Link Prediction in Social Network using Artificial Neural Network

Sharma, Anjali; Soni, Sneha; Rai, Kalpana

doi:10.5120/ijca2021921210

Cited by 2 publications

(6 citation statements)

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“…However, we cannot 100% predict human behavior and brain activity through artificial intelligence (neural network), but we have identified another model to predict the neuromarketing phenomenon [58,89]. As we know, that neural network (artificial intelligence) is successfully used in Google search engines, YouTube social media, and other online media [89], and results have coincided with the human brain [83,88,90]. Therefore, we can say that artificial intelligence (neural network) is an effective tool for predicting the human brain in social sciences [57,80,91].…”

Section: Discussionmentioning

confidence: 99%

“…One of the applications is the transition from consumer buying behavior. The second one is forecast consumer buying behavior criteria [83,88].…”

Section: Applicationmentioning

confidence: 99%

“…Figure 3 shows the mathematical functions of artificial nerve cells in layers [82]. As seen in Figure 3, the change in each input causes an inevitable change in the neuron's output, and the magnitude of this change depends on the weight of the input [83]. According to the artificial neural network methodology, neurons, the central processing unit of the biological nervous system, can be modeled mathematically and associated with each other as in biological nervous systems [74].…”

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confidence: 99%

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confidence: 99%

“…Figure 3 shows the mathematical functions of artificial nerve cells in layers [82]. As seen in Figure 3, the change in each input causes an inevitable change in the neuron's output, and the magnitude of this change depends on the weight of the input [83]. The mathematical equation of input layers, hidden layers, and output layers can be computed as Equations ( 2) and (3) as follows:…”

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confidence: 99%

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The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

et al. 2022

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This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors.

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Section: Discussionmentioning

confidence: 99%

“…One of the applications is the transition from consumer buying behavior. The second one is forecast consumer buying behavior criteria [83,88].…”

Section: Applicationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Figure 3 shows the mathematical functions of artificial nerve cells in layers [82]. As seen in Figure 3, the change in each input causes an inevitable change in the neuron's output, and the magnitude of this change depends on the weight of the input [83]. The mathematical equation of input layers, hidden layers, and output layers can be computed as Equations ( 2) and (3) as follows:…”

mentioning

confidence: 99%

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The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

et al. 2022

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Forecasting Blood Supply in Chinese Major Cities by Fractional Grey Prediction Model and Linear Regression Model

Li¹,

He²,

Zhang³

et al. 2023

Preprint

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Background and Objectives: The aim of this study is to predict the quantity of blood donations in major Chinese cities from 2023 to 2026. Blood is a scarce and perishable resource, and its availability in a particular area depends on the amount of blood donated in that area. Therefore, it is essential for healthcare providers and policymakers to have accurate predictions of future blood donation quantities in order to plan for transfusion treatments and operations. This study aims to provide insights into the future blood supply and allocation in major Chinese cities. Methods: We used a combination of linear regression and fractional grey prediction models to predict blood donation quantities in major Chinese cities from 2023 to 2026. The models were developed based on historical data of blood donation quantity, resident population, and gross domestic production. We compared the predicted values from the models to cross-check the consistency of the predictions. When they were not consistent, the predictions based on the recent historical data were chosen. We cross-checked the predictions with historical data to assess their accuracy. Results: We found that the predicted future donation quantity in major Chinese cities from 2023 to 2026 would be around $20\pm3$U per 1000 persons, and the models suggested that these predictions were credible in at least half of the cities examined. We also observed a slightly larger differential order of the grey prediction model in the cities of northeast China compared to the rest of the cities. Furthermore, we found that the reliability of the predictions varied, with over half of the cities showing consistent predictions and others showing inconsistent or unreliable results. Conclusion: Our findings provide important insights for healthcare providers and policymakers to plan for blood supply and allocation in the future. However, the reliability of the predictions varied, indicating the need for updating historical data to improve the accuracy and reliability of prediction models. We also find that in northeast of China, the blood donation quantity might be bias from 20U per 1000 persons after 2026, according to the differential order of the fractional grey prediction model. Overall, our study highlights the importance of accurate predictions of blood donation quantities for effective healthcare planning and resource allocation.

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Link Prediction in Social Network using Artificial Neural Network

Cited by 2 publications

References 0 publications

The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

Forecasting Blood Supply in Chinese Major Cities by Fractional Grey Prediction Model and Linear Regression Model

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