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Alphanumeric Journal : The Journal of Operations Research, Statistics, Econometrics and Management Information Systems
Number of Followers: 9  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2148-2225 - ISSN (Online) 2148-2225
Published by İstanbul Üniversitesi Homepage  [18 journals]
  • Analysis of the Prosperity Performances of G7 Countries: An Application of
           the LOPCOW-based CRADIS Method

    • Authors: Furkan Fahri Altıntaş
      Abstract: The prosperity policies and strategies of major economies have the potential to significantly influence both the global economy and the prosperity of other nations. Therefore, the assessment of the prosperity performance of major economies holds paramount importance. In this context, the primary aim of this research is to evaluate the prosperity performance of G7 countries using the LOPCOW-based CRADIS method, leveraging sub-component values from the Legatum Prosperity Index. The secondary objective is to examine the relationship between a country's prosperity performance assessed through the LOPCOW-based CRADIS method and its quantifiability within the Legatum Prosperity Index (LPI) framework, as well as its associations with other Multi-Criteria Decision-Making (MCDM) methodologies. The findings reveal the ranking of countries' prosperity performance as follows: Germany, the United Kingdom, Canada, Japan, the United States, France, and Italy. Additionally, an assessment of the average prosperity performance of these countries highlights that the United States, France, and Italy perform below the established average. Consequently, it is imperative for these nations to enhance their prosperity performance to make a more substantial contribution to the global economy. Furthermore, sensitivity and discrimination analysis suggest that countries' prosperity performance can be quantified within the LPI framework. Another noteworthy observation is the strong resemblance of the LOPCOW-based CRADIS method to the MEREC-based CRADIS and the LOPCOW-based MARCOS methods
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • Stock Price Forecasting with Deep Learning Techniques

    • Authors: Özgür Saracık; Aynur İncekırık
      Abstract: In this study, LSTM (Long-Short Term Memory) and GRU (Gated Recurrent Unit) techniques of deep learning, which are among the latest advanced technologies, were applied in the Google Colab software program for stock price forecasting. The dataset used in the study was obtained from Yahoo Finance and covers the dates between 02/01/2013 and 30/12/2022. Forecast models were created by considering 5 companies belonging to the XELKT (Electricity Market in Borsa Istanbul) index, which is part of BIST (Borsa Istanbul). Subsequently, the success of these forecast models was tested with the calculated model performance criteria, aiming to determine whether the techniques used were successful in stock price forecasting. Additionally, based on the results of MSE (Mean Squared Error) and MAPE (Mean Absolute Percentage Error) among the calculated model performance criteria, the techniques used were compared with each other, aiming to determine which of these techniques provided forecasts with less error. Then, through the analysis conducted on four different days, an attempt was made to identify the day that yielded the most successful forecasts. As a final step, the goal was to find a model with the least error based on techniques, epoch number, and the number of days forecasted, considering both MSE and MAPE for stocks. Since the model performance criteria outputs obtained from these analyses are below 1 for MSE and below 5% for MAPE, it can be concluded that both techniques demonstrate successful stock price forecasting. Consequently, in the comparison between these two techniques, it is observed that the LSTM technique is slightly more successful than the GRU technique.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • Using Artificial Intelligence in the Security of Cyber Physical Systems

    • Authors: Zeynep Gürkaş Aydın; Murat Kazanç
      Abstract: The prominence of cyber security continues to increase on a daily basis. Following the cyber attacks in recent years, governments have implemented a range of regulations. The advancement of technology and digitalization has led to the creation of new vulnerabilities that cyber attackers can exploit. The digitalization of facilities such as energy distribution networks and water infrastructures has enhanced their efficiency, thereby benefiting states and society. The modern sensors, controllers, and networks of these new generation facilities have made them susceptible to cyber attackers. While all forms of cyber attacks are detrimental, targeting critical cyber-physical systems presents a heightened level of peril. These assaults have the potential to disrupt the social structure and pose a threat to human lives. Various techniques are employed to guarantee the security of these facilities, which is of utmost importance. This study examined the applications of machine learning and deep learning methods, which are sub-branches of artificial intelligence that have recently undergone a period of significant advancement. Intrusion detection systems are being created for the networks that facilitate communication among the hardware components of the cyber-physical system. Another potential application area involves the development of models capable of detecting anomalies and attacks in the data generated by sensors and controllers. Cyber physical systems exhibit a wide range of diversity. Due to the wide range of variations, it is necessary to utilize specific datasets for training the model. Generating a dataset through attacks on a functional cyber-physical system is unattainable. The study also analyzed the solutions to this problem. Based on the analyzed studies, it has been observed that the utilization of artificial intelligence enhances the security of cyber physical systems.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • Time Series Prediction with Digital Twins in Public Transportation Systems

    • Authors: Mehmet Ali Ertürk
      Abstract: Classical traffic and transportation control centres are becoming insufficient with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a timeseries prediction system through Digital Twins to manage public transportation system with Facebook’s Prophet. This study presents a model framework to build Digital Twin application in Intelligent Public Transportation Systems and used a public data set to validate model with Facebook’s Prophet library by forecasting metro lines usage. Obtained results presented in the study and it is shown that forecasting error interms of Mean Absolute Percentage Error (MAPE) is 0.017 for 1 day horizon.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • Multi-Objective De Novo Programming with Type-2 Fuzzy Objective for
           Optimal System Design

    • Authors: Nurullah Umarusman
      Abstract: De Novo Programming, which is also known as Optimal System Design, regulates the resource amount of constraints depending upon the budget. Mostly, this process is managed using traditional methods, fuzzy methods and hybrid methods. When considered from this point of view, there is no certain method for the solution of De Novo Programming problems. An approach for solving the Multi-Objective De Novo Programming has been recommended using Type-2 Fuzzy Sets in this research. Without exceeding the budget in the recommended approach, Type-2 membership function for each objective function has been defined applying positive and negative ideal solutions. The solution phase of this approach, called Multi-Objective De Novo Programming with Type-2 Fuzzy Objective, has been shown step by step on the illustrative problem. Then, this illustrative problem has been solved with regards to five different approaches in the literature and the results have been compared.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • Solution Approach to Cutting Stock Problems Using Iterative Trim Loss
           Algorithm and Monte-Carlo Simulation

    • Authors: Özge Köksal; Ergün Eroğlu
      Abstract: Cutting Stock Problems are the most studied NP-Hard combinatorial problems among optimization problems. An One-dimensional Cutting Stock Problem (1-CSP), which aims to create cutting patterns to minimize trim loss, is one of the best known optimization problems. The difficulty of the solution stages and the lack of a definite solution method that can be applied to all problems have caused these problems to attract a lot of attention by researchers. This study includes a hybrid solution algorithm combined with iterative trim loss algorithm and Monte Carlo simulations, and a comparative study of the method with the solution methods in the literature, for the solution of orders to be obtained with minimum cutting loss from the same type of stocks.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
  • A Literature Review on Machine Learning in The Food Industry

    • Authors: Furkan Açıkgöz; Leyla Vercin, Gamze Erdoğan
      Abstract: Machine Learning (ML) has become widespread in the food industry and can be seen as a great opportunity to deal with the various challenges of the field both in the present and near future. In this paper, we analyzed 91 research studies that used at least two ML algorithms and compared them in terms of various performance metrics. China and USA are the leading countries with the most published studies. We discovered that Support Vector Machine (SVM) and Random Forest outperformed other ML algorithms, and accuracy is the most used performance metric.
      PubDate: Sun, 31 Dec 2023 00:00:00 +030
       
 
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Publisher: İstanbul Üniversitesi   (Total: 18 journals)   [Sort by number of followers]

Showing 1 - 11 of 11 Journals sorted alphabetically
Alphanumeric J. : The J. of Operations Research, Statistics, Econometrics and Management Information Systems     Open Access   (Followers: 9)
Anadolu Araştırmaları / Anatolian Research     Open Access   (Followers: 1)
Aquatic Sciences and Engineering     Open Access  
Art-Sanat Dergisi     Open Access   (Followers: 2)
Conservatorium / Konservatoryum     Open Access  
Darulfunun Ilahiyat     Open Access  
European J. of Biology     Open Access   (Followers: 1)
J. of Penal Law & Criminology     Open Access   (Followers: 3)
J. of Transportation and Logistics     Open Access   (Followers: 3)
Public and Private Intl. Law Bulletin     Open Access   (Followers: 3)
Studien zur deutschen Sprache und Literatur     Open Access   (Followers: 4)
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