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.

There are numerous techniques for identifying users, including cards, passwords, and biometrics. Emerging technologies such as cloud computing, smart gadgets, and home automation have raised users’ awareness of the privacy and security of their data. The current study aimed to utilise the CNN model augmented with various pre-processing filters to create a reliable identification system based on the DHV. In addition, the proposed implementing several pre-processing filters to enhance CNN recognition accuracy. The study used a dataset of 500 hand-vein images extracted from 50 patients, while the dataset training was done using the data augmentation technique. The accuracy of the proposed model in this study in classifying images without using image processing showed that 70% was approved for training. Moreover, the results indicated that using the mean filter to remove the noise gave better results, as the accuracy reached 99% in both training conditions.

A leading cause of mortality globally, skin cancer is deadly. Early skin cancer diagnosis reduces mortality. Visual inspection is the main skin cancer diagnosis tool; however, it is imprecise. Researchers propose deep-learning techniques to assist physicians identify skin tumors fast and correctly. Deep convolutional neural networks (CNNs) can identify distinct objects in complex tasks. We train a CNN on photos with merely pixels and illness labels to classify skin lesions. We train on HAM-10000 using a CNN. On the HAM10000 dataset, the suggested model scored 95.23% efficiency, 95.30% sensitivity, and 95.91% specificity.

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.

In the face of the challenges of the Digital Age and the outbreak of the pandemic COVID-19 which have changed higher education institutions remarkably, universities are urgently required to speed up their digitalization initiatives and cope up with the global digital developments to survive. For the successful implementation of digital transformation, however, data governance should be considered. Despite the extensive literature on data governance and digital transformation, there is no focus on the issue in the Saudi Higher Education institutions. In the face of this, the current study investigates data governance policies and practices in the Saudi Universities. This study is built on a case study design. Nine universities in Saudi Arabia were selected for the purpose of the study. These include public, community, and private universities that make up the Higher Education system in Saudi Arabia. For data collection purposes, three methods were selected. These were the survey, focus group discussions and in-depth interviews. The findings of this study indicate that data governance is an effective tool in the implementation of digital transformation processes in higher education institutions and thus should be embedded into strategies of the universities for using digital technologies in appropriate manners. Good data governance practices are required for smooth and effective digital transformation. Universities are required to create an effective functional team for the data governance tasks, develop an internal audit of data governance, follow-up the regulatory compliance procedures, define the priorities of data governance activities, provide frequent data governance training for employees and faculty members, set enforcement and follow-up standards, and conduct frequent assessments of data governance plans and policies. Although the study is limited to the Saudi universities, results and implications of this study can be a useful reference to choose effective data governance practices that can be successfully adopted and implemented to effectively manage critical information and use it to transform their day-to-day operations.