedures of obtaining the weights of membership functions for a set of linguistic terms are developed and calculated. The defuzzification procedure has been performed for all linguistic variables considering the table of the membership functions normalized values in the three points of the universal set division. It has been proposed the quantitative indicator of the level of the predicted quality of the printing process by the implementation of defuzzification procedure of the output fuzzy set using the mass center method. The numerical value of the level of quality of art and technical design of books H = 52,5% has been obtained during the simulation process.

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Information Technology of Forming the Quality of Art and Technical Design of Books.

Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features

Officials in the field of public health are concerned about a new monkeypox outbreak, even though the world is now experiencing an epidemic of COVID-19. Similar to variola, cowpox, and vaccinia, an orthopoxvirus with two double-stranded strands causes monkeypox. The present pandemic has been propagated sexually on a massive scale, particularly among individuals who identify as gay or bisexual. In this instance, the speed with which monkeypox was diagnosed is the most important aspect. It is possible that the technology of machine learning could be of significant assistance in accurately diagnosing the monkeypox sickness before it can spread to more people. This study aims to determine a solution to the problem by developing a model for the diagnosis of monkeypox through machine learning and image processing methods. To accomplish this, data augmentation approaches have been applied to avoid the chances of the model’s overfitting. Then, the transfer-learning strategy was utilized to apply the preprocessed dataset to a total of six different Deep Learning (DL) models. The model with the best precision, recall, and accuracy performance matrices was selected after those three metrics were compared to one another. A model called “PoxNet22” has been proposed by performing fine-tuning the model that has performed the best. PoxNet22 outperforms other methods in its classification of monkeypox, which it does with 100% precision, recall, and accuracy. The outcomes of this study will prove to be extremely helpful to clinicians in the process of classifying and diagnosing monkeypox sickness.

PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning

Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, about 2.3 million cases and 685,000 deaths have been reported worldwide. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machine-based intelligent breast cancer diagnosis system that can detect cancer at its early stage and cure it more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stacked-based ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computer-aided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images and Wisconsin Breast Cancer Database (WBCD) with 569 instances for the performance evaluation of our proposed framework. We have trained several distinct classifiers on both datasets and selected the best five classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, AdaBoost, Gradient Boosting, and KNN9 classifiers as the base learners, and the logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets and achieved testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on the BreakHis and the WBCD datasets also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores. 

/pdf/self-a-stacked-based-ensemble-learning-framework-for-breast-1h67zdmz.pdf

SELF: a stacked-based ensemble learning framework for breast cancer classification

Early predictive model for breast cancer classification using blended ensemble learning

In the present era, rapid transits are one of the most affordable means of public transport with various useful integrated application systems. The majority of the integrated applications are deplo...

K. M. Karthick Raghunath

Papers

Early predictive model for breast cancer classification using blended ensemble learning

Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system

Utilization of IoT-assisted computational strategies in wireless sensor networks for smart infrastructure management