Fruit production forecasting by neuro-fuzzy techniques

Neuro-fuzzy techniques are finding a practical application in many fields such as in model identification and forecasting of linear and non-linear systems. This paper presents a neuro-fuzzy model for forecasting the fruit production of some agriculture products (olives, lemons, oranges, cherries and pistachios). The model utilizes a time series of yearly data. The fruit forecasting is based on Adaptive Neural Fuzzy Inference System (ANFIS). ANFIS uses a combination of the least-squares method and the backprobagation gradient descent method to estimate the optimal food forecast parameters for each year. The results are compared to those of an Autoregressive (AR) model and an Autoregressive Moving Average model (ARMA).Fruit forecasting, neuro-fuzzy, ANFIS, AR, ARMA, forecasting, fruit production, Agricultural Finance, Crop Production/Industries,

Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

Greece is a low-productivity economy with an ineffective welfare state, relying almost exclusively on low wages and social transfers. Failure to come to terms with this reality hampers both the appropriateness of EU recommendations and the Greek government's capacity to deal with unemployment. Rather than finding a job in a family business or through relationship contacts, young people stay unemployed. Nor can people move back to their village of origin so easily. The underground economy, and the mass of small companies which characterize the Greek economy are booming, on paper. One in three members of the workforce are "self-employed", compared to one in seven in the EU as a whole. (International Viewpoint) An unemployed person in Greece is 2,15 times more likely to suffer poverty than a person in employment. Yet in Greece there are perhaps even more influential factors in determining increased risk of poverty. Thus while unemployment is a crucial factor in the risk of poverty, it is neither the only nor the most significant factor. The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model. The input is a time series. Classical statistics measures are calculated in order to asses the model performance. Further the results are compared with an ARMA and an AR model.forecasting, neural network, unemployment

Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System

Various methods have been developed to improve mortality forecasts. The authors proposed a neuro-fuzzy model to forecast the mortality. The forecasting of mortality is curried out by an ANFIS model which uses a first order Sugeno-type FIS. The model predicts the yearly mortality in a one step ahead prediction scheme. The method of trial and error was used in order to decide the type of membership function that describe better the model and provides the minimum error. The output of the models is the next year�s mortality. The results were presented and compared based on three different kinds of errors: RMSE, MAE, and MAPE. The ANFIS model gives good results for the case of two gbell membership functions and 500 epochs. Finally, the ANFIS model gives better results than the AR and ARMA model.ANFIS, Forecasting, Mortality, Modeling.

Χημική μελέτη γύρης Cistus creticus L. Bιολογικές δράσεις

Αντικείμενο της παρούσας διπλωματικής εργασίας, αποτέλεσε η μελέτη τριών ελληνικών δειγμάτων γύρης που σε μεγάλο ποσοστό προέρχονται από γύρη του φυτού Cistus creticus L. Το θεωρητικό μέρος αποτελείται από δύο ξεχωριστές ενότητες. Η πρώτη εστιάζει στην μέλισσα και τα προϊόντα που παράγει, και ειδικότερα στη γύρη. Γίνεται αναφορά στις βιολογικές της δράσεις, τις χρήσεις της, ιστορικά και εμπορικά στοιχεία όπως και βιβλιογραφική ανασκόπηση. Η δεύτερη ενότητα αναφέρεται στο Cistus creticus L., το φυτό από όπου προέρχεται η γύρη στην οποία επικεντρώνεται η παρούσα εργασία. Το πειραματικό μέρος ξεκινά με τη γυρεολογική μελέτη των τριών δειγμάτων γύρης (Ι, ΙΙ, ΙΙΙ). Σε βουτανολικά και μεθανολικά εκχυλίσματα από τα τρία δείγματα γύρης πραγματοποιήθηκε προσδιορισμός των ολικών φαινολικών με τη μέθοδο Folin-Ciocalteau, με πρότυπο τόσο το καφεϊκό όσο και το γαλλικό οξύ, όπως και προσδιορισμός των ολικών φλαβονοειδών ανά γραμμάριο εκχυλίσματος. Επιπρόσθετα έγινε έλεγχος της αντιοξειδωτικής δράσης τους με αναστολή της ελεύθερης ρίζας ABTS·+ και DPPH·. Αφού διαπιστώθηκε ότι το δείγμα γύρης Ι που περιείχε σχεδόν αποκλειστικά γύρη Cistus creticus L.(98%), εμφανίζει την πιο ισχυρή αντιοξειδωτική δράση και το υψηλότερο ποσοστό φλαβονοειδών και φαινολικών ενώσεων, αποφασίστηκε η περαιτέρω χημική μελέτη του με κύριο στόχο την ανίχνευση και απομόνωση δευτερογενών μεταβολιτών με πιθανό βιολογικό ενδιαφέρον. Ακολουθούν οι διαδικασίες εκχύλισης, απομόνωσης και καθορισμού της δομής φυσικών προϊόντων από την γύρη Ι που έγιναν μέσω χρωματογραφικών και φασματοσκοπικών μεθόδων. Πραγματοποιήθηκε επεξεργασία του βουτανολικού και του υδατικού εκχυλίσματος της γύρης Ι. Από αυτά τα εκχυλίσματα απομονώθηκαν 10 συνολικά δευτερογενείς μεταβολίτες. Επίσης πραγματοποιήθηκε χρωματογραφικός έλεγχος με αέριο χρωματογραφία συζευγμένη με φασματογράφο μάζας (GC-MS) στο κυκλοεξανικό και διχλωρομεθανικό εκχύλισμα από αυτή τη γύρη. Αναλυτικότερα, από το βουτανολικό εκχύλισμα της γύρης I απομονώθηκαν 3 φαινολικοί δισακχαρίτες: • καιμπφέρολο -3-Ο-β-(1→2) ραμνόσυλ-γλυκοσίδης (καιμπφέρολο-νεοεσπεριδοσίδης) (1) • κερκέτινο-3-Ο-β-(1→2) ραμνόσυλ-γλυκοσίδης (κερκέτινο-νεοεσπεριδοσίδης) (2) • μυρικέτινο- -3-Ο-β-(1→2) ραμνόσυλ-γλυκοσίδης (μυρικέτινο-νεοεσπεριδοσίδης) (3) και 7 φαινολικοί μονοσακχαρίτες: • καιμπφέρολο -7-Ο-ραμνοσίδης (4) • καιμπφέρολο-3-Ο-γαλακτοσίδης (τριφολίνη) (5) • καιμπφέρολο-3-Ο-γλυκοσίδης (αστραγαλίνη) (6) • κερκετινο-7-Ο-ραμνοσίδης (7) • κερκέτινο-3-Ο-γαλακτοσίδης (υπεροσίδης) (8) • κερκέτινο 3-Ο-γλυκοσίδης (ισοκερκετίνη) (9). • ισοραμνέτινο-3-Ο-γλυκοσίδης (10) και από το υδατικό εκχύλισμα απομονώθηκε ο φαινολικός μονοσακχαρίτης: • κερκέτινο 3-Ο- β- γλυκοσίδης (ισοκερκετίνη) (9).Bee pollen is a raw material produced by the honey-bees from flowering plants pollen, mixed with nectar and bee’s secretions. Cistus creticus is a Mediterranean evergreen shrub. Its dried leaves have been traditionally used as infusion and/or decoction and have shown gastroprotective effect, while per os administration was used to treat cough and cold, as well as against mouth and throat irritations. This study was carried out to evaluate the antiomicrobial and antioxidant properties of Greek Cistus (rock rose) bee pollen and to define its phenolic compounds, as well as to assay it for its anticholinesterase potential activities. The theoretical part of this study consists of two separate sections. The first focuses on the bee and its products, especially pollen. Reference is made to pollen’s biological activities, commercial uses as well as with historical and chemical data. Bibliographic review is also cited in this part. The second section refers to Cistus creticus L., the plant that the pollen of the present work comes from. Experimental part starts with the examination of the pollinic spectra of the three Greek bee pollen samples (I, II, III) from Cistus. It was obtained by Louveaux’s quantitative microscopical analysis and it showed that one of them (I) had Cistus sp. (Cistaceae) as abundant pollen (together with low percentage of Brassica sp., Cruciferae). Throughout the chemical analysis of the extracts, several secondary metabolites of flavonoid structure have been isolated and identified: kaempferol-3-neohesperidoside (1), quercetin-3-neohesperidoside (2), myricitetin-3-neohesperidoside (3), kaempferol-7-rhamnoside (4), trifolin (5), astragalin (6), quercetin-7-rhamnoside (7), hyperoside (8), isoquercetin (9) and isorhamnetin-3-glycoside (10). Moreover, the total phenolic content was determined by the Folin-Ciocalteu method, total flavonoid content was estimated by the aluminium chloride colorimetric assay and the free radical scavenging activity was determined by DPPH and ABTS assays. The antimicrobial activity of the extracts was tested against six Gram-positive and -negative bacteria and three human pathogenic fungi, showing an interesting antibacterial profile

Deep Learning for Forecasting Stock Returns in the Cross-Section

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201

Exploring the Behavioral Intentions of Food Tourists Who Visit Crete

Food tourism has been growing globally in recent years. Food tourism is considered as special interest tourism, attracting tourists who have a great interest in food. Tourists spend a significant percentage of their budget on the purchase of local food products and related food activities, contributing to the sustainable development of the touristic destination in the process. This survey took place in Crete, Greece, throughout the touristic period of 2021, and 4268 valid questionnaires were completed by international tourists. For the data analysis, the Structural Equation Model and an extended Theory of Planned Behavior Model, based on subjective norms, attitudes, perceived behavioral control, and satisfaction, were used to better understand the consumers’ intentions to revisit and recommend the region of Crete. The outcomes of the research pinpointed that the perceived quality and perceived value of local foods positively influenced satisfaction, which, in turn, evoked favorable intentions to revisit and recommend Crete as a touristic destination. Moreover, while satisfaction, attitude, and subjective norms seem to be the most significant drivers affecting positive behavioral intentions, perceived behavior control seems to have had no significant impact. The implications and limitations of the survey, as well as future recommendations, are also discussed

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility