Machine Learning Classifiers Based Classification For IRIS Recognition

Abstract

Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

“…, the recurrent neural 6 RNN with two hidden layers running in opposite directions allows them to accept past and future feedback. In supervised learning methods, the generative adversarial autoencoder is more popular than supervised or unsupervised learning [9]. rained with algorithms that are similar to RNN because the lateral neurons don't bind .…”

“…Deep Learning methods have been well developed and broadly implemented over the past few years to derive information from diverse data types [5][6][7]. There are many forms of architectures for Deep Learning, such as the RNN, Convolutional Neural Network (CNN), and Deep Neural Network (DNN), considering the various characteristics of input data [8] [9]. CNN and DNN are typically unable to deal with temporal input data information.…”

Flower Species Recognition Using Machine Learning Classifiers

“…Recognizing every aspect of consumer behavior will help businesses improve their communication strategies and boost sales. Additionally, this information needs to be appropriately categorized in order to maximize its value [5]. The goal of machine learning is to create computer programs that can learn to improve and adapt as they encounter new information.…”

Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

“…With a deep learning approach, filters are no longer created by human experts, but, rather, an optimization process is performed to find the best coefficients, using a training process [20]- [22]. In addition, it is common to train a classifier, such as a Support Vector Machine (SVM), Multi Perceptron Layer (MPL), or Random Forest (RF), with a training stage [14], [23], [24]. The training stage could be a limitation since, for IR, there is not a standard dataset with enough images to adjust millions of parameters.…”