•This study aims to provide a fast secure multiple-image encryption (MIE) algorithm.•Image matrix indexes and DNA sequence are basis of the proposed algorithm.•Proposed algorithm is fast and secure.
...To improve the encryption quality and increase the speed of transmission over the internet, this study aims to provide a fast secure multiple-image encryption (MIE) algorithm based on DNA sequence and image matrix indexes. Because multiple images are considered in the proposed MIE algorithm, one big concern refers to the speed of the algorithm. In the first phase of the proposed method, multiple plain- images are attached together to create a single image. Next, this image is converted to one-dimension array. Half of the array indexes are used to permute all the pixels position. During the permutation, the same indexes are associated with DNA sequence to diffuse the pixels gray level. Simulation results demonstrate that using half of indexes for permutation and diffusion make the proposed algorithm very fast and also using DNA sequence encoding gives the algorithm enough power to resist against common attacks in the era of image encryption.
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Sign languages play an essential role in the cognitive and social development of the deaf, consisting of a natural form of communication and being a symbol of identity and culture. However, hearing ...loss has a severe social impact due to an existing communication barrier, preventing access to essential services such as education and health. A bi-directional sign language translation may be the solution to bridging the communication gap between the deaf and the listener, completing a two-way communication cycle. Virtual personal assistants can benefit from this technology by extending how users interact with the intelligent system. With this idea, in this work we develop a multi-stream deep learning model to recognize signs of Brazilian (BSL), Indian (ISL), and Korean (KSL) Sign Languages. We combine different types of information for the classification task, using single-stream and multi-stream 3D Convolutional Neural Networks. In addition, considering the largest source of sign data globally – the internet – we propose a depth sensor-free classification method, with depth maps artificially generated through Generative Adversarial Networks. In order to consider the main parameters that encode sign languages, the final architecture is composed of a multi-stream network that receives the segmented hands, the faces, the distances and speeds of the points of articulation, and the RGB frames associated with artificial depth maps. Finally, we provide a visual explanation to understand which regions were important for model decision-making. The best models were obtained using the multi-stream network, presenting an accuracy of 0.91 ± 0.07, and f1-score of 0.90 ± 0.08 on publicly available BSL data set. The results suggest that the multi-stream network with artificially generated depth maps is suitable for the task of sign recognition in different languages.
•Multi-stream deep learning model to recognize signs of multiple Sign Languages.•We propose a depth sensor free classification method for sign language.•Depth maps are artificially generated with Generative Adversarial Networks.•We provide a visual explanation to understand the model and its decisions.
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We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in the proposed method, ...multivariate time series data which include hourly load data, hourly temperature time series and fuzzified version of load time series, was converted into multi-channel images to be fed to a proposed deep learning CNN model with proper architecture. By using images which have been created from the sequenced values of multivariate time series, the proposed CNN model could determine and extract related important parameters, in an implicit and automatic way, without any need for human interaction and expert knowledge, and all by itself. By following this strategy, it was shown how employing the proposed method is easier than some traditional STLF models. Therefore it could be seen as one of the big difference between the proposed method and some state-of-the-art methodologies of STLF. Moreover, using fuzzy logic had great contribution to control over-fitting by expressing one dimension of time series by a fuzzy space, in a spectrum, and a shadow instead of presenting it with exact numbers. Various experiments on test data-sets support the efficiency of the proposed method.
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A fuzzy deep learning neural network is developed for short-term load forecastingFeatures of multi-variate time series are encapsulated inside image dataUsing images to represent time series data succeeded in forecastingThe proposed method could achieve 22.84% better accuracy than LSTM
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•This paper using modified genetic algorithm (MGA) and coupled map lattices (CML), simultaneously.•MGA employs CML to generate its initial population.•This presented method outperforms recent ...available methods in literature.
Image data are considered as significant data in medical systems. The amount of medical image data available for analysis keeps increasing with the modernization of image devices and biomedical image processing techniques. To prevent from being hacked over an insecure network, medical images should be encrypted safely. This study aims at proposing a medical image encryption method based on a hybrid model of the modified genetic algorithm (MGA) and coupled map lattices. First, the proposed method employs coupled map lattice to generate the number of secure cipher-images as initial population of MGA. Next, it applies the MGA to both increase the entropy of the cipher-images and decrease the algorithm computational time. Experimental results and computer simulations both indicate that the proposed method that includes a hybrid algorithm not only performs excellent encryption but also is able to resist to various typical attacks.
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•Overview of multi-agent approach for smart-microgrid energy management and operation.•Different state-of-the-art microgrid sub-problems tackled by MAS are revised and presented.•Trends for future ...decentralized energy storage and service restoration in microgrids are highlighted.•Future MAS applications requested by efficient microgrid integration into the grid.
Mini/microgrids are a potential solution being studied for future systems relying on distributed generation. Given the distributed topology of the emerging smart grid systems, different solutions have been proposed for integrating the new components ensuring communication between existing ones. The multi-agent systems paradigm has been advocated as a useful and promising tool for a wide range of applications. In this paper, the major issues and challenges in multi-agent system and smart microgrids are discussed. We present a review of state-of-the-art applications and trends. By discussing the possibilities considering what has been done, future applications, with attention to renewable energy resources integration in emerging scenarios, are placed on the agenda. It is suggested that further studies keep growing in this direction, which will be able to decentralize the high complex energy system, allowing users to participate in the system more actively. This step may decentralize the infrastructure, giving more weight to society wishes, as well as facilitating maintenance, reducing costs and opening a the door for innovative ideas for low-cost based equipment. On the other hand, letting several combinatorial optimization problems opened to be improved and discussed along the next coming years.
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The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on ...Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1–score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.
•A literature review of ML methods applied to COVID-19 screening in routine blood tests.•Results from different ML techniques - including an ensemble - to support the diagnosis of COVID-19 using usual blood exams.•A decision tree-based methodology for the explanation of the model which can be given to the health teams.•Individual explanations in a graph that shows the relative importance of each attribute and their interactions.•Further evidence that simple blood tests might help identifying false positive/negative RT-PCR tests.
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•The Capacitated Location-Routing Problem considering environmental impact is proposed.•A new model for computing greenhouse gas emissions in vehicle routing is proposed.•The Green CLRP is formulated ...as a bi-objective mixed integer linear programming.•Using more vehicles can lead to large fuel economy in the long term and hence less emission.•More vehicles in shorter routes and prioritizing high demand clients lead to less emission.
The Capacitated Location-Routing Problem (CLRP) is a strategic-level problem involving the selection of one or many depots from a set of candidate locations and the planning of delivery routes from the selected depots to a set of customers. During the last few years, many logistics and operations research problems have been extended to include greenhouse effect issues and costs related to the environmental impact of industrial and transportation activities. In this paper a new mathematical model for the calculation of greenhouse gas emissions is developed and a new model for the CLRP considering fuel consumption minimization is proposed. This model, named Green CLRP (G-CLRP), is represented by a mixed integer linear problem, which is characterized by incorporating a set of new constraints focused on maintaining the problem connectivity requirements. The model proposed is formulated as a bi-objective problem, considering the minimization of operational costs and the minimization of environmental effects. A sensitivity analysis in instances of different sizes is done to show that the proposed objective functions are indeed conflicting goals. The proposed mathematical model is solved with the classical epsilon constraint technique. The results clearly show that the proposed model is able to generate a set of tradeoff solutions leading to interesting conclusions about the operational costs and the environmental impact. This set of solutions is useful in the decision process because several planning alternatives can be considered at strategic level.
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Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model ...with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R.sub.0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R.sub.0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.
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Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the ...accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy Time Series (FTS) with SARFIMA is proposed. To build the proposed model, certain parameters requires to be estimated. Therefore, a reliable Evolutionary Algorithm namely Particle Swarm Optimization (PSO) is employed. As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. In order to evaluate the proposed method, some experiments, using eight datasets of half-hourly load data from England and France for the year 2005 and four data sets of hourly load data from Malaysia for the year 2007, are designed. Although the focus of this research is STLF, six other seasonal long memory time series from several interesting case studies are employed to better evaluate the performance of the proposed method. The results are compared with some novel FTS methods and new state-of-the-art forecasting methods. The analysis of the results indicates that the proposed method presents higher accuracy than its counterparts, representing an efficient hybrid method for load forecasting problems.
•To increase accuracy of forecasts inside seasonal long memory time series, a hybrid method is proposed.•The proposed method is based on a combination of Fuzzy Time Series and SARFIMA.•High-order Fuzzy Time Series is adopted to be revised for developing the proposed method.•Particle Swarm Optimization is applied for parameters estimation.•Many long memory seasonal datasets, including short term load data are employed for evaluation purpose.
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•We introduce a cooperative coevolutionary algorithm for the Multi-Depot VRP.•We propose an ES with variable length genotype coupled with local search operators.•The proposed approach produces ...high-quality solutions in low computational time.•The performance is not greatly affected by the overlap between subproblems.•The proposed method could find improved solutions in many instances.
The Multi-Depot Vehicle Routing Problem (MDVRP) is an important variant of the classical Vehicle Routing Problem (VRP), where the customers can be served from a number of depots. This paper introduces a cooperative coevolutionary algorithm to minimize the total route cost of the MDVRP. Coevolutionary algorithms are inspired by the simultaneous evolution process involving two or more species. In this approach, the problem is decomposed into smaller subproblems and individuals from different populations are combined to create a complete solution to the original problem. This paper presents a problem decomposition approach for the MDVRP in which each subproblem becomes a single depot VRP and evolves independently in its domain space. Customers are distributed among the depots based on their distance from the depots and their distance from their closest neighbor. A population is associated with each depot where the individuals represent partial solutions to the problem, that is, sets of routes over customers assigned to the corresponding depot. The fitness of a partial solution depends on its ability to cooperate with partial solutions from other populations to form a complete solution to the MDVRP. As the problem is decomposed and each part evolves separately, this approach is strongly suitable to parallel environments. Therefore, a parallel evolution strategy environment with a variable length genotype coupled with local search operators is proposed. A large number of experiments have been conducted to assess the performance of this approach. The results suggest that the proposed coevolutionary algorithm in a parallel environment is able to produce high-quality solutions to the MDVRP in low computational time.
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