River Water Quality Analysis and Prediction Using GBM

Nayan, Al-Akhir; Kibria, Muhammad Golam; Rahman, Md. Obaidur; Saha, Joyeta

doi:10.1109/icaict51780.2020.9333492

Cited by 22 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A. A. Nayan et al has worked on River water quality for agriculture and fishing application [4] and identified fish diseases due to the changes in water quality [5] using Machine learning. He measured the water quality in terms of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N and predicted the output using boosting technique.…”

Section: Related Workmentioning

confidence: 99%

Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

Sen¹,

Maiti²,

Manna³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>This article focuses on the importance of the continuous collection of water parameters data from the sensors and also the prediction of water quality using the latest different Machine learning algorithms like Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, K-nearest Neighbour, XGBoost, Gradient Boosting and Naive Bayes. These Machine learning models are implemented and tested to validate and achieve a satisfactory result of water quality prediction in terms of different attributes like pH, hardness, Solids, Chloramines, Sulfate, Conductivity, organic carbon, trihalomethanes, Turbidity and potability.</p>

show abstract

Section: Related Workmentioning

confidence: 99%

Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

Sen¹,

Maiti²,

Manna³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A. A. Nayan et al worked on measuring river water quality for agriculture and fishing purposes [8] and identified fish diseases by detecting the changes in water quality [9]. They used a machine learning technique that evaluated the water quality and processed intelligent suggestions.…”

Section: Related Workmentioning

confidence: 99%

IoT based Smart Water Quality Prediction for Biofloc Aquaculture

Rashid¹,

Nayan²,

Simi³

et al. 2021

IJACSA

Self Cite

View full text Add to dashboard Cite

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.

show abstract

“…We created a dataset to train the Gradient Boosting Model (GBM) [21,22,23]. Approximately 2000 samples were collected from volunteers.…”

Section: A Dataset Configuration and Analysismentioning

confidence: 99%

Internet of Things (IoT) Based ECG System for Rural Health Care

Rahman¹,

Kashem²,

Nayan³

et al. 2021

IJACSA

Self Cite

View full text Add to dashboard Cite

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, preprocessed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System.Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.

show abstract

River Water Quality Analysis and Prediction Using GBM

Cited by 22 publications

References 8 publications

Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

IoT based Smart Water Quality Prediction for Biofloc Aquaculture

Internet of Things (IoT) Based ECG System for Rural Health Care

Contact Info

Product

Resources

About