Engagement in Video Graphic Online Learning Using the Emotional Dimensions in the Learning Context

The COVID-19 pandemic has become the prime reason for organizations across the world to shift their entire workforce onto virtual platforms. One of the major drawbacks of these virtual platforms is that it lacks a real-time metric which could be used to detect whether a person is attentive during the lectures and meetings or not. This was most evident in the case of educational institutions, where students would often fail to pay attention to the content that was being taught by teachers and professors at home. With this research work, our aim is to create a solution for this problem with the help of AI-FER (Artificial Intelligence Facial Emotion Recognition). For this, we have proposed our own Convolutional Neural Network model achieving an overall accuracy of 59.03%. We have also used several pre-trained models available in Google's Tensorflow library like DenseNET and VGG. © 2023 IEEE.

Impact of COVID-19 on Smallholder Aquaculture Farmers and Their Response Strategies: Empirical Evidence from Bangladesh

Bangladesh's aquaculture sector has contributed progressively to the nation's economy over the years, but the COVID-19 pandemic has impeded fish farmers' access to markets, reduced their production and sales capacity, resulted in lower income, and increased food security vulnerability. This study assesses how COVID-19 affects smallholder fish farmers and their response strategies by employing data collected from 250 fish farmers and traders from intensive fish-growing areas of Bangladesh. The results reveal that most farmers experienced difficulty obtaining inputs, and the price of those inputs skyrocketed during the COVID-19 period, resulting in several months of decreased production and operations. As a result of COVID-19, farm gate prices for silver carp, ruhu, common carp, grass carp, and tilapia fish dropped by 25%, 23%, 23%, 22%, 23%, and 40%, respectively. On the other hand, fish feed prices were found to increase significantly. Reduced income from fish farming and other sources has triggered a significant drop in capital for farming operations and production capacity improvement, leading to food insecurity. The most common coping strategies include reduced buying from the market (vegetables, fruits, meat, milk, etc.), relying on less expensive or less preferred food, purchasing food on credit, and selling assets. Notably, due to COVID-19, a new mode of marketing has evolved as an adaptation strategy in the fish marketing system, such as the use of the mobile phone (18%) and Facebook/internet to sell fish directly to the customer (16%). The sector requires short-term financial assistance to assist fish actors with production and marketing challenges. © 2023 by the authors.

Promoting agri-food systems resilience through ICT in developing countries amid COVID-19

An increasing body of literature has demonstrated COVID-19's harmful impact on agri-food systems, which are a major source of livelihood for millions of people worldwide. Information and communication technology (ICT) has been playing an increasing role in enhancing agri-food systems' resilience amid COVID-19. In this study, the PRISMA approach was employed to perform a systematic review of the literature from January 2020 to December 2021 on the overall impact of COVID-19 on agri-food system networks and ICT's role in enhancing agri-food system resilience in developing countries. This study reveals that COVID-19 has posed abundant obstacles to agri-food systems actors, including a lack of inputs, technical support, challenges to selling the product, transportation barriers, and low pricing. These impediments result in insufficient output, unforeseen stock, and revenue loss. COVID-19's restrictions have caused a significant food deficit by disrupting the demand and supply sides of the agri-food system networks. A high number of small-scale farmers have had to deal with food insecurity. As a result of the cumulative effects, actors in the agri-food system are getting less motivated to continue producing. This study also argues that many challenges in the agri-food systems can be overcome using ICTs, including maintaining precise farm management, product marketing, and access to production inputs. To assist stakeholders in coping with, adapting to, and building resilience in the agri-food system networks, this article emphasizes the critical need to turn to and expand the application of advanced agricultural ICTs to meet the world's growing needs for food production and to ensure the resilience and sustainability of farming systems, particularly in the face of a pandemic like COVID-19. Copyright © 2023 Alam, Khatun, Sarker, Joshi and Bhandari.

An Analysis on the Performance of Different Categories of CNN Models over COVID-19 Dataset

In recent times, there is an enormous application of machine learning (ML) and deep learning (DL) techniques in various domains. Particularly in the medical domain, DL models must have the potential to aid the medical practitioners for effective decision making. COVID-19 had caused the world to come to a grinding halt nearly 2 years ago when the first case was detected in Wuhan, China. Its ripple effects are still felt to this very day and the problem only seems to be getting worse. Studies show that COVID-19, being a virus, will continue to mutate itself into other forms so long as it isn't completely eradicated. With RT-PCR reports taking up six hours to three days to show the results, it is the need of the hour to come up with a more efficient method to detect this virus. This paper has two-fold objectives, one is to analyse the effect of Convolutional Neural Networks (CNN) models for detecting COVID-19 and another is to explore and analyse the performance of different classes of CNN over COVID-19 dataset. For this research work, a dataset of a total of 6464 images is curated for the purpose of training the various CNN models which includes 2500 images of Normal, 1464 images of COVID-19 and 2500 images of Pneumonia chest x-rays. Various pretrained models are used and compared based on their accuracies. © 2022 IEEE.

Impact of COVID-19 on the rice value chain (RVC) in Asia

COVID-19 induced lockdowns have disrupted input supply, production, processing, marketing, and consumption segments of the rice value chain. The rice value chain is facing adverse impacts of constraints on mobility, access to inputs and services, markets, finance, labour, trade and services, demand and income. The rural-urban disconnect has increased price differences between producers and consumers, decreased cash flows and hampered businesses in the value chain. To mitigate impacts of COVID-19 on the rice value chain, ensuring access to input & output markets, minimizing supply chain disruptions, promoting digital services, financial support, social safety nets, open trade, and investing in rice research will be key.

E-learning adoption by undergraduate architecture students: facilitators and inhibitors

Purpose: This study identifies the facilitators and inhibitors for the adoption of e-learning for the undergraduate students of architecture. Nine constructs are identified as facilitators and five constructs are identified as inhibitors to the adoption of online learning systems in the context of the study. These constructs were used to propose a research model. Design/methodology/approach: 596 architecture undergraduates responded to a structured questionnaire. The questionnaire was finalized after a pilot study and included standard scale items drawn from previous studies. An exploratory factor analysis was followed by structural equation modeling (SEM) to test the proposed model. Findings: All the identified facilitators emerged significant except social influence and price value. Furthermore, technology risk emerged insignificant while all other inhibitors had significant impact on Behavioral Intention to adopt e-learning. Research limitations/implications: The study has strong implications in academia as HEIs in developing countries need to make their students computer proficient, boost the implications of e-learning services by mitigating risks and motivating students to acquire knowledge through flexible e-learning modules. Originality/value: The COVID-19 pandemic forced educational institutions to switch to online modes of learning. For students of architectural programs in a developing country like India, this has been unprecedented and has brought in a new set of challenges and opportunities. With the extension of the pandemic induced lockdown in educational institutions, students – and other stakeholders – have no choice but to adapt to this new normal of dependence on remote learning. © 2021, Emerald Publishing Limited.

Identifying the drivers in the adoption of technology in online teaching, learning and assessment of an undergraduate architectural program

The study aims to examine the behavioral intention (BEI) of faculty members specializing in the field of architecture towards the adoption of online teaching and assessment for architectural courses in architecture schools in the wake of COVID 19. The population targeted for investigation consisted of qualified faculty members duly registered with the Council of Architecture (Statutory Body governing the architectural education system in India) based on their experience in teaching online courses of any kind. The study empirically analyzed and validated performance expectancy (PEX), effort expectancy (EFE), social influence (SOI), facilitating conditions (FCT) and their relationship with Behavioral Intention (BEI) to adopt online teaching and assessment. Data was collected on BEI variables from 102 respondents through online and offline surveys on a five-point Likert scale. The survey questionnaire was developed based on a detailed literature review to identify intentions that guided technology adoption to ensure construct validity. Regression was applied to substantiate multifaceted relationships among constructs. The findings of the study confirmed a significant positive association of PEX, SOI and FCT on BEI, while EFE was insignificant to propel teachers to adopt online teaching. This research will provide an insight into the factors leading to low diffusion and adoption of online teaching and assessment in architectural schools in India. These insights will facilitate universities and educational institutions to focus in the right direction by creating versatile applications along with novel strategies for an effective online teaching-learning outcome. © 2020 IEEE.

Barriers to the Adoption of Technology in Learning and Assessment of Undergraduate Architecture Students

The current study examines the barriers to the adoption of technology in learning and assessment of architectural courses in an architectural programme approved by the Council of Architecture, India. This research identifies and validates five barriers namely technological barriers, interaction barriers, evaluation constraints, time risks, and psychological barriers. Data was collected through a selfadministered and structured questionnaire targeting 311 students pursuing an undergraduate programme in reputable architecture schools of two popular private universities in north India. CFA (Confirmatory Factor Analysis) was applied to calculate validity and composite reliability. To examine the hypothesized relationships, path analysis was carried out using Structural Equation Modelling (SEM). The findings of the paper revealed that the time risk emerged as the strongest barrier followed by the interaction and technology risk respectively. In contrast, evaluation risk had the least influence on the intention to adopt online teaching and assessment and surprisingly, psychological risk had insignificant relationship. This research aims to understand hindrance factors in the adoption and assessment of online learning in the wake of COVID-19. It provides valuable insights for architecture schools to overcome these barriers and adopt online teaching learning effectively. © 2020 IEEE.