Healthcare monitoring of mountaineers by low power Wireless Sensor Networks

Garg, Rajesh; Bhola, Jyoti; Soni, Surender Kumar

doi:10.1016/j.imu.2021.100775

Cited by 40 publications

(23 citation statements)

References 28 publications

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“…Cancers are definitely more effectively treated in the early stages, resulting in less physical, mental, and financial misery. Cancer is also one of those medical disorders that has no evident indications or indicators and hence can go unnoticed for a long period within the body [ 10 , 11 ]. Spatial data from testing can be acquired by utilizing fluorescence hyperspectral imaging (FHSI), which makes it to map out images at the pixel level and makes it possible for researchers to work at capacities that cannot be accomplished utilizing the more customary optical imaging strategies.…”

Section: Introductionmentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

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Section: Introductionmentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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“…BlueDot Toronto, for instance, established the first risk-based technique for recognizing the SARS-CoV-2 epidemic, which was developed by IoT 2020 by infectious disease professionals to investigate new solutions for mitigating the initial SARS pandemic. BlueDot's previous SARS research was utilized to include advanced technologies in this impressive demonstration of AI and ML in forecasting illness outbreaks [ 25 ]. In [ 26 ], the authors have developed machine learning-based framework for diabetes prediction and named it as intelligent diabetes mellitus prediction framework (IDMPF).…”

Section: Related Workmentioning

confidence: 99%

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

Mir

Jamwal

Mehbodniya

et al. 2022

Journal of Healthcare Engineering

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COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

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“…The benefits of online in healthcare involve unequalled speed and information availability, which can assist in overcoming the challenges that leading companies and patients confront. The cloud may aid in clinical trial governance and knowledge exchange, and this cutting-edge technology has altered the scope of clinical research [2,3]. e-Health technologies comprise varied practices like sensory data collection and logging, electronic computer-aided diagnosis (e-CAD) and detail sharing, cloud data upload and decentralized data access for telemedicine, etc.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

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The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

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Healthcare monitoring of mountaineers by low power Wireless Sensor Networks

Cited by 40 publications

References 28 publications

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

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