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Prof. Giuseppe Pirlo
Department of Computer Science, University of Bari, Bari, Italy

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0 Artificial Intelligence
0 Biometrics
0 Pattern Recognition
0 Signal Processing
0 Automatic signature verification

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Automatic signature verification
Pattern Recognition
Artificial Intelligence

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Conference paper
Published: 21 February 2021 in Transactions on Petri Nets and Other Models of Concurrency XV
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Neurodegenerative disease assessment with handwriting has been shown to be effective. In this exploratory analysis, several features are extracted and tested on different tasks of the novel HAND-UNIBA dataset. Results show what are the most important kinematic features and the most significant tasks for neurodegenerative disease assessment through handwriting.

ACS Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 536 -545.

AMA Style

Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():536-545.

Chicago/Turabian Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2021. "An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 536-545.

Journal article
Published: 19 October 2020 in IEEE Access
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Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient’s life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation.

ACS Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements. IEEE Access 2020, 8, 193966 -193980.

AMA Style

Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements. IEEE Access. 2020; 8 (99):193966-193980.

Chicago/Turabian Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2020. "Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements." IEEE Access 8, no. 99: 193966-193980.

Conference paper
Published: 09 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The Internet of Things (IoT) paradigm applied to the agriculture field provides a huge amount of data allowing the employment of Artificial Intelligence for multiple tasks. In this work, solar radiation prediction is considered. To the aim, Multi-Layer Perceptron is adopted considering a complete real complex use case and real-time working conditions. More specifically the forecasting system is integrated considering three different time forecasting horizons and, given different sites, needs and data availability, multiple input features configurations have been considered. The described work allows companies to innovate and optimize their industrial business.

ACS Style

Donato Impedovo; Fabrizio Balducci; Giulio D’Amato; Michela Del Prete; Erminio Riezzo; Lucia Sarcinella; Mariagiorgia AgneseTandoi; Giuseppe Pirlo. An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 474 -483.

AMA Style

Donato Impedovo, Fabrizio Balducci, Giulio D’Amato, Michela Del Prete, Erminio Riezzo, Lucia Sarcinella, Mariagiorgia AgneseTandoi, Giuseppe Pirlo. An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():474-483.

Chicago/Turabian Style

Donato Impedovo; Fabrizio Balducci; Giulio D’Amato; Michela Del Prete; Erminio Riezzo; Lucia Sarcinella; Mariagiorgia AgneseTandoi; Giuseppe Pirlo. 2020. "An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 474-483.

Conference paper
Published: 09 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The proposed system achieves an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, thus the novelty in this study is both on phases segmentation and experimental setup.

ACS Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 596 -609.

AMA Style

Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():596-609.

Chicago/Turabian Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2020. "Sit-to-Stand Test for Neurodegenerative Diseases Video Classification." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 596-609.

Article
Published: 17 June 2020 in Multimedia Tools and Applications
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This work exploits Touch Dynamics to recognize affective states of a user while using a mobile device. To the aim, the acquired touch pattern is segmented in swipes, successively a wide set of handcrafted features is computed to characterize the swipe. The affective analysis is obtained through machine learning techniques. Data have been collected developing a specific App designed to acquire common unlock Android touch patterns. In this way the user interaction has been preserved as the more natural and neutral possible in real environments. Affective state labels have been obtained adopting a well-known psychological questionnaire. Three affective states have been considered: anxiety, stress and depression. Tests, performed on 115 users, reported an overall accuracy of 73.6% thus demonstrating the viability of the proposed approach.

ACS Style

Fabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. Affective states recognition through touch dynamics. Multimedia Tools and Applications 2020, 79, 35909 -35926.

AMA Style

Fabrizio Balducci, Donato Impedovo, Nicola Macchiarulo, Giuseppe Pirlo. Affective states recognition through touch dynamics. Multimedia Tools and Applications. 2020; 79 (47-48):35909-35926.

Chicago/Turabian Style

Fabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. 2020. "Affective states recognition through touch dynamics." Multimedia Tools and Applications 79, no. 47-48: 35909-35926.

Benchmark
Published: 13 June 2020 in Information
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This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.

ACS Style

Vito Nicola Convertini; Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo; Lucia Sarcinella. A Controlled Benchmark of Video Violence Detection Techniques. Information 2020, 11, 321 .

AMA Style

Vito Nicola Convertini, Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo, Lucia Sarcinella. A Controlled Benchmark of Video Violence Detection Techniques. Information. 2020; 11 (6):321.

Chicago/Turabian Style

Vito Nicola Convertini; Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo; Lucia Sarcinella. 2020. "A Controlled Benchmark of Video Violence Detection Techniques." Information 11, no. 6: 321.

Editorial
Published: 24 April 2020 in Applied Sciences
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Smart cities work under a more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which have led to the creation of smart enterprises and organizations that depend on advanced technologies. In this Special Issue, 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life quality.

ACS Style

Donato Impedovo; Giuseppe Pirlo. Artificial Intelligence Applications to Smart City and Smart Enterprise. Applied Sciences 2020, 10, 2944 .

AMA Style

Donato Impedovo, Giuseppe Pirlo. Artificial Intelligence Applications to Smart City and Smart Enterprise. Applied Sciences. 2020; 10 (8):2944.

Chicago/Turabian Style

Donato Impedovo; Giuseppe Pirlo. 2020. "Artificial Intelligence Applications to Smart City and Smart Enterprise." Applied Sciences 10, no. 8: 2944.

Journal article
Published: 28 November 2019 in Sensors
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Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

ACS Style

Donato Impedovo; Fabrizio Balducci; Vincenzo Dentamaro; Giuseppe Pirlo. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors 2019, 19, 5213 .

AMA Style

Donato Impedovo, Fabrizio Balducci, Vincenzo Dentamaro, Giuseppe Pirlo. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors. 2019; 19 (23):5213.

Chicago/Turabian Style

Donato Impedovo; Fabrizio Balducci; Vincenzo Dentamaro; Giuseppe Pirlo. 2019. "Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison." Sensors 19, no. 23: 5213.

Review
Published: 01 April 2019 in Pattern Recognition Letters
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ACS Style

Claudio De Stefano; Francesco Fontanella; Donato Impedovo; Giuseppe Pirlo; Alessandra Scotto di Freca. Handwriting analysis to support neurodegenerative diseases diagnosis: A review. Pattern Recognition Letters 2019, 121, 37 -45.

AMA Style

Claudio De Stefano, Francesco Fontanella, Donato Impedovo, Giuseppe Pirlo, Alessandra Scotto di Freca. Handwriting analysis to support neurodegenerative diseases diagnosis: A review. Pattern Recognition Letters. 2019; 121 ():37-45.

Chicago/Turabian Style

Claudio De Stefano; Francesco Fontanella; Donato Impedovo; Giuseppe Pirlo; Alessandra Scotto di Freca. 2019. "Handwriting analysis to support neurodegenerative diseases diagnosis: A review." Pattern Recognition Letters 121, no. : 37-45.

Editorial
Published: 19 March 2019 in Information
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Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms.

ACS Style

Donato Impedovo; Giuseppe Pirlo. eHealth and Artificial Intelligence. Information 2019, 10, 117 .

AMA Style

Donato Impedovo, Giuseppe Pirlo. eHealth and Artificial Intelligence. Information. 2019; 10 (3):117.

Chicago/Turabian Style

Donato Impedovo; Giuseppe Pirlo. 2019. "eHealth and Artificial Intelligence." Information 10, no. 3: 117.

Research article
Published: 05 February 2019 in IET Biometrics
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Reduced training sets are major problems typically found on the task of offline signature verification. To increase the number of samples, the use of synthetic signatures can be taken into account. In this work, a new method for the generation of synthetic offline signatures by using dynamic and static (real) ones is presented. The synthesis is here faced under the perspective of supervised training: the learning model is trained to perform the task of online-to-offline signature conversion. The approach is based on a deep convolutional neural network. The main goal is to enlarge offline training dataset in order to improve performance of the offline signature verification systems. For this purpose, a machine-oriented evaluation on the BiosecurID signature dataset is carried out. The use of synthetic samples (in the training phase) generated with the proposed method on a state-of-the-art classification system exhibits performance similar to those obtained using real signatures; moreover, the combination of real and synthetic signatures in the training set is also able to show improvements of the equal error rate.

ACS Style

Victor K.S.L. Melo; Byron Leite Dantas Bezerra; Donato Impedovo; Giuseppe Pirlo; Antonio Lundgren. Deep learning approach to generate offline handwritten signatures based on online samples. IET Biometrics 2019, 8, 215 -220.

AMA Style

Victor K.S.L. Melo, Byron Leite Dantas Bezerra, Donato Impedovo, Giuseppe Pirlo, Antonio Lundgren. Deep learning approach to generate offline handwritten signatures based on online samples. IET Biometrics. 2019; 8 (3):215-220.

Chicago/Turabian Style

Victor K.S.L. Melo; Byron Leite Dantas Bezerra; Donato Impedovo; Giuseppe Pirlo; Antonio Lundgren. 2019. "Deep learning approach to generate offline handwritten signatures based on online samples." IET Biometrics 8, no. 3: 215-220.

Journal article
Published: 09 December 2018 in Information
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Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.

ACS Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. LICIC: Less Important Components for Imbalanced Multiclass Classification. Information 2018, 9, 317 .

AMA Style

Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. LICIC: Less Important Components for Imbalanced Multiclass Classification. Information. 2018; 9 (12):317.

Chicago/Turabian Style

Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2018. "LICIC: Less Important Components for Imbalanced Multiclass Classification." Information 9, no. 12: 317.

Journal article
Published: 26 November 2018 in Sensors
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This work presents the practical design of a system that faces the problem of identification and validation of private no-parking road signs. This issue is very important for the public city administrations since many people, after receiving a code that identifies the signal at the entrance of their private car garage as valid, forget to renew the code validity through the payment of a city tax, causing large money shortages to the public administration. The goal of the system is twice since, after recognition of the official road sign pattern, its validity must be controlled by extracting the code put in a specific sub-region inside it. Despite a lot of work on the road signs’ topic having been carried out, a complete benchmark dataset also considering the particular setting of the Italian law is today not available for comparison, thus the second goal of this work is to provide experimental results that exploit machine learning and deep learning techniques that can be satisfactorily used in industrial applications.

ACS Style

Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods. Sensors 2018, 18, 4147 .

AMA Style

Fabrizio Balducci, Donato Impedovo, Giuseppe Pirlo. Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods. Sensors. 2018; 18 (12):4147.

Chicago/Turabian Style

Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. 2018. "Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods." Sensors 18, no. 12: 4147.

Journal article
Published: 22 October 2018 in IEEE Transactions on Emerging Topics in Computing
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On-line signature verification is typically carried out with the use of digitizing tablets specifically designed for the aim. So far, stand-alone systems have been mainly inspected, but the current distributed/cloud scenario and the amount of mobile devices in everyday life is calling for a new challenge. Within this scenario, signatures are acquired around the world with different kinds of devices and processed on multiple platforms in order to be veri-fied. Through the paper, the different phases of the signature ver-ification process in the new scenario are presented and the most valuable results are discussed considering the following aspects: accessibility and usability, interoperability, security and perfor-mance. Achievements as well as weakness are focused to high-light promising directions for further research and technology development.

ACS Style

Donato Impedovo; Giuseppe Pirlo. Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead. IEEE Transactions on Emerging Topics in Computing 2018, 9, 554 -568.

AMA Style

Donato Impedovo, Giuseppe Pirlo. Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead. IEEE Transactions on Emerging Topics in Computing. 2018; 9 (1):554-568.

Chicago/Turabian Style

Donato Impedovo; Giuseppe Pirlo. 2018. "Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead." IEEE Transactions on Emerging Topics in Computing 9, no. 1: 554-568.

Journal article
Published: 03 October 2018 in Information
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Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease.

ACS Style

Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis. Information 2018, 9, 247 .

AMA Style

Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio. Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis. Information. 2018; 9 (10):247.

Chicago/Turabian Style

Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. 2018. "Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis." Information 9, no. 10: 247.

Conference paper
Published: 01 October 2018 in 2018 AEIT International Annual Conference
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ACS Style

Fabrizio Balducci; Davide Fomarelli; Donato Impedovo; Andrea Longo; Giuseppe Pirlo. Smart Farms for a Sustainable and Optimized Model of Agriculture. 2018 AEIT International Annual Conference 2018, 1 .

AMA Style

Fabrizio Balducci, Davide Fomarelli, Donato Impedovo, Andrea Longo, Giuseppe Pirlo. Smart Farms for a Sustainable and Optimized Model of Agriculture. 2018 AEIT International Annual Conference. 2018; ():1.

Chicago/Turabian Style

Fabrizio Balducci; Davide Fomarelli; Donato Impedovo; Andrea Longo; Giuseppe Pirlo. 2018. "Smart Farms for a Sustainable and Optimized Model of Agriculture." 2018 AEIT International Annual Conference , no. : 1.

Journal article
Published: 01 September 2018 in Pattern Recognition
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The widespread availability of hand-held devices like tablets, phablets and smart phones, along with their new handwriting digitizing and their increased computing powers, enable these to process the graphomotor dimension and the lognormal trends of human handwriting. By exploiting such capacity, it becomes possible to extend these mobile devices into Personal Digital Bodyguards (PDBs). PDBs will be able to supplement people's sensitive data protection with signature verification, equipment use security with writer authentication and handwritten CAPTCHAs processing (e-security), and to enhance human-machine interaction performances through words spotting and handwriting recognition (e-recognition). For young children, these tools will turn into interactive toys helping them to learn and master their fine motor control and become better writers. For advanced students they will enable sophisticated systems for (e-learning) and (e-testing). Moreover, PDBs will also be able to provide the user with fine motor control monitoring, which can detect stress, aging and health problems (e-health). This paper presents a prospective survey of various projects dealing with these five e-fields of investigation, focussing on state of the art results and providing directions in research and development, under the theoretical umbrella of the Kinematic Theory of human movements and its Lognormality Principle. From a practical point of view, the concept of lognormality provides a fundamental common thread, an integrative psychophysical standpoint to track the graphomotor problems of signature verification, writer identification, handwriting generation, recognition and learning.

ACS Style

Réjean Plamondon; Giuseppe Pirlo; Éric Anquetil; Céline Rémi; Hans-Leo Teulings; Masaki Nakagawa. Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey. Pattern Recognition 2018, 81, 633 -659.

AMA Style

Réjean Plamondon, Giuseppe Pirlo, Éric Anquetil, Céline Rémi, Hans-Leo Teulings, Masaki Nakagawa. Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey. Pattern Recognition. 2018; 81 ():633-659.

Chicago/Turabian Style

Réjean Plamondon; Giuseppe Pirlo; Éric Anquetil; Céline Rémi; Hans-Leo Teulings; Masaki Nakagawa. 2018. "Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey." Pattern Recognition 81, no. : 633-659.

Journal article
Published: 01 September 2018 in Machines
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This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

ACS Style

Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines 2018, 6, 38 .

AMA Style

Fabrizio Balducci, Donato Impedovo, Giuseppe Pirlo. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines. 2018; 6 (3):38.

Chicago/Turabian Style

Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. 2018. "Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement." Machines 6, no. 3: 38.

Review
Published: 25 May 2018 in IEEE Reviews in Biomedical Engineering
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Neurodegenerative diseases, as for instance Alzheimer's Disease (AD) and Parkinson's Disease (PD), affect the peripheral nervous system, where nerve cells send the messages that control muscles in order to allow movements. Sick neurons cannot control muscles properly. Handwriting involves cognitive planning, coordination and execution abilities. Significant changes in the handwriting performance are a prominent feature of AD and PD. This work addresses the most relevant results obtained in the field of on-line (dynamic) analysis of handwritten trials by AD and PD patients. The survey is made from a pattern recognition point of view, so that different phases are described. Data acquisition deal not only with the device, but also with the handwriting task. Feature extraction can deal with function and parameter features. The classification problem is also discussed along with results already obtained. The paper also highlights the most profitable research direction.

ACS Style

Donato Impedovo; Giuseppe Pirlo. Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective. IEEE Reviews in Biomedical Engineering 2018, 12, 209 -220.

AMA Style

Donato Impedovo, Giuseppe Pirlo. Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective. IEEE Reviews in Biomedical Engineering. 2018; 12 (99):209-220.

Chicago/Turabian Style

Donato Impedovo; Giuseppe Pirlo. 2018. "Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective." IEEE Reviews in Biomedical Engineering 12, no. 99: 209-220.

Journal article
Published: 01 August 2017 in Expert Systems with Applications
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Novel Stability-based system for early bearing fault detection.Novel use of Dynamic Time Warping and Direct Matching Point techniques.Findings on continuous monitoring of vibration signals with low false alarms for faultsFindings on two publicly available databases under several machining conditions. This paper presents a new and straightforward system for bearing fault detection. The system computes the stability of two vibration signals by using the direct matching points (DMP) of an elastic and non-linear align function. It is able to find discriminant properties in the stability of fault-free and faulty bearing vibration signals from the early and late stages of the fault in critical bearing parts. Because training data constitutes one of the critical challenges in most expert and intelligent systems, one of the novelties of the proposed stability-based system is that it requires neither training nor fine-tuning. A significant impact on the robustness of the system is demonstrated using two publicly available vibration signal databases under several load conditions, with real faults, during multiple machine working states. Experimental results validate the use of the proposed stability-based system for predictive maintenance in bearings.

ACS Style

Moises Diaz; Patricia Henriquez; Miguel A. Ferrer; Giuseppe Pirlo; Jesus B. Alonso; Cristina Carmona-Duarte; Donato Impedovo. Stability-based system for bearing fault early detection. Expert Systems with Applications 2017, 79, 65 -75.

AMA Style

Moises Diaz, Patricia Henriquez, Miguel A. Ferrer, Giuseppe Pirlo, Jesus B. Alonso, Cristina Carmona-Duarte, Donato Impedovo. Stability-based system for bearing fault early detection. Expert Systems with Applications. 2017; 79 ():65-75.

Chicago/Turabian Style

Moises Diaz; Patricia Henriquez; Miguel A. Ferrer; Giuseppe Pirlo; Jesus B. Alonso; Cristina Carmona-Duarte; Donato Impedovo. 2017. "Stability-based system for bearing fault early detection." Expert Systems with Applications 79, no. : 65-75.