Search results for: Sabina-Adriana Floria

Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

Abstract:

Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

Procedia PDF Downloads 80

Authors: Mariana Floria, Elena-Diana Năfureanu, Diana-Mihaela Gălăţanu, Anca-Ecaterina Grumeza, Cristina Gorea-Bocîncă, Diana-Elena Iov, Aurelian-Corneliu Moraru, Dragoș-Marian Popescu

Abstract:

SARS-CoV-2 is the most deadly and devastating virus of the last one hundred years, being more highly contagious than EBOLA, HIV, Swine Influenza, Severe Acute Respiratory Syndrome, or Middle Eastern Respiratory Syndrome. After two years of pandemic, planning and budgeting for use of healthcare resources and services is very important. The aim of this study was to analyze the costs for hospital stay in patients with predominantly moderate forms of COVID-19 in a support military hospital located in Nord-East of Romania. Inpatient COVID-19 hospitalizations costs, regardless of ICD-10 procedure codes (DRG payment), in a Covid-19 support military hospital were analyzed. From August 2020 through June 2021, 241 patientswere hospitalized. Our national protocol for the treatment of Covid-19 infection was applied. The main COVID-19 manifestations were: 69% respiratory (18% with severe pneumonia, 2.9% with pulmonary embolism, diagnosed by angio-computed tomography), 3.3% cardiac, 28% digestive, and 33% psychiatric (most common anxiety) manifestations. According to COVID-19 severity, most of the patients had moderate (104 patients – 43%) and severe (50 patients - 21%) forms. Seven patients with severe form died because of multiple comorbidities, and 30 patients were transferred in hospitals with COVID-19 intensive care units.Only two patients have had procalcitonin>10 ng/mL (high probability of severe sepsis or septic shock), and 1 patient had moderate risk for septic shock (0.5 - 2 ng/mL). The average estimated costs were about 3000€/patient, without significantly differences depending on disease severity. Equipment costs were 2 times higher than for drugs and 4 times than for laboratory tests. In a Covid-19 support military hospital that took care for predominantly moderate forms of COVID-19, the costs for equipment were much higher than that for treatment. Therefore, new criteria for hospitalization of these forms of COVID-19 deserve to be analyzed to avoid useless costs.

Keywords: Covid-19, costs, hospital stay, military hospital

Procedia PDF Downloads 176

Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

Abstract:

Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

Procedia PDF Downloads 115

Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

Abstract:

Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

Procedia PDF Downloads 107