Yazar "Findik, O." seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Enhancing Predictive Maintenance in Manufacturing: A CNN-LSTM Hybrid Approach for Reliable Component Failure Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Al-Said, S.; Findik, O.; Assanova, B.; Sharmukhanbet, S.; Baitemirova, N.
    Nowadays, industry 4.0, many new ideas have come up, and one important one is predictive maintenance in modern manufacturing and production systems. This approach capitalizes on the wealth of data generated by sensors and real-time monitoring of machine components. The abundance of this data has paved the way for the application of Deep Learning (DL) techniques, enabling accurate prediction and diagnosis of failures. Consequently, precise prediction and diagnosis of component failures have become imperative for reducing machine downtime, cutting associated costs, extending machine life cycles, enhancing product quality, and fortifying overall reliability. This paper introduces an innovative framework that harnesses a hybrid approach, uniting Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), referred to as CNN-LSTM, to address the challenges of predictive maintenance. The performance and accuracy of this novel hybrid model are evaluated using the publicly accessible MetroPT dataset, with the objective of predicting component failures in Air Production Units (APUs) installed in metro vehicles. The experimental results showcase remarkable performance, achieving an F-Score about of 92% for binary classification and an impressive 97% for multiple classifications. Comparative analysis with related studies underscores the superiority of the proposed CNN-LSTM hybrid predictive maintenance approach, emphasizing its enhanced accuracy and prediction capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
  • Küçük Resim Yok
    Öğe
    Link prediction on networks created from UEFA European competitions
    (Springer, 2020) Findik, O.; Özkaynak, E.
    Link prediction is widely used in network analysis to identify future links between nodes. Link prediction has an important place in terms of being applicable to many real-world networks with dynamic structure. Networks with dynamic structure, such as social networks, scientific collaboration networks and metabolic networks, are networks in which link prediction studies are performed. In addition, it is seen that there are few studies showing the feasibility of link prediction by creating networks from different areas. In this study, in order to show the applicability of link prediction processes in different fields link prediction was made by applying traditional link prediction methods in the networks formed from the data of football competitions played after the groups between the years 2004–2017 in the UEFA European League. The AUC metric was used to measure the success of forecasting. The results show that link prediction methods can be used in sports networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.
  • Küçük Resim Yok
    Öğe
    Sentiment Analysis of Twitter: Turkey Earthquake 2023 Case
    (CEUR-WS, 2024) Rashid, A.K.; Findik, O.
    The most devastating earthquake in the past 20 years was February 6, 2023. The earthquake occurred in southern Turkey near the northern Syrian border. Thousands of people died and many more were left homeless, due to the magnitude of the event, it quickly spread all over the world. The earthquake and its damage were discussed and analyzed from all sides. In this paper, a separate analysis was proposed for tweets posted within 14 days after the earthquake. In this analysis to classify tweets, one type of label did not depend as in previous works that have been done on text classification, but three different types of labels (Manual label, NLTK_VADER label, and Cluster label) are created to classify text tweets by using machine learning algorithms. Then by using the Jaccard similarity coefficient and the cosine similarity measure the two AI labels (NLTK_VADER and Cluster) are compared which result is closer to manual labeling, according to the number of categories (positive, negative, and natural) and accuracy of sentiment in each label. In the result, we have reached that the accuracy of the VADER labeling is more effective than Cluster labeling because its accuracy is much closer to the Manual labeling. © 2024 Copyright for this paper by its authors.