Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2020

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Unit load devices (ULD) demand forecasting in the air cargo for optimal cost management

Mikram Mounia, Rhanoui Maryem, Yousf Siham, Briwa Houda

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 3

71--80

In recent decades, the airline industry has become very competitive. With the advent of large aircraft in service, unit load devices (ULD) have become an essential ele‐ ment for efficient air transport. They can load a large amount of baggage, cargo or mail using only one unit. Since this results in fewer units to load, saving time and efforts of ground crews and helping to avoid delayed flig‐ hts. However, a deficient loading of the units causes ope‐ rating irregularities, costing the company and contribu‐ ting to the dissatisfaction of the customers. In contrast, an excess load of containers is at the expense of cargo. In this paper we propose an approach to predict the de‐ mand for baggage in order to optimize the management of its ULD flow. Specifically, we build prediction models: ARIMA following the BOX‐JENKINS approach and expo‐ nential smoothing methods, in order to obtain more accu‐ rate forecasts. The approach is tested using the operatio‐ nal data of flight processing and the results are compared with four benchmark method (SES, DES, Holt‐Winters and Naive prediction) using different performance indicators: MAE, MSE, MAPE , WAPE, RMSE, SMPE. The results obtai‐ ned with the exponential smoothing methods surpass the benchmarks by providing more accurate forecasts.

Comparative study of CNN and LSTM for opinion mining in long text

Yousf Siham, Rhanoui Maryem, Mounia Mikram

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 3

50--55

The digital revolution has encouraged many companies to set up new strategic and operational mechanisms to supervise the flow of information published about them on the Web. Press coverage analysis is a part of sentiment analysis that allows companies to discover the opinion of the media concerning their activities, products and services. It is an important research area, since it involves the opinion of informed public such as journalists, who may influence the opinion of their readers. However, from an implementation perspective, the analysis of the opinion from media coverage encounters many challenges. In fact, unlike social networks, the Media coverage is a set of large textual documents written in natural language. The training base being huge, it is necessary to adopt large-scale processing techniques like Deep Learning to analyze their content. To guide researchers to choose between one of the most commonly used models CNN and LSTM, we compare and apply both models for opinion mining from long text documents using real datasets.