Browsing by Author "Karaci, A"
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Web of Science APPLICATION OF DEEP NEURAL NETWORKS IN MODELING THE CAPTURE OF Ips sexdentatus IN PHEROMONE TRAP(2022.01.01) Ozcan, GE; Karaci, A; Enez, KWeb of Science Care4HIP: An Embedded System Design for Discerning Hear-Impaired People in Traffic(2022.01.01) Akyol, K; Karaci, A; Titikci, MEPublication Detection and classification of shoulder implants from X-ray images: YOLO and pretrained convolution neural network based approach(2022-01-01) Karaci A.; Karaci, AShoulder implants may need to be replaced several months or years after insertion. In this case, it is important to determine the manufacturer or model of the implant. In some cases, the implant manufacturer and model may not be known to patients or their physicians due to uncertainty in medical records. Today, the task of identifying an implant manufacturer or model in such situations relies on meticulous examination and visual comparison of X-ray images taken from the implant by medical professionals. But this identification task is often time-consuming, error-prone and difficult for both radiologists and orthopedic surgeons. In this study, it is aimed to automatically detect the implant manufacturer using deep learning methods. For this purpose, pretrained CNN architectures and cascade models consisting of feeding these architectures with the YOLO algorithm have been proposed.Web of Science Determining students' level of page viewing in intelligent tutorial systems with artificial neural network(2014.01.01) Karaci, A; Arici, NWeb of Science Developing The Computer Adaptive Test Module for The Intelligent Tutorial Systems(2012.01.01) Karaci, A; Arici, NWeb of Science Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN(2019.01.01) Karaci, A; Yaprak, H; Ozkaraca, O; Demir, I; Simsek, OWeb of Science Expansin gene family database: A comprehensive bioinformatics resource for plant expansin multigene family(2023.01.01) Kok, BO; Altunoglu, YC; Oncul, AB; Karaci, A; Baloglu, MCPublication Intelligent tutoring system model based on fuzzy logic and constraint-based student model(2019-08-01) Karaci A.; Karaci, AA model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.Web of Science Intelligent tutoring system model based on fuzzy logic and constraint-based student model(2019.01.01) Karaci, AWeb of Science Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning(2024.01.01) Almeshrky, H; Karaci, APublication Predicting Breast Cancer with Deep Neural Networks(2020-01-01) Karaci A.; Karaci, AIn this study, a deep neural network (DNN) MODEL was developed which diagnoses breast cancer using information about age, BMI, glucose, insulin, homa, leptin, adiponectin, resistin and MCP-1. The data used in this model was collected by Patrício et al. [7] from 116 women of which 64 has breast cancer and 52 do not. While 70% of this data (81 cases) was used for instructing the DNN model, 30% (35 cases) was used for testing. The DNN model was created in Python programming language using Keras Deep Learning Library. After model creation, machine learning was conducted using probable optimisation algorithms, loss functions and activation functions and the best three models were saved. For performance evaluation of the models, metrics of specificity, sensitivity and accuracy were employed. The specificity values of the best three models were calculated as [0.882, 0.941] and sensitivity values were found to be [0.888, 0.944]. In other words, while the models predict healthy women at the rates of minimum 88.2% and maximum 94.1%; they predict women with breast cancer at the rates of minimum 88.8% and 94.4%. For both women with and without breast cancer these prediction rates are sufficient and much higher than those reported by Patrício et al. [7].Web of Science Predicting Breast Cancer with Deep Neural Networks(2020.01.01) Karaci, AWeb of Science Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks(2013.01.01) Yaprak, H; Karaci, A; Demir, IWeb of Science Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services(2020.01.01) Karaci, A; Ozkaraca, O; Acar, E; Demir, AWeb of Science Real-Time Identification from Gait Features Using Cascade Voting Method(2021.01.01) Ercin, B; Karaci, AWeb of Science Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features(2021.01.01) Karaci, A; Akyol, K; Turut, MUPublication Self-Care Problems Classification of Children with Physical and Motor Disability by Deep Neural Networks(2020.01.01) Karaci, A; Abdulkadir KARACIFiziksel ve motor engellilik bazı bireysel ana yaşam aktivitelerini büyük ölçüde sınırlandıran bir bozukluktur. Bu bozukluklardünyanın birçok ülkesinde çocukları etkilemektedir. Bunun yanı sıra fiziksel ve motor engelli bireylerin doktorlar tarafından uygunmesleki tedavilerle sınıflandırılmaları zor bir süreçtir. Çünkü değerlendirilmesi gereken birçok değişken vardır. Bu çalışmadakiamaç, fiziksel ve motor engelli çocukların öz bakım beceri problemlerini derin sinir ağlarını (DSA) kullanarak en az hata ilesınıflandırmaktır. Bu amaçla farklı parametrelere sahip DSA modelleri oluşturulmuştur. Modellerin oluşturulmasında gizli katmansayısı, gizli katmanlardaki nöron sayısı, aktivasyon fonksiyonu, optimizasyon algoritması, kayıp fonksiyonu ve epoch değeriparametreleri dikkate alınmıştır. Oluşturulan DSA modelleri SCADI (Self-Care Activities Dataset based on ICFCY) veri setivasıtasıyla eğitilmiş ve test işlemi gerçekleştirilmiştir. Modellerin sınıflandırma performansları F-1 puanı, kesinlik (precision-P),hassasiyet (recall-R) ve doğruluk (accuracy-ACC) metrikleri kullanılarak ortaya konulmuştur. En iyi sınıflandırma performansınasahip 8 modelin ayrıntıları sunulmuştur. Elde edilen bulgulara göre en iyi sınıflandırma performansı Adadelta optimizasyonalgoritmasını, Elu aktivasyon fonksiyonunu ve Categorical crossentropy kayıp fonksiyonunu kullanan DSA-1 modelinde eldeedilmiştir. Bu modelin P, R, ACC ve F1 puanı değerleri 1’dir. Yani bu model fiziksel ve motor engelli çocukların öz bakım beceriproblemlerini %100 doğrulukla tahmin etmektedir. Ayrıca, en iyi üç modelin (DSA-1, DSA-2 ve DSA-3) geçerliliğini artırmakiçin 10-fold çapraz doğrulama yöntemi ile eğitim ve test işlemi tekrar gerçekleştirilmiştir. Ortalama çapraz doğrulama accuracydeğerleri sırasıyla %85.71, % 85.71 ve % 87.14 olarak hesaplanmıştır. Mesleki terapistler, geliştirilen DSA modellerini öz bakımproblemlerini teşhis etmede doğrulayıcı bir araç olarak kullanılabilirler.Web of Science Web of Science The Impact of Online Concept Maps on Academic Achievement and Retention in Science Course(2019.01.01) Gulec, M; Karaci, A