Data uncertainty can be produced by several variables, including measurement and sampling mistakes, sensor networks, environmental monitoring, and medical diagnostics. The goal of this study is to classify uncertain data. Classifying uncertain data is critical for maintaining data quality, improving decision-making, optimizing system efficiency, and increasing predictive accuracy. Addressing data uncertainty thoroughly ensures that systems and processes run smoothly and provide accurate, actionable insights. To discover uncertainty data, we proposed a hybrid model based on two well-known deep learning approaches (CNN and ANN). In this work, the classification of the Internet of Things (IoT) data has been done, especially healthcare data. According to the findings in this work, the outcome of the proposed hybrid model (CNN + ANN) has the best results and boasts the best success rate in comparison to the traditional machine learning-based methods in terms of performance. The results of the proposed hybrid model based on famous evolution metrics (Accuracy, Precision, Recall, and F-Score) are (97 %, 96 %, 94%, and 95 %) respectively. Machine Learning and Deep Learning, etc are one of the application of the study. Our work treats classification for uncertain data. For this purpose, a collection of people’s Blood Glucose Levels (BGL) and numbers for some of their most noticeable body parts make up the dataset. Proposed a new hybrid model that combines CNN and ANN models. We have looked at measurements to see how the proposed method outperforms well-known machine learning algorithms. Finally, we have evaluated the proposed model, and based on conventional performance metrics, the tests also demonstrate that the suggested strategy finds ambiguous data more effectively than alternative approaches.

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D. M. Hussein, K. Barznji, and N. S. Mohammed “Accurate Uncertainty Dataset Classification Using Hybrid Deep Learning Models,” International Journal of Applied and Physical Sciences, vol. 10, pp. 22-30, 2024. Doi: https://dx.doi.org/10.20469/ijaps.10.50003