EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices.

Hyperspectral imaging, combined with deep learning techniques, has been employed to classify maize. However, the implementation of these automated methods often requires substantial processing and computing resources, presenting a significant challenge for deployment on embedded devices due to high GPU power consumption. Access to Ghanaian local maize data for such classification tasks is also extremely difficult in Ghana.

One pot fabrication of diamino naphthalene -AuNPs decorated graphene nanoplatform for the MRSA detection in the biological sample.

Early monitoring of MRSA can effectively mitigate the disease risk by using Penicillin-binding protein 2a (PbP2a) biomarker. Diamino naphthalene-AuNPs decorated graphene (AuNPsGO-DN) nanocomposite was synthesized for a rapid and sensitive immunosensor detecting PbP2a. The synthesized AuNPsGO-DN nanocomposites were characterized by field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (SEM-EDX), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, and X-ray diffraction spectroscopy (XRD).

CLAHE-CapsNet: Efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization.

Manual detection of eye diseases using retina Optical Coherence Tomography (OCT) images by Ophthalmologists is time consuming, prone to errors and tedious. Previous researchers have developed a computer aided system using deep learning-based convolutional neural networks (CNNs) to aid in faster detection of the retina diseases. However, these methods find it difficult to achieve better classification performance due to noise in the OCT image.

CCMT: Dataset for crop pest and disease detection.

Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others.

Uncertainty Estimation Using Variational Mixture of Gaussians Capsule Network for Health Image Classification.

Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with limited ability to express uncertainty. Many of them tend to be overconfident on out-of-distribution data, making them less trustworthy and hence reducing their suitability for practical adoption in safety-critical areas such as health and self-driving cars.

GC3558: An open-source annotated dataset of Ghana currency images for classification modeling.

The field of deep learning has led to remarkable advancements in many areas, including banking. Identifying currency denomination type and model is challenging due to intraclass variation and different illumination conditions. Although, in this domain, many datasets regarding currency denomination type and model, e.

Relaxed Exponential Stabilization for Coupled Memristive Neural Networks With Connection Fault and Multiple Delays via Optimized Elastic Event-Triggered Mechanism.

This article investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection fault of the two or some nodes can result in the connection fault of other nodes and cause iterative faults in the CMNNs. Therefore, the method of backup resources is considered to improve the fault-tolerant capability and survivability of the CMNNs.