Impact of COVID-19 Pandemic on Dermatology Teaching Program in India: A Survey on the Faculty and Residents' Perspective.

Background: Impact of COVID-19 pandemic has been immense. An innocent casualty of this disaster is medical education and training. Dermatology, which primarily deals with out-patient services, medical and surgical interventions, and in-patient services, was one of the worst hit. The National Medical Commission of India has implemented competency-based medical education (CBME) in Dermatology, Venereology, and Leprosy since 2019. The new curriculum relies on acquiring practical and procedural skills, training skills in research methodology, professionalism, attitude, and communication. Objectives: The study was undertaken to understand the implications of the COVID-19 pandemic on postgraduate dermatology CBME training in India. Materials and Methods: A questionnaire-based survey was carried out on postgraduate dermatology teachers and residents in India after obtaining ethics committee approval. An online semi-structured English questionnaire was administered by Google Forms. The calculated sample size was 366 dermatology faculty and 341 postgraduate students. Validity (Content validity ratio (CVR) ≥0.56) and reliability (Cronbach's alpha coefficient 0.7249) of the questionnaire were determined. Results: Among the 764 responses received, 51.4% reported that their institutes were converted to exclusive COVID hospitals. Domains of dermatology education affected were procedural training (n = 655), bedside clinical teaching (n = 613), outpatient department-based clinical teaching (n = 487), bedside laboratory procedures (n = 463), research activities (n = 453), histopathology (n = 412), and theory classes (n = 302). To keep up with the teaching-learning process, online platforms were mostly utilized: Zoom Meeting (n = 379), Google Meet (n = 287), and WhatsApp Interaction (n = 224). Teaching during ward rounds was significantly more affected in exclusively COVID institutes than non-exclusive COVID institutes (P < 0.001). Psychomotor skill development suffered a major jolt with 26.7% of respondents reporting a standstill (P < 0.001). Communication skills among students suffered due to social distancing, mask, and poor attendance of patients. According to 23.84% of respondents, formative assessment was discontinued. Conclusion: Online seminars, journal clubs, and assessments have been incorporated during the pandemic. Online modalities should be used as a supplementary method as psychomotor skills, communication skills, research work, and bedside clinics may not be replaced by the e-learning.

TextConvoNet: a convolutional neural network based architecture for text classification.

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based architecture for binary and multi-class text classification problems. Most of the existing CNN-based models use one-dimensional convolving filters, where each filter specializes in extracting n-grams features of a particular input word embeddings (Sentence Matrix). These features can be termed as intra-sentence n-gram features. To the best of our knowledge, all the existing CNN models for text classification are based on the aforementioned concept. The presented TextConvoNet not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. We perform an experimental study on five binary and multi-class classification datasets and evaluate the performance of the TextConvoNet for text classification. The results are evaluated using eight performance measures, accuracy, precision, recall, f1-score, specificity, gmean1, gmean2, and Mathews correlation coefficient (MCC). Furthermore, we extensively compared presented TextConvoNet with machine learning, deep learning, and attention-based models. The experimental results evidenced that the presented TextConvoNet outperformed and yielded better performance than the other used models for text classification purposes.

Predictive modeling for suspicious content identification on Twitter.

The wide popularity of Twitter as a medium of exchanging activities, entertainment, and information is attracted spammers to discover it as a stage to spam clients and spread misinformation. It poses the challenge to the researchers to identify malicious content and user profiles over Twitter such that timely action can be taken. Many previous works have used different strategies to overcome this challenge and combat spammer activities on Twitter. In this work, we develop various models that utilize different features such as profile-based features, content-based features, and hybrid features to identify malicious content and classify it as spam or not-spam. In the first step, we collect and label a large dataset from Twitter to create a spam detection corpus. Then, we create a set of rich features by extracting various features from the collected dataset. Further, we apply different machine learning, ensemble, and deep learning techniques to build the prediction models. We performed a comprehensive evaluation of different techniques over the collected dataset and assessed the performance for accuracy, precision, recall, and f1-score measures. The results showed that the used different sets of learning techniques have achieved a higher performance for the tweet spam classification. In most cases, the values are above 90% for different performance measures. These results show that using profile, content, user, and hybrid features for suspicious tweets detection helps build better prediction models.

Towards a super-resolution based approach for improved face recognition in low resolution environment.

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one. This paper develops a robust real-time face recognition approach that uses super-resolution to improve images and detect faces in the video. Many previously developed face detection systems are constrained by the severe distortion in the captured images. Further, many systems failed to handle the effect of motion, blur, and noise on the images registered on a camera. The presented approach improves descriptor count of the image based on the super-resolved faces and mitigates the effect of noise. Furthermore, it uses a parallel architecture to implement a super-resolution algorithm and overcomes the efficiency drawback increasing face recognition performance. Experimental analysis on the ORL, Caltech, and Chokepoint datasets has been carried out to evaluate the performance of the presented approach. The PSNR (Peak Signal-to-Noise-Ratio) and face recognition rate are used as the performance measures. The results showed significant improvement in the recognition rates for images where the face didn't contain pose expressions and scale variations. Further, for the complicated cases involving scale, pose, and lighting variations, the presented approach resulted in an improvement of 5%-6% in each case.