1,720,968 research outputs found
Neuro-Symbolic Integration in Artificial Intelligence and its Applications
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Neural-Symbolic System for Predicting COVID-19 Positivity
No full textThanks to the huge amount of data collected by hospitals, it is now possible to exploit Machine
Learning (ML) to build predictive models that can learn from data for identifying medical
pathologies. The potential of Deep Learning (DL) and ML algorithms are well known but, in a field
such as medicine, it is necessary to build interpretable and explainable systems instead of black-box
systems as the de facto in DL. This work applies those techniques to both clinical data and Computed
Tomography (CT) scans to predict COVID-19 positivity. To achieve an explainable model, we used
both neural systems, for classifying and analyzing CT scans images, a symbolic model, Decision
Tree, for analyzing clinical data concerning patients and a Neural-Symbolic architecture that
integrates both systems using Hierarchical Probabilistic Logic Programming (HPLP). Experiments
confirm that the proposed system provides a prediction accuracy of almost 90% and is able to
provide explanation of the classifications
A Machine Learning Pipeline to Analyse Multispectral and Hyperspectral Images: Full/Regular Research Paper (CSCI-RTHI)
No full textMachine Learning is a branch of Artificial Intelligence with the goal of learning patterns from data. These techniques fall into two big categories: supervised and unsupervised learning. The former classify data based on a given set of examples whose classification is known (hence the name supervised), while the latter try to group the data without knowing a priori the possible classes. Neural Networks and clustering algorithms are two of the most prominent examples of the two aforementioned categories. In this paper, we describe a machine learning pipeline to analyse multispectral and hyperspectral images. Our approach first adopts neural networks to identify relevant pixels and then applies a clustering algorithm to group the pixels according to two different criteria, namely intensity and variation of intensity
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
No full textEver-changing cyber threats require strong and flexible network security solutions. This paper suggests a new method to improve the performance of detecting both known and unknown attacks using a neuro-symbolic artificial intelligence (NSAI) network intrusion detection system (NIDS). Deep neural networks (DNN) learn complex network data patterns, which create a detailed overview of cyber-attack characteristics. Symbolic logic integration into the DNN allows for model training guidance by applying penalties when the DNN fails to differentiate between malicious and benign network traffic. This improves our model’s adaptability to new attacks and overcomes traditional signature-based NIDS limitations. By testing our NSAI NIDS on a large cyber dataset that includes novel attack scenarios, we show that it delivers an improvement in how accurately it detects attacks compared to traditional DNN methods. While our system maintains its high accuracy in recognizing known attacks, it outperforms conventional NIDS in discovering unknown attacks. This work improves cybersecurity by introducing a new way to detect both known and unknown network intrusions by combining DNNs with symbolic logic
Integration of Deep Generative Anomaly Detection Algorithm in High-Speed Industrial Line
Full text linkIntegration between constrained optimization and deep networks: a survey
Full text linkIntegration between constrained optimization and deep networks has garnered significant interest from both research and industrial laboratories. Optimization techniques can be employed to optimize the choice of network structure based not only on loss and accuracy but also on physical constraints. Additionally, constraints can be imposed during training to enhance the performance of networks in specific contexts. This study surveys the literature on the integration of constrained optimization with deep networks. Specifically, we examine the integration of hyper-parameter tuning with physical constraints, such as the number of FLOPS (FLoating point Operations Per Second), a measure of computational capacity, latency, and other factors. This study also considers the use of context-specific knowledge constraints to improve network performance. We discuss the integration of constraints in neural architecture search (NAS), considering the problem as both a multi-objective optimization (MOO) challenge and through the imposition of penalties in the loss function. Furthermore, we explore various approaches that integrate logic with deep neural networks (DNNs). In particular, we examine logic-neural integration through constrained optimization applied during the training of NNs and the use of semantic loss, which employs the probabilistic output of the networks to enforce constraints on the output
Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients
Full text linkRecently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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