Traditional picture fuzzy sets (PFSs) cannot capture temporal dynamics in dynamic uncertainty scenarios. This paper proposes dynamic picture fuzzy sets (DPFSs) and corresponding similarity measures. DPFSs integrate a discrete time set into the PFS framework, retaining the four-dimensional membership structure (positive, neutral, negative, refusal) while modeling time-dependent variations, and their basic operations and key properties are rigorously proven. Six types of dynamic similarity measures (Hamming, Euclidean, Minkowski, Chebyshev, Lambert, absolute sum-difference) are constructed, which are divided into Type-I (excluding refusal degree) and Type-II (including refusal degree), and time weights with a decay factor are incorporated to emphasize the cumulative effect of recent data. Two practical applications validate the effectiveness of the proposed methods: time-aware supplier selection (a multi-criteria decision-making scenario) can consistently identify the optimal alternative by amplifying the advantages of recent performance, and type 2 diabetes risk assessment (a medical diagnosis scenario) can capture dynamic symptom progression to provide clinically meaningful risk classifications, with both applications showing high consistency across different measures.
Wireless Body Area Networks (WBANs) are critical enablers of real-time, non-invasive healthcare monitoring, enabling continuous acquisition and wireless transmission of vital physiological signals such as electrocardiogram (ECG), heart rate, and motion data. However, their practical deployment faces significant challenges due to stringent energy constraints, dynamic on-body channel variations induced by mobility, and multi-class Quality-of-Service (QoS) requirements, particularly ultra-low latency ( ≤ 18 ms ) for emergency alerts and high reliability ( ≥ 99.5% PDR) for diagnostic data. Conventional routing protocols often rely on static topologies or reactive mechanisms that fail under the coupled spatio-temporal variability of physiological dynamics and wireless channels. To address these limitations, we propose QuantumEERP-Net, a novel, intelligent, and self-optimizing routing framework that synergistically integrates Multi-Agent Dueling Double Deep Q-Networks (D3QN), a Quantum-Inspired Dual Prediction Model (Q-DPM), and Context-Aware Adaptive Routing into a fully decentralized architecture. Each sensor node operates as an autonomous agent, learning optimal forwarding policies through continuous interaction with a dynamic environment modeled using log-normal shadowing ( σ = 6.500 dB ) and Markovian mobility states. The Q-DPM, implemented via a variational quantum circuit with parameter-shift gradient estimation, jointly predicts future sensor values (e.g., ECG at 125 Hz ) and residual energy levels with mean error < 0.008 J , enabling proactive suppression of redundant transmissions and substantial energy savings. A real-time context-aware engine fuses IMU and ECG inputs to detect mobility patterns (static/walking/running) and critical events (HR > 180 bpm , falls) with accuracy ≥ 0.991 , triggering low-latency routing overrides for emergency traffic. Extensive simulations over 20 000 cycles using a comprehensive hybrid dataset of 450,000 labeled samples (150,000 per class), with a held-out test set of 150,000 samples (50,000 per class), demonstrate that QuantumEERP-Net achieves a First Node Dead (FND) of 10,250 rounds (an 83.0 ≈ 3.600 J per cycle, ensuring minimal computational overhead. These gains result from the tight integration of link-quality-aware forwarding, NSGA-II-based multi-objective optimization, and predictive intelligence. By unifying quantum-inspired computation, deep reinforcement learning, and physiological context awareness, QuantumEERP-Net establishes a new paradigm for secure, energy-efficient, and adaptive communication in next-generation WBANs, offering a scalable solution for mission-critical telemedicine and remote patient monitoring systems.
Internal due-date assignment begins by estimating the completion time of a possible order, typically based on the completion of its last job. However, most machine learning and deep learning approaches focus on overall estimation accuracy, which may result in inaccurate predictions for individual cases. Consequently, the trustworthiness of individual internal due-date assignments remains uncertain, an issue that has received limited attention. To address this gap, this study proposes a neutrosophic neural network approach. Initially, a fuzzy deep neural network is constructed and trained to estimate the completion time of a possible order. The resulting predictions are then converted into neutrosophic sets utilizing three membership functions. The truth and falsity memberships are determined from global estimation accuracy based on root mean squared error, whereas the indeterminacy membership incorporates local estimation accuracy and local learning sufficiency. An internal due-date assignment is considered trustworthy when its indeterminacy falls below a prespecified threshold. When users believe that AI applications are trustworthy, their cognition shifts, which can reduce the cognitive burden on users. In addition, both fuzzy logic and deep neural network (DNN) are well-known and widely used biologically algorithms. The proposed methodology is applied to a real-world case in a wafer fabrication facility (wafer fab). Experimental results indicate 44
Young adulthood is the peak of cognitive performance, yet there exist individual differences in learning, problem-solving, and decision-making abilities. We recorded eye movements and electrodermal activity (EDA) from 89 young adults performing dual n-back tasks to investigate whether psychophysiological signals can reveal these differences. Machine learning (ML) models reliably classified individuals with high versus low working memory capacity (WMC), with eye-tracking features (fixation duration, pupil diameter, saccadic velocity) emerging as dominant predictors and EDA providing complementary arousal-related cues. By extending permutation-based feature importance, we identified a minimal set of psychophysiological measures enabling robust classification across participants. Our subsequent counterfactual ablation of this psychophysiological metric set further revealed an asymmetry wherein low-WMC individuals could be reclassified as high-WMC through perturbations of gaze and arousal features, whereas high-WMC individuals remained resistant to such manipulation. This suggests high WMC may emerge as a stable trait, while low WMC may be enhanced via interventions. Together, these findings establish multimodal ML as a robust framework for quantifying WMC through physiological markers, namely eye and EDA metrics, and highlight promising pathways for enhancing the cognitive capacity of individuals.
Student distraction remains a critical barrier to effective learning in classroom environments, yet existing detection methods relying on manual observation or post-session feedback are inherently subjective, time-intensive, and ill-suited for real-time adaptive teaching. This study presents the Artificially Intelligent Technology for Student Engagement Assessment in Classroom Environments (AI-TEACH), a novel co-teacher paradigm that automatically identifies and quantifies student distraction through multimodal fusion of asynchronized audio and video streams captured via classroom surveillance cameras. AI-TEACH integrates YOLO-NAS and ByteTrack for real-time student detection and tracking, MediaPipe for behavioral cue extraction, and Silero-VAD with Wav2Vec2-SVM for audio-based emotional distraction classification; a BiLSTM network fuses these multimodal features to generate per-incident severity scores, and a session-wide engagement report accessible through an interactive instructor dashboard. The framework was validated on a curated dataset of classroom recordings and further evaluated through a controlled experiment involving 200 students divided into experimental and control groups, achieving 90
