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Studia Universitatis Babeș-Bolyai Informatica
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This is an Open Access Journal

Open Access journal
ISSN (Print) 1224-869X - ISSN (Online) 2065-9601
Published by Babes-Bolyai University

[4 journals]

Extended Mammogram Classification From Textural Features
- Authors: A Bajcsi, C. Chira, A Andreica
  Pages: 5 - 20
  Abstract: The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated for two different mammogram databases (MIAS and DDSM) in order to assess its robustness. We discuss the changes required in the system, particularly at the level of the image preprocessing and feature extraction. Computational experiments are performed based on different methods for feature extraction, selection and classification. The results indicate an accuracy of 66.95% for the MIAS dataset and 54.1% for DDSM obtained using genetic algorithm based feature selection and Random Forest classification.
  PubDate: 2023-02-06
  DOI: 10.24193/subbi.2022.2.01
  Issue No: Vol. 67, No. 2 (2023)
Feasibility of using machine learning algorithms for yield prediction of
corn and sunflower crops based on seeding date
- Authors: A.D. Călin, H.-B. Mureșan, A.-M. Coroiu
  Pages: 21 - 36
  Abstract: In this research, our objective is to identify the relationship between the date of seeding and the production of corn and sunflower crops. We evaluated the feasibility of using prediction models on a dataset of annual average crop yields and information on plant phenology, from several states of the US. After performing data analysis and preprocessing, we trained a selection of regression models. The best results were obtained for corn using HistGradientRegressor and XGBRegressor with R^2=0.969 for both algorithms and MAE % = 8.945%, respectively MAE% = 9.423%. These results demonstrate a good potential for the problem of yield prediction based on year, state, average plating day, and crop type. This model will be further used, combined with meteorological data, to build an agricultural crop prediction model.
  PubDate: 2023-02-06
  DOI: 10.24193/subbi.2022.2.02
  Issue No: Vol. 67, No. 2 (2023)

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