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All Journal IJIIS: International Journal of Informatics and Information Systems JOURNAL OF LANGUAGE Journal of Applied Data Sciences
Hao Wu
Guangxi University for Nationalities
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences

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Exploring Transformer Life Forecasting through an In-Depth Analysis Utilizing the Random Forest Algorithm in Research and Development Gan, Lei; Wu, Hao; Ismail, Manal
International Journal of Informatics and Information Systems Vol 7, No 1: January 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i1.192

Abstract

Accurately assessing the life and operating status of transformers has important guiding significance for the formulation of maintenance strategies for power grid companies, and at the same time plays a key role in the risk management of power grid companies. However, the traditional methods for predicting the remaining life of the equipment have the problems of insufficient accuracy or long data training time. In order to achieve a more accurate assessment of the life and status of the transformer, a random forest-based transformer life prediction method is constructed in this paper. Relying on the theory of big data analysis, by mining and analyzing the accumulated data of massive transformers, the life prediction model of the transformer is established and the characteristic parameters affecting the life of the transformer are extracted to predict the life of the transformer. The experimental data research demonstrates that the model can be accurate and effective Predicting the life of transformers has higher prediction accuracy than traditional methods, providing method references for asset management and risk management of power grid companies.
Ensembling Methods for Data Privacy in Data Science Mahendiran, N; Shivakumar, B L; Maidin, Siti Sarah; Wu, Hao
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

Abstract

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.
Misuse of “Besides” in Spoken English: Error Patterns in Chinese University Students’ Group Discussions Wu, Hao
JOURNAL OF LANGUAGE Vol 8, No 1: May 2026 (In Progress)
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jol.v8i1.12897

Abstract

Conjunctions are essential for organizing discourse and signaling relationships between ideas, yet second language learners often struggle to use them accurately in spoken English. Among these, “besides” is particularly challenging due to its nuanced semantic and pragmatic functions. This qualitative study adopts Conversation Analysis (CA) to investigate the use of “besides” by Chinese university students in advanced English group discussions. Drawing on 15.9 hours of recorded interactional data from five Chinese undergraduates, the study identifies non-target-like uses of “besides” in naturally occurring talk. Five error types are found: misuse as a simple listing connector, misuse as an afterthought marker, overuse to connect aligned ideas, overuse as a redundant connector, and underuse as a clarifying connector. These errors stem from the complexity of “besides”, negative transfer from native-language conjunctions, instructional emphasis on surface-level coherence, and limited systematic training in spoken and written academic English. The study highlights the importance of understanding connectives in both semantic and pragmatic contexts and suggests pedagogical strategies to improve learners’ accurate and contextually appropriate use of “besides” in spoken English.
Co-Authors Gan, Lei Ismail, Manal Mahendiran, N Maidin, Siti Sarah Shivakumar, B L