Showing papers by "Nasreen Badruddin published in 2021"
TL;DR: In this paper, the authors presented the one resistive-capacitance (1RC) battery model with simple parameterization technique for nickel metal hydride (NiMH), which offers a good trade-off between accuracy and parameterization effort.
Abstract: Modeling the behavior of the battery is non-trivial. Nevertheless, an accurate battery model is required in the design and testing of systems such wireless sensor network (WSN) and internet of things (IoT). This paper presents the one resistive-capacitance (1RC) battery model with simple parameterization technique for nickel metal hydride (NiMH). This model offers a good trade-off between accuracy and parameterization effort. The model’s parameters are extracted through the pulse measurement technique and implemented in a physical and dynamic simulator. Finally, the performance of the model is validated with the real-life NiMH battery by applying current pulses and real wireless sensor node current profiles. The results of the voltage response obtained from both the model and experiments showed excellent accuracy, with difference of less than 2%.
2 citations
27 Jul 2021
TL;DR: In this article, a machine learning model was proposed to classify patients into different levels of cognitive frailty using parameters from blood samples, and a total of seven different classification algorithms were used to predict between 6 levels of CF, the Robust and Non-Robust groups, as well as the robust and Frail with MCI groups.
Abstract: Cognitive Frailty (CF) is a prevalent age-related disease that is affecting many individuals worldwide. Medical intervention needs to be timely, as the late stages of CF prove to be challenging for both clinicians and caretakers. While the existing clinical diagnosis and screening tools for CF are capable of detecting the syndrome, a means of prediction is needed in order to identify CF in older adults before its onset. This paper proposes a machine learning model to classify patients into different levels of CF, using parameters from blood samples. A total of 7 different classification algorithms were used to predict between 6 levels of CF, the Robust and Non-Robust groups, as well as the Robust and Frail with MCI groups. The binary classification for Robust and Frail with MCI achieved the highest accuracy, with Gaussian Naive Bayes showing the highest holdout method accuracy of 70.5%, as well as the highest cross validation accuracy of 74%.
2 citations
TL;DR: In this article, the authors proposed two algorithms that combine unsupervised eye blink artifact detection with modified EMD and Canonical Correlation Analysis (CCA) to automatically identify eye blink artifacts and remove them in an online setting.
2 citations
28 May 2021
TL;DR: In this paper, a MATLAB-based BLINKER algorithm was used to extract ocular characteristics from EEG signals and a decision tree model was used in feature selection and a Bayesian optimized ensemble bagged classifier was used for the prediction.
Abstract: One of the reasons for fatal road accidents is sleeping behind the wheel, and there are numerous methods developed to prevent these accidents. The proposed method uses a MATLAB-based BLINKER algorithm to extract ocular characteristics from EEG signals. The classification model makes this method unique to detect drivers' drowsiness using eye blinks. The decision tree model is used in feature selection and a Bayesian optimized ensemble bagged classifier for the prediction. The predictive classification model gives 88.1% accuracy.
1 citations