Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Suker, Dhia K.

doi:10.48084/etasr.4807

Cited by 4 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dropout is a technique for addressing this problem). The key idea is to randomly drop units (along with their connections) from the neural network during training [23]. This www.ijacsa.thesai.org prevents units from co-adapting too much.…”

Section: F(x)sigm=1/1+e -Xmentioning

confidence: 99%

Deep Learning to Predict Start-Up Business Success

Hsairi

2024

IJACSA

View full text Add to dashboard Cite

Over the past few decades, there has been rapid growth in the formation of new start-ups around the world. Thus, it is an important and challenging task to understand what makes start-ups successful and to predict their success. Several reasons are responsible for the success and failure of a start-up, including bad management, lack of funds, etc. This work aims to create a predictive model for start-ups based on many key factors involved in the early stages of a start-up's life. Current research on predicting success mainly focuses on financial data such as ROI, revenue, etc. Therefore, in this paper, a different approach is proposed by first investigating other non-financial factors affecting start-up success and failure. Second, the adoption of an algorithm that has not been used much in predicting start-up success, which is Convolutional Neural Network (CNN). The dataset was acquired from Kaggle. The final model was reached through a series of four experiments to determine which model predicts better. The final model was implemented using a CNN with an average accuracy of 82%, an average loss of 0.4, an average 0.9 recall and an average 0.9 precision.

show abstract

Section: F(x)sigm=1/1+e -Xmentioning

confidence: 99%

Deep Learning to Predict Start-Up Business Success

Hsairi

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…In the current study, we used 1D-CNN to process the historical data that were read. The structure of the incoming stock data was taken into consideration when selecting the convolutional kernels as well [14].…”

Section: Wwwetasrcom Ahmed Et Al: Bitcoin Price Prediction Using the ...mentioning

confidence: 99%

Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture

Ahmed,

Haji,

Ahmed

et al. 2023

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.

show abstract

“…Kimmatkar & Vijaya Babu: Human Emotion Detection with Electroencephalography Signals and … The average difference between the actual and the anticipated probability distributions is calculated via crossentropy for all classes [20]. A measure of how dissimilar one probability distribution is from another is the Leibler Kullback divergence.…”

Section: Wwwetasrcommentioning

confidence: 99%

Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Kimmatkar

Babu²

2022

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Biological brain signals may be used to identify emotions in a variety of ways, with accuracy depended on the methods used for signal processing, feature extraction, feature selection, and classification. The major goal of the current work was to use an adaptive channel selection and classification strategy to improve the effectiveness of emotion detection utilizing brain signals. Using different features picked by feature fusion approaches, the accuracy of existing classification models' emotion detection is assessed. Statistical modeling is used to determine time-domain and frequency-domain properties. Multiclass classification accuracy is examined using Neural Networks (NNs), Lasso regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). After performing hyperparameter tuning, a remarkable increase in accuracy is achieved using Lasso regression, while RF performed well for all the feature sets. 78.02% and 76.77% accuracy were achieved for a small and noisy 24 feature dataset by Lasso regression and RF respectively whereas 76.54% accuracy is achieved by Lasso regression with the backward elimination wrapper method.

show abstract

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Cited by 4 publications

References 24 publications

Deep Learning to Predict Start-Up Business Success

Deep Learning to Predict Start-Up Business Success

Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture

Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Contact Info

Product

Resources

About