A Secure IoT-Fog Enabled Smart Decision Making system using Machine Learning for Intensive Care unit

Banerjee, Anwesha; Mohanta, Bhabendu Kumar; Panda, Soumyashree S.; Jena, Debasish; Sobhanayak, Srichandan

doi:10.1109/aisp48273.2020.9073062

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…Machine learning can also be integrated with smart IoT devices for collecting patient's data accurately and those IoT devices need to have proper security protection 3 where machine learning can be a solution, . Also, the IoT devices can be used to gather physiological signals of the patients for further processing, which is supported by machine learning techniques, Banerjee et al (2020). The machine learning techniques can be solved by using Nature Inspired algorithms as discussed by Kauser et al (2017).…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Liver Disease Detection

Pan

Mukhopadhyay²,

Samanta³

2022

International Journal of Healthcare Information Systems and Informatics

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Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Liver Disease Detection

Pan

Mukhopadhyay²,

Samanta³

2022

International Journal of Healthcare Information Systems and Informatics

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“…The authors used the k-nearest neighbor approach to classify and validate the model. In 2020, Banerjee [25] proposed a model that uses machine learning to make decisions in the intensive care unit (ICU) under the fog environment of the Internet of Things. The proposed model performed real-time processing by bringing the computation closer to the data source.…”

Section: Machine Learning In Iot Enabled Fog-cloud Environmentmentioning

confidence: 99%

Early Urine Infection Prediction Framework using XGBoost Ensemble Model in IoT-Fog Environment

Gupta

Singh

2022

Preprint

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Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoTbased sensors for data collection, followed by data encoding and Infectious Risk Factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the healthrelated information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out and results are calculated based on real-time patients’ data. The statistical results of accuracy(91.45%), specificity(95.96%), sensitivity(84.79%), precision(95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.

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“…Authors in Ref. [24] provides a secure decision-making solution for the Internet of ings based on cloud computing. Accordingly, machine learning alongside IoT based on fog computing has been used to provide a safe experience in healthcare systems.…”

Section: Related Workmentioning

confidence: 99%

Security-Level Improvement of IoT-Based Systems Using Biometric Features

Moradi

Moradkhani

Tavakoli

2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is reported as a main research topic in the current decade. It will be possible to connect smart devices to each other using IoT, a platform such as the Internet. However, the expansion and intrusion of such a large network raises some new security issues and risks related to the disclosure of user confidential information where these devices are subject to hacker threats and intrusions. Traditional security systems were password based. In this paper, after reviewing the actions taken in this regard, the improvement level of biometric security compared with traditional password-based methods will be proven in section three using the Markov model. By considering the results of the evaluation, the probability of occurrence of security problems is decreased by 90.71% by applying biometric features. Then, multi-layer security architecture with biometric features and coding systems is suggested to increase security. In the first layer, the fingerprint recognition algorithm is dependent on the module, and the U.are.U 5100 module provides more security than others. In the second layer, the Hash mechanism of the MD5 algorithm is, on average, 63.21% more efficient. By determining the properties of the first two architectural layers and ultimately for the IoT application layer, empirical methods and hardware platforms for the Internet of things are used. Concerning the simulation results, the suggested mechanism enhances the system security by 120.38% on average, which is 106.23, 110.45, and 144.46% of relative improvement compared with IoT sensors, controller layer mechanisms, and application layer mechanisms, respectively.

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A Secure IoT-Fog Enabled Smart Decision Making system using Machine Learning for Intensive Care unit

Cited by 17 publications

References 12 publications

RETRACTED: Liver Disease Detection

RETRACTED: Liver Disease Detection

Early Urine Infection Prediction Framework using XGBoost Ensemble Model in IoT-Fog Environment

Security-Level Improvement of IoT-Based Systems Using Biometric Features

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