Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology. INDEX TERMS Deep learning, convolutional neural networks, generative adversarial networks, synthetic data augmentation, COVID-19 detection.

An optimized dense convolutional neural network (CNN) architecture (DenseNet) for corn leaf disease recognition and classification is proposed in this paper. Corn is one of the most cultivated grain throughout the world. Corn crops are highly susceptible to certain leaf diseases such as corn common rust, corn gray leaf spot, and northern corn leaf blight are very common. Symptoms of these leaf diseases are not differentiable in their nascent stages. Hence, the current research presents a solution through deep learning so that crop health can be monitored and, it will lead to an increase in the quantity as well as the quality of crop production. The proposed optimized DenseNet model has achieved an accuracy of 98.06%.Besides, it uses significantly lesser parameters as compared to the various existing CNN such as EfficientNet, VGG19Net, NASNet, and Xception Net. The performance of the optimized DenseNet model has been contrasted with the current CNN architectures by considering two (time and accuracy) quality measures. This study indicates that the performance of the optimized DenseNet model is close to that of the established CNN architectures with far fewer parameters and computation time.

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Bloom's taxonomy is a common paradigm for categorizing educational learning objectives into three learning levels: cognitive, affective, and psychomotor. For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom's Taxonomy. Usually, administrators of the institutions manually complete the tedious work of mapping CLOs and examination questions to Bloom's taxonomy levels. To address this issue, we propose a transformer based model named BloomNet that captures linguistic as well semantic information to classify the course learning outcomes (CLOs) . We compare BloomNet with diverse set of basic as well as strong baselines and we observe that our model performs better than all the experimented baselines. Further, we also test the generalisation capability of BloomNet by evaluating it on different distributions which our model does not encounter during training and we observe that our model is less susceptible to distribution shift compared to the other considered models. We support our findings by performing extensive result analysis. In ablation study we observe that on explicitly encapsulating the linguistic information along with semantic information improves the model's IID (independent and identically distributed) performance as well as OOD (out-of-distribution) generalization capability.

As With the rapid change in technology, there has been an equally rapid change in the employment trends. Along with the rate of increasing literacy there’s a high number of potential candidates for any job role. Thus, whenever a job posting is made public it tends to attract a lot of attention thus resulting in many responses. This creates a huge workload on the HR department of any company responsible for the hiring process. Our project aims to help solve this problem by proposing the following solution. The HR department can easily filter out candidates based on their performance in the tests set and the cv ranking in accordance with the job posted. The user will be classified into one of the 16 MTBI personality types. This personality classification of the user will be done on the basis of the data found on their social media handles and hence the MTBI personality types of the user will be given according to the probabilities. With this tool the HR can easily shortlist the more suitable candidates easing their selection process and avoid the large amount of time they would have otherwise taken going through the tedious process of reviewing each candidate’s resume and then interviewing them to gauge their personality. The HR department will be able to post any job openings they have on our website where the candidates will apply for them. The HR can also choose to select or reject candidates through our system and the respective message would be sent to the candidate