Machine Learning Algorithm on Assets' Behaviour During and Post Covid Pandemic

Global financial assets behaviour has become highly volatile during the pandemic period, especially the highly risky assets. Financial instruments like cryptocurrencies are basically speculative and the investors basically trade on these anomalies. Even though the entire world has come to standstill these markets were never. In order to understand the market anomalies during the COVID pandemic the popular asset in cryptos which is bitcoin along with the global market index such as S&P 500, Global Crude Oil prices and gold prices daily trading data are taken into consideration during and post covid. Some of the interesting aspects of Machine Learning (ML) such as variety of techniques, parameter selection, nonlinearity and generalization ability make it well suited for the problems of uncertain functional structure. Price prediction of stock markets is a challenging problem because of unpredictable noise and the number of potential variables that may impact on the prices. The research work presented in this paper involves the development of a ML algorithm which will throw light on the price behaviour of these instruments during and post crisis. © 2022 WMSCI.All rights reserved.

A Machine Learning Model for Effective Consumer Behaviour Prediction

Effective consumer behavior prediction can play a crucial role in online marketing, especially in the COVID19 scenario. In this work, we have analyzed consumer behavior to understand consumer needs and predict future requirements. For the same, we have applied the machine learning models on an amazon dataset collected from Kaggle. The dataset consists of reviewers' comments, ratings, many other parameters for the product. The model's outcome indicates that the proposed Random Forest model performs exceptionally well, and its Accuracy is approx. 98.73%. A comparative study has been done to show the efficacy of the work, and it has been observed that the performance of the proposed model is quite remarkable, and it can be a competent model for effective consumer behavior prediction. © 2021 IEEE.

Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study.

The most frequent form of dementia is Alzheimer's Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer's disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.