Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

This study evaluates the effectiveness of Indonesia’s defense industry policy from 2018 to 2023, focusing on PT Pindad, a pivotal state-owned defense enterprise. Using a Balanced Scorecard (BSC) framework, the study assesses PT Pindad’s performance across financial, customer, internal process, and learning and growth perspectives. The findings reveal strengths in financial stability (Current Ratio at 115.57% in 2023) and customer satisfaction, but challenges in Return on Investment (ROI), which fell from 6% in 2022 to 5.46% in 2023, signaling a need for further internal improvements. A mediation analysis using Shape-Restricted Regression indicates that Research and Development (R&D) serves as a crucial mediator, enhancing the impact of strategic alliances and technology transfer on PT Pindad’s self-reliance, with R&D showing a positive coefficient of β = 0.53 (p < 0.01). The systematic literature review complements these findings, underscoring the role of technology transfer, human capital development, and strategic partnerships as essential components for strengthening PT Pindad’s self-reliance and global competitiveness. Recommendations are made to enhance policy effectiveness by fostering robust technology transfer mechanisms, increasing investment in human capital, and expanding strategic partnerships. This research contributes to the literature on defense industry policies by providing a comprehensive evaluation framework that informs future policy decisions.