The primary assumption of Neuroscience is that all experiences are strongly correlated with or caused by the specifics of brain structures and their particular dynamics. The profound experiences attributed to the “se...The primary assumption of Neuroscience is that all experiences are strongly correlated with or caused by the specifics of brain structures and their particular dynamics. The profound experiences attributed to the “sensed presence” and their cultural anthropomorphisms such as deities and gods are persistent reports in human populations that are frequently associated with permanent changes in behavior, reduced depression and alleviation of pain. The majority of traditional clinical observations and modern imaging techniques have emphasized the central role of right temporal lobe structures and their directly related networks. The experimental simulation of sensed presences which can result in attributions to spiritual, deity-based or mystical sources within the clinical laboratory by the application of physiologically-patterned magnetic fields across the temporal lobes through our God Helmet requires the same precision of technology that is essential for synthesizing molecular treatments for modifying anomalous behavior, depression and pain. Despite the clinical utility of these simulated conditions within Neuroscience and Medicine, misinformation concerning the bases and efficacy of this new technology persist. Here we present detailed technical clarifications and rebuttals to refute these misconceptions. A Hegelian approach to this delay of development and impedance provides a context through which the ultimate synthesis and application of this technology may be accommodated in the near future.展开更多
Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.Howe...Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.However,existing models measure stress with physiological signals such as PPG,EDA,and blink data,which could not identify the stress level accurately.Additionally,the works face challenges with limited data,inefficient spatial relationships,security issues with health data,and long-range temporal dependencies.In this paper,we have developed a federated learning-based stress detection system for IT and hospital workers,integrating physiological and behavioral indicators for accurate stress detection.Furthermore,the study introduces a hybrid deep learning classifier called ResTFTNet to capture spatial features and complex temporal relationships to detect stress effectively.The proposed work involves two localmodels and a globalmodel,to develop a federated learning framework to enhance stress detection.Thedatasets are pre-processed using the bandpass filter noise removal technique and normalization.The Recursive Feature Elimination feature selection method improves themodel performance.FL aggregates thesemodels using FedAvg to ensure privacy by keeping data localized.After evaluating ResTFTNet with existing models,including Convolution Neural Network,Long-Short-Term-Memory,and Support VectorMachine,the proposed model shows exceptional performance with an accuracy of 99.3%.This work provides an accurate and privacy-preserving method for detecting stress in hospital and IT staff.展开更多
文摘The primary assumption of Neuroscience is that all experiences are strongly correlated with or caused by the specifics of brain structures and their particular dynamics. The profound experiences attributed to the “sensed presence” and their cultural anthropomorphisms such as deities and gods are persistent reports in human populations that are frequently associated with permanent changes in behavior, reduced depression and alleviation of pain. The majority of traditional clinical observations and modern imaging techniques have emphasized the central role of right temporal lobe structures and their directly related networks. The experimental simulation of sensed presences which can result in attributions to spiritual, deity-based or mystical sources within the clinical laboratory by the application of physiologically-patterned magnetic fields across the temporal lobes through our God Helmet requires the same precision of technology that is essential for synthesizing molecular treatments for modifying anomalous behavior, depression and pain. Despite the clinical utility of these simulated conditions within Neuroscience and Medicine, misinformation concerning the bases and efficacy of this new technology persist. Here we present detailed technical clarifications and rebuttals to refute these misconceptions. A Hegelian approach to this delay of development and impedance provides a context through which the ultimate synthesis and application of this technology may be accommodated in the near future.
文摘Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.However,existing models measure stress with physiological signals such as PPG,EDA,and blink data,which could not identify the stress level accurately.Additionally,the works face challenges with limited data,inefficient spatial relationships,security issues with health data,and long-range temporal dependencies.In this paper,we have developed a federated learning-based stress detection system for IT and hospital workers,integrating physiological and behavioral indicators for accurate stress detection.Furthermore,the study introduces a hybrid deep learning classifier called ResTFTNet to capture spatial features and complex temporal relationships to detect stress effectively.The proposed work involves two localmodels and a globalmodel,to develop a federated learning framework to enhance stress detection.Thedatasets are pre-processed using the bandpass filter noise removal technique and normalization.The Recursive Feature Elimination feature selection method improves themodel performance.FL aggregates thesemodels using FedAvg to ensure privacy by keeping data localized.After evaluating ResTFTNet with existing models,including Convolution Neural Network,Long-Short-Term-Memory,and Support VectorMachine,the proposed model shows exceptional performance with an accuracy of 99.3%.This work provides an accurate and privacy-preserving method for detecting stress in hospital and IT staff.
