169 research outputs found
Assessing Coastal Sustainability: A Bayesian Approach for Modeling and Estimating a Global Index for Measuring Risk
Integrated Coastal Zone Management is an emerg- ing research area. The aim is to provide a global view of dif- ferent and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate use- ful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second layer, the first-order in- dexes, depending on raw data, of physiographic regions are computed. The third layer estimates the second-order indexes of the analyzed physiographic regions. In the fourth layer, the global Sustainable Coastal Index is inferred. Processed data refers to 13 physiographic regions in the Province of Trapani, western Sicily, Italy. Gathered data describes the environ- mental information, the agricultural, fisheries, and economi- cal behaviors of the local population and land. The Bayesian Network was trained and tested using a real dataset acquired between 2000 and 2006. The developed system presents inter- esting results
Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features
The SARS-CoV-2 virus pandemic had devastating effects on various aspects of life: clinical cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high incidence, data-driven models can support physicians in patient management. The explainability and interpretability of machine-learning models are mandatory in clinical scenarios. In this work, clinical, laboratory and radiomic features were used to train machine-learning models for COVID-19 prognosis prediction. Using Explainable AI algorithms, a multi-level explainable method was proposed taking into account the developer and the involved stakeholder (physician, and patient) perspectives. A total of 1023 radiomic features were extracted from 1589 Chest X-Ray images (CXR), combined with 38 clinical/laboratory features. After the pre-processing and selection phases, 40 CXR radiomic features and 23 clinical/laboratory features were used to train Support Vector Machine and Random Forest classifiers exploring three feature selection strategies. The combination of both radiomic, and clinical/laboratory features enabled higher performance in the resulting models. The intelligibility of the used features allowed us to validate the models' clinical findings. According to the medical literature, LDH, PaO2 and CRP were the most predictive laboratory features. Instead, ZoneEntropy and HighGrayLevelZoneEmphasis - indicative of the heterogeneity/uniformity of lung texture - were the most discriminating radiomic features. Our best predictive model, exploiting the Random Forest classifier and a signature composed of clinical, laboratory and radiomic features, achieved AUC=0.819, accuracy=0.733, specificity=0.705, and sensitivity=0.761 in the test set. The model, including a multi-level explainability, allows us to make strong clinical assumptions, confirmed by the literature insights
Usability analysis of a novel biometric authentication approach for android-based mobile devices
Mobile devices are widely replacing the standard personal computers thanks to their small size and user-friendly use. As a consequence, the amount of information, often confidential, exchanged through these devices is raising. This makes them potential targets of malicious network hackers. The use of simple passwords or PIN are not sufficient to provide a suitable security level for those applications requiring high protection levels on data and services. In this paper a biometric authentication system, as a running Android application, has been developed and implemented on a real mobile device. A system test on real users has been also carried out in order to evaluate the human-machine interaction quality, the recognition accuracy of the proposed technique, and the scheduling latency of the operating system and its degree of acceptance. Several measures, such as system usability, users satisfaction, and tolerable speed for identification, have been carried out in order to evaluate the performance of the proposed approach
An Advanced Technique for User Identification Using Partial Fingerprint
User identification is a very interesting and
complex task. Invasive biometrics is based on traits
uniqueness and immutability over time. In forensic field,
fingerprints have always been considered an essential
element for personal recognition. The traditional issue is
focused on full fingerprint images matching. In this paper an
advanced technique for personal recognition based on
partial fingerprint is proposed. This system is based on
fingerprint local analysis and micro-features, endpoints and
bifurcations, extraction. The proposed approach starts from
minutiae extraction from a partial fingerprint image and
ends with the final matching score between fingerprint pairs.
The computation of likelihood ratios in fingerprint
identification is computed by trying every possible
overlapping of the partial image with complete image. The
first experimental results conducted on the PolyU (Hong
Kong Polytechnic University) free database show an
encouraging performance in terms of identification
accuracy
An Embedded Biometric Sensor for Ubiquitous Authentication
Communication networks and distributed technologies
move people towards the era of ubiquitous computing. An
ubiquitous environment needs many authentication sensors for
users recognition, in order to provide a secure infrastructure for
both user access to resources and services and information
management. Today the security requirements must ensure
secure and trusted user information to protect sensitive data
resource access and they could be used for user traceability inside
the platform. Conventional authentication systems, based on
username and password, are in crisis since they are not able to
guarantee a suitable security level for several applications.
Biometric authentication systems represent a valid alternative to
the conventional authentication systems providing a flexible einfrastructure
towards an integrated solution supporting the
requirement for improved inter-organizational functionality. In
this work the study and the implementation of a fingerprintsbased
embedded biometric system is proposed. Typical strategies
implemented in Identity Management Systems could be useful to
protect biometric information. The proposed sensor can be seen
as a self-contained sensor: it performs the all elaboration steps on
board, a necessary requisite to strengthen security, so that
sensible data are securely managed and stored inside the sensor,
without any data leaking out. The sensor has been prototyped via
an FPGA-based platform achieving fast execution time and a
good final throughput. Resources used, elaboration times of the
sensor are reported. Finally, recognition rates of the proposed
embedded biometric sensor have been evaluated considering
three different databases: the FVC2002 reference database, the
CSAI/Biometrika proprietary database, and the CSAI/Secugen
proprietary database. The best achieved FAR and FRR indexes
are respectively 1.07% and 8.33%, with an elaboration time of
183.32 ms and a working frequency of 22.5 MHz
Illumination Correction on Biomedical Images
RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the approach has been compared successfully against the Guillemaud filter and the SPM2 method. Moreover, the method has been successfully applied on several real MR images of the brain (0.18 T, 1.5 T and 7 T). The description of the overall technique is reported along with the experimental results that show its effectiveness in different anatomical regions and its ability to compensate both underexposed and overexposed areas. Our approach is also effective on non-radiological images, like retinal ones
Design Exploration of AES Accelerators on FPGAs and GPUs, Journal of Telecommunications and Information Technology, 2017, nr 1
The embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems and an exhaustive analysis of the state of the art of all current performance with respect to architectures, design methodologies, test and applications could be very interesting. The Advanced Encryption Standard (AES), based on the well-known algorithm Rijndael, is designed to be easily implemented in hardware and software platforms. General purpose computing on graphics processing unit (GPGPU) is an alternative to recongurable accelerators based on FPGA devices. This paper presents a direct comparison between FPGA and GPU used as accelerators for the AES cipher. The results achieved on both platforms and their analysis has been compared to several others in order to establish which device is best at playing the role of hardware accelerator by each solution showing interesting considerations in terms of throughput, speedup factor, and resource usage. This analysis suggests that, while hardware design on FPGA remains the natural choice for consumer-product design, GPUs are nowadays the preferable choice for PC based accelerators, especially when the processing routines are highly parallelizable
An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
Over the past years many researchers proposed biomedical imaging methods for computer-aided detection
and classification of suspicious regions in mammograms. Mammogram interpretation is performed by
radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings
labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method
to automatically detect suspicious regions in mammogram images. The method consists mainly of two
steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background
region from the breast profile region. In greater detail, gray levels mapping transform and histogram
specifications are used to enhance the visual representation of mammogram details. Then, local keypoints
and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are
filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and
confidence interval are very encouraging
An edge-driven 3D region growing approach for upper airways morphology and volume evaluation in patients with Pierre Robin sequence
In this paper, a semi-automatic approach for segmentation of the upper airways is
proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733%) and dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates.
The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation
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