Showing papers in "Applied Soft Computing in 2021"

TL;DR: Experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.

TL;DR: An AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia and is able to overcome a series of challenges in this particular situation and deploy the system in four weeks.

TL;DR: Two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images and it is proved that the proposed architecture shows outstanding success in infection detection.

TL;DR: The proposed WMSDE can avoid premature convergence, balance local search ability and global search ability, accelerate convergence, improve the population diversity and the search quality, and is compared with five state-of-the-art DE variants by 11 benchmark functions.

TL;DR: A novel emotion recognition method based on a novel deep learning model (ERDL) which fuses graph convolutional neural network (GCNN) and long-short term memories neural networks (LSTM) and achieves better classification results than state-of-the-art methods.

TL;DR: This paper introduces automatic fake news detection approach in chrome environment on which it can detect fake news on Facebook, and uses multiple features associated with Facebook account with some news content features to analyze the behavior of the account through deep learning.

TL;DR: An ensemble deep learning model can better meet the rapid detection requirements of the novel coronavirus disease COVID-19 and was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient.

TL;DR: In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly.

TL;DR: This paper used topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA.

TL;DR: This work presents a collaborative federated learning framework allowing multiple medical institutions screening COVID-19 from Chest X-ray images using deep learning without sharing patient data, and investigates several key properties and specificities of federatedLearning setting including the not independent and identically distributed (non-IID) and unbalanced data distributions that naturally arise.

TL;DR: This study addresses the prioritization of risks involved with self-driving vehicles by proposing new hybrid MCDM methods based on the Analytic Hierarchy Process (AHP), the Technique for order preference by similarity to an ideal solution (TOPSIS) and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) under Pythagorean fuzzy environment.

TL;DR: A novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction and mean–variance (MV) model for portfolio selection that is superior to traditional ways and benchmarks in terms of returns and risks.

TL;DR: In this article, the authors used time series models (ARIMA and SARIMA) to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are located.

TL;DR: An improved equilibrium optimization algorithm (IEOA) combined with a proposed recycling strategy for configuring the power distribution networks with optimal allocation of multiple distributed generators for enhanced distribution system performance, quality and reliability is proposed.

TL;DR: The results in different scenarios demonstrate that as compared with several existing evolutionary algorithms, the CSA method can effectively explore the decision space and produce competitive results in terms of various performance evaluation indicators.

TL;DR: A Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique under an intuitionistic fuzzy environment to rank insurance companies and yielded ten insurance companies ranking in terms of healthcare services in the era of COVID-19.

TL;DR: This is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes in COVID-19, a deadly viral infection that has brought a significant threat to human lives.

TL;DR: In this paper, an improved tunicate swarm algorithm (ITSA) was proposed for solving and optimizing the dynamic economic emission dispatch (DEED) problem, which aims to reduce the fuel cost and pollutant emission of the power system.

TL;DR: In this paper, a hotel recommendation system using sentiment analysis of the hotel reviews, and aspect-based review categorization is proposed, which is based on the queries given by a user and follows a systematic approach which first uses an ensemble of a binary classification called Bidirectional Encoder Representations from Transformers (BERT) model with three phases for positive-negative, neutral-negative and neutral-positive sentiments merged using a weight assigning protocol.

TL;DR: In this paper, a spherical vector-based particle swarm optimization (SPSO) algorithm is proposed to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the drone.

TL;DR: The results indicate that the LSTM deep-learning method outperforms the feed forward and feedback neural networks based on both accuracy and the convergence rate when reproducing the soil’s stress–strain behaviour.

TL;DR: The experimental test results indicated that the proposed deep learning ensemble model was generally more competitive when addressing imbalanced credit risk evaluation problems than other models.

TL;DR: Wang et al. as discussed by the authors presented a novel framework for disposing the problem of transfer diagnosis with sparse target data, and the main idea is to pair the source and target data with the same machine condition and conduct individual domain adaptation so as to alleviate the lack of target data.

TL;DR: This paper aims to use capsule neural networks in the fake news detection task, using different embedding models for news items of different lengths and outperforming the state-of-the-art methods on ISOT and LIAR.

TL;DR: A discrete variation of the Distributed Grey Wolf Optimizer (DGWO) for scheduling dependent tasks to VMs for maximizing the utilization of Virtual Machines (VMs) in cloud computing environments.

TL;DR: A clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series and is suitable for identifying anomalous amplitude and shape patterns in various application domains such as health care, weather data analysis, finance, and disease outbreak detection.

TL;DR: ISBPSO adopts three new mechanisms based on a recently proposed binary PSO variant, sticky binary particle swarm optimization (SBPSO), to improve the evolutionary performance and substantially reduces the computation time compared with benchmark PSO-based FS methods.

TL;DR: The results show the viability of the proposed approach which yields Bozcaada as the appropriate site, when compared to and validated using the other multi-criteria decision-making techniques from the literature, including IRN based MABAC, WASPAS, and MAIRCA.

TL;DR: A novel dual attention method called DanHAR is proposed, which introduces the framework of blending channel attention and temporal attention on a CNN, demonstrating superiority in improving the comprehensibility for multimodal HAR.

TL;DR: This work introduces CTF, a large-scale COVID-19 Twitter dataset with labelled genuine and fake tweets, and proposes Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model which partially generalises to emerging fake news as it learns from relevant external knowledge.