Volume 2, Issue 1

• Volume: 2
• Issue: 1
• Year: 2026
• Articles: 5

Using Predictive Analytics to Adjust Prices in Real-Time Retail Settings

Authors: Yusuf Adebayo | DOI: 10.63665/ijetd-y2f1a001

In the fast-moving retail world, dynamic pricing can help retailers make more money and satisfy customers. Such in-store dynamic pricing for always-open shops will be enabled by businesses using predictive analytics based on customer behavioral patterns, their demand for goods, competition, and market dynamics.

Investment Decisions in Strategic Marketing Using Causal Inference Models

Authors: John Owen | DOI: 10.63665/ijetd-y2f1a002

Correlation-based measures of marketing successes are frequently employed to aid managers in identifying where they should invest their marketing dollars. However, there is reason to believe that these measures themselves could be problematic as meaningful measures of whether or not a campaign has been successful.

Optimizing Distributed Systems for Scalable Machine Learning Workflows using AI-Driven Software Engineering

Authors: Stephen Eteng | DOI: 10.63665/ijetd-y2f1a003

The rapid growth in machine learning applications is demanding the development of distributed systems to handle large data, complex models, and high computation intensities. Among the most important challenging aspects in developing distributed systems for machine learning that have to be scaled up are data storage management, resource allocation, load balancing, and ensuring system performance.

Integrated AI Systems for Instantaneous City Monitoring

Authors: Adedokun Taofeek | DOI: 10.63665/ijetd-y2f1a004

The ever-growing trend of urbanization in metropolitan cities creates persistent problems related to traffic management, public safety concerns, and crime prevention. The demands of the emerging urban environment change every day and are often unmanageable or unreachable by traditional surveillance systems due to their intrinsically decentralized and dynamic nature.

AI-Powered Cross-Border Financial Data Integration

Authors: Mayowa Emmanuel | DOI: 10.63665/ijetd-y2f1a005

International financial institutions face different challenges in cross-border integration of financial data owing to diverse legal frameworks, inconsistent data formatting, and security concerns. The goal of this paper is to try to find the role of AI in mitigating such problems by correctly enabling compliance with the law and the efficient integration of cross-border financial data.