Browse Articles
Discover research articles across all indexed journals
A student academic performance prediction model based on the interval belief rule base
α-Synuclein Drives SNARE-Dependent Tubular Remodeling of Vesicles
Author Correction: A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning
Multistate Structure Determination and Dynamics Analysis Reveals a Unique Ubiquitin-Recognition Mechanism in Ubiquitin C-terminal Hydrolase
A scalable machine learning strategy for resource allocation in database
Abstract Modern cloud computing systems require intelligent resource allocation strategies that balance quality-of-service (QoS), operational costs, and energy sustainability. Existing deep Q-learning (DQN) methods suffer from sample inefficiency, centralization bottlenecks, and reactive decision-making during workload spikes. Transformer-based forecasting models such as Temporal Fusion Transformer (TFT) offer improved accuracy but introduce computational overhead, limiting real-time deployment. We propose LSTM-MARL-Ape-X, a novel framework integrating bidirectional Long Short-Term Memory (BiLSTM) for workload forecasting with Multi-Agent Reinforcement Learning (MARL) in a distributed Ape-X architecture. This approach enables proactive, decentralized, and scalable resource management through three innovations: high-accuracy forecasting using BiLSTM with feature-wise attention, variance-regularized credit assignment for stable multi-agent coordination, and faster convergence via adaptive prioritized replay. Experimental validation on real-world traces demonstrates 94.6% SLA compliance, 22% reduction in energy consumption, and linear scalability to over 5,000 nodes with sub-100 ms decision latency. The framework converges 3.2 $$\times$$ faster than uniform sampling baselines and outperforms transformer-based models in both accuracy and inference speed. Unlike decoupled prediction-action frameworks, our method provides end-to-end optimization, enabling robust and sustainable cloud orchestration at scale.
Unlocking a Water Coordination Environment in Co-Based Metal–Organic Frameworks for Advanced Nitrate-to-Ammonia Electroreduction
Deterministic and fractional-order modeling of measles dynamics with harmonic mean incidence rate and quarantine impact
Organocatalytic <i>meta</i>-C–H Hydroxylation of Azaarene <i>N</i>-Oxides
Automatic detection of cognitive events using machine learning and understanding models’ interpretations of human cognition
Abstract The pupillary response is a valuable indicator of cognitive workload, capturing fluctuations in attention and arousal governed by the autonomic nervous system. Cognitive events, defined as the initiation of mental processes, are closely linked to cognitive workload as they trigger cognitive responses. In this study, we detect cognitive events for the task-evoked pupillary response across four domains (vigilance, emotion processing, numerical reasoning, and short-term memory). The problem is framed as a binary classification. We train one generalized model and four task-specific models on 1-s pupil diameter and gaze position segments. Five models achieve MCC between 0.43 and 0.75. We report three key findings: (1) the generalized model reduces the specificity to enhance the sensitivity, illustrating the trade-off from specialization to generalization; (2) the permutation feature importance analyses show that both pupil dilation and gaze position contribute to model predictions, with task-specific models focusing on task-specific structure patterns to predict while the generalized model is using human cognitive responses; and (3) in an online simulation environment, models performance decreases by approximately 0.05 on MCC. The findings highlight the potential of machine learning applied to pupillary signals for rapid, individualized detection of cognitive events.