Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks

Endah H, Marselina; Wijaya, R. Nurhadi; Khotibul Ahsan, Hilmi

doi:10.52465/joscex.v4i4.226

Cited by 2 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is a computational technology to change paradigms in data processing, pattern recognition, and understanding complex systems [11], [12], [13], [14]. This method is based on the architecture of deep artificial neural networks [15], inspired by the structure and function of human neural networks [16].…”

Section: Introductionmentioning

confidence: 99%

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Gunawan,

Andriani,

Anandianskha

et al. 2024

SJI

View full text Add to dashboard Cite

Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Gunawan,

Andriani,

Anandianskha

et al. 2024

SJI

View full text Add to dashboard Cite

show abstract

“…CNN has been successfully applied in various fields, including pattern recognition in batik motifs [22]- [24]. Deep learning CNN is considered a promising approach to cirrhosis detection due to its ability to outperform traditional methods and provide accurate predictions [25]. CNN architectures, such as LeNet and MobileNet, have proven effective in recognizing complex patterns in visual data.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification

Andrian,

Herwanto,

Taufik

et al. 2024

SJI

View full text Add to dashboard Cite

Purpose: The rich cultural heritage of Indonesia includes the intricate art of batik, which varies across regions with unique patterns and motifs. This study focuses on Lampung batik, a distinctive type of batik, representing Lampung Province, Indonesia. Leveraging Convolutional Neural Network (CNN) architectures, namely LeNet-5 and MobileNet, the research compares their effectiveness in recognizing and classifying Lampung batik motifs. Data augmentation techniques, including rotation, brightness, and zoom, were employed to enhance the dataset and improve model performance.Methods: The study collected 500 Lampung batik images categorized into 10 classes which were then augmented and divided into training, validation, and testing sets. The model was created using a Deep Learning approach, LeNet And MobileNet. Both models were trained using identical hyperparameters and evaluated based on their accuracy in classifying Lampung batik motifs.Results: The results demonstrate an accuracy of 99.33% for LeNet-5 and 98.00% for MobileNet, outperforming previous studies. LeNet-5, particularly with augmentation, exhibited superior precision and recall in classifying Lampung batik motifs. This research underscores the efficacy of CNN architectures, coupled with data augmentation techniques, in accurately identifying intricate cultural artifacts like Lampung batik.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.

show abstract

Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks

Cited by 2 publications

References 37 publications

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification

Contact Info

Product

Resources

About