Comparison and Combination of Leaky ReLU and ReLU Activation Function and Three Optimizers on Deep CNN for Covid-19 Detection

COVID-19 detection is an interesting field of study in the medical world and the commonly used method is classification. In determining the best detection model, several classification architectures, such as SVM, KNN, and CNN were utilized. The CNN is a changeable architecture due to having combinations of varying numbers of hidden layers or different activation and optimizer functions. Therefore, this study uses a deep CNN architecture with a combination of Leaky ReLU activation functions and 3 different optimizers, which include Adagrad, Adadelta, and Adamax. The results showed that the combination of the Leaky ReLU activation function and the Adamax optimizer produced good and stable accuracy in the CRX and CT datasets

Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network

Olive fruit is a horticultural product of the oleaceae family with the genus Olea which has various types and unique features. One of a group of Olea species found in tropical and subtropical regions which make the plant fertile and abundant. The yields are very abundant in proportion to market needs. The random harvest of produce makes the selection of post-harvest products very important in classifying types of olives. So it is necessary to have a system that can classify automatically. Previous studies have been proposed to classify olives with considerable accuracy. However, the required speed takes a very long time because it uses a complex pretrained model. Therefore, this study aims to classify olives with a faster time and accuracy that is no less than before. The method to be used is Convolutional Neural Network (CNN) with its own architectural circuit. The results of this study get an accuracy of 92% with 30 epochs.Buah zaitun merupakan tanaman produk hortikultura rumpun oleaceae dengan genus Olea yang memiliki berbagai macam jenis dan fitur yang unik. Satu dari sekumpulan species Olea yang ditemukan di wilayah tropis dan subtropis yang menjadikan tanaman subur dan melimpah. Hasil panen yang sangat melimpah sebanding dengan kebutuhan pasar. Pemanenan produk secara acak membuat pemilihan produk pasca panen sangat penting dalam mengelompokkan jenis buah zaitun. Sehingga perlu adanya sistem yang dapat mengklasifikasi secara otomatis. Sebelumnya sudah ada penelitian yang diusulkan untuk mengklasifikasi buah zaitun dengan akurasi yang cukup tinggi. Namun kecepatan yang diperlukan butuh waktu yang sangat lama karena menggunakan model pretrained yang begitu kompleks. Oleh karena itu, penelitian ini bertujuan untuk melakukan klasifikasi buah zaitun dengan waktu yang lebih cepat dan akurasi yang tidak kalah dari sebelumnya. Metode yang akan digunakan adalah Convolutional Neural Network (CNN) dengan rangkaian arsitektur sendiri. Hasil dari penelitian ini mendapatkan akurasi sebesar 92% dengan 30 epoch

Deep learning for Chilean native flora classification: a comparative analysis

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed