In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification.

Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification of breast cancer from the histology photographs is very important for the diagnosis and effective treatment planning. Methods: In this article, we propose a DenseNet121-based deep learning model for breast cancer detection and multi-class classification. The experiments were performed using whole-slide histopathology images collected from the BreakHis dataset. Results: The proposed method attained state-of-the-art performance with a 98.50% accuracy and an AUC of 0.98 for the binary classification. In multi-class classification, it obtained competitive results with 92.50% accuracy and an AUC of 0.94. Conclusions: The proposed model outperforms state-of-the-art methods in distinguishing between benign and malignant tumors as well as in classifying specific malignancy subtypes. This study highlights the potential of deep learning in breast cancer diagnosis and establishes the foundation for developing advanced diagnostic tools.

In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions. The multimodal has been trained on a facial and vocal dataset. We have used two standard datasets, M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal expressions. The vocal expressions are in the form of audio while the faces data is in image form that is why the data is heterogenous. In order to make the data homogeneous, the voice data is converted into images by taking spectrogram. A spectrogram embeds important features of the voice and it converts the audio format into the images. Later, the dataset is passed to the multimodal for training. neural network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models.

Students’ behavioral and emotional engagement in the classroom environment may reflect the students’ learning experience and subsequent educational outcomes. The existing research has overlooked the measurement of behavioral and emotional engagement in an offline classroom environment with more students, and it has not measured the student engagement level in an objective sense. This work aims to address the limitations of the existing research and presents an effective approach to measure students’ behavioral and emotional engagement and the student engagement level in an offline classroom environment during a lecture. More precisely, video data of 100 students during lectures in different offline classes were recorded and pre-processed to extract frames with individual students. For classification, convolutional-neural-network- and transfer-learning-based models including ResNet50, VGG16, and Inception V3 were trained, validated, and tested. First, behavioral engagement was computed using salient features, for which the self-trained CNN classifier outperformed with a 97%, 91%, and 83% training, validation, and testing accuracy, respectively. Subsequently, the emotional engagement of the behaviorally engaged students was computed, for which the ResNet50 model surpassed the others with a 95%, 90%, and 82% training, validation, and testing accuracy, respectively. Finally, a novel student engagement level metric is proposed that incorporates behavioral and emotional engagement. The proposed approach may provide support for improving students’ learning in an offline classroom environment and devising effective pedagogical policies.

According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A signal processing technique is been proposed for heart sound classification. Five classifiers, Naive Bayes algorithm, Sequential (SMO), J48, Rep tree and Random Forest (RF) are used for this experiment. A detailed experimentation is performed to fine-tune the method and finally results are compared with the existing systems. The best proposed classifying technique results the overall accuracy of 91.33%.

The enormous increase in social media platforms and their usage among people have caused a massive surge in online posting. It has been observed that people find it easy to express themselves in a virtual environment rather than in a real environment. There have been multiple social media platforms where people tend to go to write their feelings out, out of which Twitter and Reddit are among the most used ones when it comes to be expressive about mental health. Therefore, pertinent questions about the efficiency of either of the platforms for the detection of anxiety disorders and depression have arisen, i.e., how much social media is effective in identifying different anxiety disorders and depression, plus the relevance of anxiety to depression. The main purpose of this research paper is to come up with a comparative study of two different platforms for discussions of mental health that are Twitter and Reddit. The finding and analysis have been focused on two distinct datasets: the Reddit dataset comprising Anxiety disorders-related posts that were manually scraped using Python Library PRAW, and the Twitter dataset with depression-related posts downloaded from an online repository Kaggle. Further, the study has been focused on finding the linguistic similarities between depression and anxiety disorder while highlighting the proposed model functioning with cross platform analysis. An array of contemporary pre-trained models as well as the machine learning models have been applied for the analysis of datasets: BERT, SVM, Decision Tree, Naive Bayes, Catboost, Gradient Boosting, XGBoost, XLNet, AACN, and some other prominent algorithms have been included. Data cleaning and feature selection have been applied initially to highlight the commonalities between both of these platform's usage datasets. The evaluation of models has been done using a validation method with 80:20 rule, having 80% data for training whereas the remaining 20% for the testing purpose. The results have been providing us insights into the platform-specific behavior with a maximum of 72% accuracy score with catboost and a 70% accuracy score with XLNet and AACN in the Reddit dataset; however, 0.98 recall was provided by XLNet outperforming other models in terms of recall. Similarly, 85% of the accuracy score has been obtained with catboost, and 82% with logistic regression has been obtained in the Twitter dataset. The highest recall has been given by catboost with a 0.90. This study's findings are expected to provide better insights with social media insights focused on improving mental health-related discussion findings. The findings could guide us in detecting platform preferences. In the future, it is suggested to apply deep learning models with large datasets.