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In the present paper, a mathematical model using the non-linear differential equations depicting the impact of Covid-19 on unemployment is discussed. The stability of the system is studied and model is reformulated as an optimal control problem. To assess the impact of unemployment on human population, two time-dependent controls are used. Providing education and training of job-oriented persons act as first control and campaigning about the awareness of coronavirus disease and self-employment business is the second control. Necessary conditions for optimal control are derived by Pontryagins maximum principle. Further, the results are illustrated by numerical simulation.
ABSTRACT
According to WHO, Skin Infection is very common but sometimes very serious and affects a large no population all over the world. Monkeypox, Chickenpox, and Measles are the major infectious disease that causes skin infection all over the world. It has been obverse that the cases of Monkeypox have drastically increased as an effect of Covid 19. This infection has spread easily and exponentially that cause serious health issues in many underdeveloped and developing countries. Some time it has been observed that people are not able to properly classify the type of skin infection well in time, which can be a main reason of serious health issues. So, it became important to propose an effective classification of Skin Disease. In this paper the authors have proposed an effective classification of Skin Disease using Deep Learning Techniques. This approach will help in classification of chicken pox, measles, and monkeypox through skin images. The authors have utilized Monkeypox Skin Images Dataset (MSID) dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The best algorithms with maximum accuracy and other parameters are Xception, EfficientNetV2L, and EfficientNetV2M, and CNN, VGG16, and VGG19 are the least favored algorithms for this research. © 2023 IEEE.
ABSTRACT
Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even the same US video. In this paper we propose to instead utilize such frames as hard negatives. We advocate mining both intra-video and cross-video negatives in a hardness-sensitive negative mining curriculum in a UCL framework to learn rich image representations. We deploy our framework to learn the representations of Gallbladder (GB) malignancy from US videos. We also construct the first large-scale US video dataset containing 64 videos and 15,800 frames for learning GB representations. We show that the standard ResNet50 backbone trained with our framework improves the accuracy of models pretrained with SOTA UCL techniques as well as supervised pretrained models on ImageNet for the GB malignancy detection task by 2–6%. We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1.5% compared to SOTA. Source code, dataset, and models are available at https://gbc-iitd.github.io/usucl. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background: Online vaccine misinformation has been rife during the COVID-19 pandemic. An important factor that influences the spread of misinformation online is how people access, use, synthesise and apply information, i.e., their 'information behaviour'. Interpretation of online vaccine-related information is also thought to be influenced by cognitive bias, defined as unconscious errors in thinking causing a misinterpretation of information, which may lead to inaccurate judgments. Only 54 percent of adults over 65 years of age are up to date with both fiveyearly pneumococcal and annual influenza vaccines. Further, vaccine misinformation accessed online is a particular problem in this group. Aims/Objectives: This study aims to investigate: (1) the relationship between information behaviour and cognitive biases amongst Australians over 65 when accessing online vaccine-related information;and (2) how this relationship influences decisionmaking regarding vaccination. Method(s): This qualitative study will involve semi-structured interviews with a maximum variation sample of Victorians over 65 years of age, recruited via a Facebook advertisement using purposive sampling. The Eisenberg and Berkowitz information behaviour model will inform data collection and analysis. Data analysis will draw upon the tenets of Grounded Theory, involving constant comparison, and open and axial coding to assess relationships between a range of cognitive biases on the one hand, and information behaviours on the other. Data collection will continue until data saturation is reached. Finding(s): The findings of the study will highlight how cognitive bias interacts with information behaviour, and how this interaction impacts upon vaccine uptake for older Australians. Implications: Many online environments are designed to manipulate cognitive biases to increase screen-time. Findings will help inform how primary care clinicians can communicate vaccine-related information in this context. Understanding the interaction between cognitive bias and information behaviour will also inform the design of interventions to tackle misinformation, ranging from consulting strategies to online information tools.
ABSTRACT
To successfully satisfy user needs, software developers need to suitably capture and implement user requirements. A critical and often overlooked characteristic of user requirements are "human aspects", which are personal circumstances affecting the use of software (e.g., age, gender, language, etc.). To better understand how human aspects can impact the use of software, this work presents an empirical study focusing on app reviews of COVID-19 contact tracing apps. We manually analyzed a dataset of 2,611 app reviews sampled from the reviews associated with 57 COVID-19 apps. To analyze the reviews, we performed qualitative and quantitative analyses. The analyses characterize the human aspects contained in the reviews and investigate whether the apps suitably address the human aspects. We identified 716 reviews related to human aspects and grouped these into nine categories. Of these 716 reviews, 8% report bugs, 14% describe future/improvement requests, and 22% detail the user experience. Our analysis of the results reveal that human aspects are important to users and we need better support to account for them as software is developed.
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Purpose: Covid-19 has forced corporate, institutions, organizations both Government and non - Government to re- think the way they work and where they work. There is both more inclusivity and greater diversity. The paper attempts to study the acceptance of the virtual platform, its magnitude and the sustenance with regards to various sectors of the service industry in India. Design/Methodology/Approach: The study uses the Technology Acceptance Model as a basic model to explore the effects of variables like perceived usefulness, perceived ease of use, facilitating conditions and social influence on the intention to use various virtual platforms. Furthermore, the impact of social and facilitating factors adapted from Unified Theory of Acceptance and use of Technology has also been considered to study the effects on the attitude towards using these platforms. The paper is a questionnaire based primary research where convenience random sampling has been used and Exploratory Factor Analysis has been run on number of items. Findings: The study has shown how performance has the magnifying effect on the intention to use virtual platforms for work related tasks. Originality/Value: The insights from prior studies on the impact of work from home arrangements do not extend to the current context since these arrangements were mostly limited to a select group of workers and/or organizations and were often self-selected. The study gives concrete reasons for the adoption and sustenance of the virtual platforms.
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During the COVID-19 pandemic, the lives of healthcare professionals are at significant threat because of the enormous workload and cross-infection risk. Ultrasound (US) imaging plays a vital role in the diagnosis and follow-up of COVID-19 patients;however, it requires a close-physical contact by the sonographer. In this context, this paper presents a Telerobotic Ultrasound (TR-US) system for complete remote control of the US probe, thereby preventing direct physical contact between patients and sonographers. The system consists of a 6-DOF robot arm at the remote site and a haptic device at the doctor's site. The control architecture precisely transmits the intended position and orientation of the US probe to the remote location for transversal and sagittal plane scanning. This architecture, when integrated with an admittance controller-based force modulation and feedback transmission, enables the radiologists to obtain high-quality images for diagnosis. The advantages and effectiveness of the system are demonstrated by conducting in-vivo feasibility study at AIIMS, Delhi, for imaging abdomen organs (liver, spleen, kidneys, bladders). The system provides image quality equivalent to a manually-guided probe, can identify various pathology and reports high acceptability among volunteers and doctors from a questionnaire survey.
ABSTRACT
The year 2020 brought us the global pandemic of COVID-19, which is not just a health crisis but a disruption to the fabric of society around the world. With no vaccine yet approved, other measures have been taken all over the world related to lockdowns, social distancing, and contact tracing to quarantine the infected individuals and suppress community transmission. The numerous challenges presented by this novel coronavirus, such as the incubation period, various symptoms, and asymptomatic superspreaders, have exacerbated the challenges of manual contact tracing.