Publications

2019

@article{Rundo_2019, title={Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for trading FX Market}, volume={9}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app9091796}, DOI={10.3390/app9091796}, number={9}, journal={Applied Sciences}, publisher={MDPI AG}, author={Rundo and Trenta and di Stallo and Battiato}, year={2019}, month={Apr}, pages={1796}}

2018

@Article{computation6030046, AUTHOR = {Rundo, Francesco and Ortis, Alessandro and Battiato, Sebastiano and Conoci, Sabrina}, TITLE = {Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis}, JOURNAL = {Computation}, VOLUME = {6}, YEAR = {2018}, NUMBER = {3}, ARTICLE-NUMBER = {46}, URL = {http://www.mdpi.com/2079-3197/6/3/46}, ISSN = {2079-3197}, ABSTRACT = {Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy.}, DOI = {10.3390/computation6030046} }

@ARTICLE{8466246, author={F. Rundo and S. Conoci and G. L. Banna and A. Ortis and F. Stanco and S. Battiato}, journal={IET Computer Vision}, title={Evaluation of Levenberg–Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up}, year={2018}, volume={12}, number={7}, pages={957-962}, abstract={Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so-called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre-processing of dermoscopy image by SC-cellular neural networks is performed, in order to obtainad-hocgrey-level skin lesion image that is further exploited to extract analytic innovative hand-crafted image features for oncological risks assessment. In the end, a pre-trained Levenberg–Marquardt neural network is used to performad-hocclustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow-up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg–Marquardt neural network for the clustering step.}, keywords={cancer;image classification;skin;image segmentation;feature extraction;neural nets;biomedical optical imaging;medical image processing;stacked autoencoders;skin lesion analysis;visual analysis;morphological analysis;skin lesion dermoscopy images;dermoscopy image;SC-cellular neural networks;ad-hoc grey-level skin lesion image;ad-hoc clustering;benign against melanoma;hand-crafted image features;Levenberg–Marquardt neural network}, doi={10.1049/iet-cvi.2018.5195}, ISSN={1751-9632}, month={},}

@Article{s18020405, AUTHOR = {Rundo, Francesco and Conoci, Sabrina and Ortis, Alessandro and Battiato, Sebastiano}, TITLE = {An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment}, JOURNAL = {Sensors}, VOLUME = {18}, YEAR = {2018}, NUMBER = {2}, ARTICLE-NUMBER = {405}, URL = {http://www.mdpi.com/1424-8220/18/2/405}, ISSN = {1424-8220}, ABSTRACT = {Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.}, DOI = {10.3390/s18020405} }

@Article{computationSUBMITTED, title={Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System}, author={Rundo, Francesco and Trenta, Francesca and Di Stallo, Agatino and Battiato, Sebastiano}, journal={Computation}, volume={7}, number={1}, pages={4}, year={2019}, publisher={Multidisciplinary Digital Publishing Institute} }