Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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All Journal Journal of Applied Data Sciences Journal of Current Research in Blockchain
Hemalatha, M.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Empirical Study of the Correlation between Social Media Content and Health Issues among College Students Using Machine Learning Hemalatha, M.; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.365

Abstract

This study analyzes the effect of social media content on college student addiction using data science techniques. It aims to examine the correlation between different types of social media content and addictive behavior in college students. The research methodology used is non-probability sampling with a sample size of 587 college students in Tamil Nadu, India. The study uses statistical tools such as correlation analysis, regression analysis, one-way ANOVA, and Friedman ranking test to analyze the data collected. The findings suggest that the factors influencing social media addiction are positively correlated with the health issues faced by college students. The study indicates that demographic variables such as age, gender, year in college, and place of living may play a role in shaping an individual's perception of social media addiction. The results of the study can inform the development of interventions and prevention strategies to reduce social media addiction among college students. The study recommends a multi-pronged approach to address the root causes of addiction and provide students with the tools and resources they need to manage their social media use and promote their physical and mental health.
A Hybrid Ensemble Framework Combining Transformer Networks, CNN-LSTM, and Prophet for Multi-Horizon Bitcoin Price Prediction Using 1-Minute Time Series Data Maidin, Siti Sarah; Hemalatha, M.; Sun, Jing
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.57

Abstract

Bitcoin price forecasting at one-minute frequency presents significant challenges due to rapid volatility and noise in high-frequency markets. This study proposes a hybrid ensemble framework integrating a CNN-LSTM model, a Transformer architecture, and a Prophet-based component to perform multi-horizon prediction using 500,000 one-minute BTC/USD observations. The model is evaluated across 5-minute, 15-minute, and 30-minute horizons. The results show that the ensemble achieves the best performance for the 5-minute horizon with MAE = 41.565 USD, RMSE = 60.722 USD, and MAPE = 0.156. This outperforms the CNN-LSTM model (MAE = 47.838 USD) and the Transformer model (MAE = 53.733 USD). Performance decreases at the 15-minute horizon due to Transformer instability, where the ensemble reaches MAE = 269.347 USD and the Transformer reaches MAE = 530.429 USD. At the 30-minute horizon, performance stabilizes, with the ensemble producing MAE = 84.481 USD, close to the CNN-LSTM result (MAE = 84.186 USD) and better than the Transformer (MAE = 153.887 USD). These findings indicate that the hybrid ensemble is highly effective for ultra-short-term forecasting but requires horizon-specific tuning to remain stable for medium-range intervals.
Co-Authors Jing Sun Maidin, Siti Sarah