Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network DOI Creative Commons

Ahmad Taufiq Akbar,

Shoffan Saifullah,

Hari Prapcoyo

и другие.

Jurnal Teknologi Informasi dan Ilmu Komputer, Год журнала: 2024, Номер 11(6), С. 1399 - 1412

Опубликована: Дек. 10, 2024

Pengenalan ekspresi wajah adalah tantangan penting dalam pengolahan citra dan interaksi manusia-komputer karena kompleksitas variasi yang ada. Penelitian ini mengusulkan arsitektur sederhana Convolutional Neural Network (CNN) untuk meningkatkan efisiensi klasifikasi emosi pada dataset kecil. Dataset digunakan Jaffe, terdiri dari 213 berukuran 256x256 piksel tujuh kategori ekspresi. Citra-citra tersebut di-resize menjadi 128x128 mempercepat pemrosesan. Data diproses menggunakan CNN 3 lapisan konvolusi, 2 subsampling, dense. Kami mengevaluasi model dengan 5-fold 10-fold cross-validation estimasi kinerja robust, serta teknik hold-out (70:30, 80:20, 85:15, 90:10) perbandingan hasil jelas. Hasil menunjukkan akurasi tertinggi sebesar 90.6% learning rate 0.001 pembagian 85% data latih 15% uji, melebihi lebih kompleks. Meskipun tidak transfer atau augmentasi data, tetap unggul dibandingkan pendekatan tradisional seperti Local Binary Pattern (LBP) Histogram Oriented Gradient (HOG). Dengan demikian, terbukti efektif pengenalan Abstract Facial expression recognition is a significant challenge in image processing and human-computer interaction due to its inherent complexity variability. This study proposes simple architecture enhance the efficiency of emotion classification on small datasets. Jaffe's consists images sized pixels across seven categories. These were resized accelerate processing. The was processed using comprising convolutional layers, subsampling dense layers. We evaluated with 5-fold- for robust performance estimation techniques clear result comparison. results indicated highest accuracy training testing split, surpassing that more complex models. Although does not employ or augmentation, it still outperforms traditional approaches such as Thus, this proves effective facial

Язык: Английский

Feature fusion of single and orthogonal polarized rock images for intelligent lithology identification DOI Creative Commons
Wen Ma, Tao Han, Zhenhao Xu

и другие.

AI in Civil Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

2
Small-scale, large impact: utilizing machine learning to assess susceptibility to urban geological disasters—a case study of urban road collapses in Hangzhou DOI

Bofan Yu,

Huaixue Xing,

Jiaxing Yan

и другие.

Bulletin of Engineering Geology and the Environment, Год журнала: 2024, Номер 83(11)

Опубликована: Окт. 23, 2024

Язык: Английский

Процитировано

4
Distinguishing Lithofacies of Flysch Formations Using Deep Learning Models: Integrating Remote Sensing Data with Morphological Indexes DOI Creative Commons
Paraskevas Tsangaratos, I. Vakalas,

Irene Zanarini

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 422 - 422

Опубликована: Янв. 26, 2025

The main objective of the present study was to develop an integrated approach combining remote sensing techniques and U-Net-based deep learning models for lithology mapping. methodology incorporates Landsat 8 imagery, ALOS PALSAR data, field surveys, complemented by derived products such as False Color Composites (FCCs), Minimum Noise Fraction (MNF), Principal Component Analysis (PCA). Dissection Index, a morphological index, calculated characterize geomorphological variability region. Three variations U-Net architecture, Dense U-Net, Residual Attention were implemented evaluate performance in lithological classification. Validation conducted using metrics accuracy, precision, recall, F1-score, mean intersection over union (mIoU). results highlight effectiveness model, which provided highest mapping accuracy superior feature extraction delineating flysch formations associated units. This demonstrates potential integrating data with advanced machine enhance geological challenging terrains.

Язык: Английский

Процитировано

0
Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement DOI
Jagrati Talreja,

Divya Chauhan

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 197 - 228

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Precision Recognition of Rock Thin Section Images With Multi‐Head Self‐Attention Convolutional Neural Networks DOI Creative Commons
Pengfei Lv, Weiying Chen,

Xinyu Zou

и другие.

Journal of Geophysical Research Machine Learning and Computation, Год журнала: 2025, Номер 2(2)

Опубликована: Май 10, 2025

Abstract Lithological thin‐section image classification is crucial in geology. Traditional manual methods rely on expert experience, being subjective and time‐consuming. Convolutional neural network (CNN)‐based automated has potential but less effective with more rock types limited training data, restricting its applications. We propose a lightweight framework that integrates the multi‐head self‐attention (MSA) mechanism into classical convolutional (CNN) architectures, hereinafter denoted as MSA‐CNN. Specifically, we employ VGG16 AlexNet backbone networks incorporate MSA to enhance feature extraction from small‐scale lithological data sets. The resultant MSA‐VGG16 MSA‐AlexNet models, after fine‐tuning, can capture geological features effectively continuously improve accuracy. conducted comprehensive experiments public set, which be partitioned 3, 34, 105 categories respectively. model exhibits strong generalization ability across all tasks. Notably, most challenging scenario categories, outperforms previously reported best‐performing same set by approximately 9.61%. These results strongly validate effectiveness of integrating CNNs for classification. They highlight this method practical applications represent significant advancement

Язык: Английский

Процитировано

0
Enhancing data reuse in tunnelling site investigation through transfer learning-based historical data mining DOI Creative Commons
Jiawei Xie, Baolin Chen, Shui‐Hua Jiang

и другие.

Underground Space, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Predicting soil stress–strain behaviour with bidirectional long short-term memory networks DOI Creative Commons
Kacper Cerek,

Arjun Gupta,

Duy Anh Dao

и другие.

Опубликована: Май 15, 2025

Purpose Artificial intelligence, particularly deep learning (DL), has increasingly influenced various scientific fields, including soil mechanics. This paper aims to present a novel DL application of long short-term memory (LSTM) networks for predicting behaviour during constant rate strain (CRS) tests. Design/methodology/approach LSTMs are adept at capturing long-term dependencies in sequential data, making them suitable the complex, nonlinear stress–strain soil. evaluates LSTM configurations, optimising parameters such as step size, batch data sampling and training subset size balance prediction accuracy computational efficiency. The study uses comprehensive set from numerical finite element method simulations conducted with PLAXIS 2D laboratory CRS Findings proposed model, trained on lower stress levels, accurately forecasts higher levels. optimal setup achieved median error 3.59% 5.10% 3.86% presenting setup’s effectiveness. Originality/value approach reduces required time complete extensive testing, aligning sustainable industrial practices. findings suggest that can enhance geotechnical engineering applications by efficiently behaviour.

Язык: Английский

Процитировано

0
Rock image classification based on improved EfficientNet DOI Creative Commons
Kai Bai, Zhaoshuo Zhang,

Siyi Jin

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 28, 2025

Язык: Английский

Процитировано

0
Deep Learning based Identification of Rock Minerals from Un-Processed Digital Microscopic Images of Undisturbed Broken-Surfaces DOI Creative Commons
M.A. Dalhat, Sami A. Osman

Artificial Intelligence in Geosciences, Год журнала: 2025, Номер unknown, С. 100127 - 100127

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0
Enhanced convolutional neural network methodology for solid waste classification utilizing data augmentation techniques DOI Creative Commons

Daniel Hogan Itam,

Ekwueme Chimeme Martin,

Ibiba Taiwo Horsfall

и другие.

Waste Management Bulletin, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1