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Koleksiyon Bilgisayar Yazılımı ...
166997

Time and Memory Comparison of Parallel K-Nearest Neighbor Algorithms on GPUs

Korucu, Gizen Mutlu | Acı, Çiğdem

2022-11-18

166446

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...

166992

SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent

Mutlu, Gizen | Acı, Çiğdem

The Support Vector Machine (SVM) method is one of the popular machine learning algorithms as it gives high accuracy. However, like most machine learning algorithms, the resource consumption of the SVM algorithm in terms of time and memory increases linearly as the dataset grows. In this study, a parallel-hybrid algorithm that combines SVM, Sequential Minimal Optimization (SMO) with Stochastic Gradient Descent (SGD) methods have been proposed to optimize the calculation of the weight costs. The performance of the proposed SVM-SMO-SGD algorithm was compared with classical SMO and Compute Unified Device Architecture (CUDA) based approaches on the well-known datasets (i.e., Diabetes, Healthcare Stroke Prediction, Adults) with 520, 5110, and 32,560 samples, respectively. According to the result...

Makale2022PARALLEL COMPUTING 2 | 0 Erişime Kapalı
166670

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...

166444

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 ...

166453

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...

167123

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...

167005

Static Analysis of Malware Detection Using Machine Learning Algorithms

Vuran, Nisa | Acı, Mehmet

2024-12-26

167124

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...

Makale2024APPLIED SCIENCES-BASEL 23 | 0 Erişime Kapalı
166996

Enhancing Driver Injury Severity Prediction Using Optimized Oversampling and Feature Selection Techniques

Korucu, Gizen Mutlu | Acı, Çiğdem | Özen, Murat

2024-12-11

166442

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...

167011

Detection of Malware by Static Analysis Using Machine Learning Methods

Vuran, Nisa | Acı, Mehmet

The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes,...

Makale2022Bilgisayar Bilimleri ve Teknolojileri Dergisi 28 | 1 Süreli Ambargolu : 26.12.2024
167060

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...

Makale2023PeerJ 24 | 1 Erişime Açık
166454

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%...

166998

MÜHENDİSLİKTE YENİLİKÇİ ÇALIŞMALAR

Mutlu, Gizen | Acı, Çiğdem

Son yıllarda, Nöral-Bulanık Sistemler’in (NBS) güçlü öğrenme yeteneği ve diğer algoritmalarla uyumlu çalışmasından faydalanarak örüntü tanıma, nesnelerin interneti, zaman serisi tahminleri gibi çeşitli teknolojik alt dallarda dikkate değer başarılar elde edilmiştir. Bilinen yapay zekâ algoritmalarının karmaşık ve belirsiz veriler üzerinde yetersiz kaldığı noktalarda NBS’nin uzmanlığından yararlanarak mevcut algoritmaların başarıları artırılmıştır. Böylelikle NBS kullanımı daha yaygın hale gelmiştir. Bu çalışmada 2019-2022 yılları arasında sağlık alanında yapılan NBS tabanlı çalışmalar araştırılmış ve sonuçlar kıyaslanmıştır. Sonuç olarak, NBS’lerin mevcut yapay zekâ algoritmaları ile birleştiğinde daha yüksek doğrulukla çalıştığı gözlemlenmiştir

166451

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...

Makale2022Information Sciences 34 | 0 Erişime Açık
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