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Conference Object Citation - Scopus: 2Reinforcement Learning for Intrusion Detection(Springer Science and Business Media Deutschland GmbH, 2023) Saad,A.M.S.E.; Yildiz,B.Network-based technologies such as cloud computing, web services, and Internet of Things systems are becoming widely used due to their flexibility and preeminence. On the other hand, the exponential proliferation of network-based technologies exacerbated network security concerns. Intrusion takes an important share in the security concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solving the intrusion problem and ensuring the secure delivery of network-based technologies and services. In this paper, we propose a novel approach using deep reinforcement learning to detect intrusions to make network applications more secure, reliable, and efficient. As for the reinforcement learning approach, Deep Q-learning is used alongside a custom-built Gym environment that mimics network attacks and guides the learning process. The NSL-KDD dataset is used to create the reinforcement learning environment to train and evaluate the proposed model. The experimental results show that our proposed reinforcement learning approach outperforms other related solutions in the literature, achieving an accuracy that exceeds 93%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Conference Object Citation - Scopus: 4Convolution Neural Network (cnn) Based Automatic Sorting of Cherries(Institute of Electrical and Electronics Engineers Inc., 2021) Park,H.; Khan,M.U.Cherries are spring fruits enriched with nutrients, and are easily available in food markets around the world. Due to their excess demand, many enterprises solely focused on their processing. Cherries are especially susceptible to pathological-, physiological-diseases and structural degradation due to their soft outer skin. The post-harvest life of the fruit is limited by various characteristics. The agricultural industry has also been at the forefront to get benefits from the advanced machine learning tools. This study presents an image processing-based system for sorting cherries using the convolutional neural network (CNN). For this study, Prunus avium L cherries of export quality, available in Turkey, tagged as ‘0900 Ziraat’, are used. Surprisingly, there exists no dataset for these cherries; hence, we developed our dataset. Through the proposed approach based upon U-Net, the binary classification accuracy of 99% is achieved. Clear identification is demonstrated by the test results of varying mixture ratios of good and bad cherries. It can therefore be said that for cherry sorting and grading, U-Net can be applied as a reliable and promising machine learning tool. ©2021 IEEEConference Object A Comparison of Neural Network Approaches for Network Intrusion Detection(Springer international Publishing Ag, 2020) Oney, Mehmet Ugur; Peker, SerhatNowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.Article Citation - WoS: 10Citation - Scopus: 16Student Engagement Research Trends of Past 10 Years: a Machine Learning-Based Analysis of 42,000 Research Articles(Springer, 2023) Gurcan, Fatih; Erdogdu, Fatih; Cagiltay, Nergiz Ercil; Cagiltay, KursatStudent engagement is critical for both academic achievement and learner satisfaction because it promotes successful learning outcomes. Despite its importance in various learning environments, research into the trends and themes of student engagement is scarce. In this regard, topic modeling, a machine learning technique, allows for the analysis of large amounts of content in any field. Thus, topic modeling provides a systematic methodology for identifying research themes, trends, and application areas in a comprehensive framework. In the literature, there is a lack of topic modeling-based studies that analyze the holistic landscape of student engagement research. Such research is important for identifying wide-ranging topics and trends in the field and guiding researchers and educators. Therefore, this study aimed to analyze student engagement research using a topic modeling approach and to reveal research interests and trends with their temporal development, thereby addressing a lack of research in this area. To this end, this study analyzed 42,517 peer-reviewed journal articles published from 2010 to 2019 using machine learning techniques. According to our findings, two new dimensions, "Community Engagement" and "School Engagement", were identified in addition to the existing ones. It is also envisaged that the next period of research and applications in student engagement will focus on the motivation-oriented tools and methods, dimensions of student engagement, such as social and behavioral engagement, and specific learning contexts such as English as a Foreign Language "EFL" and Science, Technology, Engineering and Math "STEM".Article Citation - WoS: 6Citation - Scopus: 10Beyond Rouge: a Comprehensive Evaluation Metric for Abstractive Summarization Leveraging Similarity, Entailment, and Acceptability(World Scientific Publ Co Pte Ltd, 2024) Briman, Mohammed Khalid Hilmi; Yıldız, Beytullah; Yildiz, Beytullah; Yıldız, BeytullahA vast amount of textual information on the internet has amplified the importance of text summarization models. Abstractive summarization generates original words and sentences that may not exist in the source document to be summarized. Such abstractive models may suffer from shortcomings such as linguistic acceptability and hallucinations. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) is a metric commonly used to evaluate abstractive summarization models. However, due to its n-gram-based approach, it ignores several critical linguistic aspects. In this work, we propose Similarity, Entailment, and Acceptability Score (SEAScore), an automatic evaluation metric for evaluating abstractive text summarization models using the power of state-of-the-art pre-trained language models. SEAScore comprises three language models (LMs) that extract meaningful linguistic features from candidate and reference summaries and a weighted sum aggregator that computes an evaluation score. Experimental results show that our LM-based SEAScore metric correlates better with human judgment than standard evaluation metrics such as ROUGE-N and BERTScore.Conference Object Citation - Scopus: 2AI-Driven Drought Management System: A Turkish Case Study(Institute of Electrical and Electronics Engineers Inc., 2023) Sabamehr,M.; Ekin,C.C.Nowadays, drought is one of the trending topics in the world that has turned into a challenge for the world. By developing countries and cities worldwide, especially in the economic aspect, governments started to damage the environment such as through the use of fossil fuels, pollution of the seas, unregulated use of fresh water also deforestation for personal purposes. The presented research aims to change the format of drought mitigation strategies from traditional ways into the up to date treats. Leveraging AI technologies, including machine learning algorithms and data analytics, a comprehensive AI-driven drought management system is designed and implemented. In this system, inconsistent data have been obtained from the Ministry of Agriculture and Forestry organization and transformed into insightful data and analyzed in real-Time style to provide the status of agricultural products in Turkey. This research contributes to the fields of environmental science and agriculture by innovatively augmenting traditional approaches with AI-driven solutions. Ultimately, our research offers a means to monitor weather conditions in different regions of Turkey, moving beyond manual drought prediction and guesswork that were prevalent in previous systems. Additionally, it facilitates the evaluation of vegetation health by considering precipitation and temperature averages in each area. © 2023 IEEE.Article Citation - WoS: 12Citation - Scopus: 20A Hybrid Approach for Predicting Customers' Individual Purchase Behavior(Emerald Group Publishing Ltd, 2017) Peker, Serhat; Kocyigit, Altan; Eren, P. ErhanPurpose - Predicting customers' purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers' individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach - The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings - A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications - The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value - This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers' individual purchase behaviors.Article Citation - WoS: 2Citation - Scopus: 3Real-Time Anomaly Detection System Within the Scope of Smart Factories(Springer, 2023) Bayraktar, Cihan; Karakaya, Ziya; Gokcen, HadiAnomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting of interconnected devices. Synthetic data were preferred in the study because it has difficulties such as high cost and a long time to obtain real anomaly data naturally for learning and testing processes. In order to obtain the necessary synthetic data, a simulation was developed by taking the popcorn production systems as an example. Multi-class anomalies were defined in the obtained data set, and the analysis performances were tested by creating learning models with AutoML libraries. In the field of production systems, while studies on anomaly detection generally focus on whether there is an anomaly in the system, it is aimed to determine which type of anomaly occurs in which device, together with the detection of anomaly by using multi-class tags in the data of this study. As a result of the tests, the Auto-Sklearn library presented the learning models with the highest performance on all data sets. As a result of the study, a real-time anomaly detection system was developed on dynamic data by using the obtained learning models.Review Citation - WoS: 3Citation - Scopus: 5A Systematic Review on Smart Waste Biomass Production Using Machine Learning and Deep Learning(Springer, 2023) Peng, Wei; Sadaghiani, Omid KarimiThe utilization of waste materials, as an energy resources, requires four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the waste biomass production step. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in the surveying and collecting the applications of Machine Learning in the waste biomass. To fill this gap with the current work, the kinds and resources of waste biomass as well as the role of Machine Learning and Deep Learning in their development are reviewed. Moreover, the storage and transportation of the wastes are surveyed followed by the application of Machine Learning and Deep Learning in these areas. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the waste collecting quality and quality, improve the predictions, diminish the losses, as well as increase storage and transformation conditions.Review Citation - WoS: 8Citation - Scopus: 11A Review on the Applications of Machine Learning and Deep Learning in Agriculture Section for the Production of Crop Biomass Raw Materials(Taylor & Francis inc, 2023) Peng, Wei; Karimi Sadaghiani, OmidThe application of biomass, as an energy resource, depends on four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the biomass production step with focusing on agriculture crops. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in collecting the applications of Machine Learning in crop biomass production. To fill this gap by the current work, the origin of biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. Then, the kinds and resources of biomass as well as the role of machine learning in these fields are reviewed. Meanwhile, the sustainable production of farming-origin biomass and the effective factors in this issue are explained, and the application of Machine Learning in these areas are surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in crop biomass production areas to enhance the crops production quantity, quality, and sustainability, improve the predictions, decrease the costs, and diminish the products losses. According to the statistical analysis, in 19% of the studies conducted about the application of Machine Learning and Deep Learning in crop biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Random Forest (RF) and Super Vector Machine (SVM) are the second and third most-utilized algorithms applied in 17% and 15% of studies, respectively. Meanwhile, 26% of studies focused on the applications of Machine Learning and Deep Learning in the sugar crops. At the second and third places, the starchy crops and algae with 23% and 21% received more attention of researchers in the utilization of Machine Learning and Deep Learning techniques.
