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Öğe An ANFIS Based Vehicle Sales Forecasting Model Utilizing Feature Clustering and Genetic Algorithms(Hezarfen Aeronautics and Space Technologies Institute, 2020) Yılmaz, Atınç; Kaya, Umut; Şaykol, EdizThe automotive sector is one of Turkey’s most important industries, and the developments in technology are affecting the automotive sector as well as the other sectors. The methods that have been used to date indicate that the use of AI should be increased when the demand forecasting applications take into account the developments in the industry. For this purpose, by using the data taken from the Automotive Distributors Association and Turkish Statistical Institute Internet pages, intuitive learning hybrid ANFIS method is used to forecast the sales in this study. A clustering scheme is first applied to group the features, and then the features are fed into genetic algorithms to improve the prediction model performance. The experiments show that the prediction performance of the proposed method is good when compared to existing related studies in the literature.Öğe An ANFIS Based Vehicle Sales Forecasting Model Utilizing Feature Clustering and Genetic Algorithms(2020) Şaykol, Ediz; Yılmaz, Atınç; Kaya, UmutThe automotive sector is one of Turkey’s most important industries, and the developments in technology are affecting the automotive sector as well as the other sectors. The methods that have been used to date indicate that the use of AI should be increased when the demand forecasting applications take into account the developments in the industry. For this purpose, by using the data taken from the Automotive Distributors Association and Turkish Statistical Institute Internet pages, intuitive learning hybrid ANFIS method is used to forecast the sales in this study. A clustering scheme is first applied to group the features, and then the features are fed into genetic algorithms to improve the prediction model performance. The experiments show that the prediction performance of the proposed method is good when compared to existing related studies in the literature.Öğe Designing A Neural Network Model Using K-Means Clusteing For Rısk Analysis Of Lung Cancer Disease(Hezârfen Aeronautics and Space Technologies Institute, 2018) Kaya, Umut; Yılmaz, Atınç; Şaykol, EdizAccording to the World Health Organization report in 2004, lung cancer belongs to highest mortality rate cancer type compared to others. Genetics and early starting smoke etc. become the basis for lung cancer risk. In recent years, lung cancer cases are increasing with the use of cigarettes at younger ages. One of the most important factor in the treatment of the disease is early diagnosis. Artificial intelligence methods, which have been used in many areas in recent years, are also used for early diagnosis and imaging of diseases. In this study, a hybrid artificial neural network (ANN) model was designed to bring a different perspective to the use of multilayer ANN in the literature for lung cancer risk prediction. Lung cancer risk factors were used as input data in predicting the disease. We tried to estimate the results using clustered data by K-means clustering algorithm and multi-layered NN method. When the results obtained from the normalized and clustered data set are compared with the results in the literature, the proposed model has a higher accuracy value than the other methods.Öğe DESIGNING A NEURAL NETWORK MODEL USING K-MEANS CLUSTERING FOR RISK ANALYSIS OF LUNG CANCER DISEASE(2018) Yılmaz, Atınç; Şaykol, Ediz; Kaya, UmutAccording to the World Health Organization report in 2004, lung cancer belongs to highest mortality rate cancertype compared to others. Genetics and early starting smoke etc. become the basis for lung cancer risk. In recentyears, lung cancer cases are increasing with the use of cigarettes at younger ages. One of the most important factorin the treatment of the disease is early diagnosis. Artificial intelligence methods, which have been used in manyareas in recent years, are also used for early diagnosis and imaging of diseases. In this study, a hybrid artificialneural network (ANN) model was designed to bring a different perspective to the use of multilayer ANN in theliterature for lung cancer risk prediction. Lung cancer risk factors were used as input data in predicting the disease.We tried to estimate the results using clustered data by K-means clustering algorithm and multi-layered ANNmethod. When the results obtained from the normalized and clustered data set are compared with the results in theliterature, the proposed model has a higher accuracy value than the other methods.Öğe Is There Any Advantage of Machine Learning to Multivariate Regression Analysis for Predicting Disease-Related Deaths in Patients with Gastric Cancer? Reevaluation of Retrospective Data(KARE PUBL, 2021) Yılmaz, Atınç; Kaya, Umut; Yaprak, Gökhan; Özen, AlaattinOBJECTIVE The problem in gastric cancer patients is multifactorial and it is not possible to use one factor alone to predict disease survival. Machine learning (ML) algorithms have become popular in the medical field, recently. We aimed to evaluate the power of ML algorithms for predicting deaths due to gastric cancer. METHODS We reevaluated the retrospective data published. Seven different ML algorithms (logistic regression [LR], artificial neural networks/multilayer perceptron, gradient boosted trees, support vector machine, random forest, naive Bayes, and probabilistic neural network) tried to predict disease-related deaths using the significant variables effective on disease-specific survival (DSS) obtained from univariate analysis. RESULTS Median follow-up time was 34 months (4-156 months), and the death with disease occurred in 194 (86.6%) patients in the follow-up period. The median DSS was 22 (4-139) months. Using the significant variables effective on DSS obtained from univariate analysis, the highest accuracy rate (99%) was the best in the LR, and only one patient was classified incorrectly. CONCLUSION We can successfully predict the treatment outcomes such as disease-related deaths in gastric cancer patients using ML algorithms.Öğe An Ontology Design To Represent Academic Researches(Beykent Üniversitesi, 2017) Kaya, Umut; Altan, ZeynepSemantic Web provides models and abstractions to process webaccessible information and services to be more effectively. The effective communication with semantic web is ontology, which is the concept of the entities that means concreted objects. It provides formal and explicit specification of the conceptualization in any domain. In this paper, we designed an ontology in which the academic studies have been classified. After the description of the logic rules, we realized formal conceptual analysis of the constituted ontology. All classes of our ontology include instances interacting with each other according to the object property assertions and it has been built with OWL web ontology language by using Protege. Finally, we defined SPARQL queries of our prototype.Öğe PREDICTION AND CLASSIFICATION OF PRESSURE INJURIES BY DEEP LEARNING(Termedia Publishing House Ltd, 2021) Yilmaz, Atinc; Kizil, Hamiyet; Kaya, Umut; cakir, Ridvan; Demiral, MelekPressure injuries are a serious medical problem that both negatively affects the patient's quality of life and results in significant healthcare costs. In cases where a patient doesn't receive appropriate treatment and care, death may result. Nurses play critical roles in the prevention, care, and treatment of pressure injuries as members of the healthcare team who closely monitor the health status of the patient. Today, the use of artificial intelligence is becoming more prevalent in healthcare, as in many other areas. Artificial intelligence is a method that aims to solve complex problems by using computers to mathematically simulate the way the brain works. In this article, we compile and share information about a deep learning model developed for the detection and classification of pressure injuries. Deep learning can operate on many types of data. Convolutional Neural Networks (CNN) prefer images because they can handle 2D arrays. In this case, the images, annotated according to the National Pressure Injury Advisory Panel pressure injury classification system, have been fed into a deep learning model using CNN. The developed CNN model has a 97% success in detecting and classifying pressure injuries, and as more images are collected and fed into the CNN, the prediction accuracy will increase. This deep learning model allows for the automatic detection and classification of pressure injuries, an indicator of health outcomes, at an early stage and for quick and accurate intervention. In this context, it is expected that the quality of nursing care will increase, the prevalence of pressure injury will decrease, and the economic burden of this health problem will decrease.Öğe Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri(Osman Sağdıç, 2019) Kaya, Umut; Yılmaz, Atınç; Dikmen, YalımUzun süreli tedavi gerektiren kanser ve benzeri hastalıklara yakalanan hastaların ölüm riski yüksektir. Bu riski azaltmak ve hastanın yaşam süresini uzatmak için tıpta, teknolojideki gelişmelerin de kullanıldığı çalışmalar bulunmaktadır. Bu çalışmalarda hastalığın tedavisi için çok önemli olan erken tanı yöntemlerine odaklanılmıştır. Yapay zekâ, makinelerin insan beyninin çalışmasını taklit ederek karar verme ve tahmin etme gibi çözülmesi zor olan problemlerin çözümüne imkân tanıyan bir bilim dalıdır. Yapay zekânın bir alt dalı olan makine öğrenmesi ise kodlanmış olan hazır talimatları kullanarak çözüm üretmek yerine; örneklerden öğrenerek, görüntü, resim ve ses tanıma gibi birçok zor probleme çözüm getirmektedir. Son yıllarda birçok alanda kullanılan makine öğrenmesinin, hastalıkların erken teşhisinde kullanılabilme potansiyeli de bulunmaktadır. Konu ile ilgili yapılan çalışmalar özellikle makine öğrenmesinin bir alt dalı olan derin öğrenme yöntemlerine odaklanmıştır. Bu çalışmanın amacı sağlık alanında uygulanan derin öğrenme yöntemlerinin çalışma prensiplerini ve hangi hastalıklarda kullanıldığını, ilgili literatür ışığında ortaya koymaktır. Bu çalışmanın sonucunda, hastalığın teşhisinde kullanılan verilere uygun derin öğrenme yönteminin tercih edilmesinin, hastalığa erken tanı konma başarısını arttıracağı düşünülmektedir.Öğe Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks(Mdpi, 2023) Kaya, Umut; Yilmaz, Atinc; Asar, SinanThe early diagnosis of sepsis reduces the risk of the patient's death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs.
