Deep Learning and IoT for Smart Agriculture Using WSN

Varman, S Aruul Mozhi; Baskaran, Arvind; Aravindh, S.; Prabhu, E

doi:10.1109/iccic.2017.8524140

Cited by 46 publications

(12 citation statements)

References 7 publications

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“…The proliferation of sensing systems in agricultural fields has also provided an avenue for data driven decision making and planning for farmers. This involves predicting various physical parameters which can affect crop growth like solar radiance [95] and temperature, humidity, windspeed [96][97][98][99][100] to help in decision making in terms of plant care but also classification systems for recommending crops to be sown [101,102]. It is important to note that all of these implementations are cloud based.…”

Section: Smart Agriculturementioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Agriculturementioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…Moderate lung infection was mainly seen in the 61 to 70 age group (68%), and severe lung infection was mainly seen in the age group of 41 to 60 (70%). This is the highest risk factor for COVID-19 affected patients [34][35][36][37]. Fig.…”

Section: Detection and Severity Classification Of Covid-19mentioning

confidence: 99%

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Budimirović¹,

Prabhu²,

Antonijevic³

et al. 2022

Computers, Materials &Amp; Continua

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Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature selection algorithm. The severity of lung infection is detected on the basis of age group. Fourth, image classification is done by using the proposed MWOA with the salp swarm algorithm (MWOA-SSA). MWOA-SSA has an accuracy of 97%, whereas PCA and MWOA have accuracies of 81% and 86%. The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA (84.4%) and MWOA (95.2%). MWOA-SSA outperforms other algorithms with a specificity of 97.8%. This proposed method improves the effective classification of lung affected images from large datasets.

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“…In the article [47], an intelligent agricultural system was dealt with through deep learning. In this article, in addition to predicting suitable products for subsequent crop cultivation, optimization of the irrigation system in the eld was also considered.…”

Section: Ref and Yearmentioning

confidence: 99%

Survey On The application of Deep Learning in Internet of Things (IoT)

Shadroo¹,

Rahmani²,

Rezaee³

2021

Preprint

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The Internet of Things (IoT) is a network of physical instruments, software, sensors that all are connected to the Internet. The IoT produces massive data, where, this enormous volume of data allows the use of deep learning algorithms (DLAs). Recently, the increase of the large body of data and their availability has been one of the main reasons for paying attention to this issue. Further, the recent upgrade of the hardware boosting the computational power has resulted in the utilize of deep learning alongside the IoT. Therefore, the purpose of the present research is to review the relevant conference and journal articles in IoT and deep learning from 2012 to July 2019. To review the publications, a composition of Systematic Mapping and systematic literature review has been employed for creating a survey paper. Accordingly, some questions have been raised; to answer which, 32 articles have been investigated. The papers have been categorized into four sections including a focus on data, network, computing environment, application with each being examined, and analyzed. This article would be beneficial for researchers who want to investigate the field of deep learning and IoT.

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Deep Learning and IoT for Smart Agriculture Using WSN

Cited by 46 publications

References 7 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Survey On The application of Deep Learning in Internet of Things (IoT)

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