Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

The integration of generative artificial intelligence (AI) has the potential to revolutionize cross-cultural knowledge exchange in higher education. By leveraging its ability to create culturally sensitive and contextually relevant learning materials, generative AI can enhance personalized education for students from diverse backgrounds. Overcoming language barriers through real-time translation, this technology promotes inclusive collaboration and co-creation of knowledge. However, ethical considerations such as cultural authenticity, bias mitigation, and data privacy must be carefully navigated to ensure equitable and respectful cross-cultural interactions. In essence, generative AI offers a transformative avenue for fostering a culturally enriched learning environment in higher education, necessitating a balanced approach that maximizes benefits while upholding ethical principles.

Smart healthcare is defined by the technology that leads to better diagnostic tools, better treatment for patients, and devices that improve the quality of life for anyone and everyone. Medical images have significant to facilitate that smart health environment. However, the medical images frequently get noisy in the acquisition process, which engages many different physical mechanisms. Most of the de-noising algorithms conceive the additive white Gaussian noise (AWGN). However, among the popular medical image modalities, several are degraded by some type of non-Gaussian noise, such as Poisson noise. Poisson noise is mainly associated with many imaging modalities like single-photon emission computerized tomography (SPECT), (positron emission tomography) PET, and fluorescent confocal microscopy imaging. Because of the signal-dependent nature of Poisson noise, the various de-noising filters proposed in the literature, including the Non-Local Mean (NL-Mean) filter. In literature, NL-Mean is mostly applied for Gaussian noise extraction and very rarely used for Poisson noise removal. In this work, notable efforts are put to modified NL-Mean filter, and high order NL-Mean Methods are proposed. These novel high order algorithms de-noise images by prominent the signals and noise because it takes the high order odd moment of the medical image. The visual quality of the de-noised medical image (PET) and correlation graph determines that the proposed algorithms outperform the conventional de-noising filter. This study's findings will significantly contribute to the development of a more accurate and robust image analysis model, which is the need of today's modern age of digitization.

Diabetes claims the lives of thousands each year, and many individuals remain oblivious to their condition until it reaches a critical stage. This study presents a data mining-based approach aimed at enhancing the early detection and prediction of diabetes, utilizing data from the Pima Indian Diabetes dataset. Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. Additionally, precision in data clustering is crucial; it can furnish either extensive, well-grouped data for the random forest or limited data, constraining its efficacy. Our principal objective was to enhance the accuracy of fuzzy C-means clustering and the random forest. To boost the model's performance, we incorporated PCA, fuzzy c-means, and the Random Forest approach. Various algorithmic combinations were employed, and the results unequivocally demonstrate that our model surpasses the original outcomes of the Pima Indian Diabetes Dataset in terms of accuracy. The diabetic prediction model achieved a remarkable accuracy of 97.40\% through the utilization of PCA, logistic regression, and K-Means. However, when employing PCA in conjunction with fuzzy C-means and random forests, an even higher accuracy of 98.96\% was attained. Empirical evidence confirms that the implementation of PCA significantly enhanced the accuracy of both the fuzzy C-means clustering approach and the random forest classifier, deviating from previous findings. To improve the model's performance, we used PCA, fuzzy c-means, and the Random Forest approach. Different algorithm combinations were used, and the results clearly show that our model outperforms the original Pima Indian Diabetes Dataset outcomes in terms of accuracy. The diabetic prediction model was improved to 97.40% accuracy using PCA, logistic regression, and K -Means. Using PCA with fuzzy C-means and random forests, however, we achieved an accuracy of 98.96%. Based on empirical evidence, it has been demonstrated that the implementation of PCA improved the accuracy of the fuzzy C-means clustering approach and the random forest classifier. These findings differ from previous findings.