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Informatics, Volume 12, Issue 1

March 2025 - 33 articles

Cover Story: Concept drift, a phenomenon that can lead to the degradation of classifier performance over time, is commonly addressed by retraining the classifier without considering the properties of drift. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors: severity, recurrence, frequency, and speed. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency provide the highest impact on drift, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it. View this paper
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Articles (33)

  • Article
  • Open Access
2 Citations
4,933 Views
19 Pages

The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this la...

  • Article
  • Open Access
2,203 Views
19 Pages

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing tec...

  • Article
  • Open Access
2,692 Views
32 Pages

Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms

  • Edna Rocio Bernal-Monroy,
  • Erika Dajanna Castañeda-Monroy,
  • Rafael Ricardo Rentería-Ramos,
  • Sixto Enrique Campaña-Bastidas,
  • Jessica Barrera,
  • Tania Maribel Palacios-Yampuezan,
  • Olga Lucía González Gustin,
  • Carlos Fernando Tobar-Torres and
  • Zeneida Rocio Ceballos-Villada

This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed...

  • Article
  • Open Access
1 Citations
3,892 Views
20 Pages

DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text Classification

  • Eliton Luiz Scardin Perin,
  • Mariana Caravanti de Souza,
  • Jonathan de Andrade Silva and
  • Edson Takashi Matsubara

The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with heterog...

  • Article
  • Open Access
5 Citations
7,936 Views
16 Pages

Anemia Classification System Using Machine Learning

  • Jorge Gómez Gómez,
  • Camilo Parra Urueta,
  • Daniel Salas Álvarez,
  • Velssy Hernández Riaño and
  • Gustavo Ramirez-Gonzalez

In this study, a system was developed to predict anemia using blood count data and supervised learning algorithms. Anemia, a common condition characterized by low levels of red blood cells or hemoglobin, affects oxygenation and often causes symptoms,...

  • Article
  • Open Access
10 Citations
7,128 Views
30 Pages

AI-Powered Lung Cancer Detection: Assessing VGG16 and CNN Architectures for CT Scan Image Classification

  • Rapeepat Klangbunrueang,
  • Pongsathon Pookduang,
  • Wirapong Chansanam and
  • Tassanee Lunrasri

Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and requires...

  • Article
  • Open Access
2 Citations
2,152 Views
38 Pages

Hybrid Machine Learning for IoT-Enabled Smart Buildings

  • Robert-Alexandru Craciun,
  • Simona Iuliana Caramihai,
  • Ștefan Mocanu,
  • Radu Nicolae Pietraru and
  • Mihnea Alexandru Moisescu

This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security...

  • Article
  • Open Access
2 Citations
2,168 Views
20 Pages

Automatic Translation Between Kreol Morisien and English Using the Marian Machine Translation Framework

  • Zaheenah Beebee Jameela Boodeea,
  • Sameerchand Pudaruth,
  • Nitish Chooramun and
  • Aneerav Sukhoo

Kreol Morisien is a vibrant and expressive language that reflects the multicultural heritage of Mauritius. There are different versions of Kreol languages. While Kreol Morisien is spoken in Mauritius, Kreol Rodrige is spoken only in Rodrigues, and th...

  • Systematic Review
  • Open Access
4 Citations
6,761 Views
20 Pages

Machine Learning and Deep Learning Models for Dengue Diagnosis Prediction: A Systematic Review

  • Daniel Cristobal Andrade Girón,
  • William Joel Marín Rodriguez,
  • Flor de María Lioo-Jordan and
  • Jose Luis Ausejo Sánchez

The global crisis triggered by the dengue outbreak has increased mortality and placed significant pressure on healthcare services worldwide. In response to this crisis, there has been a notable increase in research employing machine learning and deep...

  • Article
  • Open Access
9 Citations
19,502 Views
38 Pages

Advancing Cybersecurity with Honeypots and Deception Strategies

  • Zlatan Morić,
  • Vedran Dakić and
  • Damir Regvart

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for...

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Informatics - ISSN 2227-9709