Medical remotely caring with COVID-19 virus infected people using optimized wireless arm tracing system

Therib, Mohammed Azher; Al-Baghdadi, Ahmed; Marzog, Heyam A.

doi:10.12928/telkomnika.v18i6.16331

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…e network is tested with various internal process activation functions, and it is discovered that the sigmoid function provides the highest precision. Reference [29] is shown in Figure 2.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

Marzog

Haider

2022

Applied Computational Intelligence and Soft Computing

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The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.

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“…e network is tested with various internal process activation functions, and it is discovered that the sigmoid function provides the highest precision. Reference [29] is shown in Figure 2.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

Marzog

Haider

2022

Applied Computational Intelligence and Soft Computing

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“…The COVID-19 pandemic is affecting many aspects of society, including university training programs around the world [11]. As a result, many universities now employ online learning as a viable option.…”

Section: Related Workmentioning

confidence: 99%

Improving Measurement of Effectiveness of Blended Learning in Iraqi Education Using SVM

Hussein

Najeeb

2022

eijs

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E-learning has recently become of great importance, especially after the emergence of the Corona pandemic, but e-learning has many disadvantages. In order to preserve education, some universities have resorted to using blended learning. Currently, the Ministry of Higher Education and Scientific Research in Iraq has adopted e-learning in universities and schools, especially in scientific disciplines that need laboratories and a spatial presence. In this work, we collected a dataset based on 27 features and presented a model utilizing a support vector machine with regression that was enhanced with the KNN method, which identifies factors that have a substantial influence on the model for the type of education, whether blended or traditional.Furthermore, the dataset used was primarily focused on three key factors: personal information, the impact of e-Learning platforms, and the influence of the Corona virus. The attributes that were measured revealed that social status, computer skills, and the basic platform gave the user enough tools to continue the learning process. The size of the classrooms and laboratories that meet the health safety conditions is the most significant. The goal of our work is to discover a model that predicts how blended learning will be used during and after the coronavirus pandemic and to produce a model with minimal errors.

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“…[19]. Singh and Shimi [20] and Therib et al [21] are proposed using lux sensor and temperature sensor respectively to maintained and monitored of conditions of the room as light intensity and temperature of the room. Mishra et al [22] and Kruse et al [23] advanced a robotic arm by utilize Arduino uno and make hinges of a robotic arm and control it by making use of servo motor and potentiometer.…”

Section: Figure 1 Cloud Robots Interconnectionmentioning

confidence: 99%

Design and implement of robotic arm and control of moving via IoT with Arduino ESP32

Ahmed

Marzog

Abdul-Rahaim

2021

IJECE

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Every day, the technologies are expanding and developed with extra things to them. A cloud computing (CC) and Internet of things (IoT) became deeply associated with technologies of the internet of future with one supply the other a way helping it for the successful. Arduino microcontroller is used to design robotic arm to pick and place the objects by the web page commands that can be used in many industrials. It can pick and place an object from source to destination and drive the screws in into its position safely. The robot arm is controlled using web page designed by (html) language which contain the dashboard that give the commands to move the servos in the desired angle to get the aimed direction accordingly. At the receiver end there are four servo motors which are made to be interfaced with the micro controller (Arduino) which is connected to the wireless network router. One of these is for the arm horizontally movement and two for arm knee, while the fourth is for catch tings or tight movement. Two ultra-sonic sensors are used for limiting the operation area of the robotic arm. Finally, Proteus program is used for the simulation the controlling of robot before the hardware installation

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Medical remotely caring with COVID-19 virus infected people using optimized wireless arm tracing system

Cited by 8 publications

References 15 publications

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

Improving Measurement of Effectiveness of Blended Learning in Iraqi Education Using SVM

Design and implement of robotic arm and control of moving via IoT with Arduino ESP32

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