A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey

Artificial Neuro Network (ANN) Applications in Economics: A Survey of Emprical Literature and Its Using on Economic Studies

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualize complex databases for marketing segmentation. This boom in applications covers a wide range of business interests from finance management, through forecasting,to production. Neural networks can be utilized in both macro- and micro economic analysis, with clear potential to improve the timeliness and quality of economic forecasting. A form of artificial intelligence, neural networks provides significant potential in economic applications by increasing the flexibility of the process ofeconomic forecasting. In this paper, by surveying empirical literature and its using on economics studies artificial neuro network applications have been analyzed. This is anew and developing approach in economics studies. It can be useful to apply on relative areas of economics like labor economics and the other economical problems. Economists can make a forecasting using by ANN on economics studies in the near future

Estimación de áreas quemadas en incendios forestales utilizando redes neurales artificiales

Introduction: This article is the product of the research “Developing an Artificial Neural Network Based Model for Estimating Burned Areas in Forest Fires”, developed at Karadeniz Technical University in the year 2020. Problem: Forest Fires are an issue that greatly affect human life and the ecological order, leaving long-term issues. It should be estimated because it is not known when, where and how much the fire will be in the area. Objective: The objective of the research is to use artificial neural networks to estimate the burned areas in forest fires. Methodology: A feed-forward backpropagation neural network model was used for estimating the burned areas. Results: We performed a performance evaluation over the proposed model by considering Regression values, Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The results show that the model is efficient in terms of its estimation of burnt areas. Conclusions: The proposed artificial neural network model has low error rate and high estimation accuracy. It is more effective than traditional methods for estimating burned areas in forests. Originality: To the best of our knowledge, this is the first time that this real, unique data has been used for building and testing the model’s estimations and the improvements that have been made in producing results faster and more accurately than with traditional methods. Limitations: Since there are regional differences over different forest areas, effective criteria need to be analysed regarding the target regions.  Introducción: Este artículo es el producto de la investigación "Desarrollo de un modelo basado en redes neuronales artificiales para estimar áreas quemadas en incendios forestales", desarrollado en la Universidad Técnica de Karadeniz en el año 2020. Problema: los incendios forestales son un problema que afecta en gran medida la vida humana y el orden ecológico, dejando problemas a largo plazo. Debe estimarse porque no se sabe cuándo, dónde y cuánto será el incendio en el área. Objetivo: El objetivo de la investigación es utilizar redes neuronales artificiales para estimar las áreas quemadas en incendios forestales. Metodología: Se usó un modelo de red neuronal de propagación hacia atrás para estimar las áreas quemadas. Resultados: Realizamos una evaluación de desempeño sobre el modelo propuesto considerando los valores de regresión, el error de porcentaje absoluto medio (MAPE) y el error de cuadrado medio (MSE). Los resultados muestran que el modelo es eficiente en términos de su estimación de áreas quemadas. Conclusiones: El modelo de red neuronal artificial propuesto tiene una baja tasa de error y una alta precisión de estimación. Es más efectivo que los métodos tradicionales para estimar áreas quemadas en los bosques. Originalidad: según nuestro conocimiento, esta es la primera vez que esta información real y única se ha utilizado para construir y probar las estimaciones del modelo y las mejoras que se han realizado para producir resultados más rápido y con mayor precisión que con los métodos tradicionales. Limitaciones: Dado que existen diferencias regionales sobre las diferentes áreas forestales, es necesario analizar criterios efectivos con respecto a las regiones objetivo

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers\u27 financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world

Türkiye için Yapay Sinir Ağları ve Genetik Algoritmalar ile 5 Yıllık Sabit Telefon Abone Sayısı Tahmini

DergiPark: 245950trakyafbdSabit telefon hizmetinin elektronik haberleşme pazarında geçmişte olduğu gibi yakın gelecekte de önemli bir paya sahip olacağı düşünülmektedir. Bu çalışma ile doğrusal olmayan çok girdili dinamik sistemleri doğru şekilde modelleyebilen “yapay sinir ağları” ve simülasyon uygulamalarında kullanılan bir çözüm arama tekniği olan “genetik algoritmalar” birlikte kullanılmak suretiyle, bu hizmet için gelecek 5 yıla dair abone sayısı tahmini yapılmıştırIt is estimated that Fixed Telephony Service will have an important share in a near future, just like in the past. This paper discusses the “artificial neural networks”, which can correctly model non-linear multi-input dynamic systems, and forecasts the number of the subscribers of this service, comprising a period of future 5 years by using a solution searching technique used in simulation applications called “genetic algorithms

Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods

Due to copyright restrictions, the access to the full text of this article is only available via subscription.The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods