EVALUATING LAND USE TRANSITIONS THROUGH DATA-DRIVEN AGENT-BASED MODELING WITH RANDOM FOREST CLASSIFICATION
Abstract. Urban growth is making it harder to plan for land use that is beneficial for the environment. This study looks at how land use changes from 2020 to 2040 by using a combined modeling approach that uses both agent-based simulation and machine learning. We model changes in three main types of land: residential, forest, […]
MULTINOMIAL NAIVE BAYES FOR KAZAKH LANGUAGE SPAM DETECTION: A CASE STUDY WITH MORPHOLOGICAL ANALYSIS
Abstract. The growing number of spam messages in digital communication highlights the urgent need for effective spam detection systems, particularly for languages that lack sufficient digital resources, such as Kazakh. This research aims to develop a machine learning-based approach tailored for spam detection in Kazakh messages, utilizing various text preprocessing techniques and methods to enhance […]