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Optimization of Acoustic Entropy Source for Random Sequence Generation Using an Improved Grey Wolf Algorithm
Avaroğlu, Erdinç | Kahveci, Semih | Akkurt, Ramazan
The functionality of cryptographic systems necessitates unpredictable, high-quality random numbers. High-quality random numbers must possess unpredictability, non-reproducibility, and strong statistical properties. To achieve these qualities, True Random Number Generators (TRNG) are employed. The randomness quality of TRNG-derived sources depends on the entropy source used. Physical noise sources, ring oscillators, metastable, acoustic sources, and chaotic attractors are commonly used as entropy sources. In recent years, the use of acoustic signals as entropy sources has attracted attention. However, the noise in the signals affects the bit sequence to be generated. In addition, the threshold and sampling interval applied to the frequency values obtained from the signals also determine the...
A unified workflow strategy for analysing large-scale TripAdvisor reviews with BOW model
Bektaş, Jale
Nowadays, firms need to transform customer online reviews data properly into information to achieve goals such as having a competitive edge and improving the quality of service. This paper presents a unified workflow to solve the problems of analysing large-scale data with 710,450 reviews for 1,134 hotels by using text mining methods among the different touristic regions of Turkey. Firstly, a star schema dimensional data mart is built that includes one fact table and two dimensional tables. Then, a series of text mining processes which includes data cleaning, tokenisation, and analysis are applied. Text mining is implemented through standard BOW and the extended BON model. The results show significant findings through this workflow. We propose to build a dimensional model dataset before pe...
Deep Learning-Based Prediction Models for the Detection of Vitamin D Deficiency and 25-Hydroxyvitamin D Levels Using Complete Blood Count Tests
Acı, Çiğdem | Acı, Mehmet
Vitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single ”sufficient/deficient” classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient’s VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health la...
Evaluation of YOLOv8 Model Series with HOP for Object Detection in Complex Agriculture Domains
Bektaş, Jale
In recent years, many studies have been conducted in-depth investigating YOLO Models for object detection in the field of agriculture. For this reason, this study focused on four datasets containing different agricultural scenarios, and 20 dif-ferent trainings were carried out with the objectives of understanding the detec-tion capabilities of YOLOv8 and HPO (optimization of hyperparameters). While Weed/Crop and Pineapple datasets reached the most accurate measurements with YOLOv8n in mAP score of 0.8507 and 0.9466 respectively, the prominent model for Grapes and Pear datasets was YOLOv8l in mAP score of 0.6510 and 0.9641. This situation shows that multiple-species or in different developmental stages of a single species object YOLO training highlights YOLOv8n, while only object detection ...
Enhanced pyramidal residual networks for single image super-resolution
Kahveci, Semih
Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for incre...
Integrating a novel SRCRN network for segmentation with representative batch-mode experiments for detecting melanoma
Bektaş, Jale | Bektaş, Yasin | Kangal, Evrim Ersin
Melanoma is a type of skin cancer that tends to spread to other parts of the body and can be fatal if not detected at an early stage. This paper proposes an automated and non-invasive methodology to assist clinicians to detect melanoma. A two-stage framework was suggested in the study. In the first stage, the Resnet 50-based novel SRCRN Network was designed, which extracts high-dimensional distinctive features for skin lesion segmentation, and uses the advantage of stride regulation effectively. In the framework of SRCRN, pixel maps of different sizes were obtained by upsampling and downsampling methods between block layers, and the performance of segmentation was improved by selecting the most appropriate pixel map. In the second stage, the Resnet-50 network was used again for melanoma de...
EVALUATING THE EFFECT OF LESION SEGMENTATION ON THE DETECTION OF SKIN CANCER BY PRE-TRAINED CNN MODELS
Bektaş, Jale | Bektaş, Yasin | Kangal, Evrim Ersin
Early diagnosis of melanoma, which is considered to be one of the deadliest skin cancers, via medical imaging can significantly improve the course of the disease. However, expert assessments are subjective and open to errors due to large variations in dermoscopy images. To cope with this problem, a two-stage framework is proposed for the detection of melanoma in dermoscopic images. Firstly, by eliminating the presence of natural structures such as veins or hair and the variations in the pattern region, segmented images are obtained from raw image data with the help of pixel-wise image processing techniques. The second part of this framework is the recognition stage of the skin lesions by Pre-Trained Deep Networks (PTN). By using segmented input images, the PTN classifiers are optimized wit...
Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images
Bektaş, Jale
Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in low-contrast imbalanced under water images. Moreover, it employs nine model fusions as a downstream workflow task using encoder–decoder architectures with Dice Loss and Focal Loss training focusing on the imbalance data. Afterwards, the most effective two encoder–decoder fusion models, Res34+Unet and VGG19+FPN, by 0.592%, 0.590% mIoU on average and by 0.510%, 0.491% F1-score yielded better performance, respectively, than other models. Using a weight-optimization algorithm, the ensemble model with recreated IoU results improves the accuracy for b...
TARIM VE ORMANCILIK YÖNETİMİNDE NESNE TESPİT ALGORİTMALARININ KULLANIM ALANLARINA GENEL BİR BAKIŞ
Akkurt, Ramazan | Kahveci, Semih
Nesne tespit algoritmaları literatüre kazandırıldıktan kısa bir süre sonra birçok farklı alanda kullanımı yaygınlaşmıştır. Tarım ve ormancılık yönetimi de nesne tespit algoritmalarının kullanıldığı alanların en başında gelmektedir. Tarım ve ormancılık yönetimindeki birçok problemlere uyarlanan nesne tespiti algoritmaları, bu alana ciddi katkılar sağlamaktadır. Ağaç sınıflandırma, ölü ağaç örtüsü tespiti, verimli üretim, mahsul izleme, hastalık tespiti vb. birçok konu başlığında tarım ve ormancılık faaliyetlerinde yaygın şekilde kullanılmaktadır. Nesne tespit algoritmalarının sunduğu avantajlar bu alandaki insan hatalarının minimum seviyelere indirildiği, maliyetlerin azaldığı ve mahsul verimliliğin ciddi artışlar sağladığı literatürde yapılan birçok çalışma ile doğrulanmıştır. Bu çalışma k...
Segmentation of brain region of MRIs and omparisons between autistic and healthy adolescent
Bektaş, Jale
One of the most important subject in the processing MR image is segmentation, especially extraction of the brain regions, which is part of the decision of urgent operation on brain.This type medical operations need speed up process with maximum accuracy. In this study, brain is segmented by using k-means algorithm. A combination of global, adaptive thresholding techniques and at the next stage morphological operations were used for preprocessing. Moreover after this stage the main aim was setting out in the regional different of specified brain disorders to detect autism disease. Neuroimages which belong to 5 female patients in 17 years old who are diagnosed with autism and 10 female adolescents averaging 17 years old who have Typical Development were used. The parameters were slices consi...
Network neighborhood operates as a drug repositioning method for cancer treatment.
Cüvitoğlu, Ali
omputational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network...
An Overview of the Classification Problem in Unbalanced Datasets Using the Statistical Construction of European Community Economic Activities
Bektaş, Jale | Bektaş, Yasin
The use of classical classifiers in unbalanced and multi-class data sets has always been a problem. In this study, a text mining work has been applied with well-known classifiers on the definitions of Statistical Construction of Economic Activities (NACE) codes in the European Community. In the study, first of all, the application was made on the unbalanced structure of the original data, then the performance measurement was performed by retesting the result data by making it balanced by weighting on a class basis. Common classifiers such as Decision Trees, Naiv Bayes, Support Vector Machines, Diametric Based Functions and Random Forest algorithms were used in the tests. The study showed us that as a result of data balancing of Decision Trees, the F-score value increased from 17.43% to 92%...
EKSL: An effective novel dynamic ensemble model for unbalanced datasets based on LR and SVM hyperplane-distances
Bektaş, Jale
Unbalanced data is considered in many real-world classification problems, where it is often costly in practice to sample and establish a homogeneous class distribution for a minority class. The choice of methods, diversity of datasets used for structuring, and the correct kernel decision are quite decisive in the success of the system. This study develops a powerful classifier algorithm that provides an alternative solution to kernel experiments with the choice of a general-purpose, fast, automatic linear kernel. Principally based on SVM, k-means clustering in partitions is used, and logistic regression is integrated into the ensemble system. To increase the success rate and deal with the maximum convergence problem, the soft margin value of the standard SVM is changed in an adaptive struc...
Yemler Bilgisi Yem Teknolojisi ve Balık Besleme
Bilgüven, Murat
Her havuz alanında ya da yetiştirme biriminden elde edilen balık ve dolayısıyla balık yetiştiriciliğinin kârlılığı, büyük ölçüde kullanılan yemin miktarına bağlıdır. Su ürünleri sistemi ne kadar yoğun olursa yemlemenin önemi o kadar fazla olur ve toplam üretim maliyetinde yem maliyetinin payı da o kadar artar. Bilindiği üzere alabalık başta olmak üzere bazı balıkların yetiştiriciliği için akarsu sistemli tesisler kullanılmakta yada sıcaklığın, su bileşiminin ve akıntının dolayısıyla oksijen yoğunluğunun daha iyi olduğu tatlı su ve kıyılarda ise yüzer ağ kafeslerde yetiştiricilik yapılmaktadır. Su ürünleri yetiştiriciliğinin ileri durumda olduğu ülkelerde entansif yetiştiricilik uygulanmakta ve yetiştirme alanlarında birim hacimde maksimum ürün yetiştirilmeye çalışılmaktadır. Bu tür yetişti...
Role of antioxidant defense system and biochemical adaptation on stress tolerance of high mountain and steppe plants
Keleş, Yüksel
Eleven species were collected from Northwest Anatolian mountains (1500–2000 m) and 18 species were collected from the Central Anatolian steppes (850–1000 m) in June 1998 and 1999. In all the species investigated, the water and dry matter percentages and solute contents were measured. The chlorophyll, b-carotene, ascorbate and a-tocopherol contents and catalase (CAT), superoxide dismutase (SOD) andglutathione reductase (GR)enzymeactivities oftheplants werealsodetermined.Thesteppeplantshadlowerwatercontentcomparedwith alpine plants. The chlorophyll contents of the plants investigated did not change with altitude. However, the carotenoid/chlorophyll ratio of alpine plants was found to be significantly higher. The antioxidant/chlorophyll ratio of the trees and shrubs was higher than that of he...
Image contrast enhancement using a low-discrepancy population initialized gray wolf optimization algorithm
Elevi, Abdullah | Kahveci, Semih | Avaroğlu, Erdinç
Image contrast is an important factor in distinguishing objects in the image from their background. Low-contrast images, caused by various factors such as poor lighting, are insufficient for human visual perception and many image processing applications. Therefore, image contrast enhancement (ICE) is a necessary preprocessing step in different image processing applications. The main purpose of ICE is to make image objects more easily distinguishable and to improve the quality of visual information in the image. In this paper, image contrast enhancement is studied as an optimization problem. First, a modified version of the gray wolf optimization (GWO) algorithm, a population-based meta-heuristic that mimics the social leadership and hunting behavior of gray wolves in nature, is adapted to ...