Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19

Gender differentiated preferences for a community-based conservation initiative

Community-based conservation (CBC) aims to benefit local people as well as to achieve conservation goals, but has been criticised for taking a simplistic view of "community" and failing to recognise differences in the preferences and motivations of community members. We explore this heterogeneity in the context of Kenya's conservancies, focussing on the livelihood preferences of men and women living adjacent to the Maasai Mara National Reserve. Using a discrete choice experiment we quantify the preferences of local community members for key components of their livelihoods and conservancy design, differentiating between men and women and existing conservancy members and non-members. While Maasai preference for pastoralism remains strong, non-livestock-based livelihood activities are also highly valued and there was substantial differentiation in preferences between individuals. Involvement with conservancies was generally perceived to be positive, but only if households were able to retain some land for other purposes. Women placed greater value on conservancy membership, but substantially less value on wage income, while existing conservancy members valued both conservancy membership and livestock more highly than did non-members. Our findings suggest that conservancies can make a positive contribution to livelihoods, but care must be taken to ensure that they do not unintentionally disadvantage any groups. We argue that conservation should pay greater attention to individuallevel differences in preferences when designing interventions in order to achieve fairer and more sustainable outcomes for members of local communities

CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study.   This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability)