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    A Comprehensive Hybrid Approach for Indoor Scene Recognition Combining CNNs and Text-Based Features
    (Mdpi, 2025) Uckan, Taner; Aslan, Cengiz; Hark, Cengiz
    Highlights What are the main findings? Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier. Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%. What is the implication of the main finding? Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability. Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Highlights What are the main findings? Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier. Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%. What is the implication of the main finding? Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability. Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Abstract Indoor scene recognition is a computer vision task that identifies various indoor environments, such as offices, libraries, kitchens, and restaurants. This research area is particularly significant for applications in robotics, security, and assistance for individuals with disabilities, as it enables the categorization of spaces and the provision of contextual information. Convolutional Neural Networks (CNNs) are commonly employed in this field. While CNNs perform well in outdoor scene recognition by focusing on global features such as mountains and skies, they often struggle with indoor scenes, where local features like furniture and objects are more critical. In this study, the MIT 67 Indoor Scene dataset is used to extract and combine features from both a CNN and a text-based model utilizing object recognition outputs, resulting in a two-channel hybrid model. The experimental results demonstrate that this hybrid approach, which integrates natural language processing and image processing techniques, improves the test accuracy of the image processing model by 8.3%, achieving a notable success rate. Furthermore, this study offers contributions to new application areas in remote sensing, particularly in indoor scene understanding and indoor mapping.
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    Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks
    (Ieee, 2019) Seyyarer, Ebubekir; Uckan, Taner; Hark, Cengiz; Ayata, Faruk; Inan, Mevlut; Karci, Ali
    Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.
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    Extractive multi-document text summarization based on graph independent sets
    (Cairo Univ, Fac Computers & Information, 2020) Uckan, Taner; Karci, Ali
    We propose a novel methodology for extractive, generic summarization of text documents. The Maximum Independent Set, which has not been used previously in any summarization study, has been utilized within the context of this study. In addition, a text processing tool, which we named KUSH, is suggested in order to preserve the semantic cohesion between sentences in the representation stage of introductory texts. Our anticipation was that the set of sentences corresponding to the nodes in the independent set should be excluded from the summary. Based on this anticipation, the nodes forming the Independent Set on the graphs are identified and removed from the graph. Thus, prior to quantification of the effect of the nodes on the global graph, a limitation is applied on the documents to be summarized. This limitation prevents repetition of word groups to be included in the summary. Performance of the proposed approach on the Document Understanding Conference (DUC-2002 and DUC-2004) datasets was calculated using ROUGE evaluation metrics. The developed model achieved a 0.38072 ROUGE performance value for 100-word summaries, 0.51954 for 200-word summaries, and 0.59208 for 400-word summaries. The values reported throughout the experimental processes of the study reveal the contribution of this innovative method. (C) 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.
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    Extractive Text Summarization via Graph Entropy
    (Ieee, 2019) Hark, Cengiz; Uckan, Taner; Seyyarer, Ebubekir; Karci, Ali
    There is growing interest in automatic summarizing systems. This study focuses on a subtractive, general and unsupervised summarization system. It is provided to represent the texts to be summarized with graphs and then graph entropy is used to interpret the structural stability and structural information content on the graphs representing the text files. The performance of the proposed text summarizing approach for the purpose of summarizing the text on the data set of Document Understanding Conference (DUC-2002) including open access texts and summaries of these texts was calculated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation metrics. Experimental processes were repeated for 200 and 400 word abstracts. Experimental results reveale that the proposed text summarizing system performs competitively with competitive methods for different ROUGE metrics.
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    Graph-Based Suggestion For Text Summarization
    (Ieee, 2018) Hark, Cengiz; Uckan, Taner; Seyyarer, Ebubekir; Karci, Ali
    One of the methods of text summarization within the context of Natural Language Processing (NLP) works is to summarize the text by selecting sentences from the original text. There are different approaches to summarize sentence selection. In this study, texts that do not have a certain structure have been preprocessed and transfer of the proposed diagram in a structured format in the form of an expression. Different feature extraction methods could be applied on the charts. Our method uses conceptually the diagrams obtained in the representation of the text. This study aims to suggest a method of summarization of texts with a linear weighting of the importance of sentences. Moreover, the method presented does not require the use of deep linguistic knowledge and this work can be adapted to different languages.
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    A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity
    (Sage Publications Ltd, 2024) Hark, Cengiz; Uckan, Taner; Karci, Ali
    Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.
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    SSC: Clustering of Turkish Texts By Spectral Graph Partitioning
    (Gazi Univ, 2021) Uckan, Taner; Hark, Cengiz; Karci, Ali
    There is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.