Analysis of a recent heat conduction model with a delay for thermoelastic interactions in an unbounded medium with a spherical cavity

In this work, we study the thermoelastic interactions in an unbounded medium with a spherical cavity in the context of a very recently proposed heat conduction model established by Quintanilla (2011). This model is a reformulation of three-phase-lag conduction model and is an alternative heat conduction theory with a single delay term. We make an attempt to study the thermoelastic interactions in an isotropic elastic medium with a spherical cavity subjected to three types of thermal and mechanical loads in the contexts of two versions of this new model. Analytical solutions for the distributions of the field variables are found out with the help of the integral transform technique. A detailed analysis of analytical results is provided by short-time approximation concept. Further, the numerical solutions of the problems are obtained by applying numerical inversion of Laplace transform.We observe significant variations in the analytical results predicted by different heat conduction models. Numerical values of field variables are also observed to show significantly different results for a particular material. Several important points related to the prediction of the new model are highlighted

Artificial Intelligence and Exploratory-Data-Analysis-Based Initial Public Offering Gain Prediction for Public Investors

An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company’s shares is accompanied by careful consideration and study of the company’s public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company’s IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%

Artificial Intelligence and Exploratory-Data-Analysis-Based Initial Public Offering Gain Prediction for Public Investors

An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company’s shares is accompanied by careful consideration and study of the company’s public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company’s IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%