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    An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation
    (Springer, 2024) Gharehchopogh, Farhad Soleimanian; Ibrikci, Turgay
    Image segmentation is one of the most significant and required procedures in pre-processing and analyzing images. Metaheuristic optimization algorithms are used to solve a wide range of different problems because they can solve problems with different dimensions in an acceptable time and with quality results. It can show different functions in solving various problems. So, a metaheuristic algorithm should be adapted to solve the target problem with different mechanisms to find the best performance. In this paper, we have used the improved African Vultures Optimization Algorithm (AVOA) that uses the three binary thresholds (Kapur's entropy, Tsallis entropy, and Ostu's entropy) in multi-threshold image segmentation. The Quantum Rotation Gate (QRG) mechanism has increased population diversity in optimization stages, and optimal local trap escapes to improve AVOA performance. The Association Strategy (AS) mechanism is used to obtain and faster search for optimal solutions. These two mechanisms increase the diversity of production solutions in all optimization stages because the AVOA algorithm focuses on the exploration phase almost in the first half of the iterations. So, in this approach, it is possible to guarantee a wide variety of solutions and avoid falling into the local optimum trap. Standard criteria and datasets were used to evaluate the performance of the proposed algorithm and then compared with other optimization algorithms. Eight images with large dimensions have been used to evaluate the proposed algorithm so that the ability of the proposed algorithm and other compared algorithms can be accurately checked. A better solution to large-scale problems requires good performance of the algorithm in both the exploitation and exploration phases, and a balance must be created between these two phases. According to the experimental results from the proposed algorithm, it is determined that it has a good and significant performance.
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    Analysis of breast cancer classification robustness with radiomics feature extraction and deep learning techniques
    (Wiley, 2022) Rashid, Harun Ur; Ibrikci, Turgay; Paydas, Semra; Binokay, Figen; Cevik, Ulus
    Breast cancer and breast imaging diagnostic procedures are typically carried out using a variety of imaging modalities, including mammography, MRI, and ultrasound. However, ultrasound and mammography have limitations and MRI is recognized as better than other procedures. Recent computational approaches, such as radiomics, applied to image analysis have shown remarkable progress in lowering diagnostic difficulties. This research analysed the robustness of breast tumour classification with feature extraction (radiomics) and a featureless method (deep learning). The proposal consists of two stages: the first stage introduced and explored radiomics-based steps. A total of 111 tumour lesions were used to derive 74 radiomic features consisting of shape, and three separate second-order metrics. Associations of these features were used to classify tumour lesions with four different kernels from support vector machine algorithm. In the confusion matrix analysis, the SVM-RBF kernel developed optimal diagnostic efficiency with a maximum test accuracy of 97.06% on the combination of feature analysis. The second stage developed with deep learning techniques (InceptionV3 and CNN-SVM). A total of 2998 images were used to create the models. In this portion, the CNN-SVM model achieved the highest accuracy, 95.28%, with an AUC of 0.974, where the pre-trained InceptionV3 achieved an AUC of only 0.932. Finally, the obtained result in both stages was discussed together and other related studies.
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    Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital
    (Int Centre Sustainable Dev Energy Water & Env Systems-Sdewes, 2020) Timur, Oguzhan; Zor, Kasim; Celik, Ozgur; Teke, Ahmet; Ibrikci, Turgay
    Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting.
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    Optimisations of four imputation frameworks for performance exploring based on decision tree algorithms in big data analysis problems
    (Inderscience Enterprises Ltd, 2022) Bektas, Jale; Ibrikci, Turgay
    The phenomenon of how to treat missing values is a problem confronted in big data analysis. Therefore, various applications have been developed on imputation strategies. This study focused on four imputation frameworks proposing novel perspectives based on expectation-maximisation (EM), self-organising map (SOM), K-means and multilayer perceptron (MLP). Initially, several transformation steps such as normalised, standardised, interquartile range and wavelet were applied. Then, imputed datasets were analysed using decision tree algorithms (DTAs) by optimising their parameters. These analyses showed that DTAs had not been strikingly affected by any data transformation techniques except interquartile range. Even though the dataset contains a missing value ratio of 33.73%, the EM imputation framework provided a performance increase of 0.42% to 3.14%. DTAs based on C4.5 and NBTree algorithms have been more stable for all big imputed datasets. Furthermore, realistic performance measurement of any preprocessing experiment based on C4.5 can be proposed to avoid time complexity.
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    RECOGNITION OF NON-SPEECH SOUNDS USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS AND DYNAMIC TIME WARPING METHOD
    (IEEE, 2015) Disken, Gokay; Ibrikci, Turgay
    With the developing technology, speech recognition systems are getting more space in our daily lives. Sounds in our environment are not only pure speech. Because of this, it is important for cochlear implants, unmanned vehicles and security systems to be able to recognize other sounds. In this work, Mel-frequency cepstrum coefficients, one of the most widely used methods for feature extraction in speech recognition, applied to various nature and animal sounds. Because each sound does not have the same duration, dynamic time warping, one of the methods used in speech recognition, is preferred to classify the feature vectors. The difference in durations of sounds affects the lengths of the feature vectors. With dynamic time warping method, one can overcome these differences. One reference record and 10 test records obtained from 10 different sound sources. True classification rate is found as 88%.
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    Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications
    (Springer, 2023) Gharehchopogh, Farhad Soleimanian; Ucan, Alaettin; Ibrikci, Turgay; Arasteh, Bahman; Isik, Gultekin
    Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.