Advanced Expert System Using Particle Swarm Optimization Based Adaptive Network Based Fuzzy Inference System to Diagnose the Physical Constitution of Human Body

Mohammed, Amin Salih; Yuvaraj, D.; Porkodi, V.; Sivaram, M.; Yuvaraj, N.

doi:10.1007/978-981-13-8300-7_29

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…A node-level strategy has resorted to a timing window technique where the amount of trust is expected [23]. Using the confidence values calculated in a specific time range and discarding older entries.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

Vadivel¹,

Yuvaraj²,

Murthy³

et al. 2022

Computer Systems Science and Engineering

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Wireless Sensor Networks (WSN) are commonly used to observe and monitor precise environments. WSNs consist of a large number of inexpensive sensor nodes that have been separated and distributed in different environments. The base station received the amount of data collected by the numerous sensors. The current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large scale. The Trust-Based Adaptive Acknowledgement (TRAACK) Intrusion-Detection System for Wireless Sensor Networks (WSN) is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization (MPSO) has been proposed to predict knot confidence. Simulations were run for non-malicious networks (0% malicious) and different percentages of malicious nodes were discussed. The findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization (MPSO) packet delivery rate outperforms TRAACKPSO by 3.3% with 0% malicious nodes. Similarly, the packet delivery rate of TRAACKMPSO is 30% malicious, 3.5% better than TRAACKPSO in WSN.

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Section: Methodsmentioning

confidence: 99%

Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

Vadivel¹,

Yuvaraj²,

Murthy³

et al. 2022

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

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“…Therefore, information on frequency spatial arrangement, LBP information [6] and the gray level co-occurrence matrix (GLCM) [7] are the most appropriate structural features. This information helps to better analyze anomalies than removal with textural descriptors [14].…”

Section: Introductionmentioning

confidence: 99%

Liver Lesions Diagnosis Using Gwo-Anfis Framework

Babu¹,

Nanthakumar²

2019

IJEAT

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In this paper, liver abnormality is detected using an improved classification model that consists of series of process. The study reveals the liver condition to be normal or abnormal using the proposed system. The study uses both structural and statistical analysis, where both these analysis is combined with the process of classification. Initially, the noises are removed using Impulse Noise Removal and then the Segmentation is carried out using Gray Wolf Optimisation (GWO) algorithm. After the segmentation, the features are extracted through Local Binary Patters (LBP) Operator and then Artificial Neural Network Fuzzy Inference System (ANFIS) classifies the liver regions as malignant or benign. Various images collected from laboratories are used in both training and testing stages. The results are validated in terms of two different texture feature extractors namely, GLCM and LBP. The result shows that the proposed classifier using GLCM classifier obtains improved classified patterns than the existing methods.

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“…The NP-Hard [6] is this problem. The majority of algorithms are therefore based on heuristics for the partial exploration of the combinatorial search area [7], [8], [9], [10], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Biclustering Gene Expression Data Using Genetic Simulated Annealing Algorithm

Nanthakumar¹

2019

IJEAT

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DNA microarray technology produces gene expression matrix that consists of an inexorably missing entries due to poor experimental procedures. The missing values are predicted in the matrix for gene expression data are considered to be essential, since most algorithms analyse the gene expression that usually needs a matrix without missing values. In order to address this issue, the present study biclustering Genetic based Simulated Annealing (Genetic SA) algorithm to predict the items that are missing in the gene expression data. The present study uses biclustering method that is considered to be essential for clustering the gene expression data. The performance evaluation shows that the proposed Genetic SA for gene data expression predicts the missing items in an accurate manner than the existing methods.

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Advanced Expert System Using Particle Swarm Optimization Based Adaptive Network Based Fuzzy Inference System to Diagnose the Physical Constitution of Human Body

Cited by 15 publications

References 7 publications

Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

Liver Lesions Diagnosis Using Gwo-Anfis Framework

Biclustering Gene Expression Data Using Genetic Simulated Annealing Algorithm

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