Bogdan Oancea

Bogdan Oancea

Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such as range edits, ratio checks, or univariate outlier detection, are effective for identifying extreme values in individual series but are less suited for detecting unusual combinations of indicators in high-dimensional settings. This paper proposes an unsupervised machine learning framework for identifying structurally atypical regional profiles within Europe using publicly available Eurostat data. We construct a cross-sectional dataset of NUTS2 regions (2022) covering four key indicators: GDP per capita in PPS, unemployment rate, tertiary educational attainment, and population density. We apply and compare five anomaly detection techniques, univariate z-scores, Mahalanobis distance, Isolation Forest, Local Outlier Factor, and One-Class SVM, and classify a region as a structural anomaly if it is flagged by at least three of the five methods. The findings show that machine learning methods identify a consistent set of regions whose multivariate profiles diverge substantially from the EU-wide pattern. These include both highly developed metropolitan economies (Brussels, Vienna, Berlin, Prague) and regions with persistent socio-economic disadvantages (Central and Western Slovakia, Northern Hungary, Castilla-La Mancha, Extremadura), as well as Istanbul, whose profile differs markedly from EU capital regions. Importantly, these anomalies do not necessarily signal data quality issues; rather, they reflect meaningful structural divergence that warrants analytical or policy attention. The proposed framework is fully reproducible, scalable, and compatible with existing validation workflows, offering a flexible tool for early detection of unusual regional configurations within the European Statistical System.

Bogdan Oancea, Ana Maria Seciu-Grama, Nicoleta Siminea et al.

Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support. Biomedical Raman spectra are, however, high-dimensional, noisy, and affected by fluorescence background, acquisition variability, and biological heterogeneity, making robust computational analysis essential. This review examines the role of machine learning across the biomedical Raman spectroscopy pipeline, from preprocessing and signal correction to unsupervised structure discovery, supervised diagnosis and molecular stratification, representation and transfer learning, explainability, biomarker discovery, and multimodal integration with imaging, pathology, and molecular profiling. Emphasis is placed on the use of machine learning not only for diagnostic classification, but also for biologically interpretable and clinically actionable analysis. We also discuss the main barriers to clinical translation, including limited dataset sizes, inter-instrument variability, inconsistent preprocessing, insufficient external validation, reproducibility concerns, and limited sharing of software, data, and metadata. We argue that progress will require methodological advances together with standardization, robust validation, explainability, and deployment-ready analytical frameworks. By integrating methodological, biomedical, and translational perspectives, this review outlines key directions for developing reliable and clinically deployable Raman-AI systems.

Bogdan Oancea

In this paper we present the results of an experiment aimed to use machine learning methods to obtain models that can be used for the automatic classification of products. In order to apply automatic classification methods, we transformed the product names from a text representation to numeric vectors, a process called word embedding. We used several embedding methods: Count Vectorization, TF-IDF, Word2Vec, FASTTEXT, and GloVe. Having the product names in a form of numeric vectors, we proceeded with a set of machine learning methods for automatic classification: Logistic Regression, Multinomial Naive Bayes, kNN, Artificial Neural Networks, Support Vector Machines, and Decision trees with several variants. The results show an impressive accuracy of the classification process for Support Vector Machines, Logistic Regression, and Random Forests. Regarding the word embedding methods, the best results were obtained with the FASTTEXT technique.

Bogdan Oancea, ŞTefan Cristian Ciucu

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.