Trends in Using IoT with Machine Learning in Health Prediction System

Aldahiri, Amani; Alrashed, Bashair Ali; Hussain, Walayat

doi:10.3390/forecast3010012

Cited by 111 publications

(54 citation statements)

References 69 publications

(76 reference statements)

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“…The communication layer should provide a variety of communication means, including message queues, socket communication nodes, ftp/tfip communication nodes, etc. [ 18 ]. For communication of different quality attributes, different communication methods can be used for communication.…”

Section: Methodsmentioning

confidence: 99%

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things

Wang¹

2022

Journal of Healthcare Engineering

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The Internet of Medical Things has developed rapidly as an important direction in the field of Internet of Things, especially through the use of the new generation of information technology, theoretical and applied research on medical IoT intelligent health management that provides “full-service.” It has become a research hotspot of many universities and research institutions. Therefore, conducting research on intelligent health management in the network of medical things is of great engineering importance and theoretical guidance to improve the level of medical information. For health management in the network of medical things to conduct research around the goal of health management “Everything full spectrum for everyone,” analyzed the lack of sharing of health information in current health management, lack of continuous monitoring and management of health indicators, etc., a new “individual-family-community-hospital” four-level intelligent health management service model is proposed, the hardware architecture of intelligent healthcare management and the software maintenance system have been built. Through methods such as real-time multi-source data collection, mobile sensing, cloud computing, multi-network fusion technology, continuous monitoring and intelligent management of health data is realized convenient, fast and efficient. It solves the problems that the existing medical system cannot meet the multi-level health needs, personal data security and privacy protection, etc., it has achieved the goal of real-time interactive health management of regionalization, multi-level and multi-center, and whole-person, whole-process and all-round.

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Section: Methodsmentioning

confidence: 99%

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things

Wang¹

2022

Journal of Healthcare Engineering

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“…More prominently in a health care centre, the doctors are mostly not available to handle all cases [12][13][14]. In these cases a long queue is added in the hospitals and it causes lots of annoyance to the patients.…”

Section: Objectivesmentioning

confidence: 99%

IoT for Health Monitoring System Based on Machine Learning Algorithm

Balakrishnan¹,

Kumar

Lakshmanan

et al. 2021

Wireless Pers Commun

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Internet of things (IoT) is one of the futuristic and upcoming technologies which have led to the concept of automation in many daily chores. IoT has placed its fame nearly in all possible platforms and the main idea is to enhance this technology in the current healthcare system, for the purpose of automatic monitoring of hospitals and patient's health by spreading positive vibes to the vision of IoT. With the support of certain technologies such as Radio Frequency Identification (RFID), Brainsense headband, Wireless Sensor Network and smart mobile, by keeping the IoT as its connecting platform, a Smart E-Healthcare System is framed. A low power wireless personal area connection helps in incorporating these technologies together through a Constrained Application Protocol/IPv6. To track the state of the patient, Smart Healthcare Sensor (SHS) and RFID are used that are attached to the person's wrist bands automatically by using electromagnetic fields. Each tag held by the person has a unique ID and it contains information that is electronically stored in a separate cloud environment. Based on the various information sensed by the SHS sensors, required prescription is given to the victim in the absence of doctors. This technology represents a significant step in the development of health care sectors. In this paper, we are proposing pro prediction algorithm for detecting the prescription for the patient.

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“…The research community has been working for many years to recognize printed and written texts [1]. Many applications to the text recovery system help blind people to navigate specific environments [2]. The text is filtered with textual contents on the images.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

et al. 2021

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According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.

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Trends in Using IoT with Machine Learning in Health Prediction System

Cited by 111 publications

References 69 publications

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things

IoT for Health Monitoring System Based on Machine Learning Algorithm

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

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