Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262314
S. Padmapriya, R. Valli, M. Jayekumar
Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.
{"title":"Monitoring Algorithm in Malicious Vehicular Adhoc Networks","authors":"S. Padmapriya, R. Valli, M. Jayekumar","doi":"10.1109/ICSCAN49426.2020.9262314","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262314","url":null,"abstract":"Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"26 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86638949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262330
Pugazhenthi A, L. S. Kumar
This paper presents algorithms for extraction of clouds from INSAT-3D satellite image over the Indian region. The K-Means and Fuzzy C-Means clustering algorithms are applied on INSAT-3D satellite images on some specific dates and time in the year 2017, when the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite crosses the Indian region. Prior to this, the number of cluster segments k is selected from the MODIS Aqua sensor's cloud product. The result of segmentation algorithms is validated by comparing with the cloud optical thickness of the MODIS data. The comparison shows that INSAT-3D cloud segmentation matches well with the cloud optical thickness of the MODIS data.
{"title":"Cloud Extraction from INSAT-3D Satellite Image by K-Means and Fuzzy C-Means Clustering Algorithms","authors":"Pugazhenthi A, L. S. Kumar","doi":"10.1109/ICSCAN49426.2020.9262330","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262330","url":null,"abstract":"This paper presents algorithms for extraction of clouds from INSAT-3D satellite image over the Indian region. The K-Means and Fuzzy C-Means clustering algorithms are applied on INSAT-3D satellite images on some specific dates and time in the year 2017, when the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite crosses the Indian region. Prior to this, the number of cluster segments k is selected from the MODIS Aqua sensor's cloud product. The result of segmentation algorithms is validated by comparing with the cloud optical thickness of the MODIS data. The comparison shows that INSAT-3D cloud segmentation matches well with the cloud optical thickness of the MODIS data.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"45 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88300422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262441
G. Sharmila, S. Karthika, V. Rajesh, A. Yuvarani, E. Sangeetha
Aging Macular Deterioration (AMD) is a leading eye problem most commonly experienced by the old age people. If the problem is untreated over a prolonged time period, it results in permanent blindness. This eye problem is caused due to the damage of macula lutea which is a central region of retina needs for visualizing very fine details. However, only early detection can exhibit it from becoming severe and protect vision. This method proposes an automatic screening of all the three stages of AMD (i.e.) early (DMD), intermediate and late (WMD) using Convolutional Neural Network. A set of 400 color fundus images are taken for experimentation out of which 190 images are affected AMD images and 210 images are non-AMD images. Here, first the images are subjected to an image segmentation technique which adds-on the advantage of improving the accuracy of the system. Fuzzy c-means clustering is used as the image segmentation technique. Then the segmented images were trained and experimented using Convolutional Neural Network. This work thus obtained an overall accuracy of about 95.65%. The experimental results verify the effectiveness of this method.
{"title":"Computer Aided Diagnosis of Aging Macular Deterioration Via Convolutional Neural Network","authors":"G. Sharmila, S. Karthika, V. Rajesh, A. Yuvarani, E. Sangeetha","doi":"10.1109/ICSCAN49426.2020.9262441","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262441","url":null,"abstract":"Aging Macular Deterioration (AMD) is a leading eye problem most commonly experienced by the old age people. If the problem is untreated over a prolonged time period, it results in permanent blindness. This eye problem is caused due to the damage of macula lutea which is a central region of retina needs for visualizing very fine details. However, only early detection can exhibit it from becoming severe and protect vision. This method proposes an automatic screening of all the three stages of AMD (i.e.) early (DMD), intermediate and late (WMD) using Convolutional Neural Network. A set of 400 color fundus images are taken for experimentation out of which 190 images are affected AMD images and 210 images are non-AMD images. Here, first the images are subjected to an image segmentation technique which adds-on the advantage of improving the accuracy of the system. Fuzzy c-means clustering is used as the image segmentation technique. Then the segmented images were trained and experimented using Convolutional Neural Network. This work thus obtained an overall accuracy of about 95.65%. The experimental results verify the effectiveness of this method.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88316638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262304
M. Lambay, S. Pakkir Mohideen
Healthcare industry is an indispensable entity in the real world where large volumes of data is accumulated from time to time. Such data assumes characteristics of big data and it is desirable to analyze it and bring about latent relationships among variables in the healthcare data. Data in healthcare industry is rich in useful information. However, a comprehensive big data approach is essential to mine the data and acquire business intelligence. There are many use cases of big data analytics. However, in healthcare industry it is imperative to have knowledge-driven recommendations that help all stakeholders. With the emergence of cloud computing, big data analytics has become a reality. Distributed programming frameworks like Hadoop and Spark, to mention few, are available with associated Distributed File System (DFS) to manage big data. Many researchers contributed towards developing algorithms based on machine learning which is part of Artificial Intelligence (AI). Since healthcare industry is one of the sources of big data, it needs distributed environments for processing. Big data analytics is essential to analyze healthcare data in a comprehensive manner. The cloud computing and big data ecosystem is playing favorable role in realizing big data analytics for healthcare recommendations. A typical recommender system in healthcare industry is supposed to produce recommendations in various aspects of the domain. This paper throws light into different recommenders in healthcare domain that use big data analytics to generate recommendations. It not only provides useful insights but also discussed research gaps that can be used to investigate further to improve the state of the art.
{"title":"Big Data Analytics for Healthcare Recommendation Systems","authors":"M. Lambay, S. Pakkir Mohideen","doi":"10.1109/ICSCAN49426.2020.9262304","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262304","url":null,"abstract":"Healthcare industry is an indispensable entity in the real world where large volumes of data is accumulated from time to time. Such data assumes characteristics of big data and it is desirable to analyze it and bring about latent relationships among variables in the healthcare data. Data in healthcare industry is rich in useful information. However, a comprehensive big data approach is essential to mine the data and acquire business intelligence. There are many use cases of big data analytics. However, in healthcare industry it is imperative to have knowledge-driven recommendations that help all stakeholders. With the emergence of cloud computing, big data analytics has become a reality. Distributed programming frameworks like Hadoop and Spark, to mention few, are available with associated Distributed File System (DFS) to manage big data. Many researchers contributed towards developing algorithms based on machine learning which is part of Artificial Intelligence (AI). Since healthcare industry is one of the sources of big data, it needs distributed environments for processing. Big data analytics is essential to analyze healthcare data in a comprehensive manner. The cloud computing and big data ecosystem is playing favorable role in realizing big data analytics for healthcare recommendations. A typical recommender system in healthcare industry is supposed to produce recommendations in various aspects of the domain. This paper throws light into different recommenders in healthcare domain that use big data analytics to generate recommendations. It not only provides useful insights but also discussed research gaps that can be used to investigate further to improve the state of the art.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77397989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262367
H. Rosi, Ramachandram Ethrajavalli, Mohammed Iqbal Janci
Owing to their peculiar properties nanoparticles of cerium oxide have gained tremendous attention in recent years. As such, bacteria, fungus and algae are used for the development of CeO2 NPs through the use of both intracellular and extracellular microbial or enzyme cells, proteins and other biomolecule compounds. In this paper we use Sargassum wightii Greville, a biological extract, to synthesize cerium oxide (CeO2) nanoparticles. Algal-biogenic metal oxide synthesis nanoparticles is a safe and economical procedure due to the formation of compact, small nanoparticles. A number of advanced devices, such as UV-visible spectrophotometers, XRD, FTIR and SEM spectroscopy have been identified for prepared CeO2 NPs. Cerium oxide particles were studied for the antioxidant properties and their antioxidant potency was examined using an in vitro system. The antioxidant strength tests for insoluble solids were conducted using an modified DPPH process. DPPH spray increases with particle size decrease.
{"title":"Synthesis Of Cerium Oxide Nanoparticles Using Marine Algae Sargassum Wightii Greville Extract: Implications For Antioxidant Applications","authors":"H. Rosi, Ramachandram Ethrajavalli, Mohammed Iqbal Janci","doi":"10.1109/ICSCAN49426.2020.9262367","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262367","url":null,"abstract":"Owing to their peculiar properties nanoparticles of cerium oxide have gained tremendous attention in recent years. As such, bacteria, fungus and algae are used for the development of CeO2 NPs through the use of both intracellular and extracellular microbial or enzyme cells, proteins and other biomolecule compounds. In this paper we use Sargassum wightii Greville, a biological extract, to synthesize cerium oxide (CeO2) nanoparticles. Algal-biogenic metal oxide synthesis nanoparticles is a safe and economical procedure due to the formation of compact, small nanoparticles. A number of advanced devices, such as UV-visible spectrophotometers, XRD, FTIR and SEM spectroscopy have been identified for prepared CeO2 NPs. Cerium oxide particles were studied for the antioxidant properties and their antioxidant potency was examined using an in vitro system. The antioxidant strength tests for insoluble solids were conducted using an modified DPPH process. DPPH spray increases with particle size decrease.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"1 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90088173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262275
G. Vengatesh, R. Rajesh, T. Naveenkumar
This article is to improve communication with the children affected with cerebral palsy by using a computer vision. cerebral palsy is a permanent movements disorder that appears in childhood. It affects their movements, sensation, and speaking so the children differ from normal children. The technology can improve communication between the children and parents by using an open cv python programming and convolutional neural network(CNN). It detects the facial expression and body pattern of the children to give accurate results of the emotion or needs of the children. then it intimates the alert message to the parents through the mobile application.
{"title":"An Intelligent Computer Vision for Children Affected with Cerebral Palsy","authors":"G. Vengatesh, R. Rajesh, T. Naveenkumar","doi":"10.1109/ICSCAN49426.2020.9262275","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262275","url":null,"abstract":"This article is to improve communication with the children affected with cerebral palsy by using a computer vision. cerebral palsy is a permanent movements disorder that appears in childhood. It affects their movements, sensation, and speaking so the children differ from normal children. The technology can improve communication between the children and parents by using an open cv python programming and convolutional neural network(CNN). It detects the facial expression and body pattern of the children to give accurate results of the emotion or needs of the children. then it intimates the alert message to the parents through the mobile application.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"8 38 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81664878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262373
C. Someswararao, Shiva Shankar Reddy, S. V. Appaji, Vmnssvkr Gupta
The anomalous development of cells in brain causes brain tumor that may lead to death. The rate of deaths can be reduced by early detection of tumor. Most common method to detect the tumor in brain is the use of Magnetic Resonance Imaging (MRI). MR images are considered because it gives a clear structure of the tumor. In this paper we proposed an novel mechanism for detecting tumor from MR image by applying machine learning algorithms especially with CNN model.
{"title":"Brain Tumor Detection Model from MR Images using Convolutional Neural Network","authors":"C. Someswararao, Shiva Shankar Reddy, S. V. Appaji, Vmnssvkr Gupta","doi":"10.1109/ICSCAN49426.2020.9262373","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262373","url":null,"abstract":"The anomalous development of cells in brain causes brain tumor that may lead to death. The rate of deaths can be reduced by early detection of tumor. Most common method to detect the tumor in brain is the use of Magnetic Resonance Imaging (MRI). MR images are considered because it gives a clear structure of the tumor. In this paper we proposed an novel mechanism for detecting tumor from MR image by applying machine learning algorithms especially with CNN model.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"17 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83974878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262343
S. Pariselvam, Dhanuja. N, D. S, S. B
Nowadays, Hand gestures playing a important role for human interactions with the computer. Deep Learning is a part of machine learning methods which makes the recognition process easier by using Convolution Neural Networks (ConvNet/CNN). Convolution Neural Networks is a multilayer process network which includes Input layer, Convolution layer, Max pooling layer, Fully connected layer, Output layer. When compared to other algorithms, CNN can give more accurate results. CNN is mainly used to analyze visual images and for the image processing, segmentation and classification with higher accuracy. Here, this model consists of two main systems. One is voice input is converted into text and hand gestures and second approach is hand gestures conversion to text. These two systems are mainly used for abnormal people. These systems are implemented in Python and OpenCV is used to capture images. Each of these two systems has different modules. Human Computer Interaction are main source for the communication between humans and computer. So, these systems are helpful in communicating some information to humans. These systems are free from lighting conditions and background noise by using CNN algorithm.
{"title":"An Interaction System Using Speech and Gesture Based on CNN","authors":"S. Pariselvam, Dhanuja. N, D. S, S. B","doi":"10.1109/ICSCAN49426.2020.9262343","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262343","url":null,"abstract":"Nowadays, Hand gestures playing a important role for human interactions with the computer. Deep Learning is a part of machine learning methods which makes the recognition process easier by using Convolution Neural Networks (ConvNet/CNN). Convolution Neural Networks is a multilayer process network which includes Input layer, Convolution layer, Max pooling layer, Fully connected layer, Output layer. When compared to other algorithms, CNN can give more accurate results. CNN is mainly used to analyze visual images and for the image processing, segmentation and classification with higher accuracy. Here, this model consists of two main systems. One is voice input is converted into text and hand gestures and second approach is hand gestures conversion to text. These two systems are mainly used for abnormal people. These systems are implemented in Python and OpenCV is used to capture images. Each of these two systems has different modules. Human Computer Interaction are main source for the communication between humans and computer. So, these systems are helpful in communicating some information to humans. These systems are free from lighting conditions and background noise by using CNN algorithm.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87175696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262439
B. Lakshmipriya, N. Pavithra, D. Saraswathi
Deep learning has been witnessing an unprecedented growth in various applications like image classification, image recognition, object recognition and so on. In this work, a novel multifocus fusion schematic is putforth using deep learning strategy for the fusion of more than two colour images. The activations of the convolutional neural network (CNN) are used to extract the prominent deep features of the source and these features are fused by the virtue of weighted averaging technique. Finally, the weighted average outputs of the activations of the source images are considered for the recovering the enhanced fused output the image. The fused image is found to be enhanced such that the entire image is free from motion blur and defocusing. Three popular deep learning architectures namely Alexnet, VGG16 and GoogLeNet are considered in this work. It is evident from the results presented in this work that, GoogLeNet based framework performs well when compared to Alexnet and VGG16.
{"title":"Optimized Convolutional Neural Network based Colour Image Fusion","authors":"B. Lakshmipriya, N. Pavithra, D. Saraswathi","doi":"10.1109/ICSCAN49426.2020.9262439","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262439","url":null,"abstract":"Deep learning has been witnessing an unprecedented growth in various applications like image classification, image recognition, object recognition and so on. In this work, a novel multifocus fusion schematic is putforth using deep learning strategy for the fusion of more than two colour images. The activations of the convolutional neural network (CNN) are used to extract the prominent deep features of the source and these features are fused by the virtue of weighted averaging technique. Finally, the weighted average outputs of the activations of the source images are considered for the recovering the enhanced fused output the image. The fused image is found to be enhanced such that the entire image is free from motion blur and defocusing. Three popular deep learning architectures namely Alexnet, VGG16 and GoogLeNet are considered in this work. It is evident from the results presented in this work that, GoogLeNet based framework performs well when compared to Alexnet and VGG16.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"97 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86284808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262372
R. Poovendran, B. A. Kumar, V. Bhuvaneshwari, R. Aswini, K. Priya
These days, Many agriculture tasks are mechanized and numerous programmed hardware and robots accessible industrially. Two significant issues in present day agribusiness are water shortage and high work worth. The above issues are settled utilizing agribusiness task mechanization it is planned to configuration to diminish work cost [1]. ECOBOT is a robot extraordinarily intended for farming purposes. This diminishes the human work and yields the creation development with low venture of seeds. Agrobot goes about as an Internet of Things gadget which gathers the information from various sensors and passes the data to the client by means of Wi-Fi. This robot for the most part manages burrowing of land, seeding, furrowing, giving water, preparing, splashing medicinal, collecting and so forth. What's more, Microcontrollers like Arduino and Node-MCU is utilized to control and gathers the sensors data.
{"title":"Multi-Purpose Intelligent Drudgery Reducing Ecobot","authors":"R. Poovendran, B. A. Kumar, V. Bhuvaneshwari, R. Aswini, K. Priya","doi":"10.1109/ICSCAN49426.2020.9262372","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262372","url":null,"abstract":"These days, Many agriculture tasks are mechanized and numerous programmed hardware and robots accessible industrially. Two significant issues in present day agribusiness are water shortage and high work worth. The above issues are settled utilizing agribusiness task mechanization it is planned to configuration to diminish work cost [1]. ECOBOT is a robot extraordinarily intended for farming purposes. This diminishes the human work and yields the creation development with low venture of seeds. Agrobot goes about as an Internet of Things gadget which gathers the information from various sensors and passes the data to the client by means of Wi-Fi. This robot for the most part manages burrowing of land, seeding, furrowing, giving water, preparing, splashing medicinal, collecting and so forth. What's more, Microcontrollers like Arduino and Node-MCU is utilized to control and gathers the sensors data.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"53 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80744689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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