33 research outputs found
An Integrated TOE-DoI Framework for Cloud Computing Adoption in Higher Education: The Case of Sub-Saharan Africa, Ethiopia
No full textThis paper aims to propose an integrated framework based on the technology-organization-environment (TOE) framework and diffusion of innovation (DoI) theory and to show the significant factors that are pertinent to adopt cloud computing in the Ethiopian higher education (EHE) sector. Emerging technology literature based on adoption theories and the framework studied to identify a set of factors and sub-factors relevant to cloud computing adoption. It resulted in conceptualizing an integrated TOE-DoI framework for cloud computing adoption in higher education at the university in Ethiopia and in developing its reliable measures. A qualitative study is done with a questionnaire survey comprising 35 staff respondents in connection with four items (the technological factor, the organizational factor, the environmental factor, and the sociocultural factor), and cloud computing adoption in Ethiopia was established using factors and concepts adopted from studies. The study confirmed that the TOE-DoI approach education in Ethiopia is authenticated. Thus, the four-factor reliability statistics is validated with Cronbach’s alpha a = 0.918 and Cronbach’s alpha ‘a’ based on standard items: a = 0.990. This indicates that scaling the four items there subsists strong reasons to determine a cloud computing adoption in EHE with TOE-DoI integration. This research paper has a novel contribution in integrating TOE-DoI framework.This paper aims to propose an integrated framework based on the technology-organization-environment (TOE) framework and diffusion of innovation (DoI) theory and to show the significant factors that are pertinent to adopt cloud computing in the Ethiopian higher education (EHE) sector. Emerging technology literature based on adoption theories and the framework studied to identify a set of factors and sub-factors relevant to cloud computing adoption. It resulted in conceptualizing an integrated TOE-DoI framework for cloud computing adoption in higher education at the university in Ethiopia and in developing its reliable measures. A qualitative study is done with a questionnaire survey comprising 35 staff respondents in connection with four items (the technological factor, the organizational factor, the environmental factor, and the sociocultural factor), and cloud computing adoption in Ethiopia was established using factors and concepts adopted from studies. The study confirmed that the TOE-DoI approach education in Ethiopia is authenticated. Thus, the four-factor reliability statistics is validated with Cronbach’s alpha a = 0.918 and Cronbach’s alpha ‘a’ based on standard items: a = 0.990. This indicates that scaling the four items there subsists strong reasons to determine a cloud computing adoption in EHE with TOE-DoI integration. This research paper has a novel contribution in integrating TOE-DoI framework.</p
Deep Learning Approach to Recognize COVID-19, SARS and Streptococcus Diseases from Chest X-ray Images
Get PDF51-59Corona virus disease (COVID-19) became pandemic for the world in the year 2020 and large numbers of people are infected worldwide due to the rapid widespread of this infectious virus. Pathological laboratory testing of a large number of suspects becomes challenging and producing false-negative results. Therefore, this paper aims to develop a deep learning basedapproach for automatic detection of COVID-19 infection using medical X-ray images. The proposed approach is used for the fast detection of COVID-19 along with other similar diseases such as Streptococcus, and severe acute respiratory syndrome (SARS) positive cases. A 2D-convolution neural network (2D-CNN) is used to recognize the graphical features of X-ray image’s dataset of COVID-19 positive, Streptococcus and SARSpatients. The proposed approach is tested on the COVID-chest X-Ray dataset. Experiments produced individual accuraciesof COVID-19, Streptococcus, SARS disease and normal persons are 100%, 90.9%, 91.3%, and 94.7% respectively and achieved an overall accuracy of 95.73%. From the experimental results, it is proved that the performance of the proposed approach is better as compared to the mentioned state-of-art methods
Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms
Get PDFMachine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results
REAL-TIME OBJECT DETECTION IN AUTONOMOUS VEHICLES USING DEEP LEARNING
Get PDFObject detection is a crucial component of autonomous driving technology. Accurate and real-time detection of every object on the road is required to ensure the safe operation of vehicles at high speeds. In recent years, there has been a lot of research into how to balance detection speed with accuracy. Real-time object detection is one of the important technologies applied to autonomous vehicles that allow vehicles to move safely through traffic. This paper focuses on the use of deep learning, the YOLOv8 algorithm in object detection of self-driving cars. The real-world data set of real driving scenarios involved includes streets, roads, and intersections/squares. The powerful interaction of the model with the deep learning algorithms defines the objects and allows for a fast decision-making process applied in autonomous systems. The metrics used to assess the models include detection rates, accuracy of the bounding die placement, and accuracy of the objects’ detection. The outcome is beneficial in refining the object detection methods and advancing the perception capability for self-driven vehicles as well as making driving automation safer
Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms.
Get PDFMachine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results
Deep Learning Approach to Recognize COVID-19, SARS and Streptococcus Diseases from Chest X-ray Images
Get PDFCorona Virus Disease (COVID-19) became pandemic for the world in the year 2020. A large numbers of people are infected worldwide due to the rapid widespread infectious virus which is threatening many lives and economic damages. Controlling of this virus becomes challenging for the world due to non-preparedness and less availability of testing kits, necessary medical equipment, and vaccine. Pathological laboratory testing of a large number of suspects becomes challenging. Some existing pathological testing is producing false-negative results. Therefore, this paper aims to develop a method of automatic detection of transmissible diseases through medical image analysis techniques which are based on the radiological changes in the X-ray images. In this paper, a Deep Learning approach is proposed for the fast detection of COVID-19, Streptococcus, and Severe Acute Respiratory Syndrome (SARS) positive cases. In Deep Learning, 2-D Convolution Neural Network (2DCNN) is used to classify graphical features of X-ray image’s dataset of COVID-19 positive, Streptococcus and Severe Acute Respiratory Syndrome (SARS) patients. The proposed approach is implemented on the COVID-chest X-Ray dataset. Experiments produced individual accuracy of COVID-19, Streptococcus, SARS disease and normal person is 100%, 90.9%, 91.3%, and 94.7% respectively. This approach achieved an overall accuracy of 95.73% over four classes. Validation of the proposed approach results has been done using Precision, Recall, and F1-score matrices. From the experimental results, it is proved that the performance of the proposed deep learning approach is quite better as compared to the mentioned state-of-art methods to detect COVID-19, SARS, and Streptococcus disease using X-ray medical imaging
Politics in the Cloud:A Review of Cloud Technology Applications in the Domain of Politics
No full textThe cloud computing field has seen many applications in the domainsof banking, education, media, gaming, health, and other industries. However, cloud applications are not so popular in the field of politics. This paper attempts to show that the cloud is equally useful for politics. It includes a literature review on an introduction to cloud technology, the advantages of cloud technology, and the common domains where cloud is applied. It also discusses the nature of politics and how politics differs from governance, and then it mentions some problems in the political world. A keyword search for “cloud computing” and “politics” on Google scholar and other research database search engines returned few results, and a perusal of the products and services of the top cloud providers revealed the absence of applications meant for politics. Further searches revealed some cloud applications for politics including Microsoft TownHall and Google Moderator. The paper discusses that despite the paucity of cloud applications meant for politics, the cloud has certain strategic features which are particularly suited to the political domain. It then concludes with suggested cloud applications that could be built for the political world
Who gets counted? Understanding low female death registration in India
Get PDFBackground: Civil Registration and Vital Statistics (CRVS) systems are essential for governance, public health, and achieving SDGs however, gender gaps limit women’s access to rights and services, with under-registration of female vital events reinforcing their invisibility and distorting gender-responsive policies. Objectives: This study examines the drivers of low female death registration across India’s States and Union Territories, focusing on the roles of age, gender and wealth, with an aim to inform policies to strengthen CRVS systems and reduce gender disparities in vital statistics. Methods: The study utilizes data from NFHS-5 (2019–2021 for examining the factors associated with female death registration. Multivariable logistic regression models have been used to examine the impact of socio-economic and demographic factors on female death registration in India. Findings: The results highlight a significant gender gap in death registration (73% male vs. 64% female). The gap is widest in states like Bihar and Uttar Pradesh, while states like Kerala and Goa report near universal registration for both sexes. Gender gaps in housing and land ownership align with gaps in death registration, suggesting a strong association between asset ownership and registration. The results highlight association between wealth and death registration, with rates rising across quintiles for both sexes; however males consistently have higher registration rates. Among the poorest, the gap is widest which narrows down in the richest group. A gender gap in death registration persists across all age groups in India, beginning early, widening during working ages, and continuing into old age; while registration rates improve with age and wealth, women especially among the poorest remain under-registered, particularly in early and later life stages. Conclusions: Women in India encounter barriers to civil registration, and improving death registration demands systemic reforms, digital advancements, and community engagement Strengthening political commitment, collaboration, and public awareness will ensure inclusive, accurate records, enhancing CRVS for governance and policy
Burkholderia sp. from rhizosphere of Rhododendron arboretum: Isolation, identification and plant growth promotory (PGP) activities
Get PDFPlant growth promoting rhizobacteria (PGPR) is beneficial bacteria that colonize plant roots and enhance plant growth by wide variety of mechanism like phosphate solubilisation, etc. Use of PGPR has steadily increased in agriculture and offers an attractive way to replace chemical fertilizers, pesticides and supplements. The present research work was designed to isolate and characterize the PGP activity of Burkholderia sp. For this purpose rhizospheric soil from Rhododendron arboreum of Kumaun Himalaya was collected and efficient bacterial strain was screened on the basis of phosphate solubilization. Further, assessment of various parameters of plant growth promotion activity was done and enhanced production of IAA (16.4 ?gml-1) and (20.8 ?gml-1) was observed in the presence of 250?gml-1 and 500 ?g ml-1 of tryptophan, respectively. Correspondingly, in respect of 7.8 ?g ml-1 IAA without tryptophan, and their confirmation was executed by TLC. A remarkable change in color from green to reddish-brown zone on CAS plates, suggests the positive result for siderophore production, and finally the seed germination and pot trial experiment depicted the growth index of wheat plant. Therefore, the present study suggests that Burkholderia sp. is beneficial for plant growth promotion
STATUS AND IMPACT OF INVASIVE AND ALIEN SPECIES ON ENVIRONMENT, AND HUMAN WELFARE: AN OVERVIEW
Get PDFAn invasive species can be any kind of living organisms such as amphibian, plant, insect, fish, fungus, bacteria, or even an organism’s seeds or eggs—that is not native to an ecosystem and causes harm. We conducted a systematic literature review of the existing research on the status and impact of invasive species on the Environment, Socio-Economic and Humans, with the aim of providing guidance on various levels and group decisions. In order to detect patterns of publication trends and factors determining research perceptions of invasive species, qualitative as well as quantitative data were used. The majority of research papers dealing with the impact of invasive plant species have addressed the autecology of invasive species, factors facilitating the spread of these species, change in social structure and economic losses. Soil processes that adapt quickly to the invasion of plants and, in turn, affect the recruitment and growth of both native and invasive species are often ignored. The author\u27s reviews the existing literature on the impact of invasive plant species show that several soil properties and processes are substantially altered. These changes support feedback mechanisms, which could reverberate up to the landscape-level and affect ecosystem structure and biogeochemical cycles. Evidently, studies need to focus on soil processes for a better understanding of the invasion. In India total of 169 invasive alien species have documented in different ecosystems across the country. It is documented by the National biodiversity authority of India on the basis of specific criteria. In this review, an attempt has been made to understand the status of IAS in India and their impact on biodiversity, ecosystem services and human welfare. This review will helpful for the researchers to know the status of IAS in India
