Pub Date : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452827
K.Upendar Reddy, Y.Gopal Reddy, Nelakuditi Krishna Teja, Padmaveni Krishnan, D. Aravindhar, M. Sambath
Security becomes a major concern for data storage. As observed, the images in the cloud will emerge as a threat to the users. The user considers about the security of stored data device. Henceforth, the storage and maintenance of data on devices became a hectic task and it consumes more effort from the user. To overcome this problem, this research wok has attempted to implement the images on the cloud to waive off the cost problems by implementing security mechanisms for the images present on the cloud with minimal cost and security. The proposed method protects the images on the cloud by using visual cryptograpghy. The image is encrypted with the user specified key by using AES algorithm and it is further divided into multiple pieces called shares. The shares are then moved to the cloud. To decrypt the image, the shares are collected back from the cloud and combined to form a meaningful image using AES algorithm. Finally, the original image gets revealed.
{"title":"Securing images on cloud using visual cryptography","authors":"K.Upendar Reddy, Y.Gopal Reddy, Nelakuditi Krishna Teja, Padmaveni Krishnan, D. Aravindhar, M. Sambath","doi":"10.1109/ICOEI51242.2021.9452827","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452827","url":null,"abstract":"Security becomes a major concern for data storage. As observed, the images in the cloud will emerge as a threat to the users. The user considers about the security of stored data device. Henceforth, the storage and maintenance of data on devices became a hectic task and it consumes more effort from the user. To overcome this problem, this research wok has attempted to implement the images on the cloud to waive off the cost problems by implementing security mechanisms for the images present on the cloud with minimal cost and security. The proposed method protects the images on the cloud by using visual cryptograpghy. The image is encrypted with the user specified key by using AES algorithm and it is further divided into multiple pieces called shares. The shares are then moved to the cloud. To decrypt the image, the shares are collected back from the cloud and combined to form a meaningful image using AES algorithm. Finally, the original image gets revealed.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959635","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452740
G. Aparna, S. Gowri, R. Bharathi, V. S, J. J, A. P
Coronavirus disease (COVID-19) is a pandemic caused by the coronavirus SARS -CoV-2 that was not previously seen in humans. COVID-19 is spreading rapidly throughout the world. COVID-19 can be detected by a lung infection of the patients. The standard method for detecting COVID-19 is the Reverse transcription-polymerase chain reaction (RT-PCR) test. But the availability of RT-PCR tests is in short supply. As a result of this, the early detection of the disease is difficult. The easily obtainable modes like X-rays are often used for detecting infections in the lungs. It is confirmed that X-ray scans can be widely used for efficient COVID-19 diagnosis. But a physical diagnosis of X-rays of an outsized number of patients is a longterm process. A deep learning-based diagnosis process can help radiologists in detecting COVID-19 from X-ray scans. Pre-trained CNNs are commonly used in detecting diseases from datasets. This paper proposes a CNN model with a parallelization strategy that extracts the features in the X-ray images by applying filters parallelly through the images. Our proposed method aims to attain higher accuracy and a less loss rate with precision. To do so, the accuracy and loss rates of three types of CNN - VGG-16, MobileNet, and CNN are compared with the parallelization technique. Since, VGG-16 and MobileNet are pre-trained models; those two models are directly imported from Keras. Moreover, this paper utilizes two datasets consisting of COVID X-ray images and Non-COVID X-ray images for the prediction of COVID-19 using Convolution Neural Network [CNN].
{"title":"COVID-19 Prediction using X-Ray Images","authors":"G. Aparna, S. Gowri, R. Bharathi, V. S, J. J, A. P","doi":"10.1109/ICOEI51242.2021.9452740","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452740","url":null,"abstract":"Coronavirus disease (COVID-19) is a pandemic caused by the coronavirus SARS -CoV-2 that was not previously seen in humans. COVID-19 is spreading rapidly throughout the world. COVID-19 can be detected by a lung infection of the patients. The standard method for detecting COVID-19 is the Reverse transcription-polymerase chain reaction (RT-PCR) test. But the availability of RT-PCR tests is in short supply. As a result of this, the early detection of the disease is difficult. The easily obtainable modes like X-rays are often used for detecting infections in the lungs. It is confirmed that X-ray scans can be widely used for efficient COVID-19 diagnosis. But a physical diagnosis of X-rays of an outsized number of patients is a longterm process. A deep learning-based diagnosis process can help radiologists in detecting COVID-19 from X-ray scans. Pre-trained CNNs are commonly used in detecting diseases from datasets. This paper proposes a CNN model with a parallelization strategy that extracts the features in the X-ray images by applying filters parallelly through the images. Our proposed method aims to attain higher accuracy and a less loss rate with precision. To do so, the accuracy and loss rates of three types of CNN - VGG-16, MobileNet, and CNN are compared with the parallelization technique. Since, VGG-16 and MobileNet are pre-trained models; those two models are directly imported from Keras. Moreover, this paper utilizes two datasets consisting of COVID X-ray images and Non-COVID X-ray images for the prediction of COVID-19 using Convolution Neural Network [CNN].","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130114245","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453099
Ramkrishna Patel, Vikas Choudhary, D. Saxena, Ashutosh Kumar Singh
Stock prices change everyday by market forces (supply and demand). In recent years stock price prediction has been one of the most significant concern. Investors are investing on stock market on the basis of certain prediction. For prediction, stock market prices investors are applying some techniques and methods through which they get more profits and minimize their risks. Machine Learning methods are often used for the prediction of stock prices. This survey paper discusses various machine learning approaches (Supervised or Unsupervised) and methods through which the investors get to know the stock prices increase or decrease. It was done in five phases, such as data acquired, pre-processing of dataset, extraction of features, prediction of stock price using different techniques and display the result. In first phase, the data is collected from different social sites, historical data of companies. In second phase, the removal of incorrect, duplicate and dirt is done in pre-processing phase. In third phase, the reduction of data sets and the selection of useful data is done. In fourth phase, prediction is done using different machine learning techniques and approaches which is categorized as supervised and unsupervised learning techniques. Now, in last phase the accuracy is determined using different approaches.
{"title":"REVIEW OF STOCK PREDICTION USING MACHINE LEARNING TECHNIQUES","authors":"Ramkrishna Patel, Vikas Choudhary, D. Saxena, Ashutosh Kumar Singh","doi":"10.1109/ICOEI51242.2021.9453099","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453099","url":null,"abstract":"Stock prices change everyday by market forces (supply and demand). In recent years stock price prediction has been one of the most significant concern. Investors are investing on stock market on the basis of certain prediction. For prediction, stock market prices investors are applying some techniques and methods through which they get more profits and minimize their risks. Machine Learning methods are often used for the prediction of stock prices. This survey paper discusses various machine learning approaches (Supervised or Unsupervised) and methods through which the investors get to know the stock prices increase or decrease. It was done in five phases, such as data acquired, pre-processing of dataset, extraction of features, prediction of stock price using different techniques and display the result. In first phase, the data is collected from different social sites, historical data of companies. In second phase, the removal of incorrect, duplicate and dirt is done in pre-processing phase. In third phase, the reduction of data sets and the selection of useful data is done. In fourth phase, prediction is done using different machine learning techniques and approaches which is categorized as supervised and unsupervised learning techniques. Now, in last phase the accuracy is determined using different approaches.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134538997","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452988
F. A. Lincy, T. Sasikala
At present, population rate in the main cities has increased tremendously. This has increased the production of waste. The management of huge volume of waste has become more difficult and challenging. The public dustbins are overflowing and have become nobody's concern. Due to the lack of responsibility of the corporation people, the overflowing garbage wastes have created unhygienic surroundings and foul smell. So, to overcome this issue, smart dustbin is designed. This smart dustbin is built on Arduino Uno board and is interfaced with GSM, GPRS and sensors. The sensors are used to check the threshold level of the dustbin. The threshold levels are already set. If the garbage hits the mentioned threshold level, continuous alert is sent to the respective authority until the garbage is recovered and the externally fixed LED is changed into red color. Once, the garbage from the bin cleared the LED changes to green color. This alert system is triggered by the sensors to the GSM modem. A time limit (say 24 hours) is given to respective authority, where if he/she fails the duty, the alert to the higher authority is sent. By this facility, the higher authority will be able to take action on the irresponsible workers. Features like maps are used to locate the dustbins which make the authority to reach the location easily. Connectivity among the dustbins are given to establish communication among the bins and provides smart system. Thus, the implementation of smart dustbins will create a hygienic society and will make the management of waste easy. The negligence of authorities and the public may be reduced. A clean and disease free environment can be created.
{"title":"Smart Dustbin Management Using IOT and Blynk Application","authors":"F. A. Lincy, T. Sasikala","doi":"10.1109/ICOEI51242.2021.9452988","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452988","url":null,"abstract":"At present, population rate in the main cities has increased tremendously. This has increased the production of waste. The management of huge volume of waste has become more difficult and challenging. The public dustbins are overflowing and have become nobody's concern. Due to the lack of responsibility of the corporation people, the overflowing garbage wastes have created unhygienic surroundings and foul smell. So, to overcome this issue, smart dustbin is designed. This smart dustbin is built on Arduino Uno board and is interfaced with GSM, GPRS and sensors. The sensors are used to check the threshold level of the dustbin. The threshold levels are already set. If the garbage hits the mentioned threshold level, continuous alert is sent to the respective authority until the garbage is recovered and the externally fixed LED is changed into red color. Once, the garbage from the bin cleared the LED changes to green color. This alert system is triggered by the sensors to the GSM modem. A time limit (say 24 hours) is given to respective authority, where if he/she fails the duty, the alert to the higher authority is sent. By this facility, the higher authority will be able to take action on the irresponsible workers. Features like maps are used to locate the dustbins which make the authority to reach the location easily. Connectivity among the dustbins are given to establish communication among the bins and provides smart system. Thus, the implementation of smart dustbins will create a hygienic society and will make the management of waste easy. The negligence of authorities and the public may be reduced. A clean and disease free environment can be created.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"IA-21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126561185","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452842
P. Ajitha, Jeyakumar. S, Yadhu Nandha Krishna K, A. Sivasangari
One of the most significant issues in modern road safety and intelligent transportation systems is the automation of vehicle detection and identification. Many challenges have been solved in the advancement of image processing, pattern recognition, and deep learning technology in order to accomplish this goal. Vehicle Type Classification is a difficult task since the dataset has a large class imbalance, and several viewpoints for different cars can be identical. The proposed framework employs a shallow Convolutional Neural Networks (CNN) architecture to prevent overfitting and ensure that the correct features are learned, and an augmentation technique is utilized to produce synthetic images by using the image data generation model in Keras due to class imbalance. The shallow CNN is used to extract features from the generated images, and then Softmax activation is used to classify them. Finally, the proposed system will achieve the classification of vehicle type i.e. classify the different car models with efficiently by novel methodology. The findings of the experiments demonstrate that shallow CNN can do well in real-world situations.
{"title":"Vehicle Model Classification Using Deep Learning","authors":"P. Ajitha, Jeyakumar. S, Yadhu Nandha Krishna K, A. Sivasangari","doi":"10.1109/ICOEI51242.2021.9452842","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452842","url":null,"abstract":"One of the most significant issues in modern road safety and intelligent transportation systems is the automation of vehicle detection and identification. Many challenges have been solved in the advancement of image processing, pattern recognition, and deep learning technology in order to accomplish this goal. Vehicle Type Classification is a difficult task since the dataset has a large class imbalance, and several viewpoints for different cars can be identical. The proposed framework employs a shallow Convolutional Neural Networks (CNN) architecture to prevent overfitting and ensure that the correct features are learned, and an augmentation technique is utilized to produce synthetic images by using the image data generation model in Keras due to class imbalance. The shallow CNN is used to extract features from the generated images, and then Softmax activation is used to classify them. Finally, the proposed system will achieve the classification of vehicle type i.e. classify the different car models with efficiently by novel methodology. The findings of the experiments demonstrate that shallow CNN can do well in real-world situations.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131134983","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452872
M. Suri, N. Raj, Chakradhar Reddy Rendeddula, S. K., Deepa K
Electric Vehicle(EV) is chosen over the conventional vehicles, due to its less contribution in release of green-house gases. The depleted batteries in an EV can be refuelled using Battery Charging(BC) and Battery Swapping(BS) techniques. As the BS method provides, less refuelling time and flexibility in service to EV user, Battery Swapping stations (BSS) are gaining lot of acceptance from the transportation sector. BSS must plan its battery stack -with full charge to serve EV user with less waiting time. Hence, the forecasting of EV arrivals is necessary for the optimal planning of BSS. This paper presents, performance analysis of various forecasting algorithms used for EV arrivals, by using MATLAB/SIMULINK environment and results are analysed with performance metrics such as mean square error, system simulation time, correlation etc. A comparative analysis on various time series models has been carried out and results are analysed.
{"title":"Comparative Study on Forecasting methods of EV Arrivals at Battery Swapping Station","authors":"M. Suri, N. Raj, Chakradhar Reddy Rendeddula, S. K., Deepa K","doi":"10.1109/ICOEI51242.2021.9452872","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452872","url":null,"abstract":"Electric Vehicle(EV) is chosen over the conventional vehicles, due to its less contribution in release of green-house gases. The depleted batteries in an EV can be refuelled using Battery Charging(BC) and Battery Swapping(BS) techniques. As the BS method provides, less refuelling time and flexibility in service to EV user, Battery Swapping stations (BSS) are gaining lot of acceptance from the transportation sector. BSS must plan its battery stack -with full charge to serve EV user with less waiting time. Hence, the forecasting of EV arrivals is necessary for the optimal planning of BSS. This paper presents, performance analysis of various forecasting algorithms used for EV arrivals, by using MATLAB/SIMULINK environment and results are analysed with performance metrics such as mean square error, system simulation time, correlation etc. A comparative analysis on various time series models has been carried out and results are analysed.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130845335","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453094
Yuan Hu, Long Wan
Construction of immersive architectural wisdom guiding environment based on virtual reality is studied in this paper. Emerging development of the computer smart systems have provided the engineers a novel solution for the platform construction. Network virtualization is currently the most unclear and controversial concept in the industry regarding the definition of virtualization subdivisions. To improve the current study, we use the VR system to implement the platform. The wisdom guiding environment is built through the virtual data modelling and the interactive connections. The platform is implemented through the software. The test on the data analysis accuracy and the interface optimization is conducted.
{"title":"Construction of immersive architectural wisdom guiding environment based on virtual reality","authors":"Yuan Hu, Long Wan","doi":"10.1109/ICOEI51242.2021.9453094","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453094","url":null,"abstract":"Construction of immersive architectural wisdom guiding environment based on virtual reality is studied in this paper. Emerging development of the computer smart systems have provided the engineers a novel solution for the platform construction. Network virtualization is currently the most unclear and controversial concept in the industry regarding the definition of virtualization subdivisions. To improve the current study, we use the VR system to implement the platform. The wisdom guiding environment is built through the virtual data modelling and the interactive connections. The platform is implemented through the software. The test on the data analysis accuracy and the interface optimization is conducted.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957168","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452992
M. V, Manimegalai V, Sharmila B, Sinivasan M, Vadivel M, V. S
Most common man would think Electric Vehicles are the future, but it is not, Electric Vehicles are here and as we push importance of green energy in the present world EVs are becoming the best choice for environment. And most important thing in Electric Vehicles is Battery Management System (BMS). Our proposed work is helpful in selecting more suitable ways to origin of a trusted and Safe BMS. To maintain reliability and safety of battery we are going to use Lithium-ion battery which is preferred Over Lead acid battery. If not operated within safety, Lithium-ion batteries can be dangerous. Therefore, this System must be used along with Li-ion battery for better performance. The battery pack will be Connected to Battery Management System and parameters such as current, voltage, temperature will be displayed using LCD display. So, we can monitor the values at any time which will enhance our usage of batteries & its life. The main factor to the world-shattering Change in Electric Vehicles(EV) is Battery Management System.
{"title":"Estimation of State of Charge of Battery","authors":"M. V, Manimegalai V, Sharmila B, Sinivasan M, Vadivel M, V. S","doi":"10.1109/ICOEI51242.2021.9452992","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452992","url":null,"abstract":"Most common man would think Electric Vehicles are the future, but it is not, Electric Vehicles are here and as we push importance of green energy in the present world EVs are becoming the best choice for environment. And most important thing in Electric Vehicles is Battery Management System (BMS). Our proposed work is helpful in selecting more suitable ways to origin of a trusted and Safe BMS. To maintain reliability and safety of battery we are going to use Lithium-ion battery which is preferred Over Lead acid battery. If not operated within safety, Lithium-ion batteries can be dangerous. Therefore, this System must be used along with Li-ion battery for better performance. The battery pack will be Connected to Battery Management System and parameters such as current, voltage, temperature will be displayed using LCD display. So, we can monitor the values at any time which will enhance our usage of batteries & its life. The main factor to the world-shattering Change in Electric Vehicles(EV) is Battery Management System.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130968367","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453027
S. M, L. Sujihelen
In an emergency healthcare situation, it is always intended to obtain the data in an easier and secure way. To accomplish this, the proposed research work has developed a study about the implementation of blockchain in healthcare system. Electronic Medical Records (EMRs) of patients are utilized to store data in the blockchain architecture. These EMRs are very sensitive, since it contains patient's personal details. Henceforth, the usage of records should take place in a secured mode otherwise hackers will attack the data. The availability of medical records will help to diagnose the diseases very easily and deliver better treatment to the patient, when they enter the critical stages like coma and unconsciousness. In this perspective, this research work analyzes different technologies and algorithms used in the existing systems.
{"title":"A Study on Blockchain and the Healthcare System","authors":"S. M, L. Sujihelen","doi":"10.1109/ICOEI51242.2021.9453027","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453027","url":null,"abstract":"In an emergency healthcare situation, it is always intended to obtain the data in an easier and secure way. To accomplish this, the proposed research work has developed a study about the implementation of blockchain in healthcare system. Electronic Medical Records (EMRs) of patients are utilized to store data in the blockchain architecture. These EMRs are very sensitive, since it contains patient's personal details. Henceforth, the usage of records should take place in a secured mode otherwise hackers will attack the data. The availability of medical records will help to diagnose the diseases very easily and deliver better treatment to the patient, when they enter the critical stages like coma and unconsciousness. In this perspective, this research work analyzes different technologies and algorithms used in the existing systems.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131119528","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452918
A. Nagaraju, M.Ajith Kumar reddy, CH. Venugopal reddy, R. Mohandas
As already known, Machine learning is a rapidly evolving technology. Several machine learning algorithms are used to predict the crop based on our analysis and study. Artificial neural network (ANN), Random forest, Linear regression, and Gradient boosting tree are just a few examples. They also used data sets such as soil, temperature, humidity, rainfall, pH value, and so on. This project includes modules like Crop, Fertilizer, etc. In Crop Module, the data sets like Nutrition, PH value, Rainfall, State and District Data are collected. In Nutrition, the values like nitrogen, phosphorus, potassium are collected. In fertilizer, the data sets like nutrition values and crop type data are collected. In Disease module, plant disease images data set are collected and futher this research work employs Deep learning concept like Convolutional Neural Network (CNN) for performing plant disease detection. Coming to machine learning part, Six major machine learning algorithms such as Decision Tree, SVM, Random forest, Logistic Regression XG Boost, and Naive Bayes are utilized in this paper. By collecting all data sets, the data will be trained by using the aforementioned machine learning algorithms.
{"title":"Multifactor Analysis to Predict Best Crop using Xg-Boost Algorithm","authors":"A. Nagaraju, M.Ajith Kumar reddy, CH. Venugopal reddy, R. Mohandas","doi":"10.1109/ICOEI51242.2021.9452918","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452918","url":null,"abstract":"As already known, Machine learning is a rapidly evolving technology. Several machine learning algorithms are used to predict the crop based on our analysis and study. Artificial neural network (ANN), Random forest, Linear regression, and Gradient boosting tree are just a few examples. They also used data sets such as soil, temperature, humidity, rainfall, pH value, and so on. This project includes modules like Crop, Fertilizer, etc. In Crop Module, the data sets like Nutrition, PH value, Rainfall, State and District Data are collected. In Nutrition, the values like nitrogen, phosphorus, potassium are collected. In fertilizer, the data sets like nutrition values and crop type data are collected. In Disease module, plant disease images data set are collected and futher this research work employs Deep learning concept like Convolutional Neural Network (CNN) for performing plant disease detection. Coming to machine learning part, Six major machine learning algorithms such as Decision Tree, SVM, Random forest, Logistic Regression XG Boost, and Naive Bayes are utilized in this paper. By collecting all data sets, the data will be trained by using the aforementioned machine learning algorithms.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132675562","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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