Issues of Clinical Identity Verification for Healthcare Applications over Mobile Terminal Platform

Ahmad, Sultan; Abdeljaber, Hikmat A. M.; Nazeer, Jabeen; Uddin, Mohammed Yousuf; Lingamuthu, Velmurugan; Kaur, Amandeep

doi:10.1155/2022/6245397

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…After receiving the medical data, analysis can be done using a proper AI-based data transformation and interpretation technique [ 27 ]. In case of serious problems, doctors or other medical requirements can be approached with the help of smart AI-based applications in smartphones [ 28 ]. In nonserious cases, self-preventive measures can be taken.…”

Section: Related Workmentioning

confidence: 99%

“…System to be Considered during IoMT Network Design AI provides the capability of a computer or robot, which is controlled by a computer system for performing tasks that are usually done by humans via their intelligence [28,83]. In a smart healthcare system with proper data interpretation techniques, a machine can also monitor health parameters Computational Intelligence and Neuroscience using the implanted/wearable sensors on the body of the person under observation [84,85].…”

Section: Challenges Within a Smart Healthcarementioning

confidence: 99%

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Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

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Section: Related Workmentioning

confidence: 99%

Section: Challenges Within a Smart Healthcarementioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…The proposed model obtains 98.2 accuracies as compared with existing classifiers [ 34 ]. Over the last five years, Korea University Guro Hospital has accumulated data in the form of an EMR (electronic medical record) [ 35 ]. Various ML methods were then employed with the help of cross-validation.…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy

Dash

Routray

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

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“…Although the above models have achieved improvement in the accuracy of summary generation, the recurrent neural network and its variants are all time-step-based sequence structures, which seriously hinders the parallel training of the model [16][17][18], resulting in the inference process being limited by memory, resulting in reduced encoding and decoding speed of the summary generation model, and increased training overhead [19][20][21][22][23]. On the other hand, the above works optimize the model to maximize the ROUGE index or maximum likelihood without considering the coherence or fluency of the summary sentence [24][25][26] and relying on the ground-truth value of the annotated summary text in advance.…”

Section: Related Workmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

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The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.

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Issues of Clinical Identity Verification for Healthcare Applications over Mobile Terminal Platform

Cited by 11 publications

References 36 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

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