IoT-Based Real-Time Monitoring System for Epidemic Diseases Patients: Design and Evaluation

Al-Mutairi, Abdullah W.; Al-Aubidy, Kasim M.; Al-Halaiqa, Fadwa N.

doi:10.3991/ijoe.v17i01.18849

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…To verify the functioning of this system, a real-time monitoring device was developed. The secure mechanism is designed to establish a wireless connection to the monitoring unit [12]. K.Geetha in his research entitled "Enhanced Web Log Data Mining using Probability Density-Based Fuzzy C Means Clustering" aims to Cluster into groups is one of the most important tasks in the action-bound areas of net record mining.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

et al. 2021

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<p class="0abstract">Students' learning interest and motivation in programming language to date are determined by their scores and abilities to create applications. However, this is not sufficient to identify students' learning interest in programming language because some students got low scores. This study aims to identify students' learning interest in Dipanegara school of informatics management and computer (STMIK), Makassar, in Java programming language. The samples were 65 technical information students and 63 information system students. The data collection technique of this study was questionnaire and processed with data mining technique. Additionally, Fuzzy C-Means clustering method was applied on Java Netbeans programming language to classify the level of students' interest in studying Java programming language. The result of the study was a web-based application that could determine students’ learning interests. It was obtained through questionnaires and resulted as follows: 47 students had high learning interest, 45 students had moderate interest, and 36 students had fair interest out of 128 samples.</p>

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Section: Fuzzy C-meansmentioning

confidence: 99%

An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

et al. 2021

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“…Recently, internet of things (IoT) technology is widely used in many applications in most areas of life. as it is an advanced model of environmental interaction [9], [10]. In the field of agriculture and aquaculture, IoT technology has been used to monitor water quality in aquaculture in general and fish in  ISSN: 2302-9285 particular [11].…”

Section: Introductionmentioning

confidence: 99%

IoT-based smart monitoring and management system for fish farming

Al-Mutairi¹,

Al-Aubidy²

2023

Bulletin EEI

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Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.

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“…Significant advances in wireless communication technologies and sensors have enabled designers to use the internet of things (IoT) in monitoring and control systems [14], [15]. The IoT provides a huge amount of data over an internet connection.…”

Section: Introductionmentioning

confidence: 99%

IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach

Qasim Obaidi,

Derbel

2023

Bulletin EEI

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One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods.

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IoT-Based Real-Time Monitoring System for Epidemic Diseases Patients: Design and Evaluation

Cited by 11 publications

References 18 publications

An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

IoT-based smart monitoring and management system for fish farming

IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach

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