Data Cleaning in Knowledge Discovery Database-Data Mining (KDD-DM)

Rahman, Fauziah Abdul; Kassim, Rahimah; Baharum, Zirawani; Noor, Helmi Adly Mohd; Haris, Norhaidah Abu

doi:10.35940/ijeat.f1100.0986s319

Cited by 3 publications

(1 citation statement)

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“…In this research process, the data collection phase is a critical step involving the application of the knowledge discovery in database (KDD) method (Molina‐Coronado et al, 2020; Nurhachita & Negara, 2021; Plotnikova et al, 2020; Rahman et al, 2019). The KDD method is used to extract valuable information from a dataset related to milk quality.…”

Section: Methodsmentioning

confidence: 99%

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Hadi,

Sugiono,

Mizar

et al. 2024

J Food Process Engineering

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This research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real‐time decisions by analyzing critical temperature and duration data essential for the low‐temperature long‐time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1‐score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300‐L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set‐point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set‐point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector.Practical ApplicationsThe article “Enhancing Low‐Temperature Long‐Time Milk Pasteurization Process with a C4.5 Algorithm‐Driven AIoT System for Real‐Time Decision‐Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts prevent downtime. The system's scalability supports production expansion with minimal resource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry.

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Section: Methodsmentioning

confidence: 99%

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Hadi,

Sugiono,

Mizar

et al. 2024

J Food Process Engineering

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Diagnostic analysis for outlier detection in big data analytics

Ridzuan

Zainon

2022

Procedia Computer Science

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Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

et al. 2020

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This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime.

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Data Cleaning in Knowledge Discovery Database-Data Mining (KDD-DM)

Cited by 3 publications

References 1 publication

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Diagnostic analysis for outlier detection in big data analytics

Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

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