Review and study of internet of things: It's the future

Bhardwaj, Sanjeevani; Kole, Alok

doi:10.1109/icicpi.2016.7859671

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…The dataset which is measured and collected from IoT sensors, has been [12] used for Machine learning model training and validation comprises of the following attributes: pH, hardness, Solids, Chloramines, Sulfate, Conductivity, organic carbon, trihalomethanes, Turbidity and potability. The dataset is stored in a CSV file.…”

Section: Methodsmentioning

confidence: 99%

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

Sen¹,

Maiti²,

Manna³

et al. 2023

Preprint

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<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>

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Section: Methodsmentioning

confidence: 99%

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

Sen¹,

Maiti²,

Manna³

et al. 2023

Preprint

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show abstract

“…1 tion, the details of the methodology are presented that used to forecast water quality, regulate the situation, ss-wise decisions. taset which is measured and collected from IoT as been [12] used for Machine learning model training tion comprises of the following attributes: dness, Solids, Chloramines, Sulfate, Conductivity, rbon, trihalomethanes, Turbidity and potability. The stored in a CSV file.…”

Section: Flowchart Of Methodology Proposedmentioning

confidence: 99%

“…Support Vector Machine (SVM) is a supervised Machine learning used for classification and regression. The primary objective of the SVM is to search for a hyperplane that distinctly classifies the data points [12].…”

Section: State-of-the-art Machine Learning Algorithmsmentioning

confidence: 99%

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

2023

J Curr Trends Comp Sci Res

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Intelligent and Smart aquaculture is nowadays one of the most important latest development trends in the field of the aquaculture industry to overcome the problems of the farmers due to water quality monitoring, food feeding, temperature imbalance, and recycling of water. Smart water quality prediction in the real-time environment using intelligent machine learning models and IoT sensors establishes the basis for the evaluation, planning, and intelligent regulation of the aquaculture environment. The purpose of this study is to propose an intelligent Machine learning and IoT-based Biofloc system that enhances the efficiency, production, water recycling system, and automatic food feeding system. This paper proposed a system that collects process data from sensors, stores data in the cloud, and analyses it using different latest machine learning models such as a Decision tree classification model, and Random Forest Model to predict the water quality and provides real-time monitoring through an android app. 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 linear regression, Random Forest, and other ML models have been implemented and tested to validate and achieve a satisfactory result. A performance comparative analysis of the different ML algorithms has been conducted based on a few metrics such as accuracy, recall, precision, and F1-score. Random Forest Classifier has surpassed all the other models in terms of accuracy, recall, precision, and F1 score. Random Forest Classifier has exhibited an accuracy of 73.76%, recall, precision, and F1-score of 90%, 75%, and 82% respectively.

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“…It is connected to physical devices, enabling communication with people and let them know about their condition and location. For example in the logistics sector, the used of trucks or vessels can communicate with other devices and people in order to handle the supplied data [32]. These combinations can optimum the income by reducing the cost of inefficiency in business thus making work more accessible and more effective.…”

Section: Interface Layermentioning

confidence: 99%

Internet of Things: An Implementation and Its Challenges in Malaysia

Badarudin

Din

Prasetyo

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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to date, the Internet becomes one of the technologies that is rapidly evolving and changing. It has become trending over the world. The Internet of Thing is a mechanism composed of devices, sensors, networks, cloud storage, and application. Each device able to communicate with another device over the Internet to share the information and accomplished some objectives. IoT is known as one of the new future technologies and were gaining attention from various fields over the countries. Malaysia is one of the countries in the planning stage to increase the development of IoT, which is equivalent to other countries with the emerging IoT applications development. However, it was not easy to develop IoT devices due to some issues and challenges in implementing IOT devices. This paper addresses the major concern and challenges in IOT and the solution how to overcome these issues. The future trends and applications of IoT were also briefly discussed in this paper for gaining more in-depth knowledge about the IOT technology.

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Review and study of internet of things: It's the future

Cited by 13 publications

References 4 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

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

Internet of Things: An Implementation and Its Challenges in Malaysia

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