SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

<span lang="EN-US">In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpose, we develop a customer churn prediction approach with the three intelligent models Random Forest (RF), AdaBoost, and Support Vector Machine (SVM). This approach achieves the best result when the Synthetic Minority Oversampling Technique (SMOTE) is applied to overcome the unbalanced dataset and the combination of undersampling and oversampling. The method on SMOTED data has produced excellent results with a 91.90 F1 score and overall accuracy of 88.7% using RF. Furthermore, the experimental results show that RF yielded good results for the full feature-selected datasets.</span>

Recently, high dynamic range (HDR) imaging has received significant attention from research community as well as the industrial companies due to valuable applications of HDR images in better visualization and analysis. However, HDR images need to be converted to low dynamic range (LDR) images for viewing on standard LDR display screens. Several tonemapping operators have been proposed for the conversion, however, so far, no significant works have been reported employing artificial intelligence to achieve better enhancement of the output images. In this paper, we present an optimizationbased approach, to enhance the quality of the tone-mapped LDR images using metaheuristics. More specifically, the optimization process is based on the differential evolution (DE) algorithm which takes tone-mapping function of an existing histogrambased method as initial guess and refines the histogram bins iteratively leading to progressive enhancement of the quality of LDR image. The final results produced by the proposed optimized histogram-based approach (OHbA) showed better performance compared to the existing state-of-the-art tonemapping algorithms.

E-health is an exciting area of research with immense potential for benefit of mankind. The research in e-health domain is of such a nature that it provides an opportunity for researchers from different fields to work together for collective research benefit. Antenatal care is a critical issue for the health of the mother and new born. There are multiple complications which can arise during the pregnancy. The availability of all the relevant data for effective diagnosis and treatment is very important for every case of antenatal care. In this paper, we have proposed an efficient Intelligent Electronic Medical Record system for antenatal care and used Case-Based Reasoning to predict diagnosis of the patient in noisy environment. The experimental results obtained have confirmed the soundness of our approach. These results have also shown that performance of e-health can be enhanced when system development is a collective effort of various stakeholders including medical professionals, software developers and researchers.