Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria.
View Article and Find Full Text PDFThe quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape.
View Article and Find Full Text PDFAlzheimer's is one of the brain syndromes that steadily affects the brain memory. The early stage of Alzheimer's disease (AD) is referred to as mild cognitive impairment (MCI), and the growth of Alzheimer's is not certain in patients with MCI. The premature detection of Alzheimer's is crucial for maintaining healthy brain function and avoiding memory loss.
View Article and Find Full Text PDFAutomatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.
View Article and Find Full Text PDFThe power system incorporates renewable energy resources into the main utility grid, which possesses low or no inertia, and these systems generate harmonics due to the utilization of power electronic equipment. The precise and effective assessment of harmonic characteristics is necessary for maintaining power quality in distributed power systems. In this paper, the Marine Predator Algorithm (MPA) that mimics the hunting behavior of predators is exploited for harmonics estimation.
View Article and Find Full Text PDFPeerJ Comput Sci
December 2023
Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training.
View Article and Find Full Text PDFIn this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels.
View Article and Find Full Text PDFIn this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples' nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary.
View Article and Find Full Text PDFGastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck.
View Article and Find Full Text PDFObjective: Drug resistance from affordable drugs has increased the number of deaths from malaria globally. This problem has raised the requirement to design new drugs against multidrug-resistant Plasmodium falciparum parasite.
Methods: In the current project, we have focused on four important proteins of Plasmodium falciparum and used them as receptors against a dataset of four anti-malarial drugs.