Neural Network Prediction Model to Explore Complex  Nonlinear Behavior in Dynamic Biological Network

Alsharaiah, Mohammad A.; Baniata, Laith H.; Adwan, Omar; Alghanam, Orieb Abu; Abu-Shareha, Ahmad Adel; Alzboon, Laith; Mustafa, Nedal; Baniata, Mohammad

doi:10.3991/ijim.v16i12.30467

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…The main objective of this paper is to create a system that integrates a prototype to detect heartbeats and implements a Back Propagation Neural Network to determine normal and abnormal heart classification via an Android smartphone. Supervised learning encompasses various techniques, and among the most promising ones is the Backpropagation algorithm [8]. BPNN is one of the ANN methods, an information processing method adapted from biological neurons [9].…”

Section: Methodsmentioning

confidence: 99%

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Suryani,

Faizal

2024

Int. J. Onl. Eng.

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Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.

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Section: Methodsmentioning

confidence: 99%

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Suryani,

Faizal

2024

Int. J. Onl. Eng.

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“…Deep learning algorithms have recently become prevalent in solving problems across various domains. They have found applications in the medical sector for disease prediction [8,9], in understanding complex behaviors in systems such as in biology [10], and in many other areas impacting daily life [11], including customer service, sport forecasting, autonomous vehicles, and weather prediction [12]. This research aims to explore the application of deep learning methods to datasets in educational institutions.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the accuracy for the model indicates the ratio among the numbers of correctly predicted samples to the total number of input samples. This is shown in Equation (10). Finally, the F-score is calculated using Equation ( 13).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions

Baniata,

Kang,

Alsharaiah

et al. 2024

Applied Sciences

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Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout.

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“…the text area is the focus area of the subjects experiments. The text content design in the interface design has a positive effect on prolonging users' attention time [20]. It can be inferred from the experimental results of the text area that to improve the user's visual attention, clear and distinct typography with appropriate fonts can be used in the text area to improve the visual logic and relevance and help users understand the interface information quickly.…”

Section: Plos Onementioning

confidence: 99%

Research on mobile learning platform interface design based on college students’ visual attention characteristics

Zhu

Yang

2023

PLoS ONE

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To explore the visual experience characteristics and influencing factors of college students’ visual attention intervention in the interface of mobile learning platform by using eye-tracking technology, and to summarize and summarize the visual experience pattern of platform interface design and its design inspiration. Methods: Using the head-mounted eye-tracking technology, 28 images from 6 groups of typical elements in the interface layout of CGTN learning platform were selected as the test samples, and the eye-movement indexes of the subjects browsing the interface were recorded. Results: There were significant differences in the attention time, number of times of attention, visual attention rate and visual recall rate of different areas and topics of the interface (P < 0. 001). Conclusion: In the platform interface design, the analysis of the factors influencing visual attention can be found that people’s attention and visual experience is mainly influenced by color, text, and typography, and secondary areas and layout also play an important role in visual communication. The color and text areas in the interface design, as well as the innovative design of typography can effectively enhance the visual attention of college students and better communicate the information of the platform.

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Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Cited by 3 publications

References 21 publications

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions

Research on mobile learning platform interface design based on college students’ visual attention characteristics

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