An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification.

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images.

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications.

Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare.

Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease.

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope.

Structural and Optical Characteristics of Highly UV-Blue Luminescent ZnNiO Nanoparticles Prepared by Sol-Gel Method.

A simple single pot sol-gel method is used to prepare ZnNiO nanoparticles at assorted Ni doping levels, 1, 3, 7 and 10 wt.%. Structural and optical properties of nanoparticles are studied by X-ray diffraction (XRD), UV-visible diffuse reflection spectroscopy (DRS), photoluminescence (PL) measurements, scanning electron microscopy (SEM), μ-Raman and X-ray photoelectron-spectroscopy (XPS).