This contribution describes new useful geometric transformations using the tensor product. The geometric transformations are used widely in many applications, especially in CAD/CAM systems, systems for Civil Engineeri...
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In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pa...
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This research study addresses the pressing issue of diabetes prediction using advanced machine learning techniques, presenting the development of an ensemble model that significantly outperforms existing methods by 1....
This research study addresses the pressing issue of diabetes prediction using advanced machine learning techniques, presenting the development of an ensemble model that significantly outperforms existing methods by 1.6% using area under the curve as the primary performance metric. The proposed model achieved an area under the curve (AUC) of 0.946, surpassing the previously best-performing model, extreme gradient boosting (XB) by 0.7%. The improved ensemble model has outrightly outperformed other existing machine learning (ML) models from the literature by 1.6%. This improvement is particularly notable given the challenges posed by outliers and missing values in the dataset, which complicate diabetes prediction. The choice of choosing algorithms such as k-nearest neighbor (KNN), random forest (RF), AdaBoost (AB), and extreme gradient boost (XB) was meticulously informed by their distinct strengths and limitations, which are critical for the efficacy of this research study. KNN is particularly user-friendly, making it accessible for preliminary analyses; however, it exhibits sensitivity to feature scaling; RF, while robust and capable of handling a variety of data distribution, has a propensity for overfitting, which is mitigated through tuning. AB is effective in enhancing the performance of weak learners, yet it may encounter challenges with imbalanced datasets, an aspect that was addressed through soft weighted sampling vote. XB demonstrates remarkable predictive performance, bolstered by its built-in cross-validation and parallel processing; nevertheless, it remains sensitive to outliers, highlighting the importance of thorough data processing. By integrating these algorithms into an ensemble framework, this study effectively mitigated their individual limitations, leading to a more accurate and improved reliable prediction model. The innovative approach of hyperparameter tuning through randomized search further enhanced the model’s performance, marking a signific
In outdoor environment, navigation and localization services are usually provided by different global navigation satellite system (GNSS). However, indoor localization must rely on different solutions since there is no...
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The GALDIT model is used for the assessment of the aquifer vulnerability of Groundwater (GW), but it relies on expert judgment that contains uncertainty and is one of its weaknesses. To tackle the challenge of managin...
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One of the ways humans communicate is by using facial expressions. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person's emotions or ...
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Compiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation pro...
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A WBAN consists of a network of multipurpose nodes with limited storage and limited lifetimes. Keeping these nodes running on their limited battery power is a difficult undertaking. As a result, in any WSN-IoT network...
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In today's fast-paced business environment, making informed decisions is crucial for success. To achieve this, decision-makers are increasingly turning to data-oriented and business intelligence databases. As a re...
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This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the ...
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