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Aerobiology is a branch of biology that studies microorganisms passively transferred by the air. Bacteria, viruses, fungal spores, tiny insects, and pollen grains are samples of microorganisms. Pollen grains classification is essential in medicine, agronomy, economy, etc. It is performed traditionally (manually) and automatically. The automated approach is faster, more accurate, cost-effective, and with less human intervention than the manual method. In this paper, we introduce a Residual Cognitive Attention Network (RCANet) for the automated classification of pollen grains microscopic images. The suggested attention block, Ventral-Dorsal Ateetntion Block (VDAB), is designed based on the ventral (temporal) and dorsal (parietal) pathways of the occipital lobe. It is embedded in each Basic Block of the architecture of ResNet18. The VDAB is composed of ventral and dorsal attention blocks. The ventral and dorsal streams detect the structure and location of the pollen grain, respectively. According to the mentioned pathways, the Ventral Attention Block (VAB) extracts the channels related to the shape of the pollen grain, and the Dorsal Attention Block (DAB) is focused on its position. Three publicly pollen grains datasets including the Cretan Pollen Dataset (CPD), Pollen13K, and Pollen23E are employed for experiments. The ResNet18 and the proposed method (RCANet) are trained on the datasets and the proposed RCANet obtained higher performance metrics than the ResNet18 in the test step. It achieved weighted F1-score values of 98.69%, 97.83%, and 98.24% with CPD, Pollen13K, and Pollen23E datasets, respectively.

Abstract Approaches developed based on the blockchain concept can provides a framework for the realization of open science. The traditional centralized way of data collection and curation is a labor-intensive work that is often not updated. The fundamental contribution of developing blockchain format of microbial databases includes: 1. Scavenging the sparse data from different strain database; 2. Tracing a specific thread of access for the purpose of evaluation or even the forensic; 3. Mapping the microbial species diversity; 4. Enrichment of the taxonomic database with the biotechnological applications of the strains and 5. Data sharing with the transparent way of precedent recognition. The plausible applications of constructing microbial databases using blockchain technology is proposed in this paper. Nevertheless, the current challenges and constraints in the development of microbial databases using the blockchain module are discussed in this paper. Graphical Abstract

This paper aims to employ Machine Learning (ML) classifying algorithms to predict whether the patient has Parkinson's Disease (PD) or not. Motor disorders mainly characterize PD, and consequently, a variety of data sets are recorded from the motor system. These data sets consist of either physical behaviors of patients or neuroimaging data captured from their brains. However, the disease mostly begins years before the motor symptoms. Consequently, non-motor symptoms have been studied more in the last decade. Since about 90% of patients experience vocal disorders in the early stages, these symptoms can be more useful for diagnosing the disease. We will review data sets developed for PD diagnosis and some machine learning classification models applied to these data sets. We will offer some models to accurately predict PD according to vocal symptoms characteristics provided in the UCI Machine Learning database, which suffers a low number of samples compared to features and being imbalanced. The results of comparative studies demonstrate that the proposed classic classification models can outperform various Deep learning methods that have been previously used in the literature. The accuracy of 97.22% was obtained by using Logistic Regression and Voting algorithms.

Abstract Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.

Problem: The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). Aim: This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). Methods: A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. Results: For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. Conclusion: While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.