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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6% [ABSTRACT FROM AUTHOR]

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body's temperature exceeds 104 °F (40 °C). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage. [ABSTRACT FROM AUTHOR]

The subject of fatigue monitoring is becoming more important in transportation and traffic management (including, for instance, the development of systems to detect and prevent driver drowsiness). People who work in offices are also susceptible to exhaustion, but there is currently no widely deployed system that is able to monitor this condition. In most cases, the driver’s eyelids will become heavy due to exhaustion after lengthy hours of driving or in the absence of mental concentration. Typically, when the driver’s concentration begins to fade, audio alert would be provided to force the drivers awake. In recent times, drowsiness is risky since it can result in an accident. Thus, a solution has been proposed to identify driver drowsiness by comparing several algorithms to find improved accuracy and execution time. Besides, this system will alert the driver with an audible warning in the event of drowsiness is detected. [ABSTRACT FROM AUTHOR]

The main objective of this study is to identify the factors that affect consumers' intention to use AR for their online buying context. This study integrated three theories with additional constructs of perceived enjoyment and personal innovativeness. Data was collected through a personal-administered questionnaire from 265 respondents. In this study, PLS based Structural Equation Modeling was used to analyze the data. The study results show that all the estimated relationships were found significant and positive except the relationship between social identity and attitude, social identity and perceived usefulness, and personal innovativeness and perceived ease of use. Attitude significantly and positively mediates the association between two constructs of the Technology Acceptance Model (TAM) and behavioral intention. The findings of this study also reveal that self-efficacy significantly moderates the association between attitude and behavioral intention. This study validated a new model developed for examining the intention to use augmented reality in the online buying context. Here, a model is developed by integrating the Technology Acceptance Model (TAM), Social Identity Theory, and Self-identity Theory with additional constructs of perceived enjoyment and personal innovativeness. This study will contribute to the knowledge extension of using augmented reality in the online buying context. [ABSTRACT FROM AUTHOR]