Cloud Computing and Data Science

Stock Price Prediction: A Machine Learning Approach

2025-11-21T09:12:13+08:00

Accurately predicting stock prices remains a complex task due to market volatility influenced by economic indicators and geopolitical events. This study presents a Hybrid Stock Sequence Learner (HSSL) model that integrates Support Vector Machine (SVM), Support Vector Regression (SVR), and Linear Regression (LR) to improve forecasting performance in dynamic financial environments. The model employs an attention-gated mechanism and regularization to capture higher-order feature interactions while preventing overfitting. SVM captures non-linear patterns, LR enhances interpretability by calculating feature impacts, and SVR enables adaptive modelling through high-dimensional feature mapping. The HSSL model was empirically evaluated using historical stock data from five entities, namely Apple, Microsoft, Walt Disney, Alphabet, and the S&P 500 index. This data was sourced from Yahoo Finance. Results show that HSSL achieves a Mean Squared Error (MSE) of 4.414 and a Root Mean Squared Error (RMSE) of 29.843, outperforming baseline models. These results demonstrate that our model effectively reduces prediction errors and captures market trends. Our proposed approach offers a robust and interpretable forecasting framework suitable for short-term stock price prediction and decision-making in highly volatile markets, serving as a vital tool for stock investors to evaluate potential risks and adjust strategies to minimize losses.

FLUDITY Unleashed: Dynamic Blockchain-Enabled Federated Learning Architecture for Tactile IoT Applications

2025-07-01T14:05:02+08:00

The Tactile Internet of Things (TIoT) demands ultra-reliable, low-latency communication for real-time haptic applications in domains such as remote surgery and human-robot collaboration. Conventional Federated Learning (FL) architectures-centralized, hierarchical, or purely decentralized-each suffer from bottlenecks in scalability, latency, or trust. We propose Federated Learning Using Distributed Infrastructure for TIoT (FLUDITY), a hybrid FL framework leveraging Blockchain, Multi-Edge Computing (MEC), and a Dynamic Aggregation Decision Algorithm (DADA). Evaluated via the TACTO (a fast and flexible tactile simulator) tactile simulator in a remote grasping task, FLUDITY achieves up to 30% reduction in convergence time (880 s vs. 1,258 s), 15% fewer Floating Point Operations (FLOPs) (74.8 × 10¹² vs. 88 × 10¹²), and 25% lower communication overhead (1,575 MB vs. 2,099.9 MB) compared to static blockchain FL.

Leveraging AI for Continuous Quality Assurance in Agile Software Development Cycles

2025-08-18T09:06:53+08:00

In the era of Agile software development, where rapid releases and continuous integration are essential, ensuring consistent software quality becomes increasingly complex. This research explores the integration of Artificial Intelligence (AI) into Continuous Quality Assurance (CQA) within Agile Software Development Cycles. Using the PROMISE dataset comprising 10,885 entries of real-world software metrics and defect labels, we implemented and evaluated AI models for real-time defect prediction, complexity analysis, and risk assessment. The Naive Bayes classifier achieved an accuracy of 98.16%, with high precision and recall across both defective and non-defective classes. Linear Regression, applied for defect-proneness estimation, yielded a low RMSE of 0.25, indicating strong predictive performance and effectively predicting defect-prone modules. As a result, our approach led to a 15.48% reduction in post-release bugs and an 80.71% decrease in manual testing time, significantly improving sprint-level feedback and delivery quality. Compared to manual testing, the AI-driven approach significantly improved defect detection rates and reduced testing time, supporting faster and more reliable software delivery. These results validate that AI integration in Agile environments not only automates and accelerates the quality declaration development but also sustains software reliability in all iterative development cycles.

Retraction Note to "Usability of University Websites as Information Sources: A Review and Synthesis Based on 2021 Publications Indexed in Scopus Database" [Cloud Computing and Data Science, Volume 4 Issue 1 (2023), 60-76]

2025-09-16T15:39:29+08:00

The Editor-in-Chief and the Editorial Office of Cloud Computing and Data Science (CCDS) have retracted the following article:

Yap C K, Hashim H, Ainuddin M H, et al. Usability of University Websites as Information Sources: A Review and Synthesis Based on 2021 Publications Indexed in Scopus Database. Cloud Computing and Data Science. 2023; 4(2): 60-76.

This article has been retracted at the request of the Editor-in-Chief.

Following a journal-wide investigation, it was identified that this article falls outside the scope of the journal, and does not align with the thematic focus or subject matter requirements as outlined by the journal's editorial policies.

The retraction is in accordance with the Committee on Publication Ethics (COPE) guidelines, ensuring the integrity of the publication record is maintained. As part of journal's ongoing efforts to improve the quality of publications, the editorial office will continue to rigorously conduct preliminary review and peer review to ensure that only manuscripts that align with the journal's scope and meet the highest academic standards are published.

The editorial office sincerely apologize for any confusion or inconvenience this retraction may have caused.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

Retraction Note to "Design of a Smart Cabin Lighting System Based on Internet of Things" [Cloud Computing and Data Science, Volume 4 Issue 2 (2023), 112-121]

2025-09-18T11:01:09+08:00

The Editor-in-Chief and the Editorial Office of Cloud Computing and Data Science (CCDS) have retracted the
following article:

Huang Y K. Design of a Smart Cabin Lighting System Based on Internet of Things. Cloud Computing and Data
Science. 2023; 4(2): 112-121.

This article has been retracted at the request of the Editor-in-Chief.

Following a journal-wide investigation, it was identified that this article falls outside the scope of the journal, and
does not align with the thematic focus or subject matter requirements as outlined by the journal's editorial policies.

The editorial office sincerely apologize for any confusion or inconvenience this retraction may have caused.

None of the authors have responded to the Editor about this retraction notice.