A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union

In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms' and households' expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth

METODE PENGAMBANGAN LOKAL UNTUK SEGMENTASI SEL LIMFOSIT PADA CITRA DARAH MIKROSKOPIS

Abstrak. Pemeriksaan hematologi yang paling sering dilakukan untuk mendeteksi leukemia limfoblastik akut yaitu pemeriksaan mikroskop apusan darah dengan menganalisis morfologi dan volume sel limfosit. Segmentasi sel limfosit merupakan tahapan paling penting dalam sistem otomatis identifikasi leukemia limfoblastik berbasis komputer yang berfungsi sebagai alat penunjang medis sehingga membantu mempercepat tugas para hematolog di laboratorium medis. Pada penelitian ini segmentasi sel limfosit pada citra darah mikroskopis menggunakan teknik pengambangan lokal disajikan. Pada pengambangan lokal, nilai ambang Otsu dihitung pada citra yang telah direduksi dimensinya agar piksel-piksel selain limfosit jumlahnya tidak terlalu mendominasi. Ini dilakukan untuk mengurangi peluang terjadinya citra yang oversegmented. Hasil ujicoba menunjukkan bahwa segmentasi sel limfosit menggunakan metode yang diusulkan memberikan nilai rata-rata dice similarity coefficient 0,841 dengan standar deviasi sebesar 0,067. Hasil ini memberikan kesimpulan bahwa citra input yang disegmentasi menggunakan metode pengambangan lokal memiliki tingkat kemiripan tinggi dengan citra ground truth sehingga metode usulan dapat dikatakan memiliki performa yang baik.Kata kunci: segmentasi sel limfosit, limfosit, pengambangan lokal, analisis citra darah mikroskopis.  DOI : https://doi.org/10.33005/scan.v14i2.148

Big Data: an exploration of research, technologies and application cases

Big Data se ha convertido en una tendencia a nivel mundial y aunque aún no cuenta con un concepto científico o académico consensuado, se augura cada día mayor crecimiento del mercado que lo envuelve y de las áreas de investigación asociadas. En este artículo se reporta una exploración de literatura sobre Big Data, que comprende un estado del arte de las técnicas y tecnologías asociadas a Big Data, las cuales abarcan captura, procesamiento, análisis y visualización de datos. Se exploran también las características, fortalezas, debilidades y oportunidades de algunas aplicaciones y modelos que incluyen Big Data, principalmente para el soporte al modelado de datos, análisis y minería de datos. Asimismo, se introducen algunas de las tendencias futuras para el desarrollo de Big Data por medio de la definición de aspectos básicos, alcance e importancia de cada una. La metodología empleada para la exploración incluye la aplicación de dos estrategias, una primera corresponde a un análisis cienciométrico; y la segunda, una categorización de documentos por medio de una herramienta web de apoyo a los procesos de revisión literaria. Como resultados se obtiene una síntesis y conclusiones en torno a la temática y se plantean posibles escenarios para trabajos investigativos en el campo de dominio.Big Data has become a worldwide trend and although still lacks a scientific or academic consensual concept, every day it portends greater market growth that surrounds and the associated research areas. This paper reports a systematic review of the literature on Big Data considering a state of the art about techniques and technologies associated with Big Data, which include capture, processing, analysis and data visualization. The characteristics, strengths, weaknesses and opportunities for some applications and Big Data models that include support mainly for modeling, analysis, and data mining are explored. Likewise, some of the future trends for the development of Big Data are introduced by basic aspects, scope, and importance of each one. The methodology used for exploration involves the application of two strategies, the first corresponds to a scientometric analysis and the second corresponds to a categorization of documents through a web tool to support the process of literature review. As results, a summary and conclusions about the subject are generated and possible scenarios arise for research work in the field

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Applications of evolutionary computation in image processing and pattern recognition

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an optimization task. The book has been structured so that each chapter can be read independently from the others. It can serve as reference book for students and researchers with basic knowledge in image processing and EC methods