The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier

Chaudhury, Sushovan; Prasad, N Achyutha; Chakrabarti, Sudakshina; Kumar, Chevella Anil; Elashiri, Mohamed A.

doi:10.1109/iciem54221.2022.9853128

Cited by 11 publications

(20 citation statements)

References 21 publications

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“…On each and every Conv Layer with stride 1, VGG16 employs a small 3x3 kernel (filter). Layers that will extract the most dominant features from the adjusted feature map are always followed by max pooling layers [1,2]. Convolution is just the application of a filter to an input, which results in activation.…”

Section: Vgg16mentioning

confidence: 99%

“…Here, Deep tuning involves fine-tuning every layer of the deep convolutional network, whereas shallow tuning just involves the final few levels of the deep network. For extracting the features from the mammographic pictures, two CNN pretrained models are utilized, ResNet-50 and VGG16, where "Res" stands for a residual network and "VGG" for a Visual Geometry Group with a 16-layer deep model architecture [2]. The purpose of this research is to evaluate pre-trained deep learning VGG16 as a feature extractor for binary and multiclass breast cancer mammographic image classification tasks, once VGG16 has been tuned for these tasks [7].…”

mentioning

confidence: 99%

“…The experiment results surpassed the existing long-established machine learning algorithms with high precision. Moreover, in paper [2], the authors have implemented a multinet architecture established on transfer learning conception for classifying various kinds of ductal carcinomas by making use of generally available microscopic mammary gland images. In this work, they have drawn out required nanoscopic attributes with the help of three familiar models which were trained in advance together with DenseNet-201, NasNetMobile, and VGG16.…”

mentioning

confidence: 99%

“…The developed model obtained an accuracy of 99% in categorizing 2 groups whereas obtained 98% accuracy in categorizing 4 groups. Significant number of papers have been published with respect to identification of breast cancer in the early stages [2]. Most of these researches have proposed image filtering and machine learning techniques which include myriad features.…”

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confidence: 99%

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A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

Murthy,

H V,

E V

et al. 2024

IJEER

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Segmentation is a technique for separating an image into discrete areas in order to separate objects of interest from their surroundings. In image analysis, segmentation—which encompasses detection, feature extraction, classification, and treatment—is crucial. In order to plan treatments, segmentation aids doctors in measuring the amount of tissue in the breast. Categorizing the input data into two groups that are mutually exclusive is the aim of a binary classification problem. In this case, the training data is labeled in a binary format based on the problem being solved. Identifying breast lumps accurately in mammography pictures is essential for the purpose of prenatal testing for breast cancer. The proposed TLA (Transfer Learning Approach) based CNN (Convolution Neural Network) –TLA based CNN aims to offer binary classification for rapid and precise breast cancer diagnosis (benign and malignant). In order to predict the sub-type of cancer, this exploration as used Deep Learning techniques on the Histogram of Oriented Gradient (HOG) - Feature extraction technique that creates a local histogram of the image to extract features from each place in the image with CNN classifier. This research work employs two well-known pre-trained models, ResNet-50 and VGG16, to extract characteristics from mammography images. The high-level features from the Mammogram dataset are extracted using a transfer learning model based on Visual Geometry Group (VGG) with 16-layer and Residual Neural Network with 50-layers deep model architecture (ResNet-50). The proposed model TLA based CNN has achieved 96.49% and 95.48% accuracy as compared to ResNet50 and VGG16 in the breast cancer classification and segmentation.

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Section: Vgg16mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

Murthy,

H V,

E V

et al. 2024

IJEER

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“…The method with Bayes classifier is utilized to detect the subjects this is performed its work upon the same database to analyze the requirement of minimum heart beat for the verification of the person [24] and to analyze the emotional condition effect. [25] This technique was introduced by Zhang and Wei [26] The features of fudicial consisting of ECG feature points like duration, amplitude and PR and QRS fiducial intervals. Distinctive practical works done on ECG leads.…”

Section: Literature Reviewmentioning

confidence: 99%

ECG Biometric for Human Authentication using Hybrid Method

T G,

M N

2023

IJRITCC

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Recently there is more usage of deep learning in biometrics. Electrocardiogram (ECG) for person authentication is not the exception. However the performance of the deep learning networks purely relay on the datasets and trainings, In this work we propose a fusion of pretrained Convolutional Neural Networks (CNN) such as Googlenet with SVM for person authentication using there ECG as biometric. The one dimensional ECG signals are filtered and converted into a standard size with suitable format before it is used to train the networks. An evaluation of performances shows the good results with the pre-trained network that is Googlenet. The accuracy results reveal that the proposed fusion method outperforms with an average accuracy of 95.0%.

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