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Volume 13 | Issue 5 | Year 2026 | Article Id. IJEEE-V13I5P115 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I5P115

Machine Learning for Multi-User Remote Laboratory Optimization: Deep Face Verification and Reinforcement Learning-based Pipeline Scheduling

Niket Amoda, Lochan Jolly

Received Revised Accepted Published
23 Feb 2026 22 Mar 2026 21 Apr 2026 30 May 2026

Citation :

Niket Amoda, Lochan Jolly, "Machine Learning for Multi-User Remote Laboratory Optimization: Deep Face Verification and Reinforcement Learning-based Pipeline Scheduling," International Journal of Electrical and Electronics Engineering, vol. 13, no. 5, pp. 183-195, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I5P115

Abstract

Remote laboratory systems that allow multiple users to interact at the same time present serious problems related to strong user identification and smart distribution of shared hardware resources. This work proposes a unified machine learning framework composed of two synergistic modules. The first is a lightweight Siamese Convolutional Neural Network designed for edge-deployable facial identity verification, trained with a triplet loss objective incorporating curriculum-based learning. The second module is a Double Q-learning scheduler utilizing prioritized experience replay to allocate laboratory resources dynamically. On a custom dataset of 10,000 facial images spanning 50 subjects, the verification module attains a true acceptance rate of 96.30% at a false acceptance rate threshold of 0.10%, with per-inference latency of 47 ms when executed on a Raspberry Pi 4B equipped with a Coral TPU accelerator. Post-training INT8 quantization combined with structured channel pruning reduces the model footprint to 8.20 MB. The scheduling module increases experimental throughput by a factor of 2.3 relative to first-come-first-served baselines while sustaining a Jain fairness index of at least 0.92 and eliminating user starvation entirely. A supporting bidirectional long short-term memory network predicts per-session latency with a root mean squared error of 3.20 ms (coefficient of determination R² = 0.94). Comprehensive Validation encompasses five-fold cross-validation, component-wise ablation experiments, and a sixteen-week production trial involving fifty engineering students who completed 3,847 authenticated laboratory sessions. The principal technical contributions comprise triplet loss training with curriculum-guided negative mining, experience replay-driven scheduling optimization, and edge-targeted model compression, collectively establishing performance benchmarks for deploying machine learning within educational laboratory infrastructure.

Keywords

Remote Laboratory Systems, Facial Identity Verification, Reinforcement Learning Scheduling, Edge inference, Convolutional Neural Networks, Pipeline Resource Allocation, Biometric Authentication, Bidirectional LSTM, Model Compression.

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