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Intelligent Thermal Regulation Architecture Incorporating Gender-Specific Adaptation Strategies
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Keywords
Thermal Regulation
Emotion Recognition
Gender Adaptation
Embedded Systems
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Abstract
The rapid evolution of embedded and intelligent computing systems has enabled the integration of adaptive environmental control mechanisms within everyday devices such as smartphones, vehicles, and smart appliances. This study proposes an intelligent thermal regulation architecture that incorporates gender-specific adaptation strategies, leveraging multimodal inputs with particular emphasis on speech-based emotion recognition. The research builds upon advancements in acoustic signal processing, machine learning classification techniques, and affective computing to design a system capable of dynamically adjusting thermal conditions based on inferred emotional and physiological states. A hybrid framework combining feature extraction, dimensionality reduction, and ensemble classification is introduced to enhance responsiveness and personalization. The methodology integrates signal processing techniques such as Fourier analysis and wavelet entropy, alongside machine learning models including Support Vector Machines (SVM), Hidden Markov Models (HMM), and ensemble classifiers. Results indicate that incorporating gender-specific behavioral and emotional patterns significantly improves thermal comfort prediction accuracy. The study contributes to the development of context-aware embedded systems and highlights implications for human-centric design in intelligent environments.
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