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WONGA: The Future of Personal Finance Management – A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking
(IEEE, 2023-07-10) Uyanahewa, M.I.R; Jayawardana, G.V.H.D; Bandara, M.B.D.N; Hapugala, H.A.V.V; Attanayaka, B
The financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. The proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. The system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga" mobile application to help users make better financial decisions to achieve personal financial success.
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A Framework for Tracking And Summarizing Daily Activities with Mobile Phone for Healthy Life of a User
(Institute of Electrical and Electronics Engineers, 2022-09-16) Wickramasinghe, K.E.T.; Albertjanstin, N; Wijethunga, L.P. P. L; Rajapaksha, U.U.S.K; Panduwawala, P.K.K.G; Harshanath, S.M.B
People nowadays have incredibly busy lifestyles with little time to do their activities, and smartphones have become a need for many people all over the world as new features and controls have been developed, and smartphone usage has rapidly increased by individuals. People that lead complex lifestyles find it difficult to balance their activities. As a consequence, an automated method of tracking user activity on a smartphone can give a better solution for managing a user's daily life while also saving time. We take a look at some of the previous studies on user behaviour tracking methods approaches. Derived from the previous studies, we noticed some important challenges, including smartphone addiction and unhealthy posture, battery drainage concerns, managing educational activities, and finding necessary information from the huge data. In this paper, we proposed an automated monitoring approach to track user activities on smartphones and give solutions to the aforementioned difficulties. We developed different algorithms such as ANN to understand smartphone usages and battery interactions, CNN for detecting unhealthy neck postures and Word2Vec for generating similar meanings for fast search

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