96 results
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2. Derin sinir ağlarıyla Osmanlıca optik karakter tanıma.
- Author
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Dölek, İshak and Kurt, Atakan
- Subjects
- *
OPTICAL character recognition , *ARTIFICIAL neural networks , *WORD recognition , *WORD frequency , *RECOGNITION (Psychology) - Abstract
In this paper, we present a web-based optical character recognition (OCR) system that converts images of Ottoman documents printed with naskh font into text using CNN+RNN-based deep neural network models. For training, three datasets - original, synthetic, and hybrid - were prepared and three different OCR models were created. The original data set consists of 1,000 pages and the synthetic data set consists of 23,000 pages. Hybrid data set contains both. The trained models were compared with Tesseract's Arabic and Persian, Google Docs' Arabic, Abby FineReader's Arabic, and Miletos OCR model/tools with a 21-page test set. The comparison was made with 3 different texts (raw, normalized, and joined) and using 3 different criteria (character, ligature, and word recognition). The Osmanlica.com Hybrid model produced significantly better results than the others with 88.86% raw, 96.12% normalized, and 97.37% joined accuracy in character recognition; 80.48% raw, 91.60% normalized, and 97.37% joined accuracy in ligature recognition; and 44.08% raw and 66.45% normalized accuracy in word recognition. To investigate the effects of the characteristics of the alphabet on OCR, character, ligature, and word frequency analyses of Ottoman was performed. In this analysis, the characters in the alphabet were grouped according to distinctive features such as connectedness, letter body, position and number of dots, type of character, and source language; and frequencies and recognition accuracies were examined for each group. OCR results are also reported for each character. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Yoğunluk tabanlı kümeleme yöntemiyle karakteristiği oluşturulan yollar için RNN yöntemi ile kısa zamanlı trafik hız tahmini.
- Author
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Akın, Murat and Sağıroğlu, Şeref
- Subjects
- *
INTELLIGENT transportation systems , *TRAFFIC speed , *ARTIFICIAL neural networks , *CITY traffic , *TRAFFIC flow - Abstract
Intelligent transportation systems use parameters such as traffic flow, density and speed to manage city traffic. This paper presents a novel prediction model for traffic speed prediction consisting of nine stages. In the presented model, real vehicle data were passed through data filtering and map matching processes, density-based clusters were created, cluster features were generated, instant traffic state was displayed, and traffic speed prediction was performed using with the artificial neural network RNN model. In previous studies, while traffic speed prediction can be performed on a specific road with stationary data sources or on different days with distributed GPS records, in the developed model, characteristics of the interested road are created by obtaining density-based vehicle cluster features, short-term and data-driven speed prediction is made within the changeable structure of traffic. Speed prediction was tested on Eskisehir and Istanbul roads belonging to Ankara province, the error rates were determined for speed prediction using the RNN variant LSTM and GRU methods, Eskisehir road LSTM-GRU error rates were measured as 8,595-8,656 and İstanbul road error rates as 7,331-7,955, respectively. The developed model for the changeable nature of traffic has yielded successful results in near real time. It is considered that the proposed model will offer different and new solutions in the prediction of traffic parameters, accelerate the processes and assist to the users more accurate and faster services. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. ESTIMATING AND FORECASTING TRADE FLOWS BY PANEL DATA ANALYSIS AND NEURAL NETWORKS.
- Author
-
NUROĞLU, Elif
- Subjects
ARTIFICIAL neural networks ,DATA analysis ,SMALL business ,INTERNATIONAL trade - Abstract
Copyright of Journal of the Faculty of Economics / İktisat Fakültesi Mecmuası is the property of Istanbul University, Faculty of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2014
5. Deri lezyonlarının evrişimsel yapay sinir ağları ile sınıflandırılması.
- Author
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BİLGİNER, Onur, TUNGA, Burcu, and DEMİRER, Rüştü Murat
- Subjects
- *
HIGH-dimensional model representation , *HILBERT transform , *ARTIFICIAL neural networks , *BASAL cell carcinoma , *CONVOLUTIONAL neural networks , *NEVUS - Abstract
In this paper we classified 4 skin lesions (Melanoma, Melanocytic Nevus, Basal Cell Carcinoma, Benign keratosis) from ISIC 2019 dataset which was published by International Skin Imaging Collabration in 2019. We used InceptionV3 convolutional neural network model for classification. We applied two preprocessing methods: High Dimensional Model Representation (HDMR) and Hilbert Transform. In conclusion we obtained 89% accuracy on classification of Basal Cell Carcinoma using Hilbert Transform. Moreover, we obtained 78% accuracy on classification of Melanoma using Contrast Enhancement High Dimensional Model Representation (HDMR). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Grafik Sinir Ağları Üzerine Bir İnceleme.
- Author
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GÜMÜŞ, Hamza Talha and EYÜPOĞLU, Can
- Abstract
Copyright of Journal of Defense Sciences / Savunma Bilmleri Dergisi is the property of Turkish Military Academy Defense Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
7. Prediction of Air Pollutant Levels by Using Artificial Neural Networks and Statistical Methods.
- Author
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Taşpınar, Fatih, Işık, Memiş, and Kaygusuz, Kamil
- Abstract
One of the environmental problems adversely affecting human health and welfare in many part of world is air pollution. Pollution monitoring data can be utilized to predict concentrations of air pollutants for short-term using artificial intelligence approaches and multivariate regression analysis. In this paper, artificial neural networks (ANNs) and multivariate regression modeling (MRM) techniques have been comparatively employed to forecast one-hour ahead concentration of particulate air pollution (PM
10 ). An hourly based data was composed by including meteorological factors and particulate concentrations for the years 2015-2016. ANN(12-7-1) model with R² of 0.887 and RMSE of 19.89 yielded fairly rational predictions over hourly dataset while the best MRM models produced lower scores with R² of 0.848 and RMSE of 21.33 in general. ANN models simulating the time series data better among the models identified have been chosen in further tuning in the prediction of next hour's concentration of PM10 . [ABSTRACT FROM AUTHOR]- Published
- 2016
8. AŞIRI ÖĞRENME MAKİNELERİ İLE HİSSE SENEDİ FİYAT TAHMİNİ.
- Author
-
ÖZÇALICI, Mehmet
- Abstract
Copyright of Hacettepe University Journal of Economics & Administrative Sciences / Hacettepe Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Hacettepe University, Faculty of Economic & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2017
- Full Text
- View/download PDF
9. Deep Learning for Edge Detection.
- Author
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Tiken, Cihan and Ensari, Tolga
- Subjects
DEEP learning ,BOLTZMANN machine ,ARTIFICIAL neural networks ,EDGE detection (Image processing) ,COMPUTER vision ,DATABASES - Abstract
Deep learning and deep belief networks (DBNs) are one of the most used topics in machine learning and pattern recognition area in recent years. DBNs consist of stacked Restricted Boltzmann Machine (RBM) structure. RBM is energy based stochastic neural networks. DBNs have many hidden layers and it is optimized after fine-tuning process with Autoencoder (AE) architecture. AE transforms input space to new space. Edge detection is also one of the important issues in machine vision. It is generally done with gradient or Laplacian methods. Some of classical techniques, used in the literature, are canny, differential, sobel, prewitt, roberts or fuzzy logic methods. In this paper, we propose deep learning based edge detection method. Some hidden features is discovered with suitable DBNs architecture. In order to evaluate the performance of presented method we use handwritten character images from MNIST data set. Experimental results show that our approach improves the performance of edge detection process. [ABSTRACT FROM AUTHOR]
- Published
- 2015
10. Duygu analizi ve yapay sinir ağı kullanılarak envanter rotalama problemi için talep tahmini.
- Author
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İPEK, Aslı BORU, DOSDOĞRU, Ayşe Tuğba, and EROL, Rızvan
- Subjects
- *
ARTIFICIAL neural networks , *SENTIMENT analysis , *BUSINESS intelligence , *SUPPLY chains , *GENETIC algorithms , *INVENTORY control , *SOCIAL media in business , *SOCIAL media - Abstract
Social media users have been growing exponentially in recent years. This growth has evoked researchers and manager to analyze the social media and customer sentiment because it offers significant opportunities to advance business intelligence in supply chain. However, supply chain members are struggling in understanding the general sentiments in today’s business world. Therefore, various methods are used to provide valuable insights in supply chain. In this paper, SentiStrength is used to analyze customer reviews related to one type of product. The output of SentiStrength and demands of the product are then fed into Artificial Neural Network to forecast the customer demands. After, nondominated sorting genetic algorithm II (NSGA-II) based simulation optimization is employed to solve the inventory routing problem using forecasted customer demands. The results of the study demonstrated that the use of hybrid methodology containing sentiment analysis can successfully analyze the inventory routing problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
11. PANEL VERİLERİ İLE TÜRKİYE’DE KONUT FİYATLARINI ETKİLEYEN FAKTÖRLERİN TESPİTİ VE YAPAY SİNİR AĞLARI YAKLAŞIMI.
- Author
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ÇELİK, Cahit and KIRAL, Gülsen
- Subjects
ARTIFICIAL neural networks ,HOME prices ,PRICE indexes ,INTEREST rates ,ECONOMIC impact ,URBANIZATION - Abstract
Copyright of Journal of the Institute of Social Sciences Cankiri Karatekin University / Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Cankiri Karatekin University, Institute of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
12. Evrişimsel sinir ağı ve iki-boyutlu karmaşık Gabor dönüşümü kullanılarak hiperspektral görüntü sınıflandırma.
- Author
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Hanbay, Kazım
- Subjects
- *
ARTIFICIAL neural networks , *GABOR transforms , *GABOR filters , *DEEP learning , *FEATURE extraction , *ARCHITECTURE - Abstract
In this paper, a new hyperspectral image classification method based on 2-dimensional complex Gabor filtering and deep convolutional neural networks is proposed. Specifically, as a deep learning model, convolutional neural network is aimed to extract distinctive high-level features. Deep-learned and Gabor feature extraction methodologies are simultaneously performed on the input hyperspectral samples. Gabor features are calculated by implementing complex Gabor filtering only on the first three principal components of the hyperspectral image. The proposed hybrid model uses Gabor transform to obtain local image features, such as edges, corners and texture. The Gabor features of the images are calculated at multiple orientations and frequencies. Then, deep features and Gabor features are fused to obtain a more robust and discriminative feature vector. Hybrid feature vector is used as input to a softmax classifier for hyperspectral image classification. The parameters of the proposed deep learning architecture are optimized using a small training set. Thus, the over-fitting problem of the proposed convolutional neural network has been reduced to some extent. Experiments performed on two popular hyperspectral datasets show that the proposed method can achieve better classification performance than some conventional methods. Classification results demonstrates that the proposed hybrid model is an efficient method for feature extraction and classification of hyperspectral images. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
13. Türkiye'de 2. El Otomobil Fiyatlarının Tahmini ve Fiyat Belirleyicilerinin Tespiti.
- Author
-
Ecer, Fatih
- Abstract
In this study, determinants of second-hand automobile prices are examined and focused on the estimation of price using the second-hand automobile advertisements in web sites. As known, second-hand automobile pricing is a difficult matter for both automobile buyers and sellers. The primary goal of this paper is to identify the determinants of second-hand automobile pricing. Additionally, the second purpose is to compare the prediction performances of two statistical models in order to predict second-hand automobile prices. In this context, hedonic models and artificial neural networks are handled in this study. Hedonic models utilize multiple regression models on large data sets in the analyses. On the other hand, artificial neural networks are employed in this studty as an alternative method on account of potential non-linearity in the hedonic functions. As a conclusion, the present paper demonstrates both determinants of second-hand automobile prices in Turkey, and also artificial neural networks can be a better alternative method for prediction of the second-hand automobile prices. [ABSTRACT FROM AUTHOR]
- Published
- 2013
14. Eski Dilde Kullanılan Sözcükler Arasındaki Anlamsal Yakınlıkların Doğal Dil İşleme Yöntemleriyle Tespiti.
- Author
-
CANIM, Mustafa
- Abstract
Leveraging machine learning techniques in NLP domain has been a very hot research field due to the advancements in artificial intelligence area. Despite the popularity of this field, there is no known study on application of ML techniques on old Turkish language. This study aims to fill in this gap where 32000 pages of text has been downloaded from the websites of Ministry of Culture and a two-layer neural network model has been built on top of them to discover the semantic similarities between Turkish words in old Turkish language. The algorithm has been run with different parameters such as window size, dimension size, sampling size etc. and the produced vector spaces are uploaded into public servers for the purposes of enabling a RESTful API based query interface. Also a web UI has been created to provide a querying mechanism for regular users. The services that are developed can be used for two different purposes. One of them is to integrate these services into existing old Turkish language dictionary websites that are made available by third party providers as well as other institutions such as Ministry of Culture and Turkish Language Institution. Secondly, the developed services are intended to be used for mitigating the translation errors made during the translation of old Turkish texts into modern Turkish language in the areas of history and Turkish literature. Also enabling these services for public use will encourage other researchers to pursue this academic work and compare their results with the experimental results presented in this paper to make further improvements in this field. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
15. Geoteknik mühendisliğinde yapay sinir ağı uygulamaları ve bir örnek: Zemin profilinin tahmin edilmesi.
- Author
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Önalp, Akın and Arel, Ersin
- Subjects
SOIL profiles ,SOIL testing ,SOIL classification ,ENGINEERING geology ,ARTIFICIAL neural networks - Abstract
Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2011
16. Hata tanıma ve hata toleranslı kontrol: Destek vektörü makineleri yaklaşımı.
- Author
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Ortaç^Kabaoğlu, Rana and Eksın, İbrahim
- Subjects
FAULT diagnosis ,VECTOR analysis ,VECTOR fields ,FAULT tolerance (Engineering) ,FAULT-tolerant computing ,ARTIFICIAL neural networks - Abstract
Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2011
17. Karınca koloni optimizasyonuna dayalı yeni bir aritmi sınıflama tekniği.
- Author
-
Nızam, Ali and Korürek, Mehmet
- Subjects
ELECTROCARDIOGRAPHY ,ANIMAL models of arrhythmia ,ANIMAL models in research ,CARDIOLOGY ,CLUSTER analysis (Statistics) ,SIGNAL detection ,FEATURE extraction ,ARTIFICIAL neural networks ,CLASSIFICATION - Abstract
Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2011
18. İMKB endeks tahmin sistemi geliştirmede finansal parametrelerin seçimi.
- Author
-
HaznedaroĞlu, Feyzi and Taġ, Oktay
- Subjects
BUSINESS forecasting ,FINANCIAL markets ,ECONOMIC trends ,MULTIVARIATE analysis ,INVESTORS ,STOCK prices ,STOCK price indexes ,ARTIFICIAL neural networks ,FINANCIAL instruments ,MACROECONOMICS - Abstract
Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2010
19. MÜHENDİSLİK YAKLAŞIMIYLA TERMOREGÜLASYON.
- Author
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Çelık, Nevin and Bayazit, Yılmaz
- Subjects
- *
BODY temperature regulation , *HEAT transfer , *BIOTECHNOLOGY research , *TREATMENT of fever , *TEMPERATURE measurements , *COMMUNICATION of technical information , *SIMULATION methods & models , *ARTIFICIAL intelligence research , *ARTIFICIAL neural networks - Abstract
This paper aims to present technical information about the heat transfer mechanisms in human body, reaction of the human body temperature to the environment (thermoregulation), measurement of the body core temperature, and the action to be taken as the body temperature changes abnormally. Detailed information about the diseases due to abnormal temperature changes in the human body (i.e., hypothermia, hyperthermia) and about methods to prevent these diseases are reviewed. This paper is a presentation of fundamental information on bioheat transfer and thermoregulation for the interested researchers, and it is hoped to be a guide for engineering researches which aim to simulate human body. [ABSTRACT FROM AUTHOR]
- Published
- 2010
20. Düşük güçlü çok seviyeli CMOS sınıflandırıcı devresi.
- Author
-
Yildiz, Merih, Özoguz, Serdar, and Minaei, Shahram
- Subjects
PATTERN perception ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,DIGITAL computer simulation ,ELECTRONIC data processing ,TEMPLATE matching (Digital image processing) ,PATTERN recognition systems ,DIGITAL image processing ,GEOMETRIC quantization ,DIAGNOSTIC imaging - Abstract
Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2010
21. METHANOL/LIBR ILE ÇALIŞAN EJEKTÖRLÜ ABSORPSIYON SOĞUTMA SISTEMININ TERMODINAMIK ANALIZINDE YAPAY SINIR AĞLARININ KULLANILMASI.
- Author
-
Sözen, Adnan, Arcaklioğlu, Erol, and Özalp, Mehmet
- Subjects
THERMODYNAMICS ,ARTIFICIAL neural networks ,METHANOL ,OZONE layer depletion ,ABSORPTION ,SOLAR radiation ,ANALYTIC functions ,ARTIFICIAL intelligence ,TRANSFER functions - Abstract
Copyright of Teknoloji is the property of Engineering Science & Technology, an International Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2002
22. Finansal Başarısızlık Tahmini Üzerine Türkiye'de Yayımlanan Lisansüstü Tezlerin Bibliyometrik Analizi (1991-2021).
- Author
-
KARATAŞ, Bekir and CAN, Ahmet Vecdi
- Subjects
BIBLIOMETRICS ,ARTIFICIAL neural networks ,DISCRIMINANT analysis ,QUALITATIVE research ,LOGISTIC regression analysis ,BIBLIOTHERAPY - Abstract
Copyright of Muhasebe ve Vergi Uygulamalari Dergisi (MUVU) / Journal of Accounting & Taxation Studies (JATS) is the property of Ankara Serbest Muhasebeci Mali Musavirler Odasi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
23. FİNANS ALANINDA YAPAY ZEKÂ TEKNOLOJİSİNİN KULLANIMI: SİSTEMATİK LİTERATÜR İNCELEMESİ.
- Author
-
YILDIZ, Ayşe
- Subjects
ARTIFICIAL neural networks ,COMPUTER engineering ,ARTIFICIAL intelligence ,DEEP learning ,ELECTRONIC money ,INFERENCE (Logic) - Abstract
Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
24. Yapay Sinir Ağları (YSA) ve ARIMA Modelleri ile Türkiye’de Aylık Sıfır km Otomobil Satış Adetlerinin Tahmin Edilmesi.
- Author
-
EŞİDİR, Kâmil Abdullah, GÜR, Yunus Emre, YOĞUNLU, Abdulvahap, and ÇUBUK, Muhammed
- Abstract
Copyright of Pamukkale University Journal of Business Research / Pamukkale Üniversitesi İşletme Araştırmaları Dergisi is the property of Pamukkale University Journal of Business Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
25. GÖRÜNTÜ EŞLEME VE GENETİK ALGORİTMALAR KULLANARAK GÖRÜNTÜ İÇİNDE GÖRÜNTÜ ARAMA.
- Author
-
KARAKOC, Mehmet and KAVAKLIOGLU, Kadir
- Abstract
Main focus of this work is to realize image searching within another image in an efficient way. Image searching within another image is accomplished through the integrated use of image matching techniques and searching algorithms. Artificial neural networks along with various image features such as average color value, color standard deviation, correlation and edge parameters are used for image matching whereas genetic algorithms were used for image searching. In the work presented in this paper, an integrated method based on smart searching algorithms, quick image matching methods and parallel programming techniques were proposed and implemented. Proposed method was tested on several low and highresolution reference and template images. Results revealed that the proposed method can successfully match images and significantly reduce the total search time. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
26. MAKİNE ÖĞRENMESİ YÖNTEMLERİNİN İNŞAAT SEKTÖRÜNE KATKISI: BASINÇ DAYANIMI TAHMİNLEMESİ.
- Author
-
ERDAL, Hamit
- Abstract
Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC's compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM) 's applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it's obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM's can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
27. ADLİ MUHASEBEDE HİLELERİN TESPİTİNDE YAPAY SİNİR AĞI MODELİNİN KULLANIMI.
- Author
-
TERZİ, Serkan and ŞEN, İlker KIYMETLİ
- Subjects
- *
FRAUD prevention , *FORENSIC accounting , *MISLEADING financial statements , *ACCOUNTING fraud , *ARTIFICIAL neural networks - Abstract
Fraud is an intentional action in the financial statements. Fraud can be defined as an act of deception where an individual or a group obtains benefits in return for damaging another individual or group. Frauds can be classified as employee fraud and the fraudulent financial reporting. According to the 2012 report of Association of Certified Fraud Examiners, companies lose averagely more than 1 million dollars due to financial statement fraud. Therefore, forensic accounting has an important role in determining financial statemet frauds in order to provide legal support in lawsuits. The purpose of this paper is show to use artificial neural network model detecting frauds in forensic accounting. For this purpose, we conduct an empirical research in the Borsa Istanbul. In the study, the correct classification of the artificial neural network is realized as 100%. [ABSTRACT FROM AUTHOR]
- Published
- 2015
28. 112 ACİL ÇAĞRI MERKEZİNE GELEN ÇAĞRI SAYILARINI BELİRLEYEBİLMEK İÇİN BİR YAPAY SİNİR AĞLARI TAHMİNLEME MODELİ GELİŞTİRİLMESİ.
- Author
-
AYDEMİR, Erdal, KARAATLI, Meltem, YILMAZ, Gökhan, and AKSOY, Serdar
- Abstract
Forecasting studies are extremely important in the technical, social and economic research. Generally, we know it is very difficult to forecast with higher accurate about a system by using recent values. In the scientific literature, the forecasting studies of energy, personnel planning, production planning, climate changes, sales and marketing and economics etc. are frequently found. In this paper, for an emergency calls center in Isparta province of Turkey an artificial neural network (ANN) forecasting model was developed to determine the number of calls for as health, fire and security services on a pilot implementation of the emergency calls center on a single number 112. In the developed model, the gradient descent with adaptive learning and momentum (GDX) algorithm is selected as the training algorithm with feed-forward back-propagation by using 80% of input data and the 20% of input data is used for testing set data from last month. After the testing, the mean absolute percentage error (MAPE) rate is obtained as 4.5% and it is useful to test. In addition, the forecasting results of the next month are shown that the MAPE values are 2.65%, 6.40% and 5.24% with ANN, trend analysis and ARIMA (1 1 1) models respectively and, the number of calls are found separately on the types of calls in daily. Consequently, the developed model by using ANN to forecast the number of calls in an emergency call center is more accurate than the trend analysis and ARIMA models. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
29. Yapı Endüstrisinde Kullanılan Sürdürülebilir Ahşap Malzemenin Yapay Sinir Ağları İle Gelecek Tahmini.
- Author
-
Ergül, Hamdi
- Subjects
BUILDING materials research ,WOOD ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence research ,CONSTRUCTION industry - Abstract
Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2014
- Full Text
- View/download PDF
30. DÖVİZ KURU VOLATİLİTESİNİ ÖNGÖRMEDE MELEZ BİR MODEL: YAPAY SİNİR AĞI TABANLI EGARCH .
- Author
-
SAĞLAM BEZGİN, Müge and KAYA, Ebru
- Subjects
ARTIFICIAL neural networks ,GARCH model ,TURKISH lira ,U.S. dollar ,PREDICTION models ,FOREIGN exchange rates - Abstract
Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
31. KİTLESEL ÖZELLEŞTİRME ORTAMINDA SÜREÇ PLANLAMA VE ÇİZELGELEME ENTEGRASYONU: HİBRİT BİR YAKLAŞIM.
- Author
-
ŞEKER, Alper and EROL, Serpil
- Subjects
- *
ARTIFICIAL neural networks , *GENETIC algorithms , *MASS customization , *PRODUCTION planning , *PRODUCTION scheduling , *ECONOMIC demand , *JOB shops , *HEURISTIC - Abstract
In mass customization using resources efficiently with changing customer demands and manufacturing conditions is vital, since in today's competitive market conditions, this efficiency provides cost, time and labor savings to companies. In production environment, Process Planning (PP) and Scheduling are two functions that provide efficient usage of resources. Isolation and long time gap between these functions are main problems affecting the effiency of production. In this study, to solve the problems of productivity, it is aimed to build an integrated system which is able do PP and Scheduling in parallel and respond quickly to fluctuations in job floor. In existing integration models aiming to eliminate this isolation and time gap, it has been observed that if the search and solution space expands, the computational time increases rapidly. Therefore, in this research, a hybrid optimization approach, which can find the optimal solution rapidly is considered and a hybrid model combining both Genetic Algorithm (GA) and Artificial Neural Network (ANN) is proposed. To improve GA performance and increase the effiency of searching, clustering activities are carried out for building new cromosome structures. To increase population diversity, effective genetic operator schemes and efficient genetic represantations are used. In the integration module, 3 different GA structures created within the scope of our research are compared and the algorithm formed by clustering method shows better performance than the others. In this paper by using ANN method, a new system trained by data obtained from Scheduling is generated and this system is able to quickly respond the changes in shop floor and provide new schedules instantly. In rescheduling module, ANN's performance measures provide evidence to accuracy of Heuristic solution generated by Integration module. [ABSTRACT FROM AUTHOR]
- Published
- 2013
32. Yapay Sinir Ağları İçin Net Platformunda Görsel Bir Eğitim Yazılımının Gelistirilmesi.
- Author
-
Çevık, Kerim Kürsat and Dandil, Emre
- Subjects
EDUCATION software ,VISUAL education ,ARTIFICIAL neural networks ,MICROSOFT .NET Framework ,COMPUTER interfaces ,COMPUTATIONAL complexity ,BACK propagation - Abstract
Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2012
33. TÜRKİYE'NİN KARBONİZASYON İNDEKSİNİN TEMEL ENERJİ GÖSTERGELERİNE BAĞLI OLARAK YAPAY SİNİR AĞLARI İLE TANIMLANMASI.
- Author
-
ÖZDEMİR, Veysel
- Subjects
- *
CARBON dioxide & the environment , *CARBONIZATION , *ARTIFICIAL neural networks , *ENVIRONMENTAL indicators , *INDEXES , *INTERNATIONAL cooperation on climate change - Abstract
In this study, Turkey's Carbonization Index (v) depending on the basic energy indicator has determined as an analytical equation. In this paper, artificial neural networks (ANN) which is quite often in recent years and the possibility of applying to many fields, are used to obtain analytical expressions. Carbonization Index is defined the amount of CO2 emissions per energy consumption. This parameter is an important for Turkey because of has signed the Kyoto protocol, February 2009. According to the results, accuracy of the carbonization index formulated empirical was obtained as R²= 1. It is expected that this study will be helpful in demonstrating carbonization index for policy makers. [ABSTRACT FROM AUTHOR]
- Published
- 2011
34. Agregaların Temel Şekil Özellikleri Kullanılarak Yapay Sinir Ağları Yardımıyla Sınıflandırılması.
- Author
-
Sınecen, Mahmut and Makinaci, Metehan
- Subjects
- *
MINERAL aggregates , *ARTIFICIAL neural networks , *DIGITAL image processing , *DIGITAL cameras , *VECTOR analysis - Abstract
In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45° and 90° by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods. [ABSTRACT FROM AUTHOR]
- Published
- 2010
35. Agregaların Temel Şekil Özellikleri Kullanılarak Yapay Sinir Ağları Yardımıyla Sınıflandırılması.
- Author
-
SİNECEN, Mahmut and MAKİNACI, Metehan
- Abstract
In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45° and 90° by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods. [ABSTRACT FROM AUTHOR]
- Published
- 2010
36. EKONOMİK KRİZ DÖNEMİNDE FİRMA BAŞARISI TAHMİNİ: YAPAY SİNİR AĞLARI TABANLI BİR YAKLAŞIM.
- Author
-
Ekınci, Yeliz, Temur, Gül T., Çelebı, Dilay, and Bayraktar, Demet
- Subjects
- *
BUSINESS forecasting , *ARTIFICIAL neural networks , *FINANCIAL crises , *MANUFACTURING industries , *INDUSTRIAL management , *PROTOTYPES , *BUSINESS models , *BUSINESS planning - Abstract
The purpose of this paper is to present an artificial neural network (ANN) based method for the estimation of company success during economic crisis periods. There are a number of studies about recovering the crisis successfully by companies. Success or failure interpretation can be made upon financial indicators of companies for the period before the crisis. These indicators also provide estimation about the success of the companies. In this study, an ANN model is proposed estimating the success of companies after crisis period by means of financial indicators of the companies for the year before 2001 economic crisis of Turkey. A prototype model is designed with regards to homogeneous data set retrieved from the companies facilitating in manufacturing sector. [ABSTRACT FROM AUTHOR]
- Published
- 2010
37. BİLGİ EDİNME HAKKI YASASI ÇERÇEVESİNDE YAPILAN ELEKTRONİK BAŞVURULARIN YAPAY SİNİR AĞLARI İLE SINIFLANDIRMASI.
- Author
-
Kilağiz, Yavuz and Baran, Ahmet
- Subjects
- *
ELECTRONIC systems , *LABOR supply , *UNIVERSITIES & colleges , *ARTIFICIAL neural networks , *COMPUTER networks - Abstract
In this paper, within the framework of the Act on the Right of Information Acquirement, an electronic application classification system has been developed in order to eliminate such problems as the waste of workforce and time during the classification of electronic applications to the institutions. The system first classifies the electronic applications to Erzincan University then forwards them to the relevant Chambers of Affairs. Artificial Neural Network based system, which considerably accelerates the flux of work and hinders the waste of workforce, is a word based and supervised learning system and it works with 98% accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2009
38. Osteoporoz riskinin yapay sinir ağları yöntemi ile saptanması.
- Author
-
Akpolat, Veysi
- Subjects
- *
MEDICAL research , *ARTIFICIAL neural networks , *OSTEOPOROSIS , *BONE density , *DISEASE risk factors , *WOMEN'S health - Abstract
Artificial neural networks (ANNs) have become modeling tools that have found extensive acceptance and they have frequently used in applications in many disciplines for solving complex problems. Different ANN structures are valuable models, which are used in the medical field for the development of decision support systems. In this paper, the learning and classification processes are used for determining the level of bone-density (safe / risk of osteoporosis) in woman. In this study, three different structured neural networks were used for classifying of osteoporosis and the most efficient structure was determined. The training network structures were Multilayer perceptron neural network (MLP), Linear Vector Quantization (LVQ) and Self Organizing Map (SOM). Performance indicators and statistical measures were used for evaluating the structures and the results demonstrated that the MLP was the most efficient structure for classifying of osteoporosis. [ABSTRACT FROM AUTHOR]
- Published
- 2009
39. TASARIM VE UYGULAMASI YAPILAN BİR ULTRASONİK AKIŞ ÖLÇERİN KALİBRASYONU.
- Author
-
İskender, İres, Erta ş, Emre, and Genç, Naci
- Subjects
- *
FLOW meters , *ULTRASONIC equipment , *CALIBRATION , *PHYSICAL measurements , *MEASUREMENT , *ARTIFICIAL neural networks - Abstract
In this paper, the experimental studies of different flow density values have been carried on a designed and implemented flow meter being known as ultrasound working based on high frequency sound wave technology. The calibration of the flow meter has been realized using the results obtained from the study. The transit-time method has been used as the basis of the flow measure. For calibration of the flow meter, the experimental study has been carried on twenty different flow densities and the differences between the measured and the exact values have been observed. Then, to minimize these differences, two different calibration methods being 'linearequation' and 'artificial-neural-network' methods have been developed and the results have been obtained. Using the results, the two mentioned methods have been compared and those effects on the accuracy of the flow meter have been discussed. It has been shown that between these two methods the accuracy of the 'neuralnetwork' method is higher than that of the 'linear-equation' method. [ABSTRACT FROM AUTHOR]
- Published
- 2007
40. HIZLI EVRİMSEL ENİYİLEME İÇİN YAPAY SİNİR AĞI KULLANILMASI.
- Author
-
Hacioğlu, Abdurrahman
- Subjects
- *
GENETIC algorithms , *COMBINATORIAL optimization , *GENETIC programming , *ARTIFICIAL neural networks , *ARTIFICIAL intelligence , *EVOLUTIONARY computation - Abstract
In this paper, the Augmented Genetic Algorithm with Artificial Neural Network (AGANN) is expanded for optimization works, and its implementations to model problems are demonstrated. With the purpose of getting a faster algorithm, a neural network and a real coded genetic algorithm are hybridized in a new way. In this way, instead of predicting objective function calculation of a candidate, a properly trained neural network is used for predicting the candidate itself. At each step of the genetic process, using a simulated annealing based optimization procedure, the trained neural network produces an individual, which is a candidate solution of the optimization problem. Adding this candidate to the population at each step improves the exploration power of the genetic process. The proposed algorithm is tested for some test function problems. The results indicate that the computational efficiency of the implemented algorithm is tremendously high. Due to still being a genetic algorithm based technique, this method is also as robust as the pure genetic algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2006
41. ORAL VE MAKSİLLOFASİYAL RADYOLOJİ’DE YAPAY ZEKÂ.
- Author
-
ÖZKESİC, Muhammed Yasir and YILMAZ, Selmi
- Abstract
Copyright of Journal of Health Sciences / Sağlık Bilimleri Dergisi is the property of Erciyes Universitesi Saglik Bilimleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2021
- Full Text
- View/download PDF
42. MEVDUAT BANKALARININ KREDİ DERECELENDİRMESİNDE VERİ MADENCİLİĞİ YÖNTEMLERİ TAHMİN PERFORMANSININ ÖLÇÜLMESİ: TÜRKİYE ÖRNEĞİ.
- Author
-
AKSOY, Barış, TORUN, Talip, and AKEL, Veli
- Subjects
CREDIT ratings ,ARTIFICIAL neural networks ,BANK deposits ,DEPOSIT banking ,INTEREST rates ,FINANCE - Abstract
Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2021
43. Yapay Sinir Ağları ile Trafik Yoğunluğu Tahmini.
- Author
-
TAŞ, Nurullah and SEZEN, Bülent
- Abstract
Copyright of Afyon Kocatepe University Journal of Social Sciences / Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi is the property of Afyon Kocatepe University (AKU) Sosyal Bilimler Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
44. Streamflow and Sediment Load Prediction Using Linear Genetic Programming
- Author
-
Ali Unal Şorman and Ali Danandeh Mehr
- Subjects
günlük akım ,sediment ,öngörüm ,doğrusal genetik pogramlama ,yapay sinir ağları ,daily discharge ,prediction ,linear genetic programming ,artificial neural networks ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Daily flow and suspended sediment discharge are two major hydrologıcal variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used to model and predict these variables in recent studies. However, these are implicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approach has been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicit relationships (prediction rules) evolved by LGP take the form of equations or program codes, which can be checked for its physical consistency. The results showed that the LGP outperforms ANNs in terms of root mean squared error and coefficient of efficiency.
- Published
- 2018
- Full Text
- View/download PDF
45. Monthly Natural Gas Consumption’s Modelling and Its Trend Analysis For Yozgat In Turkey
- Author
-
MURAT Ay
- Subjects
yapay sinir ağları ,aylık doğal gaz tüketimi ,mann-kendall eğilim testi ,şen eğilim testi ,yozgat ,artificial neural networks ,monthly natural gas consumption ,mann-kendall trend test ,şen trend test ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
In this study an energy consumption modelling for long term (December 2006- March 2016) forecasting of monthly natural gas consumption in households and industry area for Yozgat city, Turkey was presented. In this context, it can be said that this paper has two purposes. One of them is the application and accuracy of the artificial neural networks. Estimate performances are compared with each other, and the estimates of the optimal models are evaluated with the monthly recorded natural gas consumption according to root mean square error, mean absolute error, and correlation coefficient. The other purpose of the study is to analysis trend of monthly natural gas consumption of Yozgat by using Mann-Kendall and a new method recently proposed by Şen. The results showed that the artificial neural networks gave satisfactory results in estimating monthly natural gas consumption. In the trend analysis, it was seen that both Mann-Kendall and Şen trend tests gave statistically significant increasing trend at 95% confidence level for monthly natural gas consumption of Yozgat.
- Published
- 2018
- Full Text
- View/download PDF
46. Artificial Neural Networks Based Inverse Kinematics Solution and Simulation of a Six Degree of Freedom Lighting Manipulator
- Author
-
Nihat ÇABUK and Veli BAKIRCIOĞLU
- Subjects
6-DOF Manipulator ,Kinematic Analysis ,Inverse Kinematic ,Artificial Neural Networks ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Science ,Science (General) ,Q1-390 - Abstract
Inverse kinematics problem of a robot manipulator is calculation of joint variables of the manipulator. Inverse kinematic is a challenging problem of robots. The inverse kinematics in robotic is the determination of joint angles for a desired position of the end effector. This paper proposes an Artificial Neural Network (ANN) model to find Inverse kinematic solution of a 6- DOF manipulator, which is designed instead of a manually lighting system that is used in surgery rooms. After obtaining forward kinematic equations, these equations were used to derive training data of ANN. The manipulator was designed in a computer aided design (CAD) program and this CAD model transferred to Simulink environment for a realistic and visual simulation purpose. To evaluate the trained ANN performance visually, this Simulink model were used. A test input set is introduced to the trained ANN. Results are discussed and demonstrated graphically. It was observed that obtained results are satisfactory, and the error of ANN for a reference position is acceptable.
- Published
- 2018
- Full Text
- View/download PDF
47. YAPAY SİNİR AĞLARI YARDIMI İLE ÇAĞRI MERKEZİ UYGULAMALARINDA ÖNGÖRÜ MODELLEMESİ.
- Author
-
ORTAKAYA, Sefa and TUNTAŞ, Remzi
- Abstract
Copyright of Dokuz Eylul University Journal of Graduate School of Social Sciences is the property of Dokuz Eylul University Graduate School of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2019
- Full Text
- View/download PDF
48. Yapay Zekâ Yöntemleri İle İşletmelerin Finansal Başarısızlığının Tahmin Edilmesi: Bist İmalat Sektörü Uygulaması.
- Author
-
Yürük, Muhammed Fatih and Ekşi, İbrahim Halil
- Subjects
ARTIFICIAL neural networks ,SUPPORT vector machines ,IMPACT craters ,ARTIFICIAL intelligence ,ECONOMIC structure - Abstract
Copyright of Mukaddime Journal is the property of Mukaddime Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2019
- Full Text
- View/download PDF
49. Image Searching within Another Image Using Image Matching and Genetic Algorithms
- Author
-
Kadir Kavaklıoğlu and Mehmet Karakoc
- Subjects
image processing ,image matching ,artificial neural networks ,genetic algorithms ,parallel programming image searching within another image ,görüntü içinde görüntü arama ,görüntü işleme ,görüntü eşleme ,yapay sinir ağları ,genetik algoritmalar ,paralel programlama ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Main focus of this work is to realize image searching within another image in an efficient way. Image searching within another image is accomplished through the integrated use of image matching techniques and searching algorithms. Artificial neural networks along with various image features such as average color value, color standard deviation, correlation and edge parameters are used for image matching whereas genetic algorithms were used for image searching. In the work presented in this paper, an integrated method based on smart searching algorithms, quick image matching methods and parallel programming techniques were proposed and implemented. Proposed method was tested on several low and high-resolution reference and template images. Results revealed that the proposed method can successfully match images and significantly reduce the total search time.
- Published
- 2015
50. Contribution of Machine Learning Methods to the Construction Industry: Prediction of Compressive Strength
- Author
-
Hamit Erdal
- Subjects
high performance concrete ,artificial neural networks ,support vector machines ,yüksek performanslı beton ,yapay sinir ağları ,destek vektör makineleri ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC’s compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM)’s applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it’s obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM’s can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength.
- Published
- 2015
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