Proceedings ArticleDOI
Optimized Support Vector Machine Model for Visual Sentiment Analysis
Ahammed M S Shaik Afzal
- pp 171-175
TLDR
In this article, an automatic visual sentiment analysis (VSA) model using an optimization-based support vector machine (SVM) was developed, in which the input images' features were extracted from the weighed-FC8 layer of the pre-trained ResNet-18, where the relief algorithm evaluates the updated weight.Abstract:
This research aims to develop an automatic visual sentiment analysis (VSA) model using an optimization-based support vector machine (SVM). Initially, the input images' features are extracted from the weighed-FC8 layer of the pre-trained ResNet-18, where the relief algorithm evaluates the updated weight. On the other hand, the SVM classifier is tuned optimally using a hybrid optimization technique called Holoentropy Life Choice Optimization (HELMCO) algorithm. HELMCO has the characteristic features of both the Life Choice Based Optimization (LCBO) algorithm and the Cross entropy (CE) method. The analysis is done using the Emotion-6 and Abstract Art_photo datasets based on performance parameters, such as Accuracy, Sensitivity, and Specificity. The accuracy of the proposed model is 70.7% using the Emotion-6 dataset and 76.8% using the Art_photo Dataset.read more
Citations
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Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
TL;DR: In this article , a review of NIOA-based multi-level thresholding models is presented, highlighting and exploring the major challenges encountered during the development of image multi-thresholding models.
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A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges
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TL;DR: In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed and 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling.
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A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks
Israa Khalaf Salman Al-Tameemi,Mohammad-Reza Feizi-Derakhshi,Saeed Pashazadeh,Mohammad Asadpour +3 more
TL;DR: An introduction of the data fusion strategies and a summary of existing research on visual–textual challenges and several important investigated to reduce the dimensionality of the high features are introduced.
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Optimised CNN based Brain Tumour Detection and 3D Reconstruction
TL;DR: In this article , a 3D reconstruction scheme for reconstruction of brain tumour was proposed, where an optimised convolutional neural network (CNN) was employed to detect the brain tumor, in which the training process was carried out by a new Levy-adopted Tunicate Swarm Algorithm (L-TSA) through the optimal weight tuning.
Proceedings ArticleDOI
Multitask Convolutional Neural Network for Crop Quality Vegetation Classification
TL;DR: In this article , a neural network model is proposed to extract the features automatically and classify the vegetation's sentiment and purposes, and the obtained experimental results are evaluated with the established criteria.
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