The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real-world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on 'Beach Water Quality -Automated Sensors' data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre-processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state-of-art regression algorithms in preserving the battery life of IoT devices.

COVID-19 outbreak has created havoc around the world and has brought life to a disturbing halt claiming thousands of lives worldwide and infected cases rising every day. With technological advancements in Artificial Intelligence (AI), AI-based platforms can be used to deal with COVID-19 pandemic and accelerate the processes ranging from crowd surveillance to medical diagnosis. This paper renders a response to battle the virus through various AI techniques by making use of its subsets such as Machine Learning (ML), Deep learning (DL) and Natural Language Processing (NLP). A survey of promising AI methods which could be used in various applications to facilitate the processes in this pandemic along potential of AI and challenges imposed are discussed thoroughly. This paper relies on the findings of the most recent research publications and journals on COVID-19 and suggests numerous relevant strategies. A case study on the impact of COVID-19 in various economic sectors is also discussed. The potential research challenges and future directions are also presented in the paper.

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In the trend of digital marketing, the back and front office activities are automized. For any business organization of various departmental activities, finance and marketing play a vital role. Finance and marketing are the most important functional areas of operations in any business organization, as they directly impact the financial growth as well as market steadiness of the business. As very crucial decisions taken in these functional areas of operations affect the other departments, the decisions in these financial marketing should be taken with utmost care. Henceforth, this results in greater impact on economic growth of the organization. Therefore, the decisions in finance and marketing activities should consider various factors (critical to quality) before arriving at a conclusion. In order to attain the final decision, multi-criteria decision-making (MCDM) methods can be applied. These MCDM methods consider the conflicting factors to evaluate the finance and marketing activities.