IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Saranya, S. S.; Fatima, N. Sabiyath

doi:10.1109/access.2022.3226583

Cited by 7 publications

(5 citation statements)

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“…The integration of various data sources into HR decision-making brings forth complex sustainability considerations. Future studies should not only assess the effectiveness of methods like the Improved Context-aware Data Fusion (ICDF) but also critically examine their long-term sustainability impacts, focusing on ethical data management and the potential risks associated with data-centric HR practices [124,125]. A critical evaluation of ethical challenges and bias in AI-driven HR systems is imperative, shedding light on the sustainability of these technologies in practice [126,127].…”

Section: Emerging Trends and Future Research Directionsmentioning

confidence: 99%

Fusing talent horizons: the transformative role of data integration in modern talent management

Asfahani

2024

Discov Sustain

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This study elucidates the transformative influence of data integration on talent management in the context of evolving technological paradigms, with a specific focus on sustainable practices in human resources. Historically anchored in societal norms and organizational culture, talent management has transitioned from traditional methodologies to harnessing diverse data sources, a shift that enhances sustainable HR strategies. By employing a narrative literature review, the research traces the trajectory of HR data sources, emphasizing the juxtaposition of structured and unstructured data. The digital transformation of HR is explored, not only highlighting the evolution of Human Resource Information Systems (HRIS) but also underscoring their role in promoting sustainable workforce management. The integration of advanced technologies such as machine learning and natural language processing is examined, reflecting on their impact on the efficiency and ecological aspects of HR practices. This paper not only underscores the imperative of balancing data-driven strategies with the quintessential human element of HR but also provides concrete examples demonstrating this balance in action for practitioners and scholars in sustainable human resources.

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Section: Emerging Trends and Future Research Directionsmentioning

confidence: 99%

Fusing talent horizons: the transformative role of data integration in modern talent management

Asfahani

2024

Discov Sustain

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“…. .Value N) * P(x) (7) The ICDF algorithm also provides better scalability and flexibility for the healthcare system. By using multiple data sources and context information, the algorithm can be easily adapted to different scenarios and environments, making it suitable for largescale healthcare systems.…”

Section: B Improved Context-aware Data Fusion (Icdf)mentioning

confidence: 99%

“…Fusion-based procedures sometimes include merging data from many sources, such as electronic health records (EHRs), to give a more complete picture of a patient's health, medical devices, and patient-generated data. However, there may be substantial problems with data security and privacy due to this connection [7]. One illustration is that the exchange of patient data between healthcare professionals may be limited by legal and ethical considerations, which can lead to a lack of data sharing between healthcare providers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network

Almaiah,

Yelisetti,

Arya

et al. 2023

Electronics

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IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.

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“…Mel-frequency cepstral coefficients (MFCCs) are already introduced in detail in the previous section. For localization, the MFCCs are calculated using the audio data [64]. The…”

Section: Mel-frequency Cepstral Coefficients For Localizationmentioning

confidence: 99%

“…Mel-frequency cepstral coefficients (MFCCs) are already introduced in detail in the previous section. For localization, the MFCCs are calculated using the audio data [64]. The results for different locations given by both datasets used in this study are given in Figure 11.…”

Section: Mel-frequency Cepstral Coefficients For Localizationmentioning

confidence: 99%

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

Alazeb,

Azmat,

Al Mudawi

et al. 2023

Sensors

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Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system’s accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system’s performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject’s location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

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IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Cited by 7 publications

References 50 publications

Fusing talent horizons: the transformative role of data integration in modern talent management

Fusing talent horizons: the transformative role of data integration in modern talent management

A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

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