Deep Learning Approach to Recognize COVID-19, SARS and Streptococcus Diseases from Chest X-ray Images

Corona 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

Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms

Machine 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

51-59Corona 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

Two-Stage Human Activity Recognition Using 2D-ConvNet

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences

Burkholderia sp. from rhizosphere of Rhododendron arboretum: Isolation, identification and plant growth promotory (PGP) activities

Plant 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

Can home visits for early child development be implemented with sufficient coverage and quality at scale? Evidence from the SPRING program in India and Pakistan

INTRODUCTION: There is limited evidence from low and middle-income settings on the effectiveness of early child development interventions at scale. To bridge this knowledge-gap we implemented the SPRING home visiting program where we tested integrating home visits into an existing government program (Pakistan) and employing a new cadre of intervention workers (India). We report the findings of the process evaluation which aimed to understand implementation. METHODS AND MATERIALS: We collected qualitative data on acceptability and barriers and facilitators for change through 24 in-depth interviews with mothers; eight focus group discussions with mothers, 12 with grandmothers, and 12 with fathers; and 12 focus group discussions and five in-depth interviews with the community-based agents and their supervisors. RESULTS: Implementation was sub-optimal in both settings. In Pakistan issues were low field-supervision coverage and poor visit quality related to issues scheduling supervision, a lack of skill development, high workloads and competing priorities. In India, issues were low visit coverage - in part due to employing new workers and an empowerment approach to visit scheduling. Coaching caregivers to improve their skills was sub-optimal in both sites, and is likely to have contributed to caregiver perceptions that the intervention content was not new and was focused on play activities rather than interaction and responsivity - which was a focus of the coaching. In both sites caregiver time pressures was a key reason for low uptake among families who received visits. DISCUSSION: Programs need feasible strategies to maximize quality, coverage and supervision including identifying and managing problems through monitoring and feedback loops. Where existing community-based agents are overstretched and system strengthening is unlikely, alternative implementation strategies should be considered such as group delivery. Core intervention ingredients such as coaching should be prioritized and supported during training and implementation. Given that time and resource constraints were a key barrier for families a greater focus on communication, responsivity and interaction during daily activities could have improved feasibility

Effect of the SPRING home visits intervention on early child development and growth in rural India and Pakistan: parallel cluster randomised controlled trials

INTRODUCTION: Almost 250 million children fail to achieve their full growth or developmental potential, trapping them in a cycle of continuing disadvantage. Strong evidence exists that parent-focussed face to face interventions can improve developmental outcomes; the challenge is delivering these on a wide scale. SPRING (Sustainable Programme Incorporating Nutrition and Games) aimed to address this by developing a feasible affordable programme of monthly home visits by community-based workers (CWs) and testing two different delivery models at scale in a programmatic setting. In Pakistan, SPRING was embedded into existing monthly home visits of Lady Health Workers (LHWs). In India, it was delivered by a civil society/non-governmental organisation (CSO/NGO) that trained a new cadre of CWs. METHODS: The SPRING interventions were evaluated through parallel cluster randomised trials. In Pakistan, clusters were 20 Union Councils (UCs), and in India, the catchment areas of 24 health sub-centres. Trial participants were mother-baby dyads of live born babies recruited through surveillance systems of 2 monthly home visits. Primary outcomes were BSID-III composite scores for psychomotor, cognitive and language development plus height for age z-score (HAZ), assessed at 18 months of age. Analyses were by intention to treat. RESULTS: 1,443 children in India were assessed at age 18 months and 1,016 in Pakistan. There was no impact in either setting on ECD outcomes or growth. The percentage of children in the SPRING intervention group who were receiving diets at 12 months of age that met the WHO minimum acceptable criteria was 35% higher in India (95% CI: 4-75%, p = 0.023) and 45% higher in Pakistan (95% CI: 15-83%, p = 0.002) compared to children in the control groups. DISCUSSION: The lack of impact is explained by shortcomings in implementation factors. Important lessons were learnt. Integrating additional tasks into the already overloaded workload of CWs is unlikely to be successful without additional resources and re-organisation of their goals to include the new tasks. The NGO model is the most likely for scale-up as few countries have established infrastructures like the LHW programme. It will require careful attention to the establishment of strong administrative and management systems to support its implementation

Can home visits for early child development be implemented with sufficient coverage and quality at scale? Evidence from the SPRING program in India and Pakistan

IntroductionThere is limited evidence from low and middle-income settings on the effectiveness of early child development interventions at scale. To bridge this knowledge-gap we implemented the SPRING home visiting program where we tested integrating home visits into an existing government program (Pakistan) and employing a new cadre of intervention workers (India). We report the findings of the process evaluation which aimed to understand implementation.Methods and materialsWe collected qualitative data on acceptability and barriers and facilitators for change through 24 in-depth interviews with mothers; eight focus group discussions with mothers, 12 with grandmothers, and 12 with fathers; and 12 focus group discussions and five in-depth interviews with the community-based agents and their supervisors.ResultsImplementation was sub-optimal in both settings. In Pakistan issues were low field-supervision coverage and poor visit quality related to issues scheduling supervision, a lack of skill development, high workloads and competing priorities. In India, issues were low visit coverage - in part due to employing new workers and an empowerment approach to visit scheduling. Coaching caregivers to improve their skills was sub-optimal in both sites, and is likely to have contributed to caregiver perceptions that the intervention content was not new and was focused on play activities rather than interaction and responsivity - which was a focus of the coaching. In both sites caregiver time pressures was a key reason for low uptake among families who received visits.DiscussionPrograms need feasible strategies to maximize quality, coverage and supervision including identifying and managing problems through monitoring and feedback loops. Where existing community-based agents are overstretched and system strengthening is unlikely, alternative implementation strategies should be considered such as group delivery. Core intervention ingredients such as coaching should be prioritized and supported during training and implementation. Given that time and resource constraints were a key barrier for families a greater focus on communication, responsivity and interaction during daily activities could have improved feasibility

Effect of the SPRING home visits intervention on early child development and growth in rural India and Pakistan: parallel cluster randomised controlled trials

IntroductionAlmost 250 million children fail to achieve their full growth or developmental potential, trapping them in a cycle of continuing disadvantage. Strong evidence exists that parent-focussed face to face interventions can improve developmental outcomes; the challenge is delivering these on a wide scale. SPRING (Sustainable Programme Incorporating Nutrition and Games) aimed to address this by developing a feasible affordable programme of monthly home visits by community-based workers (CWs) and testing two different delivery models at scale in a programmatic setting. In Pakistan, SPRING was embedded into existing monthly home visits of Lady Health Workers (LHWs). In India, it was delivered by a civil society/non-governmental organisation (CSO/NGO) that trained a new cadre of CWs.MethodsThe SPRING interventions were evaluated through parallel cluster randomised trials. In Pakistan, clusters were 20 Union Councils (UCs), and in India, the catchment areas of 24 health sub-centres. Trial participants were mother-baby dyads of live born babies recruited through surveillance systems of 2 monthly home visits. Primary outcomes were BSID-III composite scores for psychomotor, cognitive and language development plus height for age z-score (HAZ), assessed at 18 months of age. Analyses were by intention to treat.Results1,443 children in India were assessed at age 18 months and 1,016 in Pakistan. There was no impact in either setting on ECD outcomes or growth. The percentage of children in the SPRING intervention group who were receiving diets at 12 months of age that met the WHO minimum acceptable criteria was 35% higher in India (95% CI: 4–75%, p = 0.023) and 45% higher in Pakistan (95% CI: 15–83%, p = 0.002) compared to children in the control groups.DiscussionThe lack of impact is explained by shortcomings in implementation factors. Important lessons were learnt. Integrating additional tasks into the already overloaded workload of CWs is unlikely to be successful without additional resources and re-organisation of their goals to include the new tasks. The NGO model is the most likely for scale-up as few countries have established infrastructures like the LHW programme. It will require careful attention to the establishment of strong administrative and management systems to support its implementation

Biosorptive Removal of Ni(Ii) from Wastewater and Industrial Effluent

The objective of the present work was to investigate the removal of Ni(II) by the fresh biomass (FBM) and chemically treated leached biomass (LBM) of Calotropis procera. The scope of the work included screening of the biosorbents for their metal uptake potential, batch equilibrium, column mode removal studies and kinetic studies at varying pH (2-6), contact time, biosorbent dosages (1-25 g/L) and initial metal ion concentration (5-500 mg/L). The development of batch kinetic model and determination of order, desorption studies, column studies were investigated. It was observed that pH had marked effect on the Ni(II) uptake. Langmuir and Freundlich models were used to correlate equilibrium data on sorption of Ni(II) metallic ion by using both FBM and LBM at 28oC and pH 3 and different coefficients were calculated. It was found that both biomasses were statistically significant fit for Freundlich model. The biomass was successfully used for removal nickel from synthetic and industrial effluents and the technique appears industrially applicable and viable