A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications

Lavanya, S.; Prasanth, A.; Jayachitra, S.; Shenbagarajan, A.

doi:10.1016/j.measurement.2021.109771

Cited by 84 publications

(39 citation statements)

References 29 publications

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“…This work considered six performance metrics to compute the performance of the proposed ensemble classifier. The mathematical formulation of these metrics is as follows [35][36][37][38] :…”

Section: Resultsmentioning

confidence: 99%

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Kannan¹

2022

Concurrency and Computation

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With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing the healthcare quality and assists in taking better decisions making during emergency times. Most of the existing research concentrates on modeling an automated prediction model for heart disease and the risk factors. Nevertheless, accurate classification is a vital challenge in heart disease diagnosis where the managing of high‐dimensional data increases the execution time of existing classifiers. In this paper, a new ensemble model has been proposed with the aid of random subspace and K‐nearest neighbor (RSS‐KNN) scheme for earlier prediction of heart disease. Primarily, the proposed scheme implements an isolation‐based outlier removal mechanism to eradicate the noises and outliers in the distributed data. Subsequently, the essential features are identified using RSS by varying the testing and training errors in the evaluation phase. The extracted features are then fed into KNN for the accurate classification of heart disease. Finally, an enhanced squirrel optimizer has been employed in the proposed scheme to obtain the global results which balance the exploration as well as exploitation issues and eliminate the over‐fitting problems. The simulation results manifest that the accuracy (without features) of the proposed ensemble RSS‐KNN scheme in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, and specificity is 98.10% when compared with existing state‐of‐the‐art classifiers.

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“…This work considered six performance metrics to compute the performance of the proposed ensemble classifier. The mathematical formulation of these metrics is as follows [35][36][37][38] :…”

Section: Resultsmentioning

confidence: 99%

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Kannan¹

2022

Concurrency and Computation

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“…The finest solutions are acquired while running an optimization algorithm for multiple times R . This metric has been computed through the following equation,

Standard deviation = \sqrt{\frac{1}{R - 1} \sum {((), {X_{k}}^{*} goodbreak- Mean)}^{2}} .

Accuracy : The accuracy depicts that how precisely the optimization algorithm classifies the features 36,37 . It can be determined as,

Accuracy = \frac{TP + TN}{TP + FP + FN + TN} \times 100,

where TP ‐ true positive predicts the disease accurately of an unhealthy patient, FP ‐ false positive denotes the inaccurate classification of an unhealthy patient, TN ‐ true negative indicates the accurate prediction of a healthy patient and FN ‐ false negative represents the inaccurate classification of a healthy patient.…”

Section: Resultsmentioning

confidence: 99%

An efficient clinical support system for heart disease prediction using TANFIS classifier

Sekar

Prasanth

Haleem

et al. 2021

Computational Intelligence

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In today's world, the advancement of telediagnostic equipment plays an essential role to monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of treatment of patients and predominantly provides an expeditious diagnostic recommendation from clinical experts. However, the feature extraction is a major challenge for heart disease prediction where the high dimensional data increases the learning time for existing machine learning classifiers. In this article, a novel efficient Internet of Things-based tuned adaptive neuro-fuzzy inference system (TANFIS) classifier has been proposed for accurate prediction of heart disease. Here, the tuning parameters of the proposed TANFIS are optimized through Laplace Gaussian mutation-based moth flame optimization and grasshopper optimization algorithm. The simulation scenario can be carried out using11 different datasets from the UCI repository. The proposed method obtains an accuracy of 99.76% for heart disease prediction and it has been improved upto 5.4% as compared with existing algorithms. K E Y W O R D Sclassification, grasshopper optimization algorithm, heart disease prediction, internet of things, moth flame optimization 610

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“…This method is worked by resource node later collecting the knowledge from all installed nodes. Lavanyaa and Prasanth [31] proposed heterogeneous fault management method with energy efficient mechanism for IoT based wireless sensor network environment. They used support vector machine classification algorithm categorize faults falls in heterogeneous category where algorithm parameters were optimized by Grasshopper optimization methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective modelling of sinkhole detection algorithm for edge‐based Internet of Things (IoT) sensing devices

2022

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The sinkhole attacks for Internet of Things (IoT) situations can overcome the network and interrupt communication. Sinkhole attacker nodes can advertise the best possible shortest path towards the destination, and once the normal node starts transferring the packets by the given path, the attacker node starts troublemaking the flow of the network. In this environment, the destination is unable to receive proper information or may not receive complete information. Sinkhole attacked network keeps the problems like the enhanced end-to-end delay, less throughput, and reduced packet delivery. Furthermore, it can affect other network constraints. So, it has become important to design an effective model to prevent the IoT environment from sinkhole attacks. In this article, an intrusion detection model is proposed to protect the IoT environment from sinkhole attacks. A model proposed by using rich resourced edge nodes to detect various kinds of sinkhole attacker nodes by exchanging messages. A well-known NS2 simulator is used for the practical implementation of the model. The proposed model attains more than 95% detection rate with a near about 1.4% false-positive rate, which seems better than previously given schemes. Finally, the proposed scheme is the appropriate match for a sensitive platform like a monitoring and security system.

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A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications

Cited by 84 publications

References 29 publications

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

An efficient clinical support system for heart disease prediction using TANFIS classifier

Effective modelling of sinkhole detection algorithm for edge‐based Internet of Things (IoT) sensing devices

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