WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Author: search.filters.author.Dogdu, ErdoganPublisher: search.filters.publisher.Ieee

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Now showing 1 - 7 of 7
  • Conference Object
    Spend Portal: Linked Data Discovery Using Sparql Endpoints
    (Ieee, 2017) Yumusak, Semih; Aras, Riza Emre; Uysal, Elif; Dogdu, Erdogan; Kodaz, Halife; Oztoprak, Kasim
    We present the project SpEnD, a complete SPARQL endpoint discovery and analysis portal. In a previous study, the SPARQL endpoint discovery and analysis steps of the SpEnD system were explained in detail. In the SpEnD portal, the SPARQL endpoints are extracted from the web by using web crawling techniques, monitored and analyzed by live querying the endpoints systematically. After many sustainability improvements in the SpEnD project, the SpEnD system is now online as a portal. SpEnD portal currently serves 1487 SPARQL endpoints, out of which 911 endpoints are uniquely found by SpEnD only when compared to the other existing SPARQL endpoint repositories. In this portal, the analytic results and the content information are shared for every SPARQL endpoint. The endpoints stored in the repository are monitored and updated continuously.
  • Conference Object
    Citation - WoS: 10
    Citation - Scopus: 21
    Multi-Label Classification of Text Documents Using Deep Learning
    (Ieee, 2020) Mohammed, Hamza Haruna; Dogdu, Erdogan; Gorur, Abdul Kadir; Choupani, Roya
    Recently, studies in the field of Natural Language Processing and its related applications continue to mount up. Machine learning is proven to be predominantly data-driven in the sense that generic model building methods are used and then tailored to specific application domains. Needless to say, this has proven to be a very effective approach in modeling the complicated data dependencies we frequently experience in practice, making very few assumptions, and allowing the information to talk for themselves. Examples of these applications can be found in chemical process engineering, climate science, healthcare, and linguistic processing systems for natural languages, to name a few. Text classification is one of the important machine learning tasks that is used in many digital applications today; such as in document filtering, search engines, document management systems, and many more. Text classification is the process of categorizing of text documents into a given set of labels. Furthermore, multi-label text classification is the task of categorization of text documents into one or more labels simultaneously. Over the years, many methods for classifying text documents have been proposed, including the popularly known bag of words (BoW) method, support vector machine (SVM), tree induction, and label-vector embedding, to mention a few. These kinds of tools can be used in many digital applications, such as document filtering, search engines, document management systems, etc. Lately, deep learning-based approaches are getting more attention, especially in extreme multi-label text classification case. Deep learning has proven to be one of the major solutions to many machine learning applications, especially those involving high-dimensional and unstructured data. However, it is of paramount importance in many applications to be able to reason accurately about the uncertainties associated with the predictions of the models. In this paper, we explore and compare the recent deep learning-based methods for multi-label text classification. We investigate two scenarios. First, multi-label classification model with ordinary embedding layer, and second with Glove, word2vec, and FastText as pre-trained embedding corpus for the given models. We evaluated these different neural network model performances in terms of multi-label evaluation metrics for the two approaches, and compare the results with the previous studies.
  • Conference Object
    Citation - WoS: 1
    Topic Distribution Constant Diameter Overlay Design Algorithm (td-Cd
    (Ieee, 2017) Oztoprak, Kasim; Dogdu, Erdogan; Layazali, Sina
    Publish/subscribe communication systems, where nodes subscribe to many different topics of interest, are becoming increasingly more common in application domains such as social networks, Internet of Things, etc. Designing overlay networks that connect the nodes subscribed to each distinct topic is hence a fundamental problem in these systems. For scalability and efficiency, it is important to keep the maximum node degree of the overlay in the publish/subscribe system low. Ideally one would like to be able not only to keep the maximum node degree of the overlay low, but also to ensure that the network has low diameter. We address this problem by presenting Topic Distribution Constant Diameter Overlay Design Algorithm (TD-CD-ODA) that achieves a minimal maximum node degree in a low-diameter setting. We have shown experimentally that the algorithm performs well in both targets in comparison to the other overlay design algorithms.
  • Conference Object
    Perceptions, Expectations and Implementations of Big Data in Public Sector
    (Ieee, 2018) Ozbayoglu, Murat; Yazici, Ali; Karakaya, Ziya; Dogdu, Erdogan
    Big Data is one of the most commonly encountered buzzwords among IT professionals nowadays. Technological advancements in data acquisition, storage, telecommunications, embedded systems and sensor technologies resulted in huge inflows of streaming data coming from variety of sources, ranging from financial streaming data to social media tweets, or wearable health gadgets to drone flight logs. The processing and analysis of such data is a difficult task, but as appointed by many IT experts, it is crucial to have a Big Data Implementation plan in today's challenging industry standards. In this study, we performed a survey among IT professionals working in the public sector and tried to address some of their implementation issues and their perception of Big Data today and their expectations about how the industry will evolve. The results indicate that most of the public sector professionals are aware of the current Big Data requirements, embrace the Big Data challenge and are optimistic about the future.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 12
    Mis-Iot: Modular Intelligent Server Based Internet of Things Framework With Big Data and Machine Learning
    (Ieee, 2018) Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan; Onal, Aras Can; Berat Sezer, Omer
    Internet of Things world is getting bigger everyday with new developments in all fronts. The new IoT world requires better handling of big data and better usage with more intelligence integrated in all phases. Here we present MIS-IoT (Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning) framework, which is "modular" and therefore open for new extensions, "intelligent" by providing machine learning and deep learning methods on "big data" coming from IoT objects, "server-based" in a service-oriented way by offering services via standart Web protocols. We present an overview of the design and implementation details of MIS-IoT along with a case study evaluation of the system, showing the intelligence capabilities in anomaly detection over real-time weather data.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    Improvement of General Inquirer Features With Quantity Analysis
    (Ieee, 2018) Karadeniz, Talha; Dogdu, Erdogan
    General Inquirer is a word-affect association vocabulary having 11896 entries. Ranging from rectitude to expressiveness, it comes with a flavor of categories. Despite the extensive content, a mapping from "To be or not to be." to "How much?" can be beneficial for word representation. In this work, we apply a method of window based analysis to obtain real valued General Inquirer attributes. Sentence Completion task is chosen to calculate the effectiveness of the operation. After whitening post-process, total cosine similarity convention is followed to concentrate on embedding improvement. Results indicate that our quantity focused variant is considerable.
  • Conference Object
    Citation - WoS: 35
    Citation - Scopus: 58
    Weather Data Analysis and Sensor Fault Detection Using an Extended Iot Framework With Semantics, Big Data, and Machine Learning
    (Ieee, 2017) Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan; Onal, Aras Can; Berat Sezer, Omer
    In recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a rising trend in this field due to its ability to extract hidden features and patterns even in highly complex datasets. In this study, we used our Big Data IoT Framework in a weather data analysis use case. We implemented weather clustering and sensor anomaly detection using a publicly available dataset. We provided the implementation details of each framework layer (acquisition, ETL, data processing, learning and decision) for this particular use case. Our chosen learning model within the library is Scikit-Learn based k-means clustering. The data analysis results indicate that it is possible to extract meaningful information from a relatively complex dataset using our framework.