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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Agrawal, Poorva
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Computer Science & IT Engineering

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Elevating sentiment analysis with VGG-16's facial expression insights Mehta, Pradnya; Chhabada, Dev; Wankhade, Renuka; Patel, Dhimahi; Gote, Anirudh; Yenkikar, Anuradha; Agrawal, Poorva; Kaur, Gagandeep
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3395-3403

Abstract

In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional Neural Network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviours. To improve emotion recognition reliability and reactivity, this study examines Random Forest, Support Vector Machine (SVM), and CNN methodologies. The VGG-16 CNN model outperforms over SVM and Random Forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.
Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms Yadav, Uma; D. Bondre, Vipin; Bondre, Shweta V.; Thakre, Bhakti; Agrawal, Poorva; Thakur, Shruti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp298-306

Abstract

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.
Optimized convolution neural network with ant colony algorithm for accurate plant disease detection Bondre, Shweta V.; Yadav, Uma; Bondre, Vipin D.; Agrawal, Poorva
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3724-3733

Abstract

In India, agriculture is the primary source of income for half the people. Even in situations of fast population growth, agriculture supplies nourishment for all people. To provide food for the entire population, it is advised to detect plant diseases at an early stage. Plant leaf diseases are recognized using images of the affected leaves. Deep learning (DL) research seems to offer several opportunities for increased accuracy. Ant colony optimization with convolution-neural-network (ACO-CNN), a new deep learning technique for identifying and categorizing diseases, is presented in this article. Ant colony optimization (ACO) was used to examine the efficacy of disease diagnostics in plant leaves. The convolution neural network (CNN) classifier is used to remove texture, color, and leaf arrangement geometry from the input images. The ACO-CNN model outperformed the support vector machine (SVM) and CNN models in terms of precision, recall, and accuracy. CNN's rate is 81.6% as compared to SVM's 80% accuracy level. In the “ACO-CNN” approach, the F1-score, recall, and precision have higher rates as compared to other models, and the “F1-score” has the highest rate compared with other models since the ACO-CNN model has an accuracy rate of 91.00%.
Co-Authors Bondre, Shweta V. Bondre, Vipin D. Chhabada, Dev D. Bondre, Vipin Gote, Anirudh Kaur, Gagandeep Mehta, Pradnya Patel, Dhimahi Thakre, Bhakti Thakur, Shruti Wankhade, Renuka Yadav, Uma Yenkikar, Anuradha