Federated Learning (FL) has emerged as an essential tool for computing devices to participate in collaborative training of deep learning models. However, due to the decentralized distribution of data over clients/local computing devices, the class imbalance problem has become evident, causing severe degradation in the performance of the global model. Motivated by the emergence of FL models in emotion recognition, the current study proposes an FL-based facial emotion recognition system by addressing local imbalance data problems encountered in client devices. First, the local imbalance problem is mitigated by utilizing the data-level artificial resampling method on the client side. To address the possibility of an adversarial attack using imbalanced data, the local training is equipped with a pre-training check to verify if the data being used is imbalanced above a predefined threshold of imbalance ratio. In case of high imbalance, a pre-training step will balance the data locally without sharing any information with other participants thereby ensuring privacy in the FL framework. Experiments have been conducted by using benchmark facial emotion recognition data with a balanced testing strategy. It indicated that considerable improvement can be achieved by the proposed FL-based facial emotion recognition model.

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.

Differential evolution (DE) has established itself as a simple but efficient population-based, nonconvex optimization algorithm for continuous search spaces. Unlike the conventional real-coded genetic algorithms (GAs) and evolution strategies (ESs), DE uses a mandatory self-referential mutation for its population members, each of which are perturbed with the scaled difference(s) of the individuals from the current generation (iteration). These difference vectors determine the direction of the search moves for the individuals. However, unlike the better individuals, they are not retained in the elitist evolution cycle of DE. In this paper, we show that by archiving the most promising difference vectors from past generations and then by reusing them for generating offspring in the subsequent generations, we can strikingly improve the performance of DE. This strategy can be integrated with any classical or advanced DE variant with no serious overhead in time or space complexity. We demonstrate that when combined with the DE-based winners of the IEEE Congress on Evolutionary Computation (CEC) 2013, 2014, and 2017 competitions on real parameter optimization, the simple reuse strategy leads to a statistically significant performance improvement in the majority of test cases. We further showcase the efficacy of our proposal on a practical optimization problem concerning the design of circular antenna arrays with a prespecified radiation pattern.

Main objective of this work is to analyze the ability of the Differential Evolution (DE) framework equipped with a multi-donor mutation strategy and annealing-based local search technique to find the global minimum of the potential energy functions, which are used for molecular cluster modeling. Finding such stable molecular clusters is a significant and well-established optimization problem arising from the area of molecular distance geometry and has important implications in artificial drug design as well. Results for moderate (3, 5, 10, 15, 20, 25, and 30 atomic molecules) scale problems are presented here for the Lennard-Jones potential function based atomic clusters. Our experiments reveal that the proposed DE variant is able to yield better results than the competing state-of-art DE based optimizers and the results are with par to the best results listed in the Cambridge energy landscape database (http://doye.chem.ox.ac.uk/jon/structures/LJ.html).

A switched-parameter Differential Evolution (DE) enforced with equiprobable switching between two alternative mutation strategies, an optional blending crossover, and a threshold-based selection mechanism is proposed for optimization of complex functions corrupted with additive noise. In order to handle the noisy optimization problems, the DE framework is coupled with three new algorithmic components. Each individual is subjected to one of the two well known mutation strategies namely DE/best/1 and DE/rand/1 with equal chances. In the recombination stage, binomial and blending crossovers are opted in the same switchable strategy as done for mutation. A novel threshold-based selection mechanism is used to allow less fit offspring to survive occasionally, thus countering the noisy function behavior. Additive Gaussian noise is used to simulate the noisy behavior of functions defined over continuous search spaces. A benchmark suite comprising of 21 well-known numerical functions is considered to compare and contrast the proposed method with other state-of-the-art evolutionary algorithms specifically tailored for noisy optimization scenario. The proposed method shows very competitive performance indicating highly robust behavior against the noisy functional landscapes.