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Found 6 papers, 0 papers with code
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations.
Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms
In testing set, it achieved an accuracy of 0. 98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0. 96 for identifying non-fertile eggs.
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction
Chicken egg image data became input in image processing, with the initial process is segmentation.
K-means segmentation based-on lab color space for embryo detection in incubated egg
This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process.
Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur
The egg embryos detection is processed using a segmentation process.
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data
The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84. 99% and 82. 63%, respectively.
