0000000001039783
AUTHOR
G. Lo Bosco
Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks
Abstract The Common Fisheries Policy of the European Union aims to exploit fish stocks at a level of Maximum Sustainable Yield by 2020 at the latest. At the Mediterranean level, the General Fisheries Commission for the Mediterranean (GFCM) has highlighted the importance of reversing the observed declining trend of fish stocks. In this complex context, it is important to obtain reliable biomass estimates to support scientifically sound advice for sustainable management of marine resources. This paper presents a machine learning methodology for the classification of pelagic species schools from acoustic and environmental data. In particular, the methodology was tuned for the recognition of an…
Distributed image retrieval on DAISY
The paper describes an application of image retrieval based on DAISY architecture (distributed architecture for intelligent system). The creation of pictorial indexes may require a number of hours depending on the size of the pictorial data base. The problem can become more complex in the case of distributed database systems. In both cases a distributed architecture can be the natural and more efficient solution. DAISY architecture is based on the concept of co-operating behavioral agents supervised by a central engagement module. Preliminary experiments, to evaluate the performance of the system, have been performed on a astronomical database and coral image
HEART MOBILE LEARNING
The widespread diffusion of mobile technologies in today’s society and the technological developments of recent years offers new opportunities for learning providing innovative techniques and tools in education. In this paper, we will introduce HeARt, an augmented reality mobile Learning system to support university medical students in their learning activities during an anatomy laboratory. Students usually use, in their daily anatomy laboratory, a physical human heart model to investigate and learn about heart anatomy. Even though these models are perfect education tools to observe details and touch "with hands" all the heart sections, they need a supplementary encyclopaedia to learn all h…
A distributed architecture for autonomous navigation of robots
The paper shows a distributed architecture for autonomous robot navigation. The architecture is based on three modules that are implemented on separate and interacting agents: the target recognizer, the obsta90cle evaluator and the planner. An adaptive genetic algorithm has been studied to identify mechanisms for reaching the target and for manipulating the 2-directions of the robot; the distributed architecture has been embedded in the DAISY (Distributed Architecture for Intelligent System). Experiments have been carried out using a LEGO intelligent brick.
Classification based on Iterative Object Symmetry Transform
The paper shows an application of a new operator named the iterated object transform (IOT) for cell classification. The IOT has the ability to grasp the internal structure of a digital object and this feature can be usefully applied to discriminate structured images. This is the case of cells representing chondrocytes in bone tissue, giarda protozoan, and myeloid leukaemia. A tree classifier allows us to discriminate the three classes with a good accuracy.
Intruder Pattern Identification
This paper considers the problem of intrusion detection in information systems as a classification problem. In particular the case of masquerader is treated. This kind of intrusion is one of the more difficult to discover because it may attack already open user sessions. Moreover, this problem is complex because of the large variability of user models and the lack of available data for the learning purpose. Here, flexible and robust similarity measures, suitable also for non-numeric data, are defined, they will be incorporated on a one-class training K N N and compared with several classification methods proposed in the literature using the Masquerading User Data set (www.schonlau.net) repr…
A genetic algorithm for image segmentation
The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
DNA sequences are the basic data type that is processed to perform a generic study of biological data analysis. One key component of the biological analysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. Machine Learning (ML) methodologies have given a fundamental contribution to the solution of the problem. Among them, recently, also Deep Neural Network (DNN) models have shown strongly encouraging results. In this chapter, we deal with specific classification problems related to t…
Fuzzy Clustering of Histopathological Images Using Deep Learning Embeddings
Metric learning is a machine learning approach that aims to learn a new distance metric by increas- ing (reducing) the similarity of examples belonging to the same (different) classes. The output of these approaches are embeddings, where the input data are mapped to improve a crisp or fuzzy classifica- tion process. The deep metric learning approaches regard metric learning, implemented by using deep neural networks. Such models have the advantage to discover very representative nonlinear embed- dings. In this work, we propose a triplet network deep metric learning approach, based on ResNet50, to find a representative embedding for the unsupervised fuzzy classification of benign and maligna…
Experiments on a Prey Predators System
The paper describes a prey-predators system devoted to perform experiments on concurrent complex environment. The problem has be treated as an optimization problem. The prey goal is to escape from the predators reaching its lair, while predators want to capture the prey. At the end of the 19th century, Pareto found an optimal solutions for decision problems regarding more than one criterion at the same time. In most cases this ‘Pareto-set’ cannot be determined analytically or the computation time could be exponential. In such cases, evolutionary Algorithms (EA) are powerful optimization tools capable of finding optimal solutions of multi-modal problems. Here, both prey and predators learn i…
PGAC: A Parallel Genetic Algorithm for Data Clustering
Cluster analysis is a valuable tool for exploratory pattern analysis, especially when very little a priori knowledge about the data is available. Distributed systems, based on high speed intranet connections, provide new tools in order to design new and faster clustering algorithms. Here, a parallel genetic algorithm for clustering called PGAC is described. The used strategy of parallelization is the island model paradigm where different populations of chromosomes (called demes) evolve locally to each processor and from time to time some individuals are moved from one deme to another. Experiments have been performed for testing the benefits of the parallelisation paradigm in terms of comput…
DAISY: a distributed architecture for intelligent SYstem
Distributed perceptual systems are endowed with different kind of sensors, from which information flows to suitable modules to perform useful elaborations for decisions making. In this paper a new distributed architecture, named 'Distributed Architecture for Intelligent SYstem' (DAISY), is proposed. It is based on the concept of co-operating behavioral agents supervised by a 'Central Engagement Module'. This module integrates the processing of data coming from the behavioral agents with a symbolic level of representation, by the introduction of a 'conceptual space' intermediate analogue representation. The DAISY project is under development; experiments on navigation and exploration for an …
The Three Steps of Clustering in the Post-Genomic Era: A Synopsis
Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. Following Handl et al., it can be summarized as a three step process: (a) choice of a distance function; (b) choice of a clustering algorithm; (c) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes cluster analysis of genomic data particul…
A recurrent deep neural network model to measure sentence complexity for the Italian Language
Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS…
Higher Education Learning Methodologies and Technologies Online, 4th International Conference, HELMeTO 2022, Palermo, Italy, September 21–23, 2022, Revised Selected Papers
This volume of Communications in Computer and Information Science (CCIS) contains the post-proceedings of HELMeTO 2022, the fourth International Conference on Higher Education Learning Methodologies and Technologies Online, which took place during September 21–23, 2022 in Palermo, Italy