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Class Attendance System in Education with Deep Learning Method
Authors:
Hüdaverdi Demir,
Serkan Savaş
Abstract:
With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 ye…
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With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education; can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year.
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Submitted 23 September, 2023;
originally announced September 2023.
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Enhancing Machine Learning Model Performance with Hyper Parameter Optimization: A Comparative Study
Authors:
Caner Erden,
Halil Ibrahim Demir,
Abdullah Hulusi Kökçam
Abstract:
One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best training performance and may increase the ability to generalize using hyper parameter optimization (HPO) techniques. HPO is a popular topic that artificial intelligence studies have focused on recently and has attracted incr…
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One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best training performance and may increase the ability to generalize using hyper parameter optimization (HPO) techniques. HPO is a popular topic that artificial intelligence studies have focused on recently and has attracted increasing interest. While the traditional methods developed for HPO include exhaustive search, grid search, random search, and Bayesian optimization; meta-heuristic algorithms are also employed as more advanced methods. Meta-heuristic algorithms search for the solution space where the solutions converge to the best combination to solve a specific problem. These algorithms test various scenarios and evaluate the results to select the best-performing combinations. In this study, classical methods, such as grid, random search and Bayesian optimization, and population-based algorithms, such as genetic algorithms and particle swarm optimization, are discussed in terms of the HPO. The use of related search algorithms is explained together with Python programming codes developed on packages such as Scikit-learn, Sklearn Genetic, and Optuna. The performance of the search algorithms is compared on a sample data set, and according to the results, the particle swarm optimization algorithm has outperformed the other algorithms.
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Submitted 14 February, 2023;
originally announced February 2023.
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Object Detection and Tracking with Autonomous UAV
Authors:
A. Huzeyfe Demir,
Berke Yavas,
Mehmet Yazici,
Dogukan Aksu,
M. Ali Aydin
Abstract:
In this paper, a combat Unmanned Air Vehicle (UAV) is modeled in the simulation environment. The rotary wing UAV is successfully performed various tasks such as locking on the targets, tracking, and sharing the relevant data with surrounding vehicles. Different software technologies such as API communication, ground control station configuration, autonomous movement algorithms, computer vision, an…
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In this paper, a combat Unmanned Air Vehicle (UAV) is modeled in the simulation environment. The rotary wing UAV is successfully performed various tasks such as locking on the targets, tracking, and sharing the relevant data with surrounding vehicles. Different software technologies such as API communication, ground control station configuration, autonomous movement algorithms, computer vision, and deep learning are employed.
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Submitted 26 June, 2022;
originally announced June 2022.
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Filter design for small target detection on infrared imagery using normalized-cross-correlation layer
Authors:
H. Seçkin Demir,
Erdem Akagunduz
Abstract:
In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduc…
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In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.
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Submitted 15 June, 2020;
originally announced June 2020.
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Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
Authors:
Eda Okur,
Hakan Demir,
Arzucan Özgür
Abstract:
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised m…
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Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised method for learning continuous representations of words in vector space. We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts. We evaluated our Turkish NER system on Twitter messages and achieved better F-score performances than the published results of previously proposed NER systems on Turkish tweets. Since we did not employ any language dependent features, we believe that our method can be easily adapted to microblog texts in other morphologically rich languages.
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Submitted 19 October, 2018;
originally announced October 2018.
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PLDA-Based Diarization of Telephone Conversations
Authors:
Ahmet E. Bulut,
Hakan Demir,
Yusuf Ziya Isik,
Hakan Erdogan
Abstract:
This paper investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce using a variational Bayes (VB) approach for inference under a PLDA model for modeling segmental i-vectors in speaker diarization. Deterministic annealing (DA) algorithm is imposed in order to avoid local optimal solutions in VB iterations.…
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This paper investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce using a variational Bayes (VB) approach for inference under a PLDA model for modeling segmental i-vectors in speaker diarization. Deterministic annealing (DA) algorithm is imposed in order to avoid local optimal solutions in VB iterations. We compare our proposed system with a well-known system that applies k-means clustering on principal component analysis (PCA) coefficients of segmental i-vectors. We used summed channel telephone data from the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) as the test set in order to evaluate the performance of the proposed system. We achieve about 20% relative improvement in Diarization Error Rate (DER) compared to the baseline system.
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Submitted 29 September, 2017;
originally announced October 2017.