An early warning method for agricultural products price spike based on artificial neural networks prediction

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector

YesThe prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.EU FP7 project Policy Compass (Project No. 612133

Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization

Deep artificial neural networks have been popular for time series forecasting literature in recent years. The recurrent neural networks present more suitable architectures for forecasting problems than other deep neural network types. The simplest deep recurrent neural network type is simple recurrent neural networks according to the number of employed parameters. These neural networks can be preferred to solve forecasting problems because of their simple structure if they are trained well. Unfortunately, the training of simple recurrent neural networks is problematic because of exploding or vanishing gradient problems. The contribution of this study is proposing a new training algorithm based on particle swarm optimization. The algorithm does not use gradients so it has not vanished or exploding gradient problem. The performance of the new training algorithm is compared with long short-term memory trained by the Adam algorithm and Pi-Sigma artificial neural network. In the applications, ten-time series are used to compare the performance of the methods. The ten-time series is consisting of daily observations of the Dow-Jones and Nikkei stock exchange opening prices between the years 2014 and 2018. At the end of the analysis processes, the proposed method produces more accurate forecast results than established benchmarks

Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting

Single multiplicative neuron artificial neural networks have different importance than many other artificial neural networks because they do not have complex architecture problem, too many parameters and they need more computation time to use. In single multiplicative neuron artificial neural network, it is assumed that there is a one data generation process for time series. Many time series need an assumption that they have two data generation process or more. Based on this idea, the threshold model structure can be employed in a single multiplicative neuron model artificial neural network for taking into considering data generation processes problem. In this study, a new artificial neural network type is proposed and it is called a threshold single multiplicative neuron artificial neural network. It is assumed that time series have two data generation processes according to the architecture of single multiplicative neuron artificial neural network. Training algorithms are proposed based on harmony search algorithm and particle swarm optimization for threshold single multiplicative neuron artificial neural network. The proposed method is tested by various time series data sets and compared with well-known forecasting methods by considering different error measures. Finally, the performance of the proposed method is evaluated by a simulation study

Retrospective analysis of nasal soft tissue profile changes with maxillary surgery

Purpose: The aim of this study was to analyze the changes in the position of the nasal and labial soft tissue profile of patients undergoing bimaxillary orthognathic surgery, with special emphasis on the effect on the nasal tip projection. Materials and Methods: The lateral cephalometric radiographs of 27 consecutive patients (16 female and 11 male patients; mean age, 22 years) who had undergone maxillary advancement and mandibular setback were studied. The pretreatment and end-of-treatment lateral cephalometric radiographs were selected. The pretreatment and end-of-treatment radiographs were superimposed on the sella-nasion plane, and the case was only included if there had been no change in sella-nasion length (ie, no growth). Analyses of Pearson correlation coefficient and stepwise linear regression tests were used to compare the cephalometric measurements at the beginning and at the end of treatment. Paired-sample t tests were also performed to analyze changes in nasolabial angle (NLA) and columella-lobular angle (CLA). Results: The correlations between vertical movement of nasal tip, A-point, and maxillary incisal tip were important. Although there was an important correlation between nasal and incisal tip, interestingly, there was no correlation between nasal tip and A-point in horizontal movement. According to stepwise linear regression analysis, the best model for horizontal movement of nasal tip was as follows: Nasal anteroposterior movement = 0.241 + 0.188 × Incisal tip anteroposterior movement + 0.153 × Incisal tip superoinferior movement. For vertical movement of nasal tip, the best model was as follows: Nasal superoinferior movement = -1.117 + 0.399 × Incisal tip superoinferior movement + 0.323 × A-point anteroposterior movement. There was no significant relation in angular measurements of NLA and CLA before and after treatment. Conclusion: The results of our study suggest that both horizontal and vertical movements of nasal tip were related to incisal tip and A-point movements; however, angular changes in CLA and NLA did not affect the nasal tip. © 2011 American Association of Oral and Maxillofacial Surgeons