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Class imbalance problems have attracted the research community, but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an AdaBoost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization, and AdaBoost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of AdaBoost.
Rekha Gillala; Krishna Reddy Vuyyuru; Chandrashekar Jatoth; Ugo Fiore. An efficient chaotic salp swarm optimization approach based on ensemble algorithm for class imbalance problems. Soft Computing 2021, 1 -11.
AMA StyleRekha Gillala, Krishna Reddy Vuyyuru, Chandrashekar Jatoth, Ugo Fiore. An efficient chaotic salp swarm optimization approach based on ensemble algorithm for class imbalance problems. Soft Computing. 2021; ():1-11.
Chicago/Turabian StyleRekha Gillala; Krishna Reddy Vuyyuru; Chandrashekar Jatoth; Ugo Fiore. 2021. "An efficient chaotic salp swarm optimization approach based on ensemble algorithm for class imbalance problems." Soft Computing , no. : 1-11.
Advances in IoT, AI, Cyber-Physical Systems, Computational Intelligence, and Big Data Analytics require organizations and workforce to be able and willing to learn how to interact with digital technology. In organizations, coordination and cooperation between actors with expertise in business and technology is fundamental, but integration is hard without understanding the terminology and problems of the interlocutor. Epistemic proximity becomes prominent, underlining the importance of an education focused on flexibility, willingness to cope with the unknown, and interdisciplinarity. The main goal of this work is to provide a perspective on how the education system is evolving to support organizations in the digitization era through a quantitative analysis of literature. More than 170,000 papers were selected from the Scopus database, matching a wide set of keywords related with innovation, problem solving, and organizational change. Patterns in the co-occurrence of keywords were studied. In addition, similarities and differences in the distribution of relevant themes across disciplinary areas, as well as their evolution since 2000, were analyzed. Academic interest is found to be generally increasing over the years in all disciplines, although considerable fluctuations can be observed. This variation is found to be nonuniform in the macroareas.
Ugo Fiore; Adrian Florea; Claudiu Kifor; Paolo Zanetti. Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis. Journal of Risk and Financial Management 2021, 14, 267 .
AMA StyleUgo Fiore, Adrian Florea, Claudiu Kifor, Paolo Zanetti. Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis. Journal of Risk and Financial Management. 2021; 14 (6):267.
Chicago/Turabian StyleUgo Fiore; Adrian Florea; Claudiu Kifor; Paolo Zanetti. 2021. "Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis." Journal of Risk and Financial Management 14, no. 6: 267.
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way.
Avik Dutta; Chandrashekar Jatoth; G. R. Gangadharan; Ugo Fiore. QoS‐aware big service composition using distributed co‐evolutionary algorithm. Concurrency and Computation: Practice and Experience 2021, 1 .
AMA StyleAvik Dutta, Chandrashekar Jatoth, G. R. Gangadharan, Ugo Fiore. QoS‐aware big service composition using distributed co‐evolutionary algorithm. Concurrency and Computation: Practice and Experience. 2021; ():1.
Chicago/Turabian StyleAvik Dutta; Chandrashekar Jatoth; G. R. Gangadharan; Ugo Fiore. 2021. "QoS‐aware big service composition using distributed co‐evolutionary algorithm." Concurrency and Computation: Practice and Experience , no. : 1.
Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.
Arpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences 2021, 11, 3278 .
AMA StyleArpad Gellert, Stefan-Alexandru Precup, Bogdan-Constantin Pirvu, Ugo Fiore, Constantin-Bala Zamfirescu, Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences. 2021; 11 (7):3278.
Chicago/Turabian StyleArpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. 2021. "An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems." Applied Sciences 11, no. 7: 3278.
Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. In this work we investigate the possibility to determine when the earthquake will take place. We formulate the problem as a multiple change-point detection in the time series. In particular, we refer to the multi-scale formulation described in Fryzlewicz (Ann Stat 46(6B): 3390–3421, 2018). In that paper a bottom-up hierarchical structure is defined. At each stage, multiple neighbor regions which are recognized to correspond to locally constant underlying signal are merged. Due to their multi-scale structure, wavelets are suitable as basis functions, since the coefficients of the representation contain local information. The preprocessing stage involves the discrete unbalanced Haar transform, which is a wavelet decomposition of one-dimensional data with respect to an orthonormal Haar-like basis, where jumps in the basis vectors do not necessarily occur in the middle of their support. The algorithm is tested on data from a well-characterized laboratory system described in Rouet-Leduc et al. (Geophys Res Lett 44(18): 9276–9282, 2017).
Stefania Corsaro; Pasquale Luigi De Angelis; Ugo Fiore; Zelda Marino; Francesca Perla; Mariafortuna Pietroluongo. Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data. Dynamics of Disasters 2021, 93 -100.
AMA StyleStefania Corsaro, Pasquale Luigi De Angelis, Ugo Fiore, Zelda Marino, Francesca Perla, Mariafortuna Pietroluongo. Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data. Dynamics of Disasters. 2021; ():93-100.
Chicago/Turabian StyleStefania Corsaro; Pasquale Luigi De Angelis; Ugo Fiore; Zelda Marino; Francesca Perla; Mariafortuna Pietroluongo. 2021. "Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data." Dynamics of Disasters , no. : 93-100.
This article presents the results of the start-up of continuous production of biohydrogen from cheese whey (CW) in an anaerobic filter (AF) and anaerobic fluidized bed (AFB) with a polyurethane carrier. Heat and acid pretreatments were used for the inactivation of hydrogen-scavengers in the inoculum (mesophilic and thermophilic anaerobic sludge). Acid pretreatment was effective for thermophilic anaerobic sludge to suppress methanogenic activity, and heat treatment was effective for mesophilic anaerobic sludge. Maximum specific yields of hydrogen, namely 178 mL/g chemical oxygen demand (COD) and 149 mL/g COD for AFB and AF, respectively, were obtained at the hydraulic retention time (HRT) of 4.5 days and organic load rate (OLR) of 6.61 kg COD/(m3 day). At the same time, the maximum hydrogen production rates of 1.28 and 1.9 NL/(L day) for AF and AFB, respectively, were obtained at the HRT of 2.02 days and OLR of 14.88 kg COD/(m3 day). At the phylum level, the dominant taxa were Firmicutes (65% in AF and 60% in AFB), and at the genus level, Lactobacillus (40% in AF and 43% in AFB) and Bifidobacterium (24% in AF and 30% in AFB).
Elza R. Mikheeva; Inna V. Katraeva; Andrey A. Kovalev; Dmitriy A. Kovalev; Alla N. Nozhevnikova; Vladimir Panchenko; Ugo Fiore; Yuri V. Litti. The Start-Up of Continuous Biohydrogen Production from Cheese Whey: Comparison of Inoculum Pretreatment Methods and Reactors with Moving and Fixed Polyurethane Carriers. Applied Sciences 2021, 11, 510 .
AMA StyleElza R. Mikheeva, Inna V. Katraeva, Andrey A. Kovalev, Dmitriy A. Kovalev, Alla N. Nozhevnikova, Vladimir Panchenko, Ugo Fiore, Yuri V. Litti. The Start-Up of Continuous Biohydrogen Production from Cheese Whey: Comparison of Inoculum Pretreatment Methods and Reactors with Moving and Fixed Polyurethane Carriers. Applied Sciences. 2021; 11 (2):510.
Chicago/Turabian StyleElza R. Mikheeva; Inna V. Katraeva; Andrey A. Kovalev; Dmitriy A. Kovalev; Alla N. Nozhevnikova; Vladimir Panchenko; Ugo Fiore; Yuri V. Litti. 2021. "The Start-Up of Continuous Biohydrogen Production from Cheese Whey: Comparison of Inoculum Pretreatment Methods and Reactors with Moving and Fixed Polyurethane Carriers." Applied Sciences 11, no. 2: 510.
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.
Anca Sipos; Adrian Florea; Maria Arsin; Ugo Fiore. Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process. Processes 2020, 9, 74 .
AMA StyleAnca Sipos, Adrian Florea, Maria Arsin, Ugo Fiore. Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process. Processes. 2020; 9 (1):74.
Chicago/Turabian StyleAnca Sipos; Adrian Florea; Maria Arsin; Ugo Fiore. 2020. "Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process." Processes 9, no. 1: 74.
Research on geological disasters has made several achievements in monitoring, early warning, and risk assessment. Substantial resources are being invested in prevention projects, but, due to geographical and demographical complexity, incompleteness of data, and small number of samples, a quantitative analysis on the number of geological disasters and the entity of investments in their prevention is a difficult problem. In this work, the relation is studied between the amount of resources invested in prevention and the number of geological disasters in subsequent years. The analysis is performed on historical data, using statistical methods and a LSTM recurrent neural network.
Ugo Fiore; Zelda Marino; Francesca Perla; Mariafortuna Pietroluongo; Salvatore Scognamiglio; And Paolo Zanetti. Effectiveness of Investments in Prevention of Geological Disasters. Dynamics of Disasters 2020, 101 -108.
AMA StyleUgo Fiore, Zelda Marino, Francesca Perla, Mariafortuna Pietroluongo, Salvatore Scognamiglio, And Paolo Zanetti. Effectiveness of Investments in Prevention of Geological Disasters. Dynamics of Disasters. 2020; ():101-108.
Chicago/Turabian StyleUgo Fiore; Zelda Marino; Francesca Perla; Mariafortuna Pietroluongo; Salvatore Scognamiglio; And Paolo Zanetti. 2020. "Effectiveness of Investments in Prevention of Geological Disasters." Dynamics of Disasters , no. : 101-108.
Increasing the efficiency of heat pump systems primarily used for heat supply to buildings is an important topic. This is especially true for systems constructed according to non-standard schemes and which use low-grade heat from various sources that are rarely considered for these purposes. Such studies require special, often expensive, data acquisition systems. In this paper, a low-cost computer-based monitoring system is presented. The monitoring system incorporates solutions which are new or seldom used. It is shown that modern semiconductor thermistors can replace commonly used platinum temperature sensors and thermocouples. A proposal for processing frequency output signals from sensors through an analog-to-digital converter and a way to reduce the number of required input channels are described. The monitoring system allows optimization of various types of heat-pump-based installations. The system has been used for quite a long time to monitor the operation of the heat pump installation using low-grade heat from a surface watercourse. With its help, the feasibility of using the previously proposed submersible floating heat exchanger is justified and the optimal scheme for its placement in the watercourse is determined.
Valeriy Kharchenko; Arseniy Sychov; Pasquale Luigi De Angelis; Ugo Fiore. Monitoring System of a Heat Pump Installation for Heating a Rural House Using Low-grade Heat from a Surface Watercourse. Journal of Sensor and Actuator Networks 2020, 9, 11 .
AMA StyleValeriy Kharchenko, Arseniy Sychov, Pasquale Luigi De Angelis, Ugo Fiore. Monitoring System of a Heat Pump Installation for Heating a Rural House Using Low-grade Heat from a Surface Watercourse. Journal of Sensor and Actuator Networks. 2020; 9 (1):11.
Chicago/Turabian StyleValeriy Kharchenko; Arseniy Sychov; Pasquale Luigi De Angelis; Ugo Fiore. 2020. "Monitoring System of a Heat Pump Installation for Heating a Rural House Using Low-grade Heat from a Surface Watercourse." Journal of Sensor and Actuator Networks 9, no. 1: 11.
Learning on imbalanced datasets, where one class is underrepresented, is problematic and important at the same time. On the one hand, a limited number of positive examples restricts the generalization ability of classifiers. On the other hand, often, the class of interest is such exactly because it is rare. The Synthetic Minority Oversampling TEchnique (SMOTE) is a preprocessing method that creates new synthetic examples by interpolating between neighboring instances. In this work, an enhancement to SMOTE is proposed, which characterizes synthetic instances as solutions of attraction‐repulsion problems among the neighboring data points. Experimental evaluation shows an improvement in the positive predictive power of classification.
Ugo Fiore. Minority oversampling based on the attraction‐repulsion Weber problem. Concurrency and Computation: Practice and Experience 2019, 32, 1 .
AMA StyleUgo Fiore. Minority oversampling based on the attraction‐repulsion Weber problem. Concurrency and Computation: Practice and Experience. 2019; 32 (18):1.
Chicago/Turabian StyleUgo Fiore. 2019. "Minority oversampling based on the attraction‐repulsion Weber problem." Concurrency and Computation: Practice and Experience 32, no. 18: 1.
Given their increasing diffusion, deep learning networks have long been considered an important subject on which teaching efforts should be concentrated, to support a fast and effective training. In addition to that role, the availability of rich data coming from several sources underlines the potential of neural networks used as an analysis tool to identify critical aspects, plan upgrades and adjustments, and ultimately improve learning experience. Analysis and forecasting methods have been widely used in this context, allowing policy makers, managers and educators to make informed decisions. The capabilities of recurring neural networks—in particular Long Short-Term Memory networks—in the analysis of natural language have led to their use in measuring the similarity of educational materials. Massive Online Open Courses provide a rich variety of data about the learning behaviors of online learners. The analysis of learning paths provides insights related to the optimization of learning processes, as well as the prediction of outcomes and performance. Another active area of research concerns the recommendation of suitable personalized, adaptive, learning paths, based on varying sources, including even the tracing of eye-path movements. In this way, the transition from passive learning to active learning can be achieved. Challenges and opportunities in the application of neural networks in the educational sector are presented.
Ugo Fiore. Neural Networks in the Educational Sector: Challenges and Opportunities. Balkan Region Conference on Engineering and Business Education 2019, 3, 332 -337.
AMA StyleUgo Fiore. Neural Networks in the Educational Sector: Challenges and Opportunities. Balkan Region Conference on Engineering and Business Education. 2019; 3 (1):332-337.
Chicago/Turabian StyleUgo Fiore. 2019. "Neural Networks in the Educational Sector: Challenges and Opportunities." Balkan Region Conference on Engineering and Business Education 3, no. 1: 332-337.
The adoption of Digital and Communication Technologies (DCTs) is a critical success factor in port industry. Several empirical evidence are demonstrating that advanced knowledge infrastructures support port efficiency and competitiveness, for example through Intelligent Transport Systems, such as sensors, actuators, and platforms. In this perspective, the paper evaluates the impact of investments in DCTs—i.e., interactive websites and social media marketing solutions—on port efficiency. To this end, we perform the non-parametric method of Data Envelopment Analysis, both in its crisp and fuzzy approaches, in order to account for the vague and imprecise nature of some data. Findings pinpoint that port efficiency is generally supported by DCT solutions, and for some ports the effect is particularly relevant. The outcomes provide managerial suggestions for port authorities, policy makers, and industrial practitioners to identify critical investments for improving port competitiveness.
Rosalia Castellano; Ugo Fiore; Gaetano Musella; Francesca Perla; Gennaro Punzo; Marcello Risitano; Annarita Sorrentino; Paolo Zanetti. Do Digital and Communication Technologies Improve Smart Ports? A Fuzzy DEA Approach. IEEE Transactions on Industrial Informatics 2019, 15, 5674 -5681.
AMA StyleRosalia Castellano, Ugo Fiore, Gaetano Musella, Francesca Perla, Gennaro Punzo, Marcello Risitano, Annarita Sorrentino, Paolo Zanetti. Do Digital and Communication Technologies Improve Smart Ports? A Fuzzy DEA Approach. IEEE Transactions on Industrial Informatics. 2019; 15 (10):5674-5681.
Chicago/Turabian StyleRosalia Castellano; Ugo Fiore; Gaetano Musella; Francesca Perla; Gennaro Punzo; Marcello Risitano; Annarita Sorrentino; Paolo Zanetti. 2019. "Do Digital and Communication Technologies Improve Smart Ports? A Fuzzy DEA Approach." IEEE Transactions on Industrial Informatics 15, no. 10: 5674-5681.
In the last years, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. However, in such a specific application domain, datasets available for training are strongly imbalanced, with the class of interest considerably less represented than the other. This significantly reduces the effectiveness of binary classifiers, undesirably biasing the results toward the prevailing class, while we are interested in the minority class. Oversampling the minority class has been adopted to alleviate this problem, but this method still has some drawbacks. Generative Adversarial Networks are general, flexible, and powerful generative deep learning models that have achieved success in producing convincingly real-looking images. We trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved. Experiments show that a classifier trained on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.
Ugo Fiore; Alfredo De Santis; Francesca Perla; Paolo Zanetti; Francesco Palmieri. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences 2019, 479, 448 -455.
AMA StyleUgo Fiore, Alfredo De Santis, Francesca Perla, Paolo Zanetti, Francesco Palmieri. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences. 2019; 479 ():448-455.
Chicago/Turabian StyleUgo Fiore; Alfredo De Santis; Francesca Perla; Paolo Zanetti; Francesco Palmieri. 2019. "Using generative adversarial networks for improving classification effectiveness in credit card fraud detection." Information Sciences 479, no. : 448-455.
Sensors and intelligent applications enabling smart vehicular traffic create an opportunity for improving the welfare of people, from the viewpoints of efficiency, sustainability, and social inclusivity. Like the opportunities, challenges of such an endeavour are multifaceted, including the scalable collection and processing of the hefty data volumes generated by sensors, and the coordinated operation between selfish agents. The purpose of this work is to survey recent literature with an emphasis on applications and a multidisciplinary eye, with the aim of stimulating discussion and reflection in the scientific communities. The principal application areas of smart traffic and smart mobility are discussed, synthesizing different perspectives. Many intriguing areas for future research exist besides those relative to connectivity, data fusion, and privacy. Some research challenges pertinent to sustainability, insurance, simulation and the handling of ambiguous information are highlighted.
Ugo Fiore; Adrian Florea; Gilberto Pérez Lechuga. An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges. Journal of Sensor and Actuator Networks 2019, 8, 13 .
AMA StyleUgo Fiore, Adrian Florea, Gilberto Pérez Lechuga. An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges. Journal of Sensor and Actuator Networks. 2019; 8 (1):13.
Chicago/Turabian StyleUgo Fiore; Adrian Florea; Gilberto Pérez Lechuga. 2019. "An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges." Journal of Sensor and Actuator Networks 8, no. 1: 13.
This paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show that a set-associative prediction table is more effective than a direct mapped table and that 86% of the configurations in the Pareto front use multiple values per load. In conclusion, our experiments show that integrating an SLVP module into a superscalar microarchitecture is hardware feasible; in comparison with the case without SLVP, performance is better, energy consumption is lower, and the temperatures inside the chip decreases, remaining below 75 °C.
Arpad Gellert; Adrian Florea; Ugo Fiore; Paolo Zanetti; Lucian Vintan. Performance and energy optimisation in CPUs through fuzzy knowledge representation. Information Sciences 2019, 476, 375 -391.
AMA StyleArpad Gellert, Adrian Florea, Ugo Fiore, Paolo Zanetti, Lucian Vintan. Performance and energy optimisation in CPUs through fuzzy knowledge representation. Information Sciences. 2019; 476 ():375-391.
Chicago/Turabian StyleArpad Gellert; Adrian Florea; Ugo Fiore; Paolo Zanetti; Lucian Vintan. 2019. "Performance and energy optimisation in CPUs through fuzzy knowledge representation." Information Sciences 476, no. : 375-391.
The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.
Arpad Gellert; Adrian Florea; Ugo Fiore; Francesco Palmieri; Paolo Zanetti. A study on forecasting electricity production and consumption in smart cities and factories. International Journal of Information Management 2019, 49, 546 -556.
AMA StyleArpad Gellert, Adrian Florea, Ugo Fiore, Francesco Palmieri, Paolo Zanetti. A study on forecasting electricity production and consumption in smart cities and factories. International Journal of Information Management. 2019; 49 ():546-556.
Chicago/Turabian StyleArpad Gellert; Adrian Florea; Ugo Fiore; Francesco Palmieri; Paolo Zanetti. 2019. "A study on forecasting electricity production and consumption in smart cities and factories." International Journal of Information Management 49, no. : 546-556.
The insurance regulatory regime introduced in the European Union by the "Solvency II" Directive 2009/138, that has become applicable on January 1, 2016, is aimed to safeguard policyholders and beneficiaries by requiring insurance undertakings to hold own funds able to cover losses, in excess to the expected ones, at the 99.5% confidence level, over a one-year period. In order to assess risks and evaluate the regulatory Solvency Capital Requirement undertakings should compute the probability distribution of the Net Asset Value - i.e., value of assets minus value of liabilities - over a one-year period, with a financially inspired market consistent approach. In life insurance, given the peculiarities of the contracts, the valuation of the Net Asset Value distribution requires a nested Monte Carlo simulation, which is extremely time consuming.Machine learning techniques are considered a promising candidate to reduce the computational burden of nested simulations. This work investigates the potential of well-established methods, such as Deep Learning Networks and Support Vector Regressors, when applied to the valuation of the Solvency Capital Requirement of participating life insurance polices, by empirically assessing their effectiveness and by comparing their efficiency and accuracy, also w.r.t. the "traditional" Least Squares Monte Carlo technique.The work aims also to contribute to the global process of renewal of the European insurance industry, where Solvency II has made the board of directors fully responsible for the choice of evaluation techniques and algorithmic processes, under the periodic monitoring of national supervisory authorities.
Gilberto Castellani; Ugo Fiore; Zelda Marino; Luca Passalacqua; Francesca Perla; Salvatore Scognamiglio; Paolo Zanetti. An Investigation of Machine Learning Approaches in the Solvency II Valuation Framework. SSRN Electronic Journal 2018, 1 .
AMA StyleGilberto Castellani, Ugo Fiore, Zelda Marino, Luca Passalacqua, Francesca Perla, Salvatore Scognamiglio, Paolo Zanetti. An Investigation of Machine Learning Approaches in the Solvency II Valuation Framework. SSRN Electronic Journal. 2018; ():1.
Chicago/Turabian StyleGilberto Castellani; Ugo Fiore; Zelda Marino; Luca Passalacqua; Francesca Perla; Salvatore Scognamiglio; Paolo Zanetti. 2018. "An Investigation of Machine Learning Approaches in the Solvency II Valuation Framework." SSRN Electronic Journal , no. : 1.
Quality of Service (QoS)-aware cloud service composition is one of the pivotal problems in cloud computing. With the seamless proliferation of cloud services, it becomes challenging to obtain an optimal cloud service for composition that satisfies a user's requirements. Many composition models available in the literature compose cloud services based on one or two QoS parameters of the candidate services without considering the complete set. These composition models do not consider the connectivity constraints between the candidate cloud services for satisfying a workflow/function in a service composition. In this paper, we present a novel Optimal Fitness Aware Cloud Service Composition using Modified Invasive Weed Optimization dealing with multiple QoS parameters and satisfying the balancing of QoS parameters and the connectivity constraints of cloud service composition. We evaluate the performance of our approach on a data set of real world cloud services, to select the best optimal fitness aware cloud service composition. By performing the parametric and non-parametric test at 1% level of significance, our proposed method is statistically more accurate than the other methods compared.
Chandrashekar Jatoth; G.R. Gangadharan; Ugo Fiore. Optimal fitness aware cloud service composition using modified invasive weed optimization. Swarm and Evolutionary Computation 2018, 44, 1073 -1091.
AMA StyleChandrashekar Jatoth, G.R. Gangadharan, Ugo Fiore. Optimal fitness aware cloud service composition using modified invasive weed optimization. Swarm and Evolutionary Computation. 2018; 44 ():1073-1091.
Chicago/Turabian StyleChandrashekar Jatoth; G.R. Gangadharan; Ugo Fiore. 2018. "Optimal fitness aware cloud service composition using modified invasive weed optimization." Swarm and Evolutionary Computation 44, no. : 1073-1091.
Ugo Fiore; Adrian Florea; Arpad Gellert; Lucian Vintan; Paolo Zanetti. Optimal Partitioning of LLC in CAT-enabled CPUs to Prevent Side-Channel Attacks. Computer Vision – ACCV 2010 2018, 115 -123.
AMA StyleUgo Fiore, Adrian Florea, Arpad Gellert, Lucian Vintan, Paolo Zanetti. Optimal Partitioning of LLC in CAT-enabled CPUs to Prevent Side-Channel Attacks. Computer Vision – ACCV 2010. 2018; ():115-123.
Chicago/Turabian StyleUgo Fiore; Adrian Florea; Arpad Gellert; Lucian Vintan; Paolo Zanetti. 2018. "Optimal Partitioning of LLC in CAT-enabled CPUs to Prevent Side-Channel Attacks." Computer Vision – ACCV 2010 , no. : 115-123.
Big services are the collection of interrelated services across virtual and physical domains for analyzing and processing big data. Big service composition is a strategy of aggregating these big services from various domains that addresses the requirements of a customer. Generally, a composite service is created from a repository of services where individual services are selected based on their optimal values of Quality of Service (QoS) attributes distinct to each service composition. However, the problem of producing a service composition with an optimal QoS value that satisfies the requirements of a customer is a complex and challenging issue, especially in a Big service environment. In this paper, we propose a novel MapReduce-based Evolutionary Algorithm with Guided Mutation that leads to an efficient composition of Big services with better performance and execution time. Further, the method includes a MapReduce-skyline operator that improves the quality of results and the process of convergence. By performing T-test and Wilcoxon signed rank test at 1% level of significance, we observed that our proposed method outperforms other methods
Chandrashekar Jatoth; G.R. Gangadharan; Ugo Fiore; Rajkumar Buyya. QoS-aware Big service composition using MapReduce based evolutionary algorithm with guided mutation. Future Generation Computer Systems 2018, 86, 1008 -1018.
AMA StyleChandrashekar Jatoth, G.R. Gangadharan, Ugo Fiore, Rajkumar Buyya. QoS-aware Big service composition using MapReduce based evolutionary algorithm with guided mutation. Future Generation Computer Systems. 2018; 86 ():1008-1018.
Chicago/Turabian StyleChandrashekar Jatoth; G.R. Gangadharan; Ugo Fiore; Rajkumar Buyya. 2018. "QoS-aware Big service composition using MapReduce based evolutionary algorithm with guided mutation." Future Generation Computer Systems 86, no. : 1008-1018.