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Artificial Intelligence Techniques for Computer System Failure Prediction: Ensemble and Gradient Boosting Analysis ALaa KHudhair ali; Zeinh Sabeeh Jaseem; Ruaa Kadhim Jabir
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13271

General Background: The growing dependence on computer systems across industrial and service sectors has increased the need for reliable early failure prediction to ensure operational continuity. Specific Background: Recent advances in artificial intelligence, particularly ensemble methods, gradient boosting algorithms, Automated Machine Learning (AutoML), and Explainable AI (XAI), have demonstrated strong potential in analyzing complex operational data for predictive maintenance. Knowledge Gap: Existing studies largely address these techniques in isolation, with limited focus on their integrated application and interpretability in real-world, dynamic environments. Aims: This review examines recent AI-based approaches for computer system failure prediction, emphasizing ensemble learning, gradient boosting, AutoML, and XAI. Results: The analysis indicates that gradient boosting and ensemble models offer superior predictive accuracy, while AutoML reduces development effort and XAI enhances model transparency and trust. Novelty: The review highlights the combined role of performance-driven and explainability-focused techniques within a unified predictive framework. Implications: Integrating these approaches supports more reliable, interpretable, and cost-effective predictive maintenance strategies in modern computing systems.Keywords : Computer System Failure Prediction, Artificial Intelligence, Ensemble Methods, Gradient Boosting, Explainable Artificial IntelligenceHighlight : Combined model strategies consistently outperform conventional monitoring by capturing complex operational patterns. Sequential tree-based learners demonstrate strong suitability for large-scale, noisy, and heterogeneous operational data. Interpretation frameworks strengthen practitioner trust by clarifying decision rationales for preventive maintenance actions

Visual Artifact Detection and Correction for Digital Images Via Deep Neural Networks (Real-ESRGAN) Ruaa Kadhim Jabir; Malath Sabri Kareem
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

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This work pursues to the improvement of digital images quality and the correction of visual artifacts, in particular noise, by means of deep neural networks, through an application-oriented investigation based on pre-trained models which are ready to be used in the execution on a resource-limited platform. The study is inspired by a typical problem in processing digital images, on one hand traditional image enhancement algorithms do not work well on real world low-quality images because they cannot well trade off between over smoothing noise and preserving visual details and image structural information, on the other hand, high quality ground truth images corresponding to low quality real images are not available. The experimental part of this work was performed on a real noisy digital image from an open-source platform. The image was processed using a specialized processing pipeline developed in Python. The PyTorch library was used to run the DL model and some dedicated libraries are also included such as Real-ESRGAN as the default model to enhance the image quality, BasicSR as the generic framework to manage the process workflow, and the installation of GFPGAN and FaceXLib to facilitate optional facial restoration. Prior to all other testing were done on the CPU, these experiments were conducted using only the Central Processing Unit(DCPU) and not any high end Graphical Processing Units(GPUs) The processing approach was to use the Real-ESRGAN model only in the inference stage without any training process or modification on the model architecture. The discussion of results was grounded on qualitative visual inspection aided with a descriptive quantitative analysis of result indicators that can be obtained from the image itself before and after processing, such as the image size, the total number of pixels, and the spatial upscaling factor. The result indicates a 16 times enlargement in pixel number after processing (under the upscaling factor of ×4), as well as an intuitive improvement on clearness of details and the visual noise decreasing, which means the enhancement on perceptual quality of the image. Results show that exploiting pre-trained DNN models is a realistic and time-efficient strategy for improving quality of noisy images of the real world, even if computational power is scarce at the time of acquisition of images. In addition, the work demonstrates the feasibility of leveraging open source software in the domain of digital imaging and thus paves the way for potential future investigation incorporating larger datasets or employing standardized quantitative evaluation metrics when appropriate evaluation conditions are obtainable.

The Impact of Future Thinking on Building Computerized Databases: A Strategic Foresight and Design-Science Research Framework Dunea Taleb Kazim; Dunea Taleb Kazim; Ruaa Kadhim Jabir
Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan Vol. 7 No. 2 (2027)
Publisher : Universitas Negeri Malang

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Based on the aspects of strategic foresight, database design, data governance, data quality and information systems success literature, this study investigates the influence of future orientation on designing computerized databases. This paper introduces the Foresight-Driven Database Building Model (FDDBM), a design-science based model that seamlessly combines horizon scanning, scenario planning, anticipatory requirements engineering, governance mechanisms, metadata modeling, and environment monitoring within the database lifecycle. The paper includes theoretical figures, a quantitative model evaluation matrix, correlation and regression analyses, as well as comparisons of maturity and integrated debates of finding. It is shown that future-thinking ability is significantly correlated with database future readiness, data governance maturity, architecture flexibility, and data quality, and negatively correlated with rework effort and requirements volatility post-deployment. The paper presents a conceptual model, a practical framework, a database-design instantiation, a measurement instrument, and practice implications for the design of future-oriented electronic databases.

The Impact of Future Thinking on Building Computerized Databases: A Strategic Foresight and Design-Science Research Framework Dunea Taleb Kazim; Dunea Taleb Kazim; Ruaa Kadhim Jabir
Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan Vol. 7 No. 2 (2027)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Based on the aspects of strategic foresight, database design, data governance, data quality and information systems success literature, this study investigates the influence of future orientation on designing computerized databases. This paper introduces the Foresight-Driven Database Building Model (FDDBM), a design-science based model that seamlessly combines horizon scanning, scenario planning, anticipatory requirements engineering, governance mechanisms, metadata modeling, and environment monitoring within the database lifecycle. The paper includes theoretical figures, a quantitative model evaluation matrix, correlation and regression analyses, as well as comparisons of maturity and integrated debates of finding. It is shown that future-thinking ability is significantly correlated with database future readiness, data governance maturity, architecture flexibility, and data quality, and negatively correlated with rework effort and requirements volatility post-deployment. The paper presents a conceptual model, a practical framework, a database-design instantiation, a measurement instrument, and practice implications for the design of future-oriented electronic databases.

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