Neuro Fuzzy System with Hybrid Ant Colony Particle Swarm Optimization (HASO) and Robust Activation

Patro, Pramoda

doi:10.5373/jardcs/v12sp3/20201312

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AlexNets were used to classify benign and malignant BCs from images [8] where their classification results showed 6% more recognitions when compared to traditional machine learning approaches. Pre-trained CNN's feature vectors were used for extracting DeCAF features and used as inputs to the classifier [9]. The proposal was an example of multiple instances learning frameworks for CNNs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Convolutional Neural Network with Residual Neural Network for Breast Cancer Prediction Using Mammography Images

2023

IJIES

View full text Add to dashboard Cite

BC (Breast Cancer) is predominantly prevalent in women and is the leading cause of mortality due to cancers which can be reduced using mammography screenings. The use of CNNs (Convolution Neural Networks), a type of deep learning method has proven to be highly successful in image identifications. However, the quality of acquired mammographic images is found to be low while being used by detection models and hence this work proposed hybrid MLTs (Machine learning Techniques) for overcoming low-quality issues in the prediction of BCs. Initially, Statistical correlation analysis-based pre-processing is introduced for improving classifier performances followed by a hybrid model which predicts BCs effectively. This work also introduces a novel building block called Fuzzy Scoring based Resnets (Residual Networks) and CNNs called FS-Resnet CNNs for optimizing networks. The proposed FS-Resnet CNN model is computationally efficient, less sensitive to noises, and efficient in memory usage. Experimental results show that the proposed model achieves 95% accuracy ,95.45% precision, 93% recall rate, 94.21% f-measure and 18.113(S)-time complexity.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid Convolutional Neural Network with Residual Neural Network for Breast Cancer Prediction Using Mammography Images

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…It is widely applied as an optimization technique in areas like communications, electronics, electrical, manufacturing, grids, cloud computing, algorithms, numerical optimization, etc. [28][29][30][31][32][33][34][35][36][37][38][39][40]. PSO can be extended to non-differentiable, non-linear, large search space issues, and provides better performance with decent quality [9].…”

Section: Introductionmentioning

confidence: 99%

Improved PSO Performance using LSTM based Inertia Weight Estimation

Pawan¹,

Prakash²

2020

IJACSA

View full text Add to dashboard Cite

Particle Swarm Optimization (PSO) is first introduced in the year 1995. It is mostly an applied populationbased meta-heuristic optimization algorithm. PSO is diversely used in the areas of sciences, engineering, technology, medicine, and humanities. Particle Swarm Optimization (PSO) is improved its performance by tuning the inertia weight, topology, velocity clamping. Researchers proposed different Inertia Weight based PSO (IWPSO). Every Inertia Weight based PSO in excelling the existing PSOs. A Long Short Term Memory (LSTM) predicting inertia weight based PSO (LSTMIWPSO) is proposed and its performance is compared with constant, random, and linearly decreasing Inertia Weight PSO. Tests are conducted on swarm sizes 50, 75, and 100 with dimensions 10, 15, and 25. The experimental results show that LSTM based IWPSO supersedes the CIWPSO, RIWPSO, and LDIWPSO.

show abstract

An Intelligent Feature Extraction and Multiple Learning-Based Classification Techniques for Diagnosis of Breast Cancer

Patro,

Mishra,

Panda

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Neuro Fuzzy System with Hybrid Ant Colony Particle Swarm Optimization (HASO) and Robust Activation

Cited by 6 publications

References 0 publications

Hybrid Convolutional Neural Network with Residual Neural Network for Breast Cancer Prediction Using Mammography Images

Hybrid Convolutional Neural Network with Residual Neural Network for Breast Cancer Prediction Using Mammography Images

Improved PSO Performance using LSTM based Inertia Weight Estimation

An Intelligent Feature Extraction and Multiple Learning-Based Classification Techniques for Diagnosis of Breast Cancer

Contact Info

Product

Resources

About