Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization.

The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result, significant attention is currently being devoted to developing methods aimed at tailoring hyperparameters for specific CNN models and classification tasks.

Detection of Fungi and Oomycetes by Volatiles Using E-Nose and SPME-GC/MS Platforms.

Fungi and oomycetes release volatiles into their environment which could be used for olfactory detection and identification of these organisms by electronic-nose (e-nose). The aim of this study was to survey volatile compound emission using an e-nose device and to identify released molecules through solid phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS) analysis to ultimately develop a detection system for fungi and fungi-like organisms. To this end, cultures of eight fungi (, , , , , , , ) and four oomycetes (, , , ) were tested with the e-nose system and investigated by means of SPME-GC/MS.

Odor Detection Using an E-Nose With a Reduced Sensor Array.

Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system.

May sunshine protect women against paroxysms of atrial fibrillation?

Seasonal variation in the occurrence of atrial fibrillation (AF) has been documented, yet precise mechanisms and factors underlying the phenomenon remain unclear. We have previously observed the decrease in the number of AF paroxysms between May and August, when sunshine levels were highest. The objective of the present study was, in turn, to determine whether sunshine affects the incidence of AF episodes.

Episodes of atrial fibrillation and meteorological conditions.

Background: Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. The natural history of AF tends to begin with short paroxysms which gradually evolve into longer episodes, frequently treatment-resistant, and finally take a permanent form. It is a polyaetiological condition and single paroxysms may be caused by a variety of factors.