Future Challenges of the Internet of Things in the Health Care Domain - An Overview

Smys, S.; Raj, Jennifer S.

doi:10.36548/jtcsst.2021.4.003

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Symeonidis et al (2018) implemented soft-computing schemes called LogR, SVM, CNN and NB for examining the sentiments of tweets. The academicians and researchers are open for validating the impact of DL for SA on social media networks like Twitter (Smys and Raj, 2021; Chakrabarty, 2020).…”

Section: Related Workmentioning

confidence: 99%

Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

Sonawane¹,

Kolhe

2021

IJIUS

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PurposeThe purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations.Design/methodology/approachThe sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution.FindingsFocusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity.Originality/valueThe experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.

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Section: Related Workmentioning

confidence: 99%

Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

Sonawane¹,

Kolhe

2021

IJIUS

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“…Recent healthcare IoT edge computing solutions [36] include advanced security and privacy capabilities for fall detection systems that are not considered in this study or other conventional fall detection devices [37]. Encryption and decryption operations should also be a part of trustworthy IoT-based healthcare platforms because they become crucial features for handling real-time tasks.…”

Section: Discussionmentioning

confidence: 99%

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Reyad,

Ibrahim Shehata,

Esmail Karar

2023

Computers, Materials &Amp; Continua

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Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models-K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

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“…Additionally, trauma, stress, significant life alterations, certain medications, and pre-existing medical conditions can precipitate its development [3]. Effective management of depression often requires a comprehensive approach, including therapy, pharmacological treatment, lifestyle modifications, and support networks, which can significantly mitigate symptoms and improve quality of life [4].…”

Section: Introductionmentioning

confidence: 99%

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

Abbas,

Munir,

Raza

et al. 2024

IEEE Access

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Depression constitutes a significant mental health condition, impacting an individual's emotional state, thought processes, and ability to carry out everyday tasks. Depression is defined by ongoing feelings of sadness, diminished interest in previously enjoyed activities, alterations in hunger, sleep disturbances, decreased vitality, and challenges with focus. The impact of depression extends beyond the individual, affecting society at large through decreased productivity and higher healthcare costs. In the realm of social media, users often express their thoughts and emotions through posts, which can provide insightful data for identifying patterns of depression. This research aims to detect depression early by analyzing social media user content with machine learning techniques. We have built advanced machine learning models using a benchmark depression database containing 20,000 tagged tweets from user profiles identified as depressed or non-depressed. We are introducing an innovative BERT-RF feature engineering method that extracts Contextualized Embeddings and Probabilistic Features from textual input. The Bidirectional Encoder Representations from Transformers (BERT) model, based on the Transformer architecture, is used to extract Contextualized Embedding features. These features are then fed into a random forest model to generate class probabilistic features. These prominent features aid in enhancing the identification of depression from social media. In order to classify tweets using the features derived from the BERT-RF features selection step, we have used five popular classifiers: Random Forest (RF), Multilayer Perceptron (MLP), K-Neighbors Classifier (KNC), Logistic Regression (LR), and Long Short-Term Memory (LSTM). Evaluation experiments show that our approach, using BERT-RF for feature engineering, enables the Logistic Regression model to outperform state-of-the-art methods with a high accuracy score of 99%. We have validated the results through k-fold cross-validation and statistical T-tests. We achieved 99% k-fold accuracy during the validation of the proposed approach. This research contributes significantly to computational linguistics and mental health analytics by providing a robust approach to the early detection of user depression from social media content.

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Future Challenges of the Internet of Things in the Health Care Domain - An Overview

Cited by 12 publications

References 26 publications

Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

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