A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

Four new species of the genus Pseudosubhimalus (Hemiptera: Cicadellidae) from the Indian subcontinent.

Four new species of leafhoppers, Pseudosubhimalus asymmetricus sp. nov. (Himachal Pradesh: Katrain), P. dalangensis sp. nov. (Himachal Pradesh: Dalang), P. katraini sp. nov. (Himachal Pradesh: Katrain), P. lachungensis sp. nov. (Sikkim: Lachung), are described from the Indian subcontinent. A checklist and key to the species of Pseudosubhimalus are provided.

Tribe reassessment of the subhimalayan leafhopper genus Pseudosubhimalus (Homoptera: Cicadellidae) based on molecular phylogeny.

The phylogeny of the Pseudosubhimalus were investigated using of two different data sets, including 91 taxa and 3853 aligned nucleotide positions from the histone H3, 28S rDNA (D2 & D9-10 region). The results suggest the placement of genus in the tribe Ciacadulini, as it was clustered with Cicadulini genera. Relationships between genera of the Cicadulini were strongly supported and leads placement to tribe Cicadulini from Athysanini. Along with this, genus Pseudosubhimalus Ghauri is revised, and P. trilobatus sp. nov. (Himachal Pradesh: Katrain) is added, described from Indian subcontinent and deposited to National Pusa Collection, IARI, New Delhi, with repository number RRS1.