Changes in the feeding ecology of an opportunistic predator inhabiting urban environments in response to COVID-19 lockdown.

Urban-dwelling species present feeding and behavioural innovation that enable them to adjust to anthropogenic food subsidies available in cities. In 2020, the SARS-CoV-2 virus outbreak resulted in unprecedented reduction in the human activity worldwide associated with the human lockdown. This situation opened an excellent opportunity to investigate the capability of urban wildlife to cope with this anthropopause event. Here, we investigated the effects of the COVID-19 lockdown on the feeding strategies of the urban yellow-legged gull (Larus michahellis) population inhabiting the highly dense city of Barcelona (NE Spain). We compared the diet of chicks (through stomach content and stable isotope analyses) sampled randomly around the city of Barcelona before (2018 and 2019), during (2020) and after (2021) the COVID-19 lockdown. The results revealed that the anthropopause associated with the lockdown had an effect on the diet of this urban-dwelling predator. The diversity of prey consumed during the lockdown was lower, and consumption of urban birds (pigeons and parakeets) and marine prey (fishery discards and natural prey) decreased during the year of lockdown. Although it was not analysed, these diet changes probably were associated with variations in the availability of these resources due to the decrease in human activity during the lockdown. These results demonstrate the trophic flexibility of urban-dwelling species to cope with the changes in the availability of human-related anthropogenic resources in urban marine ecosystems.

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans.

This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.

Generalized Type-2 Fuzzy Parameter Adaptation in the Marine Predator Algorithm for Fuzzy Controller Parameterization in Mobile Robots

This article is oriented to the application of generalized type-2 fuzzy systems in the dynamic adjustment of the parameters of a recent metaheuristic based on nature that follows the rules of the best feeding strategies of predators and prey in ecosystems. This metaheuristic is called fuzzy marine predator algorithm (FMPA) and is presented as an improved variant of the original marine predator algorithm (MPA). The FMPA balances the degree of exploration and exploitation through its iterations according to the advancement of the predator. In the state of the art, it has been shown that type-2 fuzzy increases metaheuristic performance when adapting parameters, although there is also an increase in the execution time. The FMPA with generalized type-2 and interval type-2 parameter adaptations was applied to a group of benchmark functions introduced in the competition on evolutionary computation (CEC2017);the results show that generalized FMPA provides better solutions. A second case for FMPA is also presented, which is the optimal fuzzy control design, in the search for the optimal membership function parameters. A symmetrical distribution of these functions is assumed for reducing complexity in the search process for optimal parameters. Simulations were carried out considering different degrees of noise when analyzing the performance when simulating each of the used fuzzy methods.

Machine Intelligent Techniques for COVID-19 Detection: A Critical Review and Analysis

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

Convolution Neural network based Marine Predator Algorithm for COVID-19 detection

COVID-19 is among the dangerous illness in the world due to its quickly spreading, posing a new challenge for researchers to discover it early. In last few months, new covid19 virus strains have been found in South Africa, India, and United Kingdom (UK) due to the mutation of the virus. Owing this critical situation of the world health and with increased number of the cases with the absence of efficient a cure vaccine, timely quarantine and medical treatment, as well as reliable identification of COVID-19, are required to prevent and contain this pandemic. Radiology images and Artificial Intelligence techniques are the most used techniques in computer-aided medical diagnosis for Covid-19 detection. The present paper shows a Convolution Neural Network based novel metaheuristic techniques called Marine Predator Algorithm for detecting Covid-19 and well differentiate between Covid-19 and Pneumonia disease. Our proposed system achieves good results in term of classification such as 93% of accuracy, 95% of precision, 97% of recall and F1-score 95%. © 2021 IEEE.