Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The increasing complexity of modern business operations demands efficient and accurate lead time estimation to enhance decision-making processes. This study proposes a novel approach to automate lead time estimation using machine learning algorithms. Traditional lead time estimation methods often rely on manual calculations and historical averages, leading to inaccuracies and inefficiencies. In contrast, machine learning algorithms leverage historical data, contextual factors, and patterns to predict lead times dynamically. The automation of lead time estimation not only improves accuracy but also facilitates real-time decision-making. The system continuously learns from new data, adapting its predictions to changing business environments. A user-friendly interface is developed to allow easy input of relevant data and to visualize the lead time prediction.
Shivakumar Nagarajan. 2026. \u201cAutomated Lead Time Estimation for Anomaly Detection using a Machine Learning Algorithm\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 24 (GJCST Volume 24 Issue D1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: India
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Shivakumar Nagarajan, Divya T, Prasanna S (PhD/Dr. count: 0)
View Count (all-time): 254
Total Views (Real + Logic): 1286
Total Downloads (simulated): 23
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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The increasing complexity of modern business operations demands efficient and accurate lead time estimation to enhance decision-making processes. This study proposes a novel approach to automate lead time estimation using machine learning algorithms. Traditional lead time estimation methods often rely on manual calculations and historical averages, leading to inaccuracies and inefficiencies. In contrast, machine learning algorithms leverage historical data, contextual factors, and patterns to predict lead times dynamically. The automation of lead time estimation not only improves accuracy but also facilitates real-time decision-making. The system continuously learns from new data, adapting its predictions to changing business environments. A user-friendly interface is developed to allow easy input of relevant data and to visualize the lead time prediction.
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