Garuda - Garba Rujukan Digital

A Hybrid Ensemble Framework Combining Transformer Networks, CNN-LSTM, and Prophet for Multi-Horizon Bitcoin Price Prediction Using 1-Minute Time Series Data Siti Sarah Maidin; M. Hemalatha; Jing Sun
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

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.

Transforming Agriculture: An Insight into Decision Support Systems in Precision Farming Ding Yi; Luo Jun; Gao Haodic; Zhang Xing; Ye Lie; Siti Sarah Maidin; Wan Hussain Wan Ishak; Walton Wider
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.v5i4.274

Precision agriculture seamlessly incorporates advanced technologies and data analysis to improve farming efficiency and sustainability through immediate resource allocation. Therefore, this study aims to synthesize research findings related to agriculture, Decision Support Systems, and precision agriculture through a systematic literature review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was performed on the Scopus database, specifically focusing on publications published in English between the years 2017 and 2023. Out of 126 periodicals, a rigorous process was used to determine which publications met the specific criteria for inclusion and exclusion. As a result, only 8 relevant studies were chosen. The review emphasizes the substantial capacity of Decision Support Systems in precision agriculture, demonstrating that DSS has the capability to enhance crop yields by 15% and decrease water consumption by 20%. Through the utilization of big data, machine learning, and advanced technologies, Decision Support Systems has the potential to transform the agricultural industry by enhancing productivity, optimizing resource allocation, and enabling early identification of pests and diseases. The utilization of real-time data from Decision Support Systems empowers farmers to make well-informed choices, effectively managing production while upholding environmental sustainability. This, in turn, plays a crucial role in ensuring the economic viability of farms and enhancing global food security. However, addressing challenges like data privacy concerns, enhancing user-friendly interfaces, establishing robust data administration infrastructure, and providing adequate training and support for end-users is imperative for the successful implementation of data-driven Decision Support Systems in precision agriculture.

Transforming Agriculture: An Insight into Decision Support Systems in Precision Farming Ding Yi; Luo Jun; Gao Haodic; Zhang Xing; Ye Lie; Siti Sarah Maidin; Wan Hussain Wan Ishak; Walton Wider
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.v5i4.274

Precision agriculture seamlessly incorporates advanced technologies and data analysis to improve farming efficiency and sustainability through immediate resource allocation. Therefore, this study aims to synthesize research findings related to agriculture, Decision Support Systems, and precision agriculture through a systematic literature review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was performed on the Scopus database, specifically focusing on publications published in English between the years 2017 and 2023. Out of 126 periodicals, a rigorous process was used to determine which publications met the specific criteria for inclusion and exclusion. As a result, only 8 relevant studies were chosen. The review emphasizes the substantial capacity of Decision Support Systems in precision agriculture, demonstrating that DSS has the capability to enhance crop yields by 15% and decrease water consumption by 20%. Through the utilization of big data, machine learning, and advanced technologies, Decision Support Systems has the potential to transform the agricultural industry by enhancing productivity, optimizing resource allocation, and enabling early identification of pests and diseases. The utilization of real-time data from Decision Support Systems empowers farmers to make well-informed choices, effectively managing production while upholding environmental sustainability. This, in turn, plays a crucial role in ensuring the economic viability of farms and enhancing global food security. However, addressing challenges like data privacy concerns, enhancing user-friendly interfaces, establishing robust data administration infrastructure, and providing adequate training and support for end-users is imperative for the successful implementation of data-driven Decision Support Systems in precision agriculture.

An Effective Investigation of Genetic Disorder Disease Using Deep Learning Methodology B. Vidhya; B. L. Shivakumar; Siti Sarah Maidin; Jing Sun
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.370

This study evaluates the performance of four neural network models—Artificial Neural Network (ANN), ANN optimized with Artificial Bee Colony (ANN-ABC), Multilayer Feedforward Neural Network (MLFNN), and Forest Deep Neural Network (FDNN)—across different iteration levels to assess their effectiveness in predictive tasks. The evaluation metrics include accuracy, precision, Area Under the Curve (AUC) values, and error rates. Results indicate that FDNN consistently outperforms the other models, achieving the highest accuracy of 99%, precision of 98%, and AUC of 99 after 250 iterations, while maintaining the lowest error rate of 2.8%. MLFNN also shows strong performance, particularly at higher iterations, with notable improvements in accuracy and precision, but does not surpass FDNN. ANN-ABC offers some improvements over the standard ANN, yet falls short compared to FDNN and MLFNN. The standard ANN model, though improving with iterations, ranks lowest in all metrics. These findings highlight FDNN's robustness and reliability, making it the most effective model for high-precision predictive tasks, while MLFNN remains a strong alternative. The study underscores the importance of model selection based on performance metrics to achieve optimal predictive accuracy and reliability.

Ensembling Methods for Data Privacy in Data Science N Mahendiran; B L Shivakumar; Siti Sarah Maidin; Hao Wu
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.v5i4.341

The rapid advancement of technology has unified systems, data storage, applications, and operations, providing continuous services to organizations. However, this integration also introduces new vulnerabilities, particularly the risk of cyber-attacks. Malware and digital piracy pose significant threats to data security, with the potential to compromise sensitive information, leading to severe financial and reputational damage. This study aims to develop an effective method for detecting malware-infected files on storage devices within the Internet of Things (IoT) environment. The proposed approach utilizes a stacked regression ensemble for data pre-processing and the Sea Lion Optimization Algorithm (sLOA) to extract salient features, enhancing the classification process. Using malware data from an intrusion detection dataset, an ensemble classification technique is applied to identify malicious infections. The experimental results demonstrate that the proposed method achieved an accuracy of 98%, a precision of 99.6%, a recall of 96%, and an F-measure of 95% by the final iteration, significantly outperforming existing techniques in addressing cyber-security challenges within IoT systems.

Cellular Traffic Prediction Models Using Convolutional Long Short-Term Memory A Sunil Samson; N Sumathi; Siti Sarah Maidin; Qingxue Yang
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.472

Precise cellular traffic modeling and prediction is essential to future big data-based cellular network management for providing autonomic control and user-satisfied stable mobile services. However, the traditional methods have difficulty learning the complex hidden patterns of the users’ traffic data from cross-domains because of their shallow learning characteristics. Deep learning (DL)-based methods could somewhat identify these hidden patterns by learning the underlying spatial and temporal features and their dependencies. Yet, they too have constraints in handling the noisy and sparse data, reducing the prediction accuracy with increased computation time and associated storage costs. Therefore, this paper presents an intelligent cellular traffic prediction model (ICTPM) using two improved deep learning algorithms to tackle the negative impacts of noisy and sparse traffic datasets. Firstly, the Enhanced Stacked Denoising Auto-Encoder (ESDAE) is introduced to eliminate the noise in the traffic data by an adaptive Morlet wavelet transform. Secondly, Multi-dimensional Spatiotemporal Sparse-representation Convolutional Long Short-Term Memory (MDSTS-CLSTM) is used to learn the hidden patterns by extracting the spatial-temporal dependencies and predict the cellular usage in the presence of data sparsity problem. This MDSTS-CLSTM is developed by combining the Long Short-Term Memory (LSTM) with the Convolutional Neural Networks (CNN) and improvising the multi-dimensional feature learning, spatial-temporal analysis, and sparse representation properties of the hybrid DL algorithm. Evaluated over real-world cellular traffic cross-domain datasets from Telecom Italia and Open-CellID, the proposed ICTPM outperforms the state-of-the-art methods with 5-10% better performance enhancements.

Optimizing Emergency Logistics Identification: Utilizing A Deep Learning Model in the Big Data Era V. Sumathi; B. L. Shivakumar; Siti Sarah Maidin; Wu Ge
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.369

This study investigates the dynamics of commodity flow across different facilities and settings, evaluating the performance of Simulation and Feed Forward Neural Network (FFNN) methods in optimizing these flows. Analyzing data from various configurations, the research reveals significant variations in commodity distribution patterns. At Facility_1 from the K1 disposer market, the flow of Commodity_1 increased from 770 units to 830 units, while Commodity_2 decreased from 192 units to 166 units. At Facility_2, Commodity_1's flow decreased from 851 units to 793 units, and Commodity_2's flow slightly increased from 139 units to 148 units. Similar trends are observed at facilities from the K2 disposer market, reflecting the complex impact of different settings on commodity flow. The comparative analysis of Simulation and FFNN methods demonstrates their relative strengths. In Setting I, the Simulation method achieved an objective value of 1,800,574.36 Rs with a computation time of 46.78 seconds, while the FFNN method yielded a slightly lower objective value of 1,800,352.24 Rs in 42.01 seconds. In Setting II, the Simulation method provided an objective value of 1,801,025.36 Rs with a computation time of 103.86 seconds, whereas FFNN achieved a comparable objective value of 1,800,847.27 Rs in 63.05 seconds. In Setting III, Simulation resulted in an objective value of 1,801,527.36 Rs with a computation time of 61.12 seconds, while FFNN produced a higher objective value of 1,806,997.32 Rs in 50.03 seconds. The results highlight the trade-offs between solution quality and computational efficiency. The Simulation method consistently delivers higher objective values but requires more time, making it suitable for applications where result accuracy is crucial. Conversely, the FFNN method offers faster computation with competitive or superior objective values, making it advantageous for scenarios where time constraints are significant. This study underscores the importance of selecting appropriate computational methods based on specific operational needs, optimizing both the efficiency and effectiveness of commodity flow management.

Title

Found 7 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 7 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 7 Documents
Search