Yazar "Tiryaki, Ali Murat" seçeneğine göre listele

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    An Ontology Based Approach for Next Generation Customer Relationship Management Systems
    (İsmail SARITAŞ, 2020) Tiryaki, Ali Murat; Atalay, Muhammet
    Customer relationship management - CRM systems aim to increase customer satisfaction and loyalty to the company through actions that can be performed by monitoring and analyzing customer behaviours for companies. In these systems, the data obtained from different sources such as sales process, service, and call center are interpreted by the CRM experts as a whole, and the necessary action is taken for the customer. With the rapid progress in information technologies, the diversity of channels that are used to collect data, the size and dynamism of the data have increased. This situation has made it impossible for experts to wholly interpret big-size data from a wide variety of sources in a short period of time. In the CRM area, there is a need for technologies that automatically integrate the data obtained from several sources and can interpret it semantically. In this study, an ontology-based approach is proposed, which allows automatic semantic interpretations on the data obtained from different sources. Based on the proposed approach, a tool has been developed that can unify data from different CRM systems in a common format and make customer-centric interpretations of this data. The tool continuously monitors the systems it is integrated with, and when it detects the conditions defined by the users on the data, it can automatically perform the actions determined by the users. The tool developed was experienced by integrating a CRM system of a company and the evaluation results obtained in this process were discussed.
  • Küçük Resim
    Öğe
    An Ontology based product recommendation system for next generation e-retail
    (Taylor and Francis Ltd., 2023) Tiryaki, Ali Murat; Yücebaş, Sait Can
    The number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.
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    Bilgisayar ağ teknolojileri ve tasarım teknikleri
    (Çanakkale Onsekiz Mart Üniversitesi, 2001) Tiryaki, Ali Murat; Baysal, Hüsnü
    oz Tezin amacı yerel alan ağ teknolojilerini tasarım kriterleri açısından incelemektir. Bu amaçla varolan ve gelişmekte olan ağ teknolojileri karşılaştırmalı olarak incelenmiştir. Örnek olarak Çanakkale Onsekiz Mart Üniversitesi için bir ağ tasarımı hazırlanmıştır. Ağ sistemlerinin gelecekteki gelişimlerine yönelik tahmin ve yorumlar yapılmıştır. Anahtar Kelimeler Bilgisayar ağlan, OSI, kablosuz ağ sistemleri, Ethernet, ağ protokolleri, LAN topolojileri,
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    Discrimination accuracy of haploid and diploid maize seeds using NIR spectroscopy coupled with different machine learning algorithms and data pretreatment methods
    (Taylor & Francis Inc, 2025) Kahriman, Fatih; Polat, Adem; Tiryaki, Ali Murat; Eskizeybek, Volkan; Fidan, Sertug; Songur, Umut
    Spectral data collected at the single seed level allows determination of the biochemical content of the seed sample, as well as to identify the seed class. NIR (Near Infrared) spectroscopy provides a more precise method for differentiating haploid and diploid seeds in maize than traditional visual examination. In this study, classification models that can be used in the separation of haploid and diploid maize seeds were developed using spectra collected between 900-1700 nm from a single maize seed. In the study, 427 diploid and 311 haploid samples obtained by crossing 10 donor materials and 3 inducer lines and separated by eye according to the Navajo marker were used. Spectral measurements were conducted over the wavelength range of 900 to 1700 nm for each sample. The robust PCA (Principal Component Analysis) method was used to detect spectral outliers. Spectral data were treated with none, FD (First Derivative), SD (Second Derivative), SNV (Standard Normal Variate), and their binary combinations. Logistic Regression, Support Vector Machine with a linear kernel (SVM-C), Random Forest, and XGBoost methods were employed as machine learning techniques. The performance of the developed machine learning models was assessed using metrics such as Sensitivity, Specificity, Recall, F1-Score, and Accuracy. The Boosting method demonstrated the best performance with 94.9% accuracy, 95.1% sensitivity, 94% specificity, and an F1 Score of 96%, particularly when using raw reflectance data. These results obtained from raw data show that high accuracy can be achieved in classification models without requiring additional preprocessing steps. D2 preprocessing was found to be unsuitable for intact seed spectra, whereas SNV and D1 applications improved the classification success of other modeling techniques. The study revealed that the Boosting-Raw combination is a powerful and feasible method for classifying haploid and diploid samples.
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    Implementation of the FIPA English Auction Protocol with a Role-Based Multi Agent Framework
    (Osman SAĞDIÇ, 2021) Tiryaki, Ali Murat; Güven, Varol
    Agents are autonomous and complex software units. One of the major characteristics of agents is sociality. In general, agents do not work single to achieve their objectives. They try to achieve own objectives by working collaboratively with other agents situated in same multi agent system(s). Collaboration among agents situated in same or different agent platform(s) is realizable if and only if correspondence among agents is based on specific communication and interaction standards. Definition of standards increase acceptability of agent technology by industry. In this study, the interaction protocol named as “English Auction” which has been specified by FIPA was implemented by using the role-based development environment provided by SEAGENT framework. As a conclusion of the study, the agent roles and the agent plans that achieve the goals of these roles in the FIPA English Auction Protocol were obtained. These generic plans can be simply reused in other multi agent system applications developed using SEAGENT framework
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    Load sharing based on moving roles in multiagent systems
    (Tubitak Scientific & Technological Research Council Turkey, 2012) Bora, Sebnem; Tiryaki, Ali Murat; Dikenelli, Oguz
    In this paper, we present a load-sharing approach based on the refactoring of agents. According to our approach, the role(s) that makes an agent overloaded is identified and transferred to less loaded agents. The excess workload of this heavily loaded agent is then transferred to the new agent. This approach defines a new agent, called the monitor agent, which monitors the workload of agents in the organization and decides about the refactoring of the agents. The monitor agent uses the platform ontology, which explicitly describes the components of the agent organization, including agents and their roles, plans, and workloads. This ontology is updated by the monitor agent in every monitor cycle.
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    Yaygın Kullanılan Çevik Yöntemlerin Küçük Ölçekli Bir Uygulamanın Geliştirilmesi Sürecinde Değerlendirilmesi Üzerine Bir Çalışma
    (Osman SAĞDIÇ, 2021) Tiryaki, Ali Murat
    Yazılım yaşam döngüsünde dış etkenlerden gelecek değişikliklerin kaçınılmaz olduğunu kabul ederek bu değişikliklere daha hzılı ve daha kolay cevap verilebilmesini hedefleyen çevik yazılım geliştirme modelinin ticari sektördeki kullanımı giderek artmaktadır. Çevik geliştirme yaklaşımının tanıtılmasından sonra bu modeli temel alarak farklı bakış açıları ile çözümler sunan pek çok çevik yöntem önerilmiştir. Yöntemlerin belirli bir proje için uygunluğu projenin tipi, büyüklüğü, geliştirme ekibinin büyüklüğü ve deneyimi, organizasyonel etkenler gibi parametreler değerlendirilerek belirlenmelidir. Çevik yöntemlerin sayısının artması ile, projeler için bu yöntemlerin uygunluğunun belirlenerek uygun yöntemin seçimi önemli bir konu haline gelmiştir. Bu çalışmada çevik yöntemler arasında en yaygın olarak kullanılmakta olan Aşırı Programlama (Extreme Programming), SCRUM, Rasyonel Birleştirilmiş Süreç (Rational Unified Process – RUP) ve Kanban yöntemlerinin küçük ölçekli projelerde kullanımının değerlendirilmesi hedeflenmiştir. Bu hedef doğrultusunda bu dört yöntem bir yükseklisans dersi bünyesinde proje olarak geliştirilen küçük çaplı bir uygulamanın geliştirilmesinde farklı ekipler tarafından deneyimlenerek belirli parametrelere göre karşılaştırılmıştır