Multi-Tier Cluster Based Smart Farming Using Wireless Sensor Network

Narayan, Vipul; Daniel, A. K.

doi:10.1109/icccs49678.2020.9277072

Cited by 21 publications

(12 citation statements)

References 26 publications

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“…Work by [72] presents WSN deployment from three perspectives: node movement (dynamic, static, hybrid), node type (homogeneous, heterogeneous) and system hierarchy (single-tier, multi-tier). In static node deployment, nodes are placed in a fixed position for the entire sensing duration [122]. The dynamic node set-up allows mobility of nodes, and a hybrid set-up integrates static and dynamic nodes.…”

Section: Sensor Deployment Schemesmentioning

confidence: 99%

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture

Bayih

Morales

Assabie

et al. 2022

Sensors

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Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.

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Section: Sensor Deployment Schemesmentioning

confidence: 99%

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture

Bayih

Morales

Assabie

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Generic adversarial Networks (GANs) provide a way to read in-depth presentations without detailed training data. The study concludes that Resnet18 is an in-depth transmission model that is most suitable for measurement accuracy and has achieved 99% use of GAN as image enhancement Vipul Narayan et al [28].…”

Section: Related Workmentioning

confidence: 86%

“…The intense attributes of deep convolution neural network models are derived and used by SVM classifier for noticing of COVID-19. In some studies, Generative Adversarial Network (GAN) was utilized by Khalifa et al [28] in pneumonia detection in CXR images. The author spoke about the crisis and demanded its resilience with the rise of GAN production images.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of COVID-19 in the Lungs X-ray Images using Pre-trained Deep Learning Model CNN

2022

pnr

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COVID-19 is a highly contagious epidemic, and detection in the incipient phase is essential to curb the expansion of the disease. Chest Xrays are used in detecting COVID-19 infection. Lung's images and CT -Scan photos are available for coronavirus analysis. This paper is composed of deep learning techniques and methods used to detect COVID-19 contamination in the lung images. The methods employed and collected datasets used for testing metrics are summed up. The Analytical metrics utilized by the methods which are completely comparable. Through this work, we have taken a perspective on COVID-19 affected chest x-ray scanners and healthy patients. After sorting and pre-processing the images and implementing the data addition, we applied deep-learning-based CNN models to compare their performance with other models. The aim is to provide a helping hand to the most distressed medical professionals who are analyzing images with two eyes, detect COVID-19 . According to this analysis we provide a proposed methodology that uses deep learning , dropout technique with python language on Google Colab platform for reduces over-fitting by this deep learning technology. During the testing phase, I got 98.1% accuracy by increasing convolution layer and dropout layer. Our proposed methodology gives better accuracy than other compared models. The primary goal of this paper is to present research on medical image processing and define and implement the proposed CNN model.

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“…According to Figure 2 several phases have applied to the hypothesized model using the dataset to get accurate results are as follows [13].…”

Section: Figure 2 Proposed Methodologymentioning

confidence: 99%

Adoption of Mobile Payment Systems during COVID-19 in India

2022

pnr

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The digitalization process has been increasing rapidly worldwide today. There is various technologies developed and continue developing tremendously in the world, such as mobile payment systems. Such type of emerging technologies has made a very significant transformation in people lives everywhere. Technologies such as mobile devices facilitate people to finish their work in less time and effort required usually. That is why, these innovations and advancements in technologies led people to be more inclined toward their use. Furthermore, in the current scenario, mobile payments have become an integral part of the daily lives of people due to the impact of the novel coronavirus (COVID-19) disease on society. The presence of mobile payment systems can be considered for years, but the use of mobile payment systems has increased in the current situation of the COVID-19 pandemic, which is the core basis of this research paper. However, the user's acceptance of mobile payment systems is still low due to some pros and cons of this technology which are examined in this paper by applying a hybrid machine learning model. We collected primary data through Google Form from the institute in the Meerut region and other sources from India with a total of 222 respondents. In this study, the data is processed and applied on to the machine learning models. Data analysis and findings of the research showed that the proposed ensemble of classifier algorithms outperforms other exiting machine models in terms of accuracy, precision, recall and F1-score. The research and findings will help the organizations have a clear and correct analysis of the scenario of the adoption of mobile payments in India and also design better mobile payment systems to curb the situation like the COVID-19 pandemic.

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Multi-Tier Cluster Based Smart Farming Using Wireless Sensor Network

Cited by 21 publications

References 26 publications

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture

Automatic Detection of COVID-19 in the Lungs X-ray Images using Pre-trained Deep Learning Model CNN

Adoption of Mobile Payment Systems during COVID-19 in India

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