Detecting Outliers in Non-IID Data: A Systematic Literature Review

Siddiqi, Shafaq; Qureshi, Faiza; Lindstaedt, Stefanie; Kern, Roman

doi:10.1109/access.2023.3294096

Cited by 2 publications

(1 citation statement)

References 107 publications

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“…In recent research, the application of AI techniques to non-IID medical data has garnered substantial attention. Data oversampling approaches have been explored to mitigate the impact of non-IID characteristics [11], [12]. Oversampling strategies leverage knowledge gained from one domain to enhance performance in another, addressing the challenge of limited labeled data in non-IID settings.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Putra,

Prasetyo Kusumo,

Prayitno

et al. 2024

JOIV : Int. J. Inform. Visualization

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Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

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Section: Introductionmentioning

confidence: 99%

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Putra,

Prasetyo Kusumo,

Prayitno

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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Predicting Crop Yield Productivity Using Machine Learning Algorithms: A Comparison of Linear and Non-linear Approaches

Zeer,

Abu Sara,

Alkhateeb

et al. 2024

Ahliya Journal of Business Technology and MEAN Economies

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Predicting crop yield productivity is crucial for farmers and the agricultural sector to gain insights into crop productivity and returns. With advancements in technology and artificial intelligence, predicting crop yield using machine learning algorithms has become an important innovation. This study aimed to predict crop yield productivity using various machine learning algorithms and techniques. The dataset was sourced from Kaggle, preprocessed, and analyzed using linear algorithms such as Linear Regression, LASSO, and Ridge, as well as non-linear algorithms including SVR, KNN Regressor, and Polynomial Regression. Mean Squared Error (MSE) was computed to evaluate algorithms performance. Comparing the efficacy of linear versus non-linear algorithms on the dataset revealed that non-linear algorithms outperformed linear ones, indicating that the dataset's non-linear nature. Therefore, non-linear machine learning algorithms like SVR, KNN Regressor, and Polynomial Regression were recommended for better accuracy. Among these, KNN Regressor performed the best with an MSE of 0.00025, followed by SVR and Polynomial Regression with MSE values of 0.00142 and 0.0024, respectively.

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Detecting Outliers in Non-IID Data: A Systematic Literature Review

Cited by 2 publications

References 107 publications

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Predicting Crop Yield Productivity Using Machine Learning Algorithms: A Comparison of Linear and Non-linear Approaches

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