TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING

Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques.  Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values

Identifying Industrial Productivity Factors with Artificial Neural Networks

534-536Productivity is an important issue in recent literature because it encourages cost savings and efficiency in the use of industrial resources in all countries. However, the study of the factors that explain the productivity levels reached by the companies presents controversy, and the existing research demands new analysis models that can more accurately identify the causes of industrial productivity. The present study aims to develop a new model that allows determining with high accuracy the factors that explain productivity in the construction industry. For this, an important sample of industrial companies and techniques of artificial neural networks has been used. The results obtained provide levels of accuracy that exceed those obtained by the previous literature, and have allowed us to identify that the aspects related to turnover, liquidity, and growth of companies provide an excellent strategy for promoting industrial productivity

Identifying explanatory factors of bitcoin price with artificial neural networks

This study aims to develop a new model that allows determining with high precision the factors that explain the price of bitcoin. To do this, an extensive database of variables related to bitcoin and artificial neural network techniques has been used. The results obtained have made it possible to identify that aspects related to the number of forum posts, the volume of transactions on the blockchain, and the hash rate provide an excellent strategy for predicting the price of bitcoi

Artificial neural network for predicting values of residuary resistance per unit weight of displacement

This paper proposes the usage of an Artificial neural network (ANN) to predict the values of the residuary resistance per unit weight of displacement from the variables describing ship’s dimensions. For this purpose, a Multilayer perceptron (MLP) regressor ANN is used, with the grid search technique being applied to determine the appropriate properties of the model. After the model training, its quality is determined using R2 value and a Bland-Altman (BA) graph which shows a majority of values predicted falling within the 95% confidence interval. The best model has four hidden layers with ten, twenty, twenty and ten nodes respectively, uses a relu activation function with a constant learning rate of 0.01 and the regularization parameter L2 value of 0.001. The achieved model shows a high regression quality, lacking precision in the higher value range due to the lack of data

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque

Identifying influencing factors on cryptocurrency price: Evidence for bitcoin and ethereum

El presente estudio tiene como objetivo el desarrollo de nuevos modelos para determinar con alta precisión los factores que explican el precio de las principales criptomonedas. Para ello, se ha utilizado una amplia base de datos de variables relacionadas con el bitcoin y el ethereum, y se han aplicado técnicas de redes neuronales artificiales. Los resultados obtenidos han permitido identificar que los aspectos relacionados con el número de publicaciones en foros, el volumen de transacciones en blockchain y la tasa de hash proporcionan una excelente estrategia para predecir el precio del bitcoin. También, que el volumen de transacciones, el tamaño de los bloques, las comisiones de mineros y los precios del petróleo son los mejores predictores del valor de mercado del ethereu

Modelo basado en redes neuronales para la predicción de precios de inmuebles Piura - 2021

Esta investigación tuvo como objetivo determinar la efectividad de un modelo de redes neuronales en la predicción de precios de inmuebles en Piura 2021. El enfoque de esta investigación fue cuantitativo, porque el modelo se analizó mediante estadística descriptiva y de regresión, por lo tanto, el tipo de investigación fue aplicada, el diseño de investigación fue pre experimental y de nivel explicativo. Entre los resultados se obtuvo que el modelo elegido que se implementó, fue el modelo secuencial debido a que se basa en función a varias entradas y única salida. Se obtuvo un set de entrenamiento procesado de 21000 inmuebles entre ventas realizadas y puestos en venta. Se obtuvo también que la red neuronal tuvo 3 simulaciones, donde arrojó un score de varianza de 0,8 en la tercera simulación y una efectividad de 25% como resultado mínimo, utilizando 7 variables de entrada para el modelo y para su posterior validación. En conclusión, se determinó que el modelo de RNA es efectivo no solo por las correctas configuraciones realizadas tras cada simulación, sino por el uso de métricas de sklearn para regresiones y la selección más óptima de modelo, que permitieron evaluarlo en base a su precisión durante el entrenamiento

Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

Sky image classification is crucial in meteorology to forecast weather and climatic conditions. The fine-grained cloud detection and recognition (FGCDR) algorithm is use to extract colour, inside texture and neighbour texture features from multiview of superpixels sky images. However, the FGCDR produced a substantial amount of redundant and insignificant features. The ant colony optimisation (ACO) algorithm have been used to select feature subset. However, the ACO suffers from premature convergence which leads to poor feature subset. Therefore, an improved feature extraction and selection for sky image classification (FESSIC) algorithm is proposed. This algorithm consists of (i) Gaussian smoothness standard deviation method that formulates informative features within sky images; (ii) nearest-threshold based technique that converts feature map into a weighted directed graph to represent relationship between features; and (iii) an ant colony system with self-adaptive parameter technique for local pheromone update. The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. The Friedman test result is presented for the performance rank of six benchmark feature selection algorithms and FESSIC algorithm. The Man-Whitney U test is then performed to statistically evaluate the significance difference of the second rank and FESSIC algorithms. The experimental results for the proposed algorithm are superior to the benchmark image classification algorithms in terms of similarity value on Kiel, SWIMCAT and MGCD datasets. FESSIC outperforms other algorithms for average classification accuracy for the KSVM, MLP, RF and DT classifiers. The Friedman test has shown that the FESSIC has the first rank for all classifiers. Furthermore, the result of Man-Whitney U test indicates that FESSIC is significantly better than the second rank benchmark algorithm for all classifiers. In conclusion, the FESSIC can be utilised for image classification in various applications such as disaster management, medical diagnosis, industrial inspection, sports management, and content-based image retrieval