KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System

Misra, Sudip; Pal, Saswati; Deb, Pallav Kumar; Gupta, Eeshan

doi:10.1109/jsyst.2023.3239395

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…On the other hand, nine proposals used a microcontroller board, such as an Arduino Uno (Arduino, Scarmagno, Italy) and/or Raspberry Pi (Raspberry Pi Foundation, Cambridge, UK): Moghadas, Rezazadeh and Farahbakhsh [ 50 ], Al et al [ 53 ], Farag [ 54 ], Scrugli et al [ 55 ], Cheikhrouhou et al [ 56 ], Sanamdikar, Hamde, and Atsutkar [ 57 ], Belaid et al [ 59 ], Medhi, Ahmed and Hussain [ 61 ], and Misra et al [ 64 ]. Zhao et al [ 49 ] used a Lenovo smart ECG chest (Lenovo Group Ltd., Beijing, China), while Farahani et al [ 48 ] used an unspecified set of electrodes and Yasin et al [ 47 ] used a custom-made VA processor/SoC.…”

Section: Research On Cvd Detection Using Iot/iomtmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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Section: Research On Cvd Detection Using Iot/iomtmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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“…The fuzzy logic used is a type of FIS [63]. The FIS model used is FIS Tsukamoto [64], [65]. This fuzzy logic will play a role in adjusting the temperature of the air conditioner according to the input temperature and humidity of the room.…”

Section: A Stages Of Researchmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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“…The K-edge implementation provides better results regarding patient condition. The results demonstrated an accuracy of 98.68% for respiratory conditions and was suitable for performance in resource-restricted environments [ 62 ]. In another method, to increase the efficacy of data transmission in the WBAN context, the aggregation node dynamically selects the aggregation frame.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT

Khan,

Tang,

Cao

et al. 2023

Sensors

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The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

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KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System

Cited by 5 publications

References 24 publications

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT

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