Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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  • Book Part
    Artificial Intelligence in Dentistry
    (CRC Press, 2025) Cagiltay, Nergiz Ercil; Kılıçarslan, Mehmet Ali; Basmaci, Fulya
    Today, with advanced technologies, collecting detailed and big data from the environment and analyzing it using intelligent techniques has become possible, providing important insights into phenomena as well as future predictions. Big data is characterized by its high volume, velocity, and variety. Here, the volume is the amount and size of the data, which is measured in terabytes, petabytes, exabytes, or zettabytes. Velocity is the offered form of big data, which can be batch, near-real-time, real-time, or streaming. Finally, variety is the structure of the big data, which can be structured, such as in relational or dimensional models, as in warehouses, or unstructured, which is stored without any organization. It can also be in semi-structured form, where the data is unstructured but there is some meta-data or some tags for describing the data. Today, these forms of data are being collected for different dental purposes in several formats, such as images, raw data, or coordinates. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Perceptions, Expectations and Implementations of Big Data in Public Sector
    (IEEE, 2018) Doğdu, Erdoğan; Özbayoğlu, Murat; Yazıcı, Ali; Karakaya, Ziya
    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 - Scopus: 1
    Unmanned Surface Vehicle With Drown Map System
    (Institute of Electrical and Electronics Engineers Inc., 2019) Al-Dakheel, S.; Ozyer, S.T.; Can Ozdemir, F.; Karadag, A.; Al-Tekreeti, M.
    The drowning cases that are happened whether in sea or ocean specially increasing cases among refugees requires to employ the advanced technology and tools to encounter this problem. Internet of Things (IoT) and cloud computing techniques will be applied in marine sector to address the problems of drowning. Internet of things that represented in sensors, actuators...etc. generate a vast number of data that globally known as a Big data, due to the limited storage and processing of the physical units with the big number of data, a cloud computing is adopted to solve this problem. In this paper, an Unmanned Surface Vehicle (USV) contains GPS and Force Sensitive Resistor (FSR) sensors will be built to discover the location and approximate number of people exposed to drowning. In addition to USV, a real-time map system will be carried out to display this information. All the data and information that generating from the sensors and map system will be stored in a cloud in real-time. This work is a part of the research and development project which is accepted in Turkey Government with the collaboration of the University and Industry. © 2019 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Distributed Query Processing and Reasoning Over Linked Big Data
    (Springer Science and Business Media Deutschland GmbH, 2022) Mohammed, H.H.; Doğdu, E.; Choupani, R.; Zarbega, T.S.A.
    The enormous amount of structured and unstructured data on the web and the need to extract and derive useful knowledge from this big data make Semantic Web and Big Data Technology explorations of paramount importance. Open semantic web data created using standard protocols (RDF, RDFS, OWL) consists of billions of records in the form of data collections called “linked data”. With the ever-increasing linked big data on the Web, it is imperative to process this data with powerful and scalable techniques in distributed processing environments such as MapReduce. There are several distributed RDF processing systems, including SemaGrow, FedX, SPLENDID, PigSPARQL, SHARD, SPARQLGX, that are developed over the years. However, there is a need for computational and qualitative comparison of the differences and similarities among these systems. In this paper, we extend a previous comparative analysis to a diverse study with respect to qualitative and quantitative analysis views, through an experimental approach for these distributed RDF systems. We examine each of the selected RDF query systems with respect to the implementation setup, system architecture, underlying framework, and data storage. We use two widely used RDF benchmark datasets, FedBench and LUBM. Furthermore, we evaluate and examine their performances in terms of query execution time, thus, analyzing how those different types of large-scale distributed query engines, support long-running queries over federated data sources and the query processing times for different queries. The results of the experiments in this study show that SemaGrow distributed system performs more efficiently compared to FedX and Splendid, even though in smaller queries the former performs slower. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • 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: 140
    Citation - Scopus: 214
    Intrusion Detection Using Big Data and Deep Learning Techniques
    (Assoc Computing Machinery, 2019) Dogdu, Erdogan; Faker, Osama
    In this paper, Big Data and Deep Learning Techniques are integrated to improve the performance of intrusion detection systems. Three classifiers are used to classify network traffic datasets, and these are Deep Feed-Forward Neural Network (DNN) and two ensemble techniques, Random Forest and Gradient Boosting Tree (GBT). To select the most relevant attributes from the datasets, we use a homogeneity metric to evaluate features. Two recently published datasets UNSW NB15 and CICIDS2017 are used to evaluate the proposed method. 5-fold cross validation is used in this work to evaluate the machine learning models. We implemented the method using the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library to implement the deep learning technique while the ensemble techniques are implemented using Apache Spark Machine Learning Library. The results show a high accuracy with DNN for binary and multiclass classification on UNSW NB15 dataset with accuracies at 99.16% for binary classification and 97.01% for multiclass classification. While GBT classifier achieved the best accuracy for binary classification with the CICIDS2017 dataset at 99.99%, for multiclass classification DNN has the highest accuracy with 99.56%.