Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management
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| Publicado en: | International Journal of Advanced Computer Science and Applications vol. 16, no. 6 (2025) |
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| Autor principal: | |
| Publicado: |
Science and Information (SAI) Organization Limited
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160661 |2 doi | |
| 035 | |a 3231644681 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Resource planning and cost optimization are essential elements of effective project management. Conventional models are weak in changing environments because they cannot keep pace with intricate task interdependencies and changing project constraints. To overcome such weaknesses, this research envisions an LSTM-based predictive analytics model that deploys temporal trends and past project information for precise predictions of task duration, resource allocations, and possible delays. The proposed method combines sequential data modeling with Long Short-Term Memory (LSTM) networks, along with data preprocessing and optimization, to enhance project scheduling and cost control decision-making. With TensorFlow implementation, the proposed LSTM-PRO model resulted in a Mean Squared Error (MSE) of 0.0025, Root Mean Squared Error (RMSE) of 0.05, and an R² score of 0.96, which was far better than ARIMA and other baseline models. The model resulted in a cost saving of 20% on project costs and 20% rise in resource utilization from 65% to 85%. The outcome proves the effectiveness and applicability of the model in actual project settings. | |
| 653 | |a Resource utilization | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Project management | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Changing environments | ||
| 653 | |a Effectiveness | ||
| 653 | |a Scheduling | ||
| 653 | |a Predictive analytics | ||
| 653 | |a Computer science | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Trends | ||
| 653 | |a Decision making | ||
| 653 | |a Computer engineering | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Automation | ||
| 653 | |a Time series | ||
| 653 | |a Cost control | ||
| 653 | |a Strategic planning | ||
| 653 | |a Risk management | ||
| 653 | |a Resource management | ||
| 653 | |a Case studies | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 6 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231644681/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231644681/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |