Facial emotion detection has witnessed a surge in demand across numerous applications, including human-computer interaction, healthcare, and security. Accurate expression recognition is crucial for improving human-computer interactions and understanding human behavior. Existing facial emotion detection models face challenges in achieving both high accuracy and real-time processing due to complex architectures. Our goal is to create an efficient yet accurate solution that can work on resource-constrained devices. To address the challenge of accurately recognizing emotions from facial expressions, we propose a novel hybrid approach that combines the strengths of pretrained Lightweight Convolutional Neural Networks (CNN), and Attention-based Vision Models. The pretrained Lightweight CNN serves as a feature extractor, efficiently capturing facial features, while the attention model refines the feature representation to focus on crucial regions of the face associated with different expressions. This enables our model to achieve state-of-the-art (SOTA) accuracy with reduced computational requirements. The proposed model, EmoVisioNet, achieves superior performance across multiple datasets, attaining 99.97 % accuracy on CK+, 96.23 % on RAF-DB, 93.88 % on FER2013, and 96.91 % on FERPlus. The obtained results surpass the current state-of-the-art in this field, demonstrating the EmoVisioNet’s superior performance in facial expression recognition.

Abstract The recent pandemic saw the operations of many businesses shifting to virtual mode. Tasks like psychometric analysis of individuals, for various applications, are conducted online. In this article, we introduce a novel system to analyze the semantics of an individual's tweets from their Twitter profile using LIWC and SALLEE scores. These scores can be used to evaluate less fortunate, thin‐filed candidates using their Twitter profiles. With increased access to phones and the internet, many organizations are focusing on making credit systems available to the masses by introducing psychometric analysis. This article proposes a dynamic model for evaluating the personality of a Twitter user using the textual content shared on their page. The model will allow stakeholders to ascertain the personality of user according to any personality model. To analyze if this is viable and flexible approach to model any kind of personality model, we take MBTI personality dataset and train classifier to predict personality types. Then these results are correlated with a linguistic score to find correlation between the two. We found that proposed approach, outperformed the other relevant works also some aspects of these linguistic scores show a heavy correlation with certain personality types.

Social media can render content circulating to reach millions with a knack to influence people, despite the questionable authencity of the facts. Internet sources are the most convenient and easy approach to obtain any information these days. Fake news has become the topic of interest for academicians and the rest of society. This kind of propaganda has the power to influence the general perception, offering political groups the ability to control the results of democratic affairs such as elections. Automatic identification of fake news has emerged as one of the significant problems due to the high risks involved. It is challenging in a way because of the complexity levels of accurately interpreting the data. An extensive search has already been performed on English language news data. Our work presents a comparative analysis of fake news classifiers on the low resource Bengali language ‘ban fake news’ dataset from Kaggle. The analysis presented compares deep learning techniques such as LSTM (Long short-term Memory) and BiLSTM (Bi-directional Long short-term Memory) and machine learning methods like Naive Bayes, Passive Aggressive Classifier (PAC), and Random Forest. The comparison has been drawn based on classification metrics such as accuracy, precision, recall, and F1 score. The deep learning method BiLSTM shows 55.92% accuracy while Random Forest, in contrast, has outperformed all the other methods with an accuracy of 62.37%. The work presented in this paper sets a basis for researchers to select the optimum classifiers for their approach towards fake news detection.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

In the past few years, the intelligent driving systems have witnessed rapid development, either it is self-driving cars or driver assistant systems. All these systems are built around perceiving the environment of the vehicle and taking appropriate steps in the given context. Computer vision has been playing a significant role in reducing the number of costly sensors used to perceiving the environment. In the past, the velocity of the vehicle was major estimated using sensors. In this paper, we propose a data-based methodology to estimate the relative velocity of vehicles using monocular cameras hence omitting the need for costly sensors such as lidars. Our proposed methods achieve a low mean velocity square error of 1.806, for estimating the velocity of the vehicle in a real-time environment.