Arnisha Akhter

TL;DR: In this paper , a hybrid ensemble feature extraction model was proposed to detect lung and colon cancer using deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets, which achieved an accuracy of 99.05%, 100%, and 99.30% for lung, colon, and (lung and colon) cancer.

TL;DR: In this paper, a deep learning-based COVID-19 case detection model was developed with a dataset consisting of chest CT scans and X-ray images, which achieved an accuracy of 96.452% for four-class cases (COVID,19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class case (coVID-, normal controls and confirmed viral and bacterial pneumonia cases).

TL;DR: In this article , a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability was proposed, which ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection.

TL;DR: In this article , a CNN-based method to detect distracted drivers and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization was proposed, which was trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics.

TL;DR: The novel noise aware Level Based Routing Protocol (LBRP) aims to provide a noise aware, level based and energy efficient under water routing protocol.