This paper addresses the disconnect between classical full implementation theory - which assumes sophisticated agent capable of achieving Nash equilibrium - and the reality of boundedly rational agents who often rely on simple heuristics in decision-making. Drawing on learning theory that demonstrates how elementary adaptive procedures steer behavior toward equilibrium, we investigate the implementation in correlated equilibria (CE).

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.

This article proposes a machine learning augmented technique to predict the coronavirus disease (COVID-19) outbreak in India by combining Internet search trends along with social media data retrieved from Twitter. A comprehensive list of suitable search words has been used to select a large collection of Tweets, and the Internet search trends of the same keywords have been fetched. First, a lag correlation analysis is conducted to find the number of days, ahead of the current time, required to make an accurate prediction of COVID-19 cases. Second, both shallow and deep learning methods are engaged to predict the number of COVID-19 cases in a specific geospatial location in India. Thereafter, statewise air pollution data collected from the Central Pollution Control Board, Government of India, are amalgamated to understand the effect of air pollution in spreading of COVID-19 disease. The air pollution monitoring parameters have been combined to understand their effects in the prediction of COVID-19 cases in the Indian context. Experimental results reveal that accurate predictions can be made 85 days ahead of the current time using the proposed method ($r >0.85$), thereby establishing its ingenuity in the prediction of COVID-19 spread in advance.

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.