Andrea Manno

University of L'Aquila | Università dell'Aquila · Department of Information Engineering, Computer Science and Mathematics

We propose an ensemble artificial neural network (EANN) methodology for predicting the day ahead energy demand of a district heating operator (DHO). Specifically, at the end of one day, we forecast the energy demand for each of the 24 h of the next day. Our methodology combines three artificial neural network (ANN) models, each capturing a differen...

Background Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an additional tool to improve cancer diagnosis, particularly for tumor...

Decision trees are widely-used classification and regression models because of their interpretability and good accuracy. Classical methods such as CART are based on greedy approaches but a growing attention has recently been devoted to optimal decision trees. We investigate the nonlinear continuous optimization formulation proposed in Blanquero et...

Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Machine Learning deep architecture known as Echo State N...

The forecasts of electricity and heating demands are key inputs for the efficient design and operation of energy systems serving urban districts, buildings, and households. Their accuracy may have a considerable effect on the selection of the optimization approach and on the solution quality. In this work, we describe a supervised learning approach...

This paper proposes a novel optimization algorithm for constrained black-box problems, where the objective function and some constraints are computed by a simulation code. The basic idea of the optimization algorithm, referred to as SCR (Surrogate-CMAESRQLIF), is to (i) build separate Kriging surrogates for the objective function and black-box cons...

Decision trees are widely-used classification and regression models because of their interpretability and good accuracy. Classical methods such as CART are based on greedy approaches but a growing attention has recently been devoted to optimal decision trees. We investigate the nonlinear continuous optimization formulation proposed in Blanquero et...

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SU...

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMP...

Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Deep Learning method known as Echo State Networks, is co...

A variety of engineering applications are tackled as black-box optimization problems where a computationally expensive and possibly noisy function is optimized over a continuous domain. In this paper we present a derivative-free local method which is well-suited for such problems, and we describe its application to the optimization of the start-up...

Relying on multiobjective programming techniques, we have developed an optimization software to improve the services provided by the social co-operative OMNIA. OMNIA’s mission is to supply home-care assistance to children and people with disabilities. Our method is intended to optimize the social workers’ shift planning aiming at, on the one hand,...

We present a mixed integer nonlinear mathematical programming model, covering a broad range of operations research (OR)-related topics. The case is designed to allow students to use knowledge acquired from OR and management science classes to model, analyze, and provide concrete solutions for the considered problem. Because of its high practicality...

In Europe, computation of displacement demand for seismic assessment of existing buildings is essentially based on a simplified formulation of the N2 method as prescribed by Eurocode 8 (EC8). However, a lack of accuracy of the N2 method in certain conditions has been pointed out by several studies. This paper addresses the assessment of effectivene...

We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines. We propose a decomposition algorithmic scheme suitable to parallel implementations and we prove global convergence under suitable c...

This paper addresses seismic vulnerability assessment at urban scale. Particularly, it focuses on the differences in damage distribution obtained from the application of several simplified methods for displacement demand determination. The results obtained for two cities of Switzerland (Sion and Martigny) highlight the related impact. Displacement...

This paper addresses seismic vulnerability assessment at an urban scale by focusing on the displacement demand determination for building damage prediction. The study is based on the comparison of urban seismic damage distributions obtained by the displacement demand computed using non-linear time-history analysis (NLTHA) with three simplified meth...

In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well known, the learning problem of MLPs can be a difficult nonlinear nonconvex optimization problem. Typical difficulties can be the presence of extensive flat regions and steep sided valleys in the e...

The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms grow rapidly with the increase of the data. To overcome these drawbacks, we propose a parallel decomposition algorithmic scheme for SVMs training for which we prove global...

The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms grow rapidly with the increase of the data. To overcome these drawbacks a lot of parallel algorithms have been implemented, but they lack of convergence prop-erties. In th...