Cloud Computing and Machine Learning for Scalable Predictive Analytics and Automation - A Framework for Solving Real-world Problems

Aug 4, 2026

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This study presents a scalable framework that combines cloud computing and machine learning to tackle real-world challenges in predictive maintenance, anomaly detection, and sentiment analysis. Using cloud platforms such as AWS and Microsoft Azure, the approach processes large-scale datasets for efficient deployment across industries. Predictive maintenance reports 92% accuracy, 89% precision, 94% recall, and 91% F1, while anomaly detection achieves 89% precision, 85% recall, and 87% F1. Sentiment analysis on 100,000 social posts yields positive 45%, neutral 35%, negative 20%, with confidence between 85% and 98%, supporting automation, reduced costs, and improved decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

1.0 Introduction

Motivation and background

Research gap and study aims

Cloud Computing and Machine Learning for Scalable Predictive Analytics and Automation: A Framework for Solving Real-world Problems David Adetunji Ademilua* and Edoise Areghan Received: 14 November 2024/Accepted: 28 January 2025/Published: 05 February 2025 [https://dx.doi.org/10.4314/cps.v12i2.16](https://dx.doi.org/10.4314/cps.v12i2.16) [Abstract:](Abstract: This study presents a framework for)[ This study presents a framework for](Abstract: This study presents a framework for)[ ](Abstract: This study presents a framework for)harnessing cloud computing and machine learning (ML) to address real-world challenges in predictive maintenance, anomaly detection, and sentiment analysis. Leveraging cloud platforms such as AWS and Microsoft Azure, the framework processes large-scale datasets, enabling scalable and efficient solutions across various industries. In the predictive maintenance use case, a machine learning model achieved an accuracy of 92%, precision of 89%, recall of 94%, and an F1 score of 91%, demonstrating its capability to predict equipment failures with high reliability. For anomaly detection, network traffic data was analyzed, yielding a precision of 89%, recall of 85%, and an F1 score of 87%, illustrating the model's efficiency in identifying security threats. In the sentiment analysis task, a subset of 100,000 social media posts was processed, revealing that 45% of the posts were classified as positive, 35% neutral, and 20% negative. The high confidence levels in sentiment predictions, ranging from 85% to 98%, underscore the accuracy and effectiveness of the employed natural language processing (NLP) models. The results align with contemporary studies, which highlight the transformative impact of cloud-based ML systems in enhancing operational efficiency, real-time decision-making, and customer satisfaction across diverse domains (Kairo, 2024;Ucaret al., 2026; Hassan et al., 2024). These findings underscore the potential of combining cloud computing with advanced machine learning algorithms to drive automation, reduce operational costs, and optimize business processes in the digital era. Keywords: Solution, real world problem, Cloud computing, ML, predictive analysis, scalability, automation David Adetunji Ademilua* Computer Information Systems and Information Technology, University of Central Missouri. USA. Email: [davidademilua@gmail.com](davidademilua@gmail.com) [Orcid id: 0009-0006-9012-8420](Orcid id: 0009-0006-9012-8420)[ ](Orcid id: 0009-0006-9012-8420)Edoise Areghan Cybersecurity and Information Assurance, University of Central Missouri. USA. Email: [edoise.areghan@gmail.com](edoise.areghan@gmail.com) [Orcid id:: 0009-0005-5214-2646](Orcid id:: 0009-0005-5214-2646) [1.0 Introduction](1.0 Introduction) The convergence of cloud computing and machine learning (ML) represents a significant shift in how organizations process, store, and analyze data, offering solutions to a range of real-world problems. Cloud computing enableson-demand access to scalable computational resources, eliminating the need for costly infrastructure investments (Aljohani, 2023; Selvarani, et al., 2023) . ML complements this capability by leveraging algorithms to identify patterns, perform predictive analytics, and automate decision-making processes (Costa et al., 2024) . Together, these technologies empower sectors such as healthcare, manufacturing, electrical sectors and urban management to enhance efficiency, reduce costs, and improve outcomes (Fanifosi et al., 2022; Agidike et al., 2024; Ojo et al., 2023) . For instance, ML models hosted on cloud platforms have been used to optimize medical diagnostics and analyze large-scale patient data, yielding critical insights that enhance patient care (Selvarani, et al., 2023; Aljohani, 2023) . Similarly, predictive maintenance systems in manufacturing have reduced equipment downtime and operational inefficiencies, while smart cities have employed cloud-based anal

FAQs

What problems does the proposed framework address?

It targets predictive maintenance, anomaly detection, and sentiment analysis using cloud-hosted machine learning.

Which cloud platforms are used in the framework?

The framework leverages platforms such as AWS and Microsoft Azure to process large-scale datasets.

How effective are the models reported in the study?

Predictive maintenance reaches 92% accuracy and 91% F1; anomaly detection achieves 87% F1; sentiment predictions show confidence levels ranging from 85% to 98%.

Aug 4, 2026

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