Search

Dependable land cover data are required to aid in the resolution of a broad spectrum of environmental issues. Land cover classification at a broad scale has been carried out using data from traditional ground‐based information from the Advanced Very High‐Resolution Radiometer. From the merits as well as demerits of the existing works discussed in the literature, this article seeks to establish a novel technique for automatic, fast, as well as precise land cover classification deploying remote sensing data. The proposed approach follows feature extraction and classification stages. From input information, the statistical characteristics are extracted as well as they are subjected to classification via optimized deep convolutional neural network. Particularly, the convolutional layers are optimized for by means of a new Proposed Lion Algorithm with a new Cub pool Update (PLACU) approach. The established model is the advanced level of the standard lion algorithm. The superiority of the established technique is determined by the extant techniques regarding positive and negative metrics. The accuracy of the work that is being presented (PLACU) is superior to the existing methods like Dragonfly algorithm, Jaya algorithm, sea lion optimization, and lion algorithm techniques by 20%, 15%, 112%, and 10%, respectively. [ABSTRACT FROM AUTHOR]

Image retrieval aims to locate similar image based on given query from large-sized image dataset. In the recent decades, different methods are developed to retrieve similar images, but increasing the retrieval performance is a challenging task. Hence, an effective method named Lion Henry Gas Solubility Optimization-based Deep Fuzzy Clustering (LHGSO-based DFC) is developed to increase the retrieval efficiency of image retrieval. Based on the features extraction, images are indexed efficiently to the corresponding cluster using clustering model. The proposed method retrieves the relevant images with respect to query image based on the similarity. The similarity measure is computed using Jaro Winkler distance, in which the images corresponding to the cluster that have higher similarity measure is retrieved more effectively. The proposed method achieved higher performance in terms of the metrics, like F -measure, precision and recall with the values of 0.887, 0.891 and 0.882. [ABSTRACT FROM AUTHOR]

During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination. [ABSTRACT FROM AUTHOR]

Pure and aluminum (Al) doped ZnO (Al:ZnO) nanorods (NRs) were deposited on silicon substrates by the hydrothermal method. The Al composition was kept at 2 % and 5 % for the Al:ZnO NR samples. The surface morphology and structural properties of the pure and Al:ZnO NRs were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD), respectively. The XRD study revealed the hexagonal phase of the ZnO with (101), (002) and (100) peaks and it also revealed that the major orientation of ZnO NRs was along the (002) planes. The SEM micrographs showed perfectly grown ZnO NRs with hexagonal shaped tips. The electrical characterization of the pure and Al:ZnO NR thin film surface was done by scanning tunneling microscopy (STM). Local electron spectroscopy was conducted to measure the tunneling current with respect to the applied bias. The n-type behavior and bandgap of the pure and Al:ZnO NRs were confirmed from the dI/dV -- V characteristics. These studies are of fundamental importance for the fabrication of pure and Al:ZnO NR based nanodevices. [ABSTRACT FROM AUTHOR]

This study reports hydrothermally grown zinc oxide nanorod-based metal-semiconductor-metal ultraviolet detectors with palladium metal as the electrodes. The zinc oxide nanorods were deposited on glass substrates in two steps, seed layer deposition and growth of nanorods. The structural and optical properties of nanorods were examined using scanning electron microscopy and ultraviolet–vis spectroscopy, respectively. The scanning electron microscopy image showed that the growth of nanorods was uniform, and the ultraviolet–vis results indicate that the bandgap of zinc oxide nanorods was 3.23 eV. For metal-semiconductor-metal devices, interdigited metal electrodes with equal interelectrode spacing and a width of 0.3 mm were deposited above the zinc oxide nanorod thin films with a shadow mask using a thermal evaporation system. The current–voltage characteristics of the metal-semiconductor-metal detector were investigated and it showed a contrast ratio of approximately 2.10 and responsivity of approximately 0.199 A/W at 1.8 V. These results are expected to be beneficial to fabricating cheap and practical ultraviolet detection applications. [ABSTRACT FROM AUTHOR]

The paper presents the results of our experimental investigation pertaining to tailoring of energy bandgap and other associated characteristics of undoped and Al doped ZnO (AZO) thin film by varying the atomic concentration of Al in ZnO. Thin films of ZnO and ZnO doped with Al (1, 3, and 5 atomic percent (at.%)) were deposited on silicon substrate for structural characterization and on glass substrate for optical characterization. The dependence of structural and optical properties of Al doped ZnO on the atomic concentration of Al added to ZnO has been reported. On the basis of the experimental results an empirical formula has been proposed to calculate the energy bandgap of AZO theoretically in the range of 1 to 5 at.% of Al. The study revealed that AZO films are composed of smaller and larger number of grains as compared to pure ZnO counterpart and density of the grains was found to increase as the Al concentration increased (from 1 to 5 at.%). The transmittance in the visible region was greater than 90% and found to increase with increasing Al concentration up to 5 at.%. The optical bandgap was found to increase initially with increase in atomic concentration of Al concentration up to 3 at.% and decrease thereafter with increasing concentration of Al.