arXiv subjects

Agnieszka Jastrzębska

Publications and source records attributed to Agnieszka Jastrzębska.

4 recordsLinked to original sources

arXiv2026

Dual-Mode SERS and Colorimetric Sensor for Lung Cancer VOC-Biomarker Detection Using Hydrogel Patches

Hexanal, a volatile organic compound (VOC), is a potential biomarker for the early detection of lung cancer. In this study, we developed a dual-mode flexible biosensor that integrates surface-enhanced Raman scattering (SERS) and colorimetric detection for the quantitative analysis of hexanal in human exhaled breath. The biosensor employs Ag nanocubes wrapped with Co-Ni layered double hydroxide (AgNCs@Co-Ni LDH) as a functional matrix, offering both superior SERS enhancement and efficient VOC adsorption properties. To achieve selective detection, AgNCs@Co-Ni LDH were incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH). The resulting MBTH-AgNCs@Co-Ni LDH/hydrogel-patch facilitate the oxidation of hexanal, enabling simultaneous colorimetric and SERS signal generation, while producing acrizine, a blue-colored reaction product. This hydrogel-based dual-mode sensing platform exhibits high selectivity, excellent stability, and precision in SERS-based hexanal detection. The detection limit for the SERS method was determined to be as low as 3.34x10-13 M. Furthermore, developed and optimized compact CNN-based multi-terminal intelligent recognition system for enhanced hydrogel-patch detection through AI-driven, portable, and real-time colorimetric analysis. Therefore, this work not only enables the effective detection of hexanal in the exhaled breath of suspected lung cancer patients, underscoring its potential for early lung cancer screening, but also establishes a foundation for the development of multimodal hydrogel biosensors for broader applications in disease diagnosis.

physics.med-ph↗

arXiv2025

Data Classification with Dynamically Growing and Shrinking Neural Networks

The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights, but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled "Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search [26]". In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by a Monte Carlo tree search procedure which simulates network behavior and allows to compare several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture's ability to adapt dynamically, allowing independent modifications for each time series. The approach is supplemented by Python source code for reproducibility. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method's robustness and adaptability.

cs.LG↗

arXiv2022

Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task

Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time series classification. We distinguish between two modes of HMM application. The first, in which a single model is built for each class. The second, in which one HMM is built for each time series. We then transfer both approaches for classifier construction to the domain of Fuzzy Cognitive Maps. The identified four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN, and FCM 1C are then studied in a series of experiments. We compare the performance of different models and investigate the impact of their hyperparameters on the time series classification accuracy. The empirical evaluation shows a clear advantage of the one-model-per-series approach. The results show that the choice between HMM and FCM should be dataset-dependent.

cs.LG↗

arXiv2022

COSTI: a New Classifier for Sequences of Temporal Intervals

Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-of-the-art algorithms from the latter domain on the new representation to obtain significantly better accuracy than the state-of-the-art methods from the former field. We discuss limitations of this workflow and address them by developing a novel method for classification termed COSTI (short for Classification of Sequences of Temporal Intervals) operating directly on sequences of temporal intervals. The proposed method remains at a high level of accuracy and obtains better performance while avoiding shortcomings connected to operating on transformed data. We propose a generalized version of the problem of classification of temporal intervals, where each event is supplemented with information about its intensity. We also provide two new data sets where this information is of substantial value.

cs.LG↗