Search results for " image processing."

An Agent-Based Model of Extortion Racketeering

2016

Mafias can be considered as criminal organisations that are in the business of producing, promoting, and selling protection. Here, we describe the Palermo Scenario, an agent-based model of protection rackets aimed to deepen our understanding of protection rackets, and help policymakers to evaluate methods for destabilising them. Additionally, since the system is explicitly specified, we can use it to investigate the entire causal pathway from cause to effect: not only from actions to Mafia destabilisation, but also the intermediate actions along the path and actors' internal mental representations among the population.

Characterization of the consistent completion of analytic hierarchy process comparison matrices using graph theory

2018

GRASP with exterior path-relinking and restricted local search for the multidimensional two-way number partitioning problem

2017

In this work, we tackle multidimensional two-way number partitioning (MDTWNP) problem by combining GRASP with Exterior Path Relinking. In the last few years, the combination of GRASP with path relinking (PR) has emerged as a highly effective tool for finding high-quality solutions for several difficult problems in reasonable computational time. However, in most of the cases, this hybridisation is limited to the variant known as interior PR. Here, we couple GRASP with the "exterior form" of path relinking and perform extensive experimentation to evaluate this variant. In addition, we enhance our GRASP with PR method with a novel local search method specially designed for the MDTWNP problem. …

Sign Languages Recognition Based on Neural Network Architecture

2017

In the last years, many steps forward have been made in speech and natural languages recognition and were developed many virtual assistants such as Apple’s Siri, Google Now and Microsoft Cortana. Unfortunately, not everyone can use voice to communicate to other people and digital devices. Our system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.

A Paradigm Interpreting the City and the Analytic Network Process for the Management of Urban Transformations

2018

When urban and environmental transformations occur in areas where the equilibrium between nature and culture is complex and fragile, public ad-ministrations could decide to induce private investments using several tools, such as financial contributions to those projects of refurbishment that better re-spect the purpose of improving the environmental quality and of preserving the local architecture. Multicriteria models may support public decision process re-garding this issue, but it is essential to adopt a scientific paradigm that provides a major theoretical reference. This study proposes the development of a net-work model based on the scientific paradigm by Rizzo and the Analytic Net-wo…

Ancestral Reconstruction and Investigations of Genomic Recombination on some Pentapetalae Chloroplasts

2019

Abstract In this article, we propose a semi-automated method to rebuild genome ancestors of chloroplasts by taking into account gene duplication. Two methods have been used in order to achieve this work: a naked eye investigation using homemade scripts, whose results are considered as a basis of knowledge, and a dynamic programming based approach similar to Needleman-Wunsch. The latter fundamentally uses the Gestalt pattern matching method of sequence matcher to evaluate the occurrences probability of each gene in the last common ancestor of two given genomes. The two approaches have been applied on chloroplastic genomes from Apiales, Asterales, and Fabids orders, the latter belonging to Pe…

An environment based approach for the ant colony convergence

2020

Abstract Ant colony optimization (ACO) algorithms are a bio inspired solutions which have been very successful in combinatorial problem solving, also known as NP-hard problems, including transportation system optimization. As opposed to exact methods, which could give the best results of a tested problem, this meta-heuristics is based on the stochastic logic but not on theoretical mathematics demonstration (or only on certain well defined applications). According to this, the weak point of this meta-heuristics is his convergence, its termination condition. We can finds many different termination criteria in the scientific literature, yet most of them are costly in resources and unsuitable f…

Lensless object scanning holography for two-dimensional mirror-like and diffuse reflective objects

2013

Recently proposed lensless object scanning holography (LOSH) [Opt. Express 20, 9382 (2012)] is a fully lensless method capable of improving the image quality in digital Fourier holography applied to one-dimensional (1D) reflective objects and it involves a very simplified experimental setup. LOSH is based on the recording and digital postprocessing of a set of digital lensless Fourier transform holograms, which finally results in a synthetic image with improved resolution, field-of-view (FOV), signal-to-noise ratio (SNR), and depth of field. In this paper, LOSH is extended to the cases of two-dimensional (2D) mirror-like and 1D diffuse-based objects. For 2D mirror-like objects, the experime…

Computer-aided-diagnosis for ocular abnormalities from a single color fundus photography with deep learning

2023

Any damage to the retina can lead to severe consequences like blindness. This visual impairment is preventable by early detection of ocular abnormalities. Computer-aided diagnosis (CAD) for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). The main objectives of this thesis are to build two CAD models, one to detect the microaneurysms (MAs), the first visible symptom of diabetic retinopathy, and the other for multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using deep learning-based approaches. Two methods were proposed for MAs detection: ensemble-based and c…

Sequential Mining Classification

2017

Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …