This page has only limited features, please log in for full access.
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.
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 StyleDonato 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 StyleDonato 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.
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.
Fabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. Affective states recognition through touch dynamics. Multimedia Tools and Applications 2020, 79, 35909 -35926.
AMA StyleFabrizio 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 StyleFabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. 2020. "Affective states recognition through touch dynamics." Multimedia Tools and Applications 79, no. 47-48: 35909-35926.
New Information and Communication Technologies have a large potential to improve general public awareness of the importance of Cultural Heritage (CH) and to provide tools that can make visits to historical sites more interesting and enjoyable. The Internet of Things (IoT) technology can further contribute to these goals, by allowing visitors to museum and CH sites to manipulate smart objects by receiving information that stimulates emotions, understanding and appropriation of the contents. In our research, interaction paradigms and innovative methods are developed to allow curators and guides of cultural sites (i.e., domain experts) to manage interactive IoT-based environments, in order to create Smart Interactive Experiences, which are usage situations created by synchronizing many available smart objects to specific situations that might better satisfy the needs of the visitors. This article illustrates a system that, by means of a tangible user interface, integrated by pattern recognition and computer vision techniques, supports CH experts in creating Smart Interactive Experiences by properly tailoring the behavior of the involved smart objects. An experimental evaluation of the used techniques has been performed and it is presented and discussed.
Fabrizio Balducci; Paolo Buono; Giuseppe Desolda; Donato Impedovo; Antonio Piccinno. Improving smart interactive experiences in cultural heritage through pattern recognition techniques. Pattern Recognition Letters 2019, 131, 142 -149.
AMA StyleFabrizio Balducci, Paolo Buono, Giuseppe Desolda, Donato Impedovo, Antonio Piccinno. Improving smart interactive experiences in cultural heritage through pattern recognition techniques. Pattern Recognition Letters. 2019; 131 ():142-149.
Chicago/Turabian StyleFabrizio Balducci; Paolo Buono; Giuseppe Desolda; Donato Impedovo; Antonio Piccinno. 2019. "Improving smart interactive experiences in cultural heritage through pattern recognition techniques." Pattern Recognition Letters 131, no. : 142-149.
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.
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 StyleDonato 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 StyleDonato 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.
Domenico Rotondi; Leonardo Straniero; Marco Saltarella; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. Semantics for Wastewater Reuse in Agriculture*. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019, 1 .
AMA StyleDomenico Rotondi, Leonardo Straniero, Marco Saltarella, Fabrizio Balducci, Donato Impedovo, Giuseppe Pirlo. Semantics for Wastewater Reuse in Agriculture*. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 2019; ():1.
Chicago/Turabian StyleDomenico Rotondi; Leonardo Straniero; Marco Saltarella; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. 2019. "Semantics for Wastewater Reuse in Agriculture*." 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , no. : 1.
The analysis of air quality data may reveal the quality of life and can prevent dangers for the citizen health. Assuming that some chemical compounds in the air produce a bad smell, people may detect that something is going wrong acting as sensors that alerts potential risks. This work presents a visual analytics approach to support air quality experts in the analysis of data produced by electronic nose devices. The approach consists in setting workflows to manage and transform raw data offering clustering and visualization techniques to analyze such information. The analysis is supported by calendar, map and line graph visualization techniques also maneuvering the clustering attributes. The interactive map is used to show the position of monitoring stations in order to support making hypothesis related to the data source locations.
Paolo Buono; Fabrizio Balducci. MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose. Multimedia Tools and Applications 2019, 78, 33023 -33040.
AMA StylePaolo Buono, Fabrizio Balducci. MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose. Multimedia Tools and Applications. 2019; 78 (23):33023-33040.
Chicago/Turabian StylePaolo Buono; Fabrizio Balducci. 2019. "MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose." Multimedia Tools and Applications 78, no. 23: 33023-33040.
This paper proposes a novel technique for an automatic detection of dementia based on the Attentional Matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as input to machine learning algorithms to automatize the disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as her motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating; while, the ensemble of the best classification models over the three matrices provided the best classification performance (i.e., an AUC of 87.30% and a sensitivity of 86.11%). Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support a mass screening of the population.
Maria Teresa Angelillo; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. Attentional Pattern Classification for Automatic Dementia Detection. IEEE Access 2019, 7, 57706 -57716.
AMA StyleMaria Teresa Angelillo, Fabrizio Balducci, Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio. Attentional Pattern Classification for Automatic Dementia Detection. IEEE Access. 2019; 7 (99):57706-57716.
Chicago/Turabian StyleMaria Teresa Angelillo; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. 2019. "Attentional Pattern Classification for Automatic Dementia Detection." IEEE Access 7, no. 99: 57706-57716.
Paolo Buono; Fabrizio Balducci; Fabio Cassano; Antonio Piccinno. EnergyAware: a non-intrusive load monitoring system to improve the domestic energy consumption awareness. Proceedings of the 2nd ACM SIGSOFT International Workshop on Ensemble-Based Software Engineering for Modern Computing Platforms - EnSEmble 2019 2019, 1 .
AMA StylePaolo Buono, Fabrizio Balducci, Fabio Cassano, Antonio Piccinno. EnergyAware: a non-intrusive load monitoring system to improve the domestic energy consumption awareness. Proceedings of the 2nd ACM SIGSOFT International Workshop on Ensemble-Based Software Engineering for Modern Computing Platforms - EnSEmble 2019. 2019; ():1.
Chicago/Turabian StylePaolo Buono; Fabrizio Balducci; Fabio Cassano; Antonio Piccinno. 2019. "EnergyAware: a non-intrusive load monitoring system to improve the domestic energy consumption awareness." Proceedings of the 2nd ACM SIGSOFT International Workshop on Ensemble-Based Software Engineering for Modern Computing Platforms - EnSEmble 2019 , no. : 1.
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.
Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods. Sensors 2018, 18, 4147 .
AMA StyleFabrizio 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 StyleFabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. 2018. "Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods." Sensors 18, no. 12: 4147.
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 StyleFabrizio 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 StyleFabrizio 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.
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.
Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines 2018, 6, 38 .
AMA StyleFabrizio Balducci, Donato Impedovo, Giuseppe Pirlo. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines. 2018; 6 (3):38.
Chicago/Turabian StyleFabrizio Balducci; Donato Impedovo; Giuseppe Pirlo. 2018. "Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement." Machines 6, no. 3: 38.
The analysis of air quality data may reveal the quality of life and can prevent dangers for the citizen health. This paper presents an approach for air quality data analysis, which exploits Data Mining and InfoVis techniques to support the analysts daily work. The proposed approach addresses data generated by the electronic nose, a device that detects chemical compounds perceived by humans through the smell. A working pipeline implements a workflow for data processing with clustering techniques; an enhanced powerful calendar visualization combined with more traditional line graph and geo-referenced visualizations shows data to the analyst allowing to detect temporal trends and making immediate comparisons.
Paolo Buono; Fabrizio Balducci. A Web App for Visualizing Electronic Nose Data. 2018 22nd International Conference Information Visualisation (IV) 2018, 198 -203.
AMA StylePaolo Buono, Fabrizio Balducci. A Web App for Visualizing Electronic Nose Data. 2018 22nd International Conference Information Visualisation (IV). 2018; ():198-203.
Chicago/Turabian StylePaolo Buono; Fabrizio Balducci. 2018. "A Web App for Visualizing Electronic Nose Data." 2018 22nd International Conference Information Visualisation (IV) , no. : 198-203.
Fabrizio Balducci; Paolo Buono. Building a qualified annotation dataset for skin lesion analysis trough gamification. Proceedings of the 2018 International Conference on Distance Education and Learning 2018, 36 .
AMA StyleFabrizio Balducci, Paolo Buono. Building a qualified annotation dataset for skin lesion analysis trough gamification. Proceedings of the 2018 International Conference on Distance Education and Learning. 2018; ():36.
Chicago/Turabian StyleFabrizio Balducci; Paolo Buono. 2018. "Building a qualified annotation dataset for skin lesion analysis trough gamification." Proceedings of the 2018 International Conference on Distance Education and Learning , no. : 36.
Nowadays it is claimed that one method to learn how to execute a task is to present it as a gaming activity: in this way a teacher can offer a safe and controlled environment for learners also arousing excitement and engagement. In this work we present the design of the serious game ‘Annote’, to exploit a medical digital library with the aim to help dermatologists to teach students how to approach the examination of skin lesion images to prevent melanomas.
Fabrizio Balducci. Annote: A Serious Game for Medical Students to Approach Lesion Skin Images of a Digital Library. Communications in Computer and Information Science 2017, 120 -126.
AMA StyleFabrizio Balducci. Annote: A Serious Game for Medical Students to Approach Lesion Skin Images of a Digital Library. Communications in Computer and Information Science. 2017; ():120-126.
Chicago/Turabian StyleFabrizio Balducci. 2017. "Annote: A Serious Game for Medical Students to Approach Lesion Skin Images of a Digital Library." Communications in Computer and Information Science , no. : 120-126.
In recent years the interest of biomedical and computer vision communities in acquisition and analysis of epidermal images increased because melanoma is one of the deadliest form of skin cancer and its early identification could save lives reducing unnecessary medical treatments. User-friendly automatic tools can be very useful for physicians and dermatologists in fact high-resolution images and their annotated data, combined with analysis pipelines and machine learning techniques, represent the base to develop intelligent and proactive diagnostic systems. In this work we present two skin lesion detection pipelines on dermoscopic medical images, by exploiting standard techniques combined with workarounds that improve results; moreover to highlight the performance we consider a set of metrics combined with pixel labeling and classification. A preliminary but functional evaluation phase has been conducted with a sub-set of hard-to-treat images, in order to check which proposed detection pipeline reaches the best results.
Fabrizio Balducci; Costantino Grana. Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images. Privacy Enhancing Technologies 2017, 10485, 444 -455.
AMA StyleFabrizio Balducci, Costantino Grana. Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images. Privacy Enhancing Technologies. 2017; 10485 ():444-455.
Chicago/Turabian StyleFabrizio Balducci; Costantino Grana. 2017. "Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images." Privacy Enhancing Technologies 10485, no. : 444-455.
Each human activity involves feelings and subjective emotions: different people will perform and sense the same task with different outcomes and experience; to understand this experience, concepts like Flow or Boredom must be investigated using objective data provided by methods like electroencephalography. This work carries on the analysis of EEG data coming from brain-computer interface and videogame “Neverwinter Nights 2”: we propose an experimental methodology comparing results coming from different off-the-shelf machine learning techniques, employed on the gaming activities, to check if each affective state corresponds to the hypothesis fixed in their formal design guidelines.
Fabrizio Balducci; Costantino Grana. Affective Classification of Gaming Activities Coming from RPG Gaming Sessions. Computer Vision 2017, 93 -100.
AMA StyleFabrizio Balducci, Costantino Grana. Affective Classification of Gaming Activities Coming from RPG Gaming Sessions. Computer Vision. 2017; ():93-100.
Chicago/Turabian StyleFabrizio Balducci; Costantino Grana. 2017. "Affective Classification of Gaming Activities Coming from RPG Gaming Sessions." Computer Vision , no. : 93-100.
Melanoma is one of the deadliest form of skin cancers so it becomes crucial the developing of automated systems that analyze and investigate epidermal images to early identify them also reducing unnecessary medical exams. A key element is the availability of user-friendly annotation tools that can be used by non-IT experts to produce well-annotated and high-quality medical data. In this work, we present an annotation tool to manually crate and annotate digital epidermal images, with the aim to extract meta-data (annotations, contour patterns and intersections, color information) stored and organized in an integrated digital library. This tool is obtained following rigid usability principles also based on doctors interviews and opinions. A preliminary but functional evaluation phase has been conducted with non-medical subjects by using questionnaires, in order to check the general usability and the efficacy of the proposed tool.
Fabrizio Balducci; Guido Borghi. An Annotation Tool for a Digital Library System of Epidermal Data. Communications in Computer and Information Science 2017, 173 -186.
AMA StyleFabrizio Balducci, Guido Borghi. An Annotation Tool for a Digital Library System of Epidermal Data. Communications in Computer and Information Science. 2017; ():173-186.
Chicago/Turabian StyleFabrizio Balducci; Guido Borghi. 2017. "An Annotation Tool for a Digital Library System of Epidermal Data." Communications in Computer and Information Science , no. : 173-186.
Automatic layout analysis has proven to be extremely important in the process of digitization of large amounts of documents. In this paper we present a mixed approach to layout analysis, introducing a SVM-aided layout segmentation process and a classification process based on local and geometrical features. The final output of the automatic analysis algorithm is a complete and structured annotation in JSON format, containing the digitalized text as well as all the references to the illustrations of the input page, and which can be used by visualization interfaces as well as annotation interfaces. We evaluate our algorithm on a large dataset built upon the first volume of the “Enciclopedia Treccani”.
Andrea Corbelli; Lorenzo Baraldi; Fabrizio Balducci; Costantino Grana; Rita Cucchiara. Layout Analysis and Content Classification in Digitized Books. Communications in Computer and Information Science 2017, 153 -165.
AMA StyleAndrea Corbelli, Lorenzo Baraldi, Fabrizio Balducci, Costantino Grana, Rita Cucchiara. Layout Analysis and Content Classification in Digitized Books. Communications in Computer and Information Science. 2017; ():153-165.
Chicago/Turabian StyleAndrea Corbelli; Lorenzo Baraldi; Fabrizio Balducci; Costantino Grana; Rita Cucchiara. 2017. "Layout Analysis and Content Classification in Digitized Books." Communications in Computer and Information Science , no. : 153-165.
Game science has become a research field, which attracts industry attention due to a worldwide rich sell-market. To understand the player experience, concepts like flow or boredom mental states require formalization and empirical investigation, taking advantage of the objective data that psychophysiological methods like electroencephalography (EEG) can provide. This work studies the affective ludology and shows two different game levels for Neverwinter Nights 2 developed with the aim to manipulate emotions; two sets of affective design guidelines are presented, with a rigorous formalization that considers the characteristics of role-playing genre and its specific gameplay. An empirical investigation with a brain–computer interface headset has been conducted: by extracting numerical data features, machine learning techniques classify the different activities of the gaming sessions (task and events) to verify if their design differentiation coincides with the affective one. The observed results, also supported by subjective questionnaires data, confirm the goodness of the proposed guidelines, suggesting that this evaluation methodology could be extended to other evaluation tasks.
Fabrizio Balducci; Costantino Grana; Rita Cucchiara. Affective level design for a role-playing videogame evaluated by a brain–computer interface and machine learning methods. The Visual Computer 2016, 33, 413 -427.
AMA StyleFabrizio Balducci, Costantino Grana, Rita Cucchiara. Affective level design for a role-playing videogame evaluated by a brain–computer interface and machine learning methods. The Visual Computer. 2016; 33 (4):413-427.
Chicago/Turabian StyleFabrizio Balducci; Costantino Grana; Rita Cucchiara. 2016. "Affective level design for a role-playing videogame evaluated by a brain–computer interface and machine learning methods." The Visual Computer 33, no. 4: 413-427.