Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025In-depth review of industry 4.0: research implications and future perspectives; [Endüstri 4.0'ın derinlemesine incelemesi: araştırma sonuçları ve gelecek perspektifleri]
Gurcan, Fatih; Cam, Handan; Ahmed, Sana
The term “Industry 4.0” signifies a pivotal stage in industrial development, representing the current wave of transformation characterized by the integration of digital technologies into manufacturing and business processes. This concept spans multiple disciplines, including industrial engineering, data science, marketing, and strategic management. While a considerable volume of research has been conducted under the umbrella of Industry 4.0, there remains a lack of a comprehensive synthesis that captures the full scope and evolution of the field. The objective of this study is to systematically analyze, classify, and evaluate published research related to Industry 4.0, while identifying key gaps and forecasting potential future research directions. To accomplish this, a total of 10,696 journal articles indexed in the SCOPUS database from 2013 to 2023 were analyzed. The Latent Dirichlet Allocation (LDA) algorithm—a widely used probabilistic topic modeling technique in text mining—was applied to uncover latent themes within the literature. The analysis revealed 41 major topics, which were subsequently organized into five overarching categories: Sustainability, Manufacturing, Technology, Process, and Management. Findings indicate a notable shift in research emphasis over the past decade—from predominantly technical and engineering-focused issues toward broader concerns involving process optimization and organizational management. This transition highlights the growing importance of interdisciplinary approaches in addressing the complex challenges posed by Industry 4.0. Ultimately, this study not only maps the intellectual structure of existing research but also serves as a valuable resource for scholars, helping to navigate emerging trends and target underexplored areas in future investigations. This comprehensive thematic overview supports academics in pinpointing neglected subject areas, aligning future investigations with emerging priorities, and refining the strategic focus of Industry 4.0 research agendas.
- Kitap BölümüYalnız künye2025Sentiment analysis of twitter data for US airlines' finance
Cam, Handan; Demirel, Ugur
The airline industry significantly promotes cultural, social, and financial globalization by facilitating connections between nations and individuals. Airline finance encompasses the intricate aspects of managing the economic, operational, and strategic elements of airline corporations on a global scale. In this industry, it is necessary to address various factors, including revenue management, risk management, cost control, and market competition. The interaction of these factors in the global aviation sector influences the financial situation of airlines, requiring strategic planning, flexibility, and a complete understanding of market dynamics. Sentiment analysis in aviation finance uses text and data mining techniques to examine emotional elements derived from social networks, consumer feedback, and news. This facilitates understanding consumer preferences, new trends, investment opportunities, risks, and marketing strategies. Sentiment analysis also helps manage brand reputation by monitoring sentiments expressed by passengers and the general public, allowing proactive measures to maintain a positive reputation. Airline companies rely on conventional customer feedback forms, which can be extremely laborious and timeconsuming. Twitter data is valuable for collecting consumer feedback tweets and conducting sentiment analysis. In this paper, we work on a dataset comprising tweets from the top major US airlines and perform a sentiment analysis in airline finance that can be used more efficiently and effectively in decision-making processes. This would allow airlines to adopt a more knowledgeable, targeted, and competitive strategy by understanding and assessing emotional context.
- MakaleAçık Erişim2024Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers
Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana
This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.
- MakaleAçık Erişim2021Predicting stock prices using machine learning methods and deep learning algorithms: The sample of the istanbul stock exchange
Demirel, Ugur; Cam, Handan; Unlu, Ramazan
Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index (ISE-100) using well-known machine learning methods, Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) models and deep learning algorithm, Long Short Term Memory (LSTM) by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error (RMSE), mean squared error (MSE) and R-squared (R2). The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.
- Kitap BölümüAçık Erişim2016The effects of an accounting information system on the institutionalisation level of enterprises
Cam, Handan; Tosunoglu, Busra
An accounting information system (AIS) is a system of collecting, storing, and processing financial and accounting data that is used by decision makers. An accounting information system is generally a computer-based method for tracking accounting activity in conjunction with information technology resources. The purpose of this study is to determine the effects of an accounting information system on the institutionalisation level of enterprises. In this regard, a model has been developed and tested. Five hypotheses were constituted to test the model and a questionnaire was conducted to 150 firms that are operating in small and medium sized enterprises in Trabzon. The structural equation model was used to test the hypotheses. According to findings of the analysis it may be seen that an interdepartmental information system, compliance with an accounting information system, and an accounting information system activity have an effect on the institutionalisation level of enterprises.