Artificial intelligence inspired multilanguage framework for note-taking and qualitative content-based analysis of lectures.

With the advent of technology and digitization, the use of Information and Communication Technology (ICT) and its tools for the imperative dissemination of information to learners are gaining more ground. During the process of the conveyance of lectures, it is mostly observed that students (learners) are supposed to take notes (minutes) of the subject matter being delivered to them. The existence of different factors like disturbance (noise) from the environment, learner's lack of interest, problems with the tutor's voice, and pronunciation, or others, may hinder the practice of preparing (or taking) lecture notes effectively. To tackle such an issue, we propose an artificial intelligence-inspired multilanguage framework for the generation of the lecture script (of complete) and minutes (only important contents) of the lecture (or speech). We also aimed to perform a qualitative content-based analysis of the lecture's content. Furthermore, we have validated the performance(accuracy) of the proposed framework with that of the manual note-taking method. The proposed framework outperforms its counterpart in terms of note-taking and performing the qualitative content-based analysis. In particular, this framework will assist the tutors in getting insights into their lecture delivery methods and materials. It will also help them improvise to a better approach in the future. The students will be benefited from the outcomes as they do not have to invest valuable time in note-taking/preparation.

Describing Engagement in the HIV Care Cascade: A Methodological Study.

Introduction: Engagement in the HIV care cascade is required for people living with HIV (PLWH) to achieve an undetectable viral load. However, varying definitions of engagement exist, contributing to heterogeneity in research regarding how many individuals are actively participating and benefitting from care. A standardized definition is needed to enhance comparability and pooling of data from engagement studies. Objectives: The objective of this paper was to describe the various definitions for engagement used in HIV clinical trials. Methods: Articles were retrieved from CASCADE, a database of 298 clinical trials conducted to improve the HIV care cascade (https://hivcarecascade.com/), curated by income level, vulnerable population, who delivered the intervention, the setting in which it was delivered, the intervention type, and the level of pragmatism of the intervention. Studies with engagement listed as an outcome were selected from this database. Results: 13 studies were eligible, of which five did not provide an explicit definition for engagement. The remaining studies used one or more of the following: appointment adherence (n=6), laboratory testing (n=2), adherence to antiretroviral therapy (n=2), time specification (n=5), intervention adherence (n=5), and quality of interaction (n=1). Conclusion: This paper highlights the existing diversity in definitions for engagement in the HIV care cascade and categorize these definitions into appointment adherence, laboratory testing, adherence to antiretroviral therapy, time specification, intervention adherence, and quality of interaction. We recommend consensus on how to describe and measure engagement.

Measures of Retention in HIV Care: A Study Within a Review.

People living with HIV (PLHIV) need lifelong medical care. However, retention in HIV care is not measured uniformly, making it challenging to compare or pool data. The objective of this study within a review (SWAR) is to describe the assortment of definitions used for retention in HIV care in randomized controlled trials (RCTs). We conducted a SWAR, drawing data from an overview of systematic reviews on interventions to improve the HIV care cascade. Ethics review was not required for this analysis of secondary data. We identified RCTs of interventions used to improve retention in care for PLHIV, including all age groups and extracted the definitions used and their characteristics. We identified 50 trials that measured retention published between 2007 and 2021 and provided 59 definitions for retention in care. The definitions consisted of nine different characteristics with follow-up time (n = 47), and clinical visits (n = 36) most used. The definitions of retention in HIV care are highly heterogeneous. In this study, we present the pros and cons of characteristics used to measure retention in HIV care.

An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images.

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew's correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.

The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.