Browsing by Author "Kumarasinghe, H"

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Embargo
Business Intelligence Assistant for Human Resource Management for IT Companies
(IEEE, 2020-11-04) Athukorala, C; Kumarasinghe, H; Dabare, K; Ujithangana, P; Thelijjagoda, S; Liyanage, P
The advancement in technology is exponential. Moore's law supports this argument, by stating that the computing power doubles every two years. In such a premise, many IT companies have risen to meet the challenges. These companies provide various solutions in various fields of enterprises, pushing the limits of technology. Human resource is considered the most important asset in any organization. In order to utilize this asset beneficially, an organization must have great Human Resource Management practices. This includes practices from recruitment until employee termination. One great employee can offset the work of several regular employees. IT companies strive to acquire and retain such talent. But this is not a simple task. It requires resources including manpower and time. There should be knowledgeable individuals to handle important human resource processes, and many organizations lack these. They do not have enough time or labor to invest in good human resource processes. This research proposes a solution to this problem by creating a Business Intelligence Assistant for Human Resource Management Targeting Information Technology companies. It delves into Human Resource Management practices revolving around employee recruitment, job placing, employee engagement and human resource decision making. The solution consists of four solutions, namely; Structured Resume Analyzer, Smart Candidate Ranker, Employee Engagement Survey Generator and Business Intelligence Processor. Each component will enable the organization to streamline certain processes helping them save both time and labor. The individual components will make use of various applications of artificial intelligence to aid in the decision-making of an organization.
Open Access
Integrating machine learning and IoT for real-Time wildlife tracking and crowd sourcing
(SPIE, 2025-12-09) Saubhagya, S; Wijerathne, N; Sachintha, K; Kumarasinghe, H
This research introduces a smart system to enhance wildlife safari experiences by integrating real-Time location tracking, animal behavior prediction, and emergency communication technologies. Traditional safari tours rely heavily on human guides, often leading to inefficiencies in wildlife spotting and navigation. Proposed system employs machine learning, GPS tracking, and voice assistance to provide an interactive and informative safari experience. The machine learning algorithm uses past and current movement patterns of animals and generates predictive information to guide tourists to optimal wildlife observation points. The system uses LoRa-based offline communication to ensure seamless connectivity in network-poor regions, facilitating smooth coordination between safari vehicles and park authorities. The voice guidance feature also enhances accessibility by providing real-Time educational content on observed wildlife. This study adds to wildlife tourism with a technology-based framework that improves visitor experience, minimizes environmental footprint by route optimization, and aids conservation through data-driven monitoring of wildlife behavior.