Search

Your search keyword '"Sansone, Carlo"' showing total 981 results
Start Over Author "Sansone, Carlo"
981 results on '"Sansone, Carlo"'

302. REAL TIME DETECTION OF NOVEL ATTACKS BY MEANS OF DATA MINING TECHNIQUES.

Author

Chen, Chin-Sheng, Filipe, Joaquim, Seruca, Isabel, Cordeiro, José, Esposito, Marcello, Mazzariello, Claudio, Oliviero, Francesco, Romano, Simon Pietro, and Sansone, Carlo

Abstract

Rule-based Intrusion Detection Systems (IDS) rely on a set of rules to discover attacks in network traffic. Such rules are usually hand-coded by a security administrator and statically detect one or few attack types: minor modifications of an attack may result in detection failures. For that reason, signature based classification is not the best technique to detect novel or slightly modified attacks. In this paper we approach this problem by extracting a set of features from network traffic and computing rules which are able to classify such traffic. Such techniques are usually employed in off line analysis, as they are very slow and resource-consuming. We want to assess the feasibility of a detection technique which combines the use of a common signature-based intrusion detection system and the deployment of a data mining technique. We will introduce the problem, describe the developed architecture and show some experimental results to demonstrate the usability of such a system. [ABSTRACT FROM AUTHOR]

Published
2006
Full Text
View/download PDF

304. Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review

Author

Fusco, Roberta, Sansone, Mario, Filice, Salvatore, Carone, Guglielmo, Amato, Daniela, Sansone, Carlo, and Petrillo, Antonella

Abstract

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

Published
2016
Full Text
View/download PDF

305. A secure, scalable and versatile multi-layer client–server architecture for remote intelligent data processing

Author

Piantadosi, Gabriele, Marrone, Stefano, Sansone, Mario, and Sansone, Carlo

Abstract

In recent years, the need for data collection and analysis is growing in many scientific disciplines. This is consequently causing an increase of research in automated data management and data mining to create reliable methods for data analysis. To deal with the need for smart environments and big computational resources, some previous works proposed to address the problem by moving on remote processing, with the aim of sharing supercomputer resources, algorithms and costs. Following this trend, in this work we propose an architecture for advanced remote data processing in a secure, smart and versatile client–server environment that is capable of integrating pre-existing local software. In order to assess the feasibility of our proposal, we developed a case study in the context of an image-based medical diagnostic environment. Our tests demonstrated that the proposed architecture has several benefits: increase of the system throughput, easy upgradability, maintainability and scalability. Moreover, for the scenario we have considered, the system showed a very low transmission overhead which settles on about 2.5 % for the widespread 10/100 mbps. Security has been achieved using client–server certificates and up-to-date standards.

Published
2015
Full Text
View/download PDF

307. Legal Information Retrieval systems: State-of-the-art and open issues.

Author

Sansone, Carlo and Sperlí, Giancarlo

Subjects
*INFORMATION storage & retrieval systems, *LEGAL literature, *EXTRACTION techniques, *DATA mining, *INFORMATION retrieval
Abstract

In the last years, the legal domain has been revolutionized by the use of Information and Communication Technologies, producing large amount of digital information. Legal practitioners' needs, then, in browsing these repositories has required to investigate more efficient retrieval methods, that assume more relevance because digital information is mostly unstructured. In this paper we analyze the state-of-the-art of artificial intelligence approaches for legal domain, focusing on Legal Information Retrieval systems based on Natural Language Processing, Machine Learning and Knowledge Extraction techniques. Finally, we also discuss challenges – mainly focusing on retrieving similar cases, statutes or paragraph for supporting latest cases' analysis – and open issues about Legal Information Retrieval systems. • Legal Information Retrieval aims to model information search from legal practitioners. • LIR is an IR task to identify most suitable information w.r.t the input query. • LIR systems can be classified in NLP, Deep Learning and Ontology based techniques. • Several challenges are arising for Legal Information Extraction and Entailment. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

309. Transformers in the Real World: A Survey on NLP Applications.

Author

Patwardhan, Narendra, Marrone, Stefano, and Sansone, Carlo

Subjects
*TRANSFORMER models, *NATURAL language processing
Abstract

The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP. In this survey, we review the open-access and real-world applications of transformers in NLP, specifically focusing on those where text is the primary modality. Our goal is to provide a comprehensive overview of the current state-of-the-art in the use of transformers in NLP, highlight their strengths and limitations, and identify future directions for research. In this way, we aim to provide valuable insights for both researchers and practitioners in the field of NLP. In addition, we provide a detailed analysis of the various challenges faced in the implementation of transformers in real-world applications, including computational efficiency, interpretability, and ethical considerations. Moreover, we highlight the impact of transformers on the NLP community, including their influence on research and the development of new NLP models. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

310. Cooperative 3D Air Quality Assessment with Wireless Chemical Sensing Networks.

Author

De Vito, Saverio, Fattoruso, Grazia, Liguoro, Raffaele, Oliviero, Antonio, Massera, Ettore, Sansone, Carlo, Casola, Valentina, and Di Francia, Girolamo

Abstract

Abstract: Indoor Air Quality assessment is an emerging application field for chemical sensing due to raising concerns about indoor VOC pollution levels. Local and distributed assessment of chemicals concentrations is also significant for safety (gas spills detection, pollution monitoring) and security applications as well as for HVAC automation for energy efficiency. Mobile robot based and wireless sensor network based approaches are under investigation for providing efficient solutions. Here, we report results obtained by a network of wireless intelligent electronic noses in a semi-controlled environment for the detection of pollutants in complex mixtures. The w-noses are equipped with local discrimination and quantification capabilities sustained by trained artificial neural networks overcoming interferents issues. 3D chemicals concentration reconstruction is obtained by a sensor fusion algorithm at datasink on the basis of the w-noses responses. [Copyright &y& Elsevier]

Published
2012
Full Text
View/download PDF

311. A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI.

Author

Galli, Antonio, Marrone, Stefano, Piantadosi, Gabriele, Sansone, Mario, and Sansone, Carlo

Subjects
IMAGE segmentation, MAGNETIC resonance mammography, DEEP learning, BREAST cancer, ACQUISITION of data
Abstract

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a "naive" use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new "Eras/Epochs" training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

312. A MULTI-EXPERT SYSTEM FOR SHOT CHANGE DETECTION IN MPEG MOVIES.

Author

de Santo, Massimo, Percannella, Gennaro, Sansone, Carlo, and Vento, Mario

Subjects
MPEG (Video coding standard), VIDEO compression standards, DIGITAL video standards, ARTIFICIAL intelligence, PATTERN recognition systems, DATABASES
Abstract

Shot Change Detection (SCD) in MPEG coded videos is a complex and still open research problem whose interest is growing up more and more due to the diffusion of Video Databases and Digital Libraries. Techniques providing fully satisfactory performances on complex video domains are not yet available even if a number of proposals exist; such proposals show very often to be complementary in their results. In this context, the Authors investigated the use of Multi-Expert Systems (MES) for approaching the SCD problem. In the present paper, we propose and discuss a strategy to select the SCD techniques to be combined and a method for choosing an effective combining rule. In order to assess the performance of the proposed MES, we set up a database that is significantly wider than the ones commonly used in the field. Experimental results demonstrate that the proposed system performs better than each of the single SCD technique considered. [ABSTRACT FROM AUTHOR]

Published
2004
Full Text
View/download PDF

313. To Reject or Not to Reject: That is the Question--an Answer in Case of Neural Classifiers.

Author

De Stefano, Claudio and Sansone, Carlo

Subjects
*CLASSIFICATION, *ARTIFICIAL neural networks
Abstract

Proposes a method defining a reject option applicable to a give 0-reject neural network classifiers. Reject option based on an estimate of the classification reliability; Introduction of a function that characterizes the reject option's adequacy to the domain; Criteria for defining the appropriate evaluators within the classification paradigm.

Published
2000
Full Text
View/download PDF

314. Fire and smoke detection from videos: A literature review under a novel taxonomy.

Author

Gragnaniello, Diego, Greco, Antonio, Sansone, Carlo, and Vento, Bruno

Subjects
*LITERATURE reviews, *METADATA, *ACQUISITION of data, *FLAME, *FIREFIGHTING
Abstract

The recent development of deep learning based fire detection techniques and the availability of smart cameras able to execute these algorithms on the edge paved the way for sophisticated and efficient video-based firefighting systems. However, the limited available data to train these algorithms cast shadows on their robustness and generalization capability. In this survey, we review 153 papers published in the literature and 17 publicly available fire detection datasets with the aim of identifying application scenarios that better describe real-world fire detection challenges. In the proposed taxonomy, these are characterized by two features: i) the fire size in the framed scene that depends on several parameters, foremost the distance from the fire but also the camera optic; ii) the background activity, due to the presence of moving objects that may mislead the detector. On this basis, we analyzed the existing methods under a common scheme according to this new taxonomy and matched the solutions with the needs of specific application scenarios. Similarly, for 9 interesting video datasets acquired from cameras, we labeled 536 videos according to the proposed taxonomy and shared these annotations with the community. The aim of this fire detection review is two-fold: on one hand, we classify the existing scientific works according to the real application scenarios, determining the features that are promising in specific operative conditions; on the other hand, we provide a detailed analysis and annotation of available datasets to promote the development of more reliable validation protocols and the collection of data from missing scenarios. • We propose an application-oriented taxonomy of video-based fire detection approaches. • We analyzed more than 130 works proposing techniques for fire or smoke detection. • We highlight the key features that make a method suitable for a specific scenario. • We share metadata for public datasets to classify each video in a specific scenario. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

320. Empowering Cybersecurity: CyberShield AI Advanced Integration of Machine Learning and Deep Learning for Dynamic Ransomware Detection

Author

Ali, Sijjad, Ali, Asad, Uzair, Muhammad, Amir, Hamza, Bari, Rana Zaki Abdul, Sharif, Hamid, Jamil, Maryam, Hunza, M., Akram, Nabel, Allaberdiev, Sharofiddin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

321. Time Series Prediction for Anomalies Detection in Concentrating Solar Power Plants Using Long Short-Term Memory Networks

Author

Olbrych, Sylwia, Jungnickel, Robert, Zeng, Michael, Tan, Cher Dao, Kemmerling, Marco, Abdelrazeq, Anas, Schmitt, Robert H., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

322. OBBabyFace: Oriented Bounding Box for Infant Face Detection

Author

Reyes-Hernández, José Carlos, Alomar, Antonia, Rubio, Ricardo, Piella, Gemma, Sukno, Federico, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

328. Geometrical Realization for Time Series Forecasting

Author

Bayeh, Ali, Mouhoub, Malek, Sadaoui, Samira, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

331. Deep Learning for Cattle Face Identification

Author

Dede, Sinan, Vrochidou, Eleni, Kanakaris, Venetis, Papakostas, George A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

336. CNN-N-BEATS: Novel Hybrid Model for Time-Series Forecasting

Author

Aiwansedo, Konstandinos, Bosche, Jérôme, Badreddine, Wafa, Kermia, M. H., Djadane, Oussama, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

339. Version 8 of YOLO for Wildfire Detection

Author

Abreu, César, Nunes, Creison, Lisboa, Adriano, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

342. Detecting Flow via a Machine Learning Model in a MOOC Context

Author

Ramírez Luelmo, Sergio Iván, El Mawas, Nour, Bachelet, Rémi, Heutte, Jean, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

343. Self-supervised Learning for Robust Surface Defect Detection

Author

Aqeel, Muhammad, Sharifi, Shakiba, Cristani, Marco, Setti, Francesco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

346. Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0

Author

Bendaouia, Ahmed, Abdelwahed, El Hassan, Qassimi, Sara, Boussetta, Abdelmalek, Benzakour, Intissar, Ahricha, Mustapha, Amar, Oumkeltoum, Bourzeix, François, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

348. Towards Natural-Sounding Speech to Text in English

Author

Saulitis, Kriss, Urtans, Evalds, Caune, Vairis, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

350. Few-Shot Learning with Novelty Detection

Author

Bjerge, Kim, Bodesheim, Paul, Karstoft, Henrik, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Hadjali, Allel, editor, Gusikhin, Oleg, editor, and Sansone, Carlo, editor

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results