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Accepted Papers
Personalized Metro Route Optimization and a Recommendation System

Hadeel Alobaidy, Tasnime Tantawy and Shahad Talal, Department of Computer Science, University of Bahrain, Bahrain, Zallaq

ABSTRACT

This research aims to develop an optimal metro railway that connects the most popular spots for tourist places in the Kingdom of Bahrain using Dijkstra’s algorithm, complemented by a recommendation system using modified collaborative filtering and haversine algorithm. The proposed system employs software engineering principles involving agile methodology as a software process model for enhancing the adaptability and flexibility of the proposed system. The application embeds the railway route generated by Dijkstra’s algorithm to enhance the recommendations provided by the collaborative filtering algorithm, resulting in an accurate system with extraordinary potential for travellers and business owners in the future.

KEYWORDS

Bahrain Metro, Software Engineering, Agile process model, Recommendation System, Human-Computer Interaction, User- experience.


Sustainable Investments and ESG: Portfolio Optimization using Genetic Algorithms

Larissa Luize de Faria Cardoso, Electrical Engineering Department Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

ABSTRACT

Sustainable investments, guided by ESG (Environmental, Social, and Governance) criteria, have become a central focus for investors worldwide. The integration of ESG criteria into investment decisions has been shown to lead to better financial performance and lower long-term risk for companies. This study aims to develop and apply a Genetic Algorithm (GA) to optimize investment portfolios that balance financial, return, ESG criteria, and risk. The proposed methodology creates a robust and adaptable model suitable for real-world sustainable investment scenarios. By using data from companies such as Apple, Microsoft, and Tesla, this study demonstrates the effectiveness of GAs in achieving an optimal portfolio allocation. The results highlight the potential of GAs to consider multiple objectives simultaneously and provide a balanced solution that meets financial and sustainability goals.

KEYWORDS

Sustainable Investments, ESG, Genetic Algorithms, Portfolio Optimization, Financial Performance.


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