Attitudes and perceptions of Nigerians regarding receiving COVID-19 vaccines: an online cross-sectional study.

Introduction: the success of controlling pandemics like COVID-19 can be achieved through its vaccination program. Besides masks, social distance, and good hand hygiene, a rapid vaccine program is crucial in controlling this COVID-19 pandemic. Thus, this study aimed to assess the attitudes and perceptions of Nigerians regarding accepting the COVID-19 vaccine. Methods: a cross-sectional study was carried out among 334 respondents aged 18 and above from the Southeastern region of Nigeria. A validated questionnaire was used for the data collection through an online Google form. The data analysis was done using SPSS version 25. The association of socio-demographics with attitudes and perceptions was analysed using chi-square tests and Fisher exact tests. At the 95 percent confidence level, a p-value of 0.05 was deemed statistically significant. Results: sixty point two percent (60.2%) (n = 201) of respondents showed positive attitudes with a mean of (13.96±2.97). Gender was the only demographic factor associated with attitudes (p< 0.001). Respondents with poor perceptions were higher by 53.0% (n = 177) with a mean value of (3.30±1.17). Age, education, gender, and marital status were seen to be associated with perceptions of vaccine acceptance (p<0.05). There was a link between attitudes and perceptions (P> 0.001), as those with positive attitudes also exercised good perceptions. Conclusion: this study revealed that respondents had positive attitudes regarding COVID-19 vaccination acceptance but negative perceptions of it. As a result, community and health promotion professionals, religious leaders, and local celebrities should use their platforms to raise awareness about the benefits of COVID-19 immunization.

Deep Neural Networks for Medical Image Segmentation.

Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques.

This study presents an optimization approach for scheduling the operation room for emergency surgeries, considering the priority of surgeries. This optimization model aims to minimize the costs associated with elective and emergency surgeries and maximize the number of scheduled surgeries. In this study, surgeon assistants to perform each surgery are considered in order to achieve the goals. Since the time of each surgery varies according to the conditions of the patient, this parameter is considered as an uncertain one, and a robust optimization method is applied to deal with uncertainty. To demonstrate the effectiveness of the proposed method, a case study in one of the East Asian hospitals is presented and analyzed using GAMS software. Moreover, hybrid simulation and gray wolf optimization algorithm (GWO) have been implemented to solve the optimization model in different scenarios. The results show that increasing the risk parameters in the robust optimization model will increase the system costs. Moreover, in case of uncertainty, the solutions obtained from the GWO simulation method are on average 73.75% better than the solutions obtained from the GWO algorithm.

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Prevalence and factors affecting the utilization of antenatal care in rural areas of Southwestern Ethiopia.

BACKGROUND: Antenatal care (ANC) is a health care intervention intended to ensure the safety of pregnancy. According to the World Health Organization, at least four ANC visits are recommended for a healthy pregnancy. However, whether this recommended number of visits was followed or not in the rural areas of Southwestern Ethiopia is not known. Therefore, the study aimed to investigate the prevalence of, and the associated factors of ANC utilization by pregnant women in the rural areas of Southwestern Ethiopia. METHODS: A community-based cross-sectional study design was used in three rural zones. The data were collected from n = 978 women through a structured questionnaire with face-to-face interview. The collected data were analyzed using descriptive statistics and a multiple binary logistic regression model. RESULTS: The results showed that 56% of women made the recommended minimum number of ANC visits and the remaining 44% of them underutilized the ANC service. The multiple binary logistic regression model identified zone, marital status of the woman, educational level of the husband, occupation of the husband, knowledge of danger signs of pregnancy, birth interval, source of information, timely visits, and transportation problem to be statistically significant factors affecting the prevalence of ANC visit utilization of women. Bench Maji zone had smaller odds ratio of ANC visit prevalence as compared to Kaffa zone. Women who lived in the rural area of Sheko zone are 2.67 times less likely to utilize ANC visit than those who lived in the rural area of Kaffa zone keeping other variables constant. CONCLUSION: The study results highlight the need to increase the number of ANC visits, and the importance of using an appropriate model to determine the important socio-demographic factors that ANC service providers shall focus on to improve the health of the unborn baby and the mother during pregnancy.

Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches.

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

Network Management System for IoT Based on Dynamic Systems.

The Internet of Things (IoT) has the potential to transform the public sector by combining the leading technical and business trends of mobility, automation, and data analysis to dramatically alter the way public bodies collect data and information. Embedded sensors, actuators, and other devices that capture and transmit information about network activity in real-time are used in the Internet of Things to connect networks of physical objects. The design of a network management system for an IoT network is presented in this paper, which uses the edge computing model. This design is based on the Internet management model, which uses the SNMP protocol to communicate between managed devices, and a gateway, which uses the SOAP protocol to communicate with a management application. This work allowed for the identification and analysis of the primary network management system initiatives for IoT networks, in which there are four fundamental device management requirements for any deployment of IoT devices: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance.