Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

DESIGN AND DELIVERY OF ELECTRONIC SERVICES: IMPLICATIONS FOR CUSTOMER VALUE IN ELECTRONIC FOOD RETAILING

Electronic food retailers can satisfy their customers more effectively if they understand how this particular market works. As in other service segments, the emergence of electronic business-to-customer services in the retail food industry poses questions for managers about the design of new food retailing services and the redesign of existing services for delivery through electronic channels. Important topics include characteristics of electronic service offerings, the typical operational configurations used to deliver electronic services, and the ways in which they relate to the effectiveness of electronic service delivery. We address this issue by developing a product-process matrix for understanding and analyzing electronic retailing services in general. We tailor the matrix to food retailing in particular. The product-process matrix allows electronic food retailers to determine in advance what features they need in a web site to serve their chosen market effectively.Consumer/Household Economics, Marketing, Research and Development/Tech Change/Emerging Technologies,

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

Visual Interpretability of Image-based Real Estate Appraisal

Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%

Spationomy

This open access book is based on "Spationomy – Spatial Exploration of Economic Data", an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works

Aplicación de técnicas de minería de datos geo-referenciados en los circuitos de comercialización alternativa de productos agrícolas en Ecuador

A nivel mundial se utilizan sistemas de información para realizar el seguimiento y optimización de la producción agrícola. En el Ecuador el ministerio de Agricultura y Ganaderia (MAG), tiene un pro-grama orientado a fortalecer la asociación de productores agrícolas familiares que comercializan sus productos de manera directa con el consumidor, en un denominado circuito alternativo de comercialización (CIALCO). A la información recolectada por el MAG, de ferias tipo Cialco, ubicadas en las provincias de Tungurahua y Chimborazo, se aplican técnicas de minería de datos descriptivas y predictivas, para descubrir patrones de comportamiento que permitan optimizar la utilización del suelo y mejorar los ingreso en la comercialización de productos agrícolas de este sector. En la parte descriptiva, basados en la inducción de reglas de asociación, generadas utilizando los algoritmos A priori y FP-growth con parámetros mínimos de soporte y confianza, se genera un conjunto que se compone de todos los elementos resultado de obtener las mejores reglas. El conjunto asociativo resultante se integra por los productos cebolla blanca, tomate de árbol, zanahoria, brócoli y tomate riñón. En la parte predictiva se busca realizar una estimación pronóstica utilizando dos dimensiones: tiempo y ubicación geográfica. Con un solo predictor, se genera una serie de tiempo utilizando el algoritmo SMOReg, para realizar una extrapolación pronostica con la que se encuentra valores de comercialización de productos agrícolas fuera del periodo de registro de información. Adicionando coordenadas geográficas a la información inicial se ubican espacialmente las ferias en la región de estudio, compuesto por las provincias de Tungurahua y Chimborazo, para utilizar la dimensión espacial y en base a procesos de kriging realizar interpolación pronóstica para estimar va-lores de comercialización en lugares donde no se tiene información. Una vez desarrollados estos tres procesos de minería de datos se propone una metodología qué, utilizando el conjunto asociativo como predictor, vuelve a calcular la estimación pronostica para la dimensión tiempo y la dimensión espacio. La comparación de resultados con un solo predictor frente a los resultados de estimación pronóstica utilizando el conjunto asociativo como predictor indican que los porcentajes de error en la estimación pronostica multivariable disminuyen de manera considerable. Para validar los resultados obtenidos de mejora de estimación pronostica, se crean dos modelos de datos utilizando variables externas al proceso de comercialización población y piso climático. En los resultados finales, se aprecia que las dos variables de forma independiente muy poco aportan en la disminución del error de estimación, mientras que si se las hace interactuar con el conjunto asociativo se vuelve a encontrar una disminución en el error de estimación pronóstica obtenido.At the world level, information systems are used to monitor and optimize agricultural production. In Ecuador, the Ministry of Agriculture and Livestock has a program aimed at strengthening the association of family agricultural producers, who market their products directly with the consumer, in a socalled alternative marketing circuit (CIALCO). To the information collected from Cialcos-type fairs, located in the provinces of Tungurahua and Chimborazo, descriptive and predictive data mining techniques are applied. To discover patterns of behavior that allow to optimize the use of the soil and improve the income in the commercialization of agricultural products. In the descriptive part, based on the induction of association rules, generated using the Apriori and FP-growth algorithms with minimum support and Confidence parameters, a set is generated that consists of all the elements resulting from obtaining the best rules. The resulting associative set is integrated by the products white onion, tree tomato, carrot, broccoli and tomato kidney. The resulting associative set is integrated by the products: white onion, tree tomato, carrot, broccoli and tomato kidney. In the predictive part, a prognostic estimation is sought using two dimensions: time and geographic location. With a single predictor, a series of time is generated using the SMOReg algorithm, to perform a forecast extrapolation with which commercialization values of agricultural products are found outside the period of information registration. By adding geographical coordinates to the initial information, the fairs are located spatially in the study region, composed of the provinces of Tungurahua and Chimborazo, to use the spatial dimension and based on kriging processes to perform prognostic interpolation to estimate marketing values in places where you do not have information. Once these three processes of data mining have been developed, it is proposed to establish a methodology that, using the associative set as a predic-tor, recalculates the forecast forecast for the time dimension and the space dimension. The comparison of results with a single predictor versus the results of prognostic estimation using the associative set as a predictor they indicate that the percentages of error in the multivariable forecast estimate decrease considerably. In order to validate the results obtained from improvement of forecast estimation, two data models are created using variables external to the population and climatic floor marketing process. In the final results, it can be seen that the two variables independently contribute very little in the reduction of the estimation error, whereas if they are made to interact with the associative set, they will find a decrease in the error obtained.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Javier Bajo Pérez.- Secretario: Miguel Ángel Patricio Guisado.- Vocal: Ana María Bernardos Barboll

Spationomy

This open access book is based on "Spationomy – Spatial Exploration of Economic Data", an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works

Forex Trading Signal Extraction with Deep Learning Models

The rise of AI technology has popularized deep learning models for financial trading prediction, promising substantial profits with minimal risk. Institutions like Westpac, Commonwealth Bank of Australia, Macquarie Bank, and Bloomberg invest heavily in this transformative technology. Researchers have also explored AI's potential in the exchange rate market. This thesis focuses on developing advanced deep learning models for accurate forex market prediction and AI-powered trading strategies. Three deep learning models are introduced: an event-driven LSTM model, an Attention-based VGG16 model named MHATTN-VGG16, and a pre-trained model called TradingBERT. These models aim to enhance signal extraction and price forecasting in forex trading, offering valuable insights for decision-making. The first model, an LSTM, predicts retracement points crucial for identifying trend reversals. It outperforms baseline models like GRU and RNN, thanks to noise reduction in the training data. Experiments determine the optimal number of timesteps for trend identification, showing promise for building a robotic trading platform. The second model, MHATTN-VGG16, predicts maximum and minimum price movements in forex chart images. It combines VGG16 with multi-head attention and positional encoding to effectively classify financial chart images. The third model utilizes a pre-trained BERT architecture to transform trading price data into normalized embeddings, enabling meaningful signal extraction from financial data. This study pioneers the use of pre-trained models in financial trading and introduces a method for converting continuous price data into categorized elements, leveraging the success of BERT. This thesis contributes innovative approaches to deep learning in algorithmic trading, offering traders and investors precision and confidence in navigating financial markets

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation