The COVID-19 pandemic has devastated nearly every sector of the global economy. The research seeks to examine the COVID-19 pandemic's effects on economy and its interdependencies, specifically on mining and minerals, energy, and agriculture sectors. We propose a mathematical model by combining...

Infrastructural projects based on public-private partnership (PPP) schemes usually struggle with large amounts of resources. Given such projects' relatively long lifetime, it is expected to encounter a wide range of risks when executing them. This implies that increasing productivity based on...

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...

Existing studies on Twitter-based natural disaster analysis suffer from shortcomings like limitations on supported languages, lack of sentiment analysis, regional restrictions, lack of end-to-end automation, and lack of Mobile App support. In this study, we design and develop a fully-automated...

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to...

The number of people using the Internet of Things (IoT) devices has exploded in recent years. The instantaneous development in deploying constrained devices in numerous areas makes them vulnerable to assaults due to limited resources. Advanced cryptography cannot be constructed in these modest...

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable...

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable...

Supplier selection is an important process in supply chain management that sets a foundation for a long-term partnership with suppliers that can greatly contribute to the success or failure of a business. This study aims to identify, validate and propose a comprehensive list of supplier...

Breast cancer is the most common life-threatening cancer in women and one of the leading causes of death. Early diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for cancer detection and prediction. This study presents a...