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In recent years, the field of Topic Modeling (TM) has grown in importance due to the increasing availability of digital text data. TM is an unsupervised learning technique that helps uncover latent semantic structures in large sets of documents, making it a valuable tool for finding relevant patterns. However, evaluating the performance of TM algorithms can be challenging as different metrics and datasets are often used, leading to inconsistent results. In addition, many current surveys of TM algorithms focus on a limited number of models and exclude state-of-the-art approaches. This paper has the objective of addressing these issues by presenting a comprehensive comparative study of five TM algorithms across three different benchmark datasets using five different metrics. We offer an updated survey of the latest TM approaches and evaluation metrics, providing a consistent framework for comparing different algorithms while introducing state-of-the art approaches that have been disregarded in the literature. The experiments, which primarily use Context Vectors (CV) Topic Coherence as an evaluation metric, show that Top2Vec is the best-performing model across all datasets, disrupting the tendency for Latent Dirichlet Allocation to be the best performer. Doi: 10.28991/ESJ-2024-08-01-09 Full Text: PDF

The objective of this article is to provide a comparative analysis of two novel genetic programming (GP) techniques, differentiable Cartesian genetic programming for artificial neural networks (DCGPANN) and geometric semantic genetic programming (GSGP), with state-of-the-art automated machine learning (AutoML) tools, namely Auto-Keras, Auto-PyTorch and Auto-Sklearn. While all these techniques are compared to several baseline algorithms upon their introduction, research still lacks direct comparisons between them, especially of the GP approaches with state-of-the-art AutoML. This study intends to fill this gap in order to analyze the true potential of GP for AutoML. The performances of the different tools are assessed by applying them to 20 benchmark datasets of the imbalanced binary classification field, thus an area that is a frequent and challenging problem. The tools are compared across the four categories average performance, maximum performance, standard deviation within performance, and generalization ability, whereby the metrics F1-score, G-mean, and AUC are used for evaluation. The analysis finds that the GP techniques, while unable to completely outperform state-of-the-art AutoML, are indeed already a very competitive alternative. Therefore, these advanced GP tools prove that they are able to provide a new and promising approach for practitioners developing machine learning (ML) models. Doi: 10.28991/ESJ-2023-07-04-021 Full Text: PDF

A new methodology is presented that measures density in urban systems. By combining highly detailed height measurements with, amongst others, topographical data we are able to quantify urban volume. This new approach is demonstrated in two separate case studies that relate to the temporal and spatial dimension of the urban environment, respectively. In the first study the growth of the city of Amsterdam over the past century is studied. The urban-volume indicator is used to visualise and quantify the urban extension and intensification process. To critically analyse the spatiotemporal development of Amsterdam the self-organising-map approach is applied. Special attention is given to highlighting any signs of recent polynuclear development. The second case study compares the building-height frequency and spatial distribution of high-density zones in the four major Dutch cities. Additionally, the presence of built-up areas and the actual urban-volume values are explained simultaneously using a Heckman selection model.

Cruz-Jesus, F., Oliveira, T., & Bação, F. (2018). The global digital divide: evidence and drivers. Journal Of Global Information Management, 26(2), 1-26. DOI:10.4018/JGIM.2018040101 ; This article presents an analysis of the global digital divide, based on data collected from 45 countries, including the ones belonging to the European Union, OECD, Brazil, Russia, India, and China (BRIC). The analysis shows that one factor can explain a large part of the variation in the seven ICT variables used to measure the digital development of countries. This measure is then used with additional variables, which are hypothesised as drivers of the divide for a regression analysis using data from 2015, 2013, and 2011, which reveals economic and educational imbalances between countries, along with some aspects of geography, as drivers of the digital divide. Contrary to the authors' expectations, the English language is not a driver. ; publishersversion ; published