Identifying Real Estate Opportunities using Machine Learning

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table

On the statistical comparison of feature selection methods and the role of experts: the case of Las Vegas strip

A statistical comparison of feature selection methods is performed. Feature selection is an important issue in Data Mining and Data Science, and a comparison of the results obtained from different methods is hard to be performed. Then, the evaluation of metrics and ways of comparisons is an important matter of study. Our study is performed on a real dataset previously analyzed in the literature containing a small number of records, drawing the attention on the conclusions to be applied where poor statistical confidence levels of significance can be obtained because of a relative low number of samples are present. The use of inter rater agreement coefficients is introduced as a novel approach extending a previous study. Boruta and tree-based methodologies perform rather well even in small data as it is shown. Our metrics can be used to guide the expert opinion in order to take the final decision. This work extends the results obtained in a previous analysis performed on the mentioned dataset.Sociedad Argentina de Informátic

Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

Neural networks, widely used in pattern recognition, security applications and robot control have been chosen for the task of object recognition within this system. One of the main drawbacks of the implementation of traditional neural networks in reconfigurable hardware is the huge resource consuming demand. This is due not only to their intrinsic parallelism, but also to the traditional big networks designed. However, modern FPGA architectures are perfectly suited for this kind of massive parallel computational needs. Therefore, our proposal is the implementation of Tiny Neural Networks, TNN -self-coined term-, in reconfigurable architectures. One of most important features of TNNs is their learning ability. Therefore, what we show here is the attempt to rise the autonomy features of the system, triggering a new learning phase, at run-time, when necessary. In this way, autonomous adaptation of the system is achieved. The system performs shape identification by the interpretation of object singularities. This is achieved by interconnecting several specialized TNN that work cooperatively. In order to validate the research, the system has been implemented and configured as a perceptron-like TNN with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefit

Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.

Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs)specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units(layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits

A case study: the rehabilitation of residential buildings in Parque Alcosa district, analysis of common diseases and intervention proposal

La barriada del Parque Alcosa se localiza en el noroeste del núcleo urbano de Sevilla, y está formada por un conjunto de 10.640 viviendas de promoción pública construida durante los años 69-72 por el constructor valenciano Alfredo Corral. Existen tres modelos de edificación diferentes que responden a fases de construcción, siendo objeto de estudio en este artículo la correspondiente a la primera fase, comprendidas por las calles Ciudad de Játiva, Gandía, Sueca, Onteniente, Carcagente, Burjasot, Godella, Alfafar, Buñol, Paterna y Oliva. El presente trabajo expone el análisis de las patologías constructivas comunes existentes en la fase 1 del Parque Alcosa, relacionadas con el carácter potencialmente expansivo de los terrenos donde se ubica. Así mismo se desarrollan las soluciones constructivas de la intervención proyectada, llevado a cabo mediante el programa de Rehabilitación Singular de Edificios de la Empresa Pública del Suelo de Andalucía.The Parque Alcosa district is located in northwestern area of Seville. It consists of 10,640 public housing development,that was promoted throughout the years 69-72 by the builder Alfredo Corral. There are three different building types which correspond to the different building stages. This article focuses on the the first one, which includes the streets, Ciudad de Jativa, Gandía, Sueca, Onteniente, Carcagente, Burjasot, Godella, Alfafar, Buñol, Paterna y Oliva. The present paper provides a constructive analisys of common building pathologies in phase 1 of Parque Alcosa, related to the potentially expansive features of the land where it is located. This paper also describes the structural sollutions for the projected intervention designed by the architect who subscribes, and was supported by the Public Land Company of Andalusia, under the Singular Building Rehabilitation program

Evolutionary design and optimization of Wavelet Transforms for image compression in embedded systems

This paper describes the initial studies of an Evolution Strategy aimed at implementation on embedded systems for the evolution of Wavelet Transforms for image compression. Previous works in the literature have already been proved useful for this application, but they are highly computationally intensive. Therefore, the work described here, deals with the simplifications made to those algorithms to reduce their computing requirements. Several optimizations have been done in the evaluation phase and in the EA operators. The results presented show how the proposed algorithm cut outs still allow for good results to be achieved, while effectively reducing the computing requirements

Implementation of bio-inspired adaptive wavelet transforms in FPGAs. Modeling, validation and profiling of the algorithm

Providing embedded systems with adaptation capabilities is an increasing importance objective in design community. This work deals with the implementation of adaptive compression schemes in FPGA devices by means of a bioinspired algorithm. A simplified version of an Evolution Strategy using fixed point arithmetic is proposed. Specifically, a simpler than the standard (hardware friendly) mutation operator is designed, modelled and validated using a high-level language. HW/SW partitioning issues are considered and code profiling accomplished to validate the proposal. Preliminary results of the proposed hardware architecture are also show

Evolutionary Approach to Improve Wavelet Transforms for Image Compression in Embedded Systems

A bioinspired, evolutionary algorithm for optimizing wavelet transforms oriented to improve image compression in embedded systems is proposed, modelled, and validated here. A simplified version of an Evolution Strategy, using fixed point arithmetic and a hardware-friendly mutation operator, has been chosen as the search algorithm. Several cutdowns on the computing requirements have been done to the original algorithm, adapting it for an FPGA implementation. The work presented in this paper describes the algorithm as well as the test strategy developed to validate it, showing several results in the effort to find a suitable set of parameters that assure the success in the evolutionary search. The results show how high-quality transforms are evolved from scratch with limited precision arithmetic and a simplified algorithm. Since the intended deployment platform is an FPGA, HW/SW partitioning issues are also considered as well as code profiling accomplished to validate the proposal, showing some preliminary results of the proposed hardware architecture

FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration

Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows expandability by means of mapping several Basic units (layers) and dynamic reconfiguration; depending on the application specific demands. One of the most important features of Tiny Neural Networks (TNN) is their learning ability. Weight modification and architecture reconfiguration can be carried out at run time. Our system performs shape identification by the interpretation of their singularities. This is achieved by interconnecting several specialized TNN. The results of several tests, in different conditions are reported in the paper. The system detects accurately a test shape in almost all the experiments performed. The paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and was configured as a perceptron network with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits

Efecto del almacenamiento sobre la viabilidad y germinación de dos especies arbóreas tropicales

Cochlospermum vitifolium (Willd.) Spreng. y Quararibea funebris (La Llave) Vischer, son árboles tropicales nativos de México con importancia ecológica, social y económica; de los que no se tiene una práctica sustentable para el manejo. El objetivo fue evaluar la viabilidad y la germinación en semillas almacenadas a 0, 3, 6, 9 y 12 meses. Las semillas a los 0 meses de almacenamiento fueron viables en un 99.66 y 99.33% para C. vitifolium y Q. Funebris, respectivamente, mientras que a los 12 meses se tuvo disminución de la viabilidad, presentando una germinación nal del 71.3% para C. vitifolium y 61.3% para Q. Funebri