Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects

Identifying the Factors that influence the Process Optimization of New Investment Appraisal

The FDI approval process is one of the decisive factors of successful implementation of investments in a country. This paper attempts to identify the relationship and association between several factors that involves in new FDI appraisal process by the host country. It identifies factors such as global presence of the intended investor; the type of Industry; expected contribution from the investment; potential for gaining competence regarding human resource; expected developments in the country’s infrastructure. The study suggests that above factors are critical to the relevant authorities who involved in FDI promotion. This research also ranks these factors to make the conclusions more useful in the real-life application. It also highlights the number of employments generated for local workers under the new investment. The new investments bring additional knowledge, skills, and competence that usually not quantified at the appraisal level. The success rate of similar investment in other countries also to be critically evaluated. The findings of this research could be extremely useful for countries who wish to host FDIs. A clear understanding about the key influencing factors and their association with the investment appraisal process would be the key to optimize the process

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

Abstract With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning