Estimating the Category of Districts in a State Based on COVID Test Positivity Rate (TPR): A Study Using Supervised Machine Learning Approach

Bhoi, Sourav Kumar; Mallick, Chittaranjan; Nayak, Rajendra Prasad; Mohapatra, Debasis; Jena, Kalyan Kumar

doi:10.1007/978-981-19-1018-0_40

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The simulation is performed in Orange data analytics tool [14] on a machine of 8GB RAM and a Core-i3 processor. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Resultsmentioning

confidence: 99%

“…The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. However, we mainly focus on the CA of the models for selecting the best model for the proposed system to classify the device category.…”

Section: Resultsmentioning

confidence: 99%

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A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

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Purpose: Identification of Internet of Thing (IoT) devices in smart home is the most important function for a local server/controller to administer and control the home smoothly. The IoT devices continuously send and receive requests, acknowledgments, packets, etc. for efficient data communication and these communication patterns need to be classified. Design/Methodology/Approach: Therefore, to run the smart home smoothly, in this work a framework using cloud computing is proposed to identify the correct IoT device communicating with the local server based on supervised machine learning. The best supervised machine intelligence model will be installed at the local server to classify the devices on the basis of data communication patterns. Findings/Result: Simulation is performed using Orange 3.26 data analytics tool by considering an IoT devices data communication dataset collected from Kaggle data repository. From the simulation results it is observed that Random Forest (RF) shows better performance than existing supervised machine learning models in terms of classification accuracy (CA) to classify the IoT devices with high accuracy. Originality/Value: A cloud based framework is proposed for a smart home to identify the correct IoT device communicating with the local server based on supervised machine learning. Based on the data communication pattern of the IoT devices, an IoT device is accurately identified. Paper Type: Methodology Paper.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

Self Cite

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“…The performance of the model is assessed in Orange tool [15] that is installed in a machine with 64 bit OS, 2.4 GHz processor speed, and 8 Gb ram. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] for the selection of the best model using the performance metrics like AUC, CA, F1, precision (PR) and recall. The description of performance metrics can be referred from [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Resultsmentioning

confidence: 99%

“…The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] for the selection of the best model using the performance metrics like AUC, CA, F1, precision (PR) and recall. The description of performance metrics can be referred from [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. CA is mainly considered to classify the activity accurately.…”

Section: Resultsmentioning

confidence: 99%

A Cloud Based Machine Intelligent Human Activity Recognition System Using Internet of Things to Support Elderly Healthcare

Bhoi

K.²

2022

IJMTS

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Purpose: Human activity recognition is now a major concern in elderly healthcare perspective. Regular monitoring of daily activities is strongly needed for the elderly or old age persons at home. Therefore, the Internet of Things (IoTs) can be a solution to this problem. Design/Methodology/Approach: In this paper, a cloud-based machine intelligent human activity recognition (HAR) system using IoT is proposed to recognize the regular activity of old person at home. In this system, the IoT device or wearable device connected to the body is embedded with activity recognition sensors those sense the physical activity and send the readings to the device. The device then sends the readings to the cloud using the Internet for classifying the actual activity of the person. The cloud is installed with a machine intelligent model which accurately classifies the activities. For the selection of this model, in this work we considered many standards supervised machine intelligence models. Findings/Result: Simulation is done using Orange 3.26 python-based tool by considering Kaggle activity recognition data. Results state that NN shows better performance than other models in classifying the activities of the elderly person. Originality/Value: A new cloud-based machine intelligent HAR system for smart home using IoTs is proposed to monitor the regular activity of the old person. Paper Type: Methodology Paper.

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“…The AI technology has many algorithms for solving the classification, regression, and clustering problems. The algorithms mostly used in machine learning (ML) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are supervised, unsupervised, and hybrid. So, ML is also an important component of the proposed design where the classifier installed in the cloud will detect the actual disease.…”

Section: Introductionmentioning

confidence: 99%

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

Bhoi¹,

K²,

Thinakaran³

2022

STAIQC

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Rice is an important food in our day-to-day life. It has rich sources of carbohydrates that are highly essential for body growth and development. Rice is an important crop in agriculture, where it enhances a country’s economy. However, if rice plants arediseased and not monitored regularly then the crop in the field is wasted and it reduces the proper production rate. Therefore, there should be a mechanism which regularly monitors the crop in a field to detect any disease to rice plant. In this paper, a framework is proposed for identification of rice leaf disease using IoT based Unmanned Aerial Vehicle (UAV) system. Here, the UAV monitors an entire field, capture the images and sends the images to the machine intelligent cloud for detection of rice leaf diseases. The cloud is installed with a proposed stacking classifier that classify the diseased rice plant images received from UAV into different categories. The dataset of these rice leaf diseases is collected from Kaggle source. The performance of the stacking classifier installed at the cloud is evaluated using Python based Orange 3.26 tool. It is observed form the results that stacking classifier outperforms the conventional machine learning models in detecting the actual disease with a classification accuracy (CA) of 86.7%.

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Estimating the Category of Districts in a State Based on COVID Test Positivity Rate (TPR): A Study Using Supervised Machine Learning Approach

Cited by 3 publications

References 17 publications

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

A Cloud Based Machine Intelligent Human Activity Recognition System Using Internet of Things to Support Elderly Healthcare

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

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