Scopus İndeksli Yayınlar Koleksiyonu

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

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Now showing 1 - 10 of 24
  • Article
    Enhanced Mapping of Rainfall Induced Landslide Susceptibility Using a Deep Feedforward Neural Network with Soft Computing
    (Techno-Press, 2026) Zhu, Licai; Akagic, Amila; Nanehkaran, Yaser A.; Pusatli, Tolga; Mahmud, Elkhan; Jian, Dong
    The presented study attempted to propose enhanced rainfall-induced landslide susceptibility mapping method by using the Deep Feedforward Neural Network (DFNN) which is developed for analysis the non-liner feature detection in landslide susceptibility analysis. To evaluate our approach, a comprehensive dataset of triggering factors was compiled, encompassing historical landslide occurrences with total of 107 records, rainfall data, geological information, seismicity, human-activities, and topographic attributes. Through rigorous training and testing procedures, the DFNN demonstratedsuperior ability for generalization and superior performance. The effectiveness of the selected method is demonstrated on the data from the Zanjan County, known for its diverse geographical, geological, and hydrological characteristics, which are pivotal factors in mapping of landslide susceptibility. Results showcased a substantial enhancement in the accuracy of mapping of rainfall-induced landslide susceptibility for the Zanjan County, which is compared with benchmark learning classifiers. According to the results of the study, it appeared that the northeastern and southwestern area of the Zanjan County can be deemed to have a high to very-high risk of landslide occurrence, which is validated via benchmark classifiers. The western part of the Zanjan County was observed to have a very low to low risk.
  • Conference Object
    Arabic Sign Language Paradigm Enhancement
    (American Institute of Physics, 2026) Aljuboori, Mohammed Khaleel Naser; Tasel, Faris Serdar
  • Conference Object
    Publicly Available Datasets for Smart and Precision Agriculture: A Systematic Review
    (Institute of Electrical and Electronics Engineers Inc., 2026) Arabaci, Hatice Elif; Keskin, Mustafa Berk; Kahraman, Arda; Bozdag, Oyku Eylul
  • Conference Object
    Reinforcement Learning Meets the Cloud: A Q Learning Framework for Efficient Task Scheduling
    (Institute of Electrical and Electronics Engineers Inc., 2025) Boke, Kivilcim Naz; Qadri, Syed Shah Sultan Mohiuddin; Kabarcik, Ahmet
  • Conference Object
    Enhancing Data Transmission Efficiency in Computer Networks Using Hybrid SVM and Deep Neural Networks for Traffic Classification
    (Institute of Electrical and Electronics Engineers Inc., 2025) Fadhil, Ibrahim; Sever, Hayri
  • Conference Object
    Enhancing File Security with an Optimized Auto-Classification Framework Based on Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Açikgöz, Zeliha; Arslan, Recep Sinan; Arslan, Serdar
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri
    (Institute of Electrical and Electronics Engineers Inc., 2025) Calis, Ahmet Gokay; Ergezer, Halit
    In this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Hand Gesture Recognition in Variable Length Sequences
    (2005) Choupanı, Roya; Choupani, R.; Tolun, M.R.; Tolun, Mehmet Reşit; Bilgisayar Mühendisliği; Yazılım Mühendisliği
    Using hand gestures in human computer interaction has been a major challenge during the recent years. Many of the hand gesture recognition systems however, have been based on the recognition of hand postures and estimating the related gesture which is restricted to a few numbers of possible movements. However when dealing with applications such as understanding sign languages which include a large number of classes, an automatic learning method based on matching a sequence of postures with the characterizing feature sequence of each class is necessary. An important characteristic of this method is that each sample sequence of a class may have a variable length and different position of the key features. In this paper a syntactic method has been proposed for classifying the input sequences. An algorithm foe extracting the grammar of the method during training stage is also given.
  • Conference Object
    Predicting Electric Vehicle Adoption in the Eu: Analyzing Classification Performance and Influencing Attributes Across Countries, Gender, and Education Level
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kumbasar, M.; Tokdemir, G.; Labben, T.G.; Ertek, G.
    Electric vehicles (EVs) have been one of the trending technologies in recent decades, as they are expected to transform the current automotive technology and transportation systems. To this end, the scope of this study is analyzing survey data on European consumers' EV purchase decisions. The objective is comparing the predictive quality of various classification algorithms in predicting EV adoption, across country, gender and education level of the participants, as well as the analysis of the influencing attributes. Initially, the data is filtered for each value of the chosen categorical attribute (country, gender or education level) with the missing values being imputed. Then, several classification algorithms in the Python sklearn package are applied through 5-fold-cross validation and the performance of the algorithms are compared based on standard classification metrics. There are notable variations in classification performance and influencing attributes depending on the values of the selected categorical attributes. © 2024 IEEE.
  • 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.