45 research outputs found
Tatar Author Möhemmet Mehdiyev And Hıs Novel Named Frontovıklar (Revıew Text)
No full textÇağdaş Tatar edebiyatının önemli isimlerinden, Abdullah Tukay Devlet Ödülü ve
Tataristan Cumhuriyeti Millî Yazarı unvanı sahibi Möhemmet Mehdiyev, Tatar Türkleri
tarafından sevilen ve eserleri ilgiyle okunan bir yazardır. Tatar nesrine getirdiği yeniliklerle de
tanınan yazar, edebiyat bilimci ve eğitimci kimlikleriyle de Tatar içtimai hayatında birçok
çalışmaya imza atmış, Tatar Türklüğüne önemli hizmetlerde bulunmuştur. Tatarların özel
önem verdiği yazarlarından olan Mehdiyev'e ve onun eserlerine ait Türkiye'de bu zamana
kadar yapılmış kapsamlı hiçbir inceleme bulunmamaktadır. Bu konudaki büyük bir boşluğu
dolduracağı düşünülen bu tez çalışması, XX. yüzyıl Tatar edebiyatına, özellikle de Tatar
romanına dair bilgi verirken, esas olarak ise dönemin önemli yazarlarından Möhemmet
Mehdiyev'in biyografisi (hayatı-edebi kişiliği-eserleri) ve Frontoviklar adlı ilk romanının
incelenmesi başlıklarını içermektedir. Ayrıca Türkiye Türkçesine de aktarılan romanda;
Sovyetler Birliği döneminde, II. Dünya Savaşı sonrasında öğretmenlik yapan cephedekilerin
ve eğitim uğruna emek harcayan Tatarların hayatlarının anlatıldığı görülür. Bu sebeple eserde
sosyolojik ve tarihî unsurlar yoğun bir şekilde yer alır. Hem siyasi hem de toplumsal sonuçlar
barındıran roman, edebiyat sosyolojisi araştırmaları için bize birçok veri sunar ve günümüz
okuyucusuna dönemin sosyal ve siyasi atmosferini anlama imkânı tanırOne of the important names for contemporary Tatar literature, who has Gabdulla Tukay
Governmental Award and the title National Author of Tatarstan Republic, Möhemmet
Mehdiyev is a author, whose the works are loved and readed interestedly by Tatar Turks. The
writer that also known with the innovations brought by him to Tatar prose, has undertaken
several studies in Tatar social life with own litterateur and educator identities, and has made
significant service for Tatar Turks. So far there is no any comprehensive study in Turkey on
Mehdiyev that has particular attention of Tatars and on his works. This work that will fill a
large gap in this issue includes the following titles: information about the Tatar literature and
the novel of 20th century, Möhemmet Mehdiyev's biography (his life, literary personality and
works) and the review of the first novel of him Frontoviklar. In the novel also translated to
Turkish is told the life stories of the Tatars working as a teacher during the Soviet Union after
World War II. Therefore sociological and historical factors take place intensively in the novel.
The novel contains both political and social consequences, gives us a lot of data for the
researches on sociology of literature and allows to understand the social and political
atmosphere of the period to contemporary readers
Tatar Yazar Möhemmet Mehdiyev Ve Frontoviklar Adlı Romanı (İnceleme Metin)
No full textÇağdaş Tatar edebiyatının önemli isimlerinden, Abdullah Tukay Devlet Ödülü ve Tataristan Cumhuriyeti Millî Yazarı unvanı sahibi Möhemmet Mehdiyev, Tatar Türkleri tarafından sevilen ve eserleri ilgiyle okunan bir yazardır. Tatar nesrine getirdiği yeniliklerle de tanınan yazar, edebiyat bilimci ve eğitimci kimlikleriyle de Tatar içtimai hayatında birçok çalışmaya imza atmış, Tatar Türklüğüne önemli hizmetlerde bulunmuştur. Tatarların özel önem verdiği yazarlarından olan Mehdiyev'e ve onun eserlerine ait Türkiye'de bu zamana kadar yapılmış kapsamlı hiçbir inceleme bulunmamaktadır. Bu konudaki büyük bir boşluğu dolduracağı düşünülen bu tez çalışması, XX. yüzyıl Tatar edebiyatına, özellikle de Tatar romanına dair bilgi verirken, esas olarak ise dönemin önemli yazarlarından Möhemmet Mehdiyev'in biyografisi (hayatı-edebi kişiliği-eserleri) ve Frontoviklar adlı ilk romanının incelenmesi başlıklarını içermektedir. Ayrıca Türkiye Türkçesine de aktarılan romanda; Sovyetler Birliği döneminde, II. Dünya Savaşı sonrasında öğretmenlik yapan cephedekilerin ve eğitim uğruna emek harcayan Tatarların hayatlarının anlatıldığı görülür. Bu sebeple eserde sosyolojik ve tarihî unsurlar yoğun bir şekilde yer alır. Hem siyasi hem de toplumsal sonuçlar barındıran roman, edebiyat sosyolojisi araştırmaları için bize birçok veri sunar ve günümüz okuyucusuna dönemin sosyal ve siyasi atmosferini anlama imkânı tanırOne of the important names for contemporary Tatar literature, who has Gabdulla Tukay Governmental Award and the title National Author of Tatarstan Republic, Möhemmet Mehdiyev is a author, whose the works are loved and readed interestedly by Tatar Turks. The writer that also known with the innovations brought by him to Tatar prose, has undertaken several studies in Tatar social life with own litterateur and educator identities, and has made significant service for Tatar Turks. So far there is no any comprehensive study in Turkey on Mehdiyev that has particular attention of Tatars and on his works. This work that will fill a large gap in this issue includes the following titles: information about the Tatar literature and the novel of 20th century, Möhemmet Mehdiyev's biography (his life, literary personality and works) and the review of the first novel of him Frontoviklar. In the novel also translated to Turkish is told the life stories of the Tatars working as a teacher during the Soviet Union after World War II. Therefore sociological and historical factors take place intensively in the novel. The novel contains both political and social consequences, gives us a lot of data for the researches on sociology of literature and allows to understand the social and political atmosphere of the period to contemporary readers
A New Hybrid Approach For Forecasting Interest Rates
No full textAbstractThe dynamic, non-linear, volatile and complex nature of interest rates makes it hard to predict their future movements. In order to deal with these complexities, the authors propose a two-stage neuro-hybrid forecasting model. In the initial data preprocessing stage, multiple regression analysis is implemented to determine the variables that have the strongest prediction ability. The selected variables are then provided as inputs to a Fuzzy Inference Neural Network to forecast future interest rate values. The proposed hybrid model is implemented using data from the U.S. interest rate market
PRESCRIPTIVE PROCESS ANALYTICS WITH DEEP LEARNING AND EXPLAINABLE ARTIFICIAL INTELLIGENCE
No full textThe proliferation of enterprise information systems allows to capture the digital footprints generated over various user interaction phases. Such transaction data describe the details of the user interactions and the underlying processes on a fine granular level. Building capabilities to analyse the transactional process data is a key success differentiation factor that enables to grasp the user behavior more effectively. In this study, we aim to propose a prescriptive process analytics approach by combining the approaches from the machine learning, process mining and explainable artificial intelligence (XAI) research domains. After examining predictability of the business processes by employing an advanced deep learning approach, this study applies for the first time both in the business process prediction and customer journey analytics research domains an XAI technique, Partial Dependence Plots (PDP), to generate causal explanations. The real-life process data delivered by various information systems of a Dutch autonomous administrative authority were used to investigate the appropriateness of the proposed prescriptive analytics approach. The applied deep learning approach achieves a very good performance with an Area Under ROC Curve of 0.933. The generated explanations with PDP give insights to identify a set of alternative courses-of-actions to prevent the undesired outcomes
Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates
No full textAbstractThe following paper discusses the use of a hybrid model for the prediction of short-term US interest rates. The model consists of a differential evolution-based fuzzy type-2 clustering with a fuzzy type-2 inference neural network, after input preprocessing with multiple regression analysis. The model was applied to forecast the US 3- Month T-bill rates. Promising model performance was obtained as measured using root mean square error
Interest Rate Prediction: A Neuro-hybrid Approach with Data Preprocessing
No full textThe following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed model is compared against nonparametric models, such as locally weighted regression and least squares support vector machines, along with two linear benchmark models, the autoregressive model and the random walk model. The root mean square error is reported for comparison. © 2014 Taylor & Francis
LOCAL POST-HOC EXPLANATIONS FOR PREDICTIVE PROCESS MONITORING IN MANUFACTURING
No full textThis study proposes an innovative explainable predictive quality analytics solution to facilitate the data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate the relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
No full textInterpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review
No full textThis study presents a systematic literature review on the explainability and interpretability
of machine learning models within the context of predictive process monitoring. Given the
rapid advancement and increasing opacity of artificial intelligence systems, understanding
the "black-box" nature of these technologies has become critical, particularly for models
trained on complex operational and business process data. Using the PRISMA framework,
this review systematically analyzes and synthesizes the literature of the past decade, in cluding recent and forthcoming works from 2025, to provide a timely and comprehen sive overview of the field. We differentiate between intrinsically interpretable models and
more complex systems that require post-hoc explanation techniques, offering a structured
panorama of current methodologies and their real-world applications. Through this rig orous bibliographic analysis, our research provides a detailed synthesis of the state of
explainability in predictive process mining, identifying key trends, persistent challenges
and a clear agenda for future research. Ultimately, our findings aim to equip researchers
and practitioners with a deeper understanding of how to develop and implement more
trustworthy, transparent and effective intelligent systems for predictive process analytics
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
No full textThis paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
