Molecular dynamics calculation of the spectral densities of plasma fluctuations

Panarese, Antonio; Bruno, D.; Tolias, Panagiotis; Ratynskaia, S.; Longo, S.; Angelis, U. de

doi:10.1017/s0022377818000491

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, as shown in the following Figure 5, the accuracy of the model progressively increases as the available data increases. Performance measurement is very important for both basic and industrial research [1], [21]- [23]. In the present case study, the number of records that make up the dataset is crucial to improve network performance.…”

Section: Resultsmentioning

confidence: 99%

Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities

Massaro¹,

Panarese²,

Gargaro³

et al. 2021

IJAIA

View full text Add to dashboard Cite

Data processing is crucial in the insurance industry, due to the important information that is contained in the data. Business Intelligence (BI) allows to better manage the various activities as for companies working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes it possible to improve the efficiency of decisions and processes, by improving them to the individual characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help insurance companies to understand the current market and to anticipate future trends. The purpose of the present paper is to discuss a case study, which was developed within the research project "DSS / BI HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering, and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by changing the number of records in the dataset. More precisely, as the number of records increases, the accuracy increases up to a value equal to 0.9987.

show abstract

Section: Resultsmentioning

confidence: 99%

Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities

Massaro¹,

Panarese²,

Gargaro³

et al. 2021

IJAIA

View full text Add to dashboard Cite

show abstract

“…XGBoost is a high-performance algorithm, in fact, it can be implemented on parallel architectures and its training is very fast [21,[35][36][37]. Additionally, XGBoost has an embedded routine that optimizes the management of missing values.…”

Section: Xgboost Algorithmmentioning

confidence: 99%

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach

Panarese¹,

Settanni²,

Vitti³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%.

show abstract

“…The n_jobs = −1 option, permitting one to use all CPU cores, saves time, especially for trees that use a large number of nodes. Parallel computing is widely used in various sectors [12,[53][54][55] because it optimizes computational costs.…”

mentioning

confidence: 99%

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

Massaro¹,

Panarese²,

Giannone³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

show abstract

Molecular dynamics calculation of the spectral densities of plasma fluctuations

Cited by 3 publications

References 21 publications

Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities

Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

Contact Info

Product

Resources

About