DSpace Arşivi :: by Yazar "Duru, Adil Deniz" değerine göre listeleniyor

Yazar "Duru, Adil Deniz" seçeneğine göre listele

Listeleniyor 1 - 20 / 35

An expert system to predict eye disorder using deep convolutional neural network
(Academic Platform Journal of Engineering and Science, 2021) Duru, Adil Deniz
Glaucoma according to the W.H.O is one of the major causes of blindness worldwide. Due to its complexity and silent nature early detection of this disease makes it hard to detect. There have been several techniques over the years for classification which have shown significant improvement over the past decade or two. Some of the many classification models are SVM (support vector machine), KNN (K- Nearest Neighbors), Decision tree, Logistic Regression and ANN (Artificial Neural Network) back propagation. For this paper we would consider different procedure and method of early detection of the glaucoma disease using the MATLAB Deep Convolutional Neural Network (DCNN). The DCNN based expert system basically works like the human brain with input, neurons, hidden layers and output. For this project Fundus image of both healthy image and glaucoma image are collected with good lighting condition so that all hidden features can be identify. The Fundus image are then passed through different image processing method such as Grayscale, B&W, Complement, Robert, Resize and power Transform. The fundus is then passed through a texture feature extraction algorithm know as Deep Convolutional Neural Network (DCNN). The features gotten are Contrast, Correlation, energy, Homogeneity, Entropy, Mean, Standard deviation, Variance, skewness and Kurtosis. After the feature extraction the data are arrangement on a spreadsheet which serves as a means of record. Lastly, a deep convolutional neural network is written with one hidden layer, 16 input neuron and 2 output either healthy or not. The data are split into train and test dataset with 70% for training 15% validation and 15% for testing. Accuracy of detection was 92.78% with the execution time of 5.33s only depending on the number of iteration or epochs.
Classification of brain tumors using MRI images based on convolutional neural network and supervised machine learning algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022) Ali, Rawaa; Al-Jumaili, Saif; Duru, Adil Deniz; Uçan, Osman Nuri; Boyacı, Aytuğ; Duru, Dilek Göksel
Brain tumor is abnormal cells that originate from cranial tissue and is considered one of the most destructive diseases, and lead to the cause of death, where the early diagnosis is crucial for accelerating the therapy of brain tumors. Examining the patient's MRI scans is one traditional way of distinguishing brain cancers. The conventional approaches take a long time and are prone to human error, especially when dealing with huge amounts of data and diverse brain tumor classes. Artificial Intelligence (AI) is extremely useful for the strict detection and classification of several diseases in the brain. Convolutional Neural Network (CNN) is one of the modes techniques which act as a tumor classifier due to it shows high effectiveness for diagnosing brain tumors. That's why, in this research, we presented a hybrid method that merged a group of pre-Trained deep learning CNN patterns with a group of supervised classifiers in machine learning called, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). We used an MRI image that consist of images of four brain tumor classes, namely glioma, meningioma, pituitary, and no tumor. We deduced the features extracted from the images by hiring three types of CNN called (GoogleNet, Shuffle-Net, and NasNet-Mobile). Depending upon the experimental consequences, ShuffleN et with SVM achieved the highest results according to the four categories of metrics evaluation that are Accuracy of 98.40%, Precision of 97%, Recall of 96.75%, and Fl-Score of 96.75%. Finally, we compared our results with different state-of-The-Art papers recently published and our proposed method show outperforms compared them.
Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on fourier transform
(American Institute of Physics Inc., 2022) Al-Azzawi, Athar Hussein Ali; Al-Jumaili, Saif; Ibrahim, Abdullahi Abdu; Duru, Adil Deniz
Epilepsy classification techniques are one of the areas that are still under searching till now as long as there is no specific method for detection seizures. The brain consists of more than 100 billion nerves that generate electrical activity. These activities are recorded using an Electroencephalogram (EEG) by electrodes attached to the scalp. EEG is considered a big footstep in the medical and technical field where it allows the detection of brain disorders. However, this paper aims to identify the most efficient classification algorithm for classifying EEG signals of epileptic seizures. Therefore, we applied two classification techniques namely Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), which rely on the features extracted from the data by the Fast Fourier Transform (FFT) method. The results show SVM obtained the highest accuracy value compared to KNN, accurate scores were 99.5% and 99%, respectively.
Classification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG)
(Hindawi Ltd, 2020) Mohamed, Ahmed M. A.; Uçan, Osman Nuri; Bayat, Oğuz; Duru, Adil Deniz
An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.
Classification of single epochs in event related potentials
(Ieee, 2019) Çiçek, Kerime Dilşad; Bayat, Oğuz; Uçan, Osman Nuri; Duru, Adil Deniz
In the concept of this thesis, single trial event related potential measurements were classified. Classification performances of Decision Trees, logistic regression, random forest, Support Vector Machines and XGBoost methods are evaluated. In this context, EEG was collected during the presentation of two different stimulus. The resulting feature set is given as an input to decision trees, logistic regression, random forest, support vector machines, and xgboost classifiers. Due to the limited test data obtained, synthetic Minority oversampling technique(SMOTE) was applied to the data and classification was performed with the updated dataset. As a result of the study, 91% accuracy was obtained for the training dataset in random forest and XGBoost classification methods. For the test set xgboost_tuned has a 62% accuracy and 71% F1 value. To conclude, superior results were found from other classifiers using the xgboost classification method.
Classification of the level of alzheimer’s disease using anatomical magnetic resonance images based on a novel deep learning structure
(CRC Press, 2023) Al-Jumaili, Saif; Al-Azzawi, Athar; Uçan, Osman Nuri; Duru, Adil Deniz
Alzheimer’s is an incurable neurodegenerative disease that generally begins slowly and progresses gradually with time. In the early stage, symptoms of memory loss are mild, while in the late stage it clearly shows the deterioration in cognitive functions. Due to its irreversible nature, early detection reflects positively on reducing restraining the spread and preventing damage to the brain cells thus avoiding reaching the dementia stage. Till now, deep learning is considered to be one of the most significant methodologies used to detect and classify different types of neurological diseases from MRI images. However, in this study, we proposed a novel two-dimensional deep convolutional neural network to classify four stages of Alzheimer’s disease. The dataset consists of four types, namely nondemented, very mild demented, mild demented, and moderate demented subject MR images. First, we have applied a preprocessing technique to resize the image for compliance with our models. Then, we performed Reduce Atmospheric Haze techniques that can decrease the atmospheric haze making all images sharp and clear to feed to the model. We implemented the model 30 times and obtained more than 99.46% for evaluation metrics. The proposed method shows an outstanding performance compared to other papers reported in the literature.
Classify bird species audio by augment convolutional neural network
(Institute of Electrical and Electronics Engineers Inc., 2022) Jasim, Hasan Abdullah; Ahmed, Saadaldeen R.; Ibrahim, Abdullahi Abdu; Duru, Adil Deniz
Using convolutional neural networks, this thesis aims to create a system for fully automated identification of bird species based on spectrogram images. Spectrogram analysis is more difficult when trying to make an advance identification of a bird species. On a publicly available dataset of 8000 audio examples, we've begun by analyzing the challenges of bird species detection, segmentation, and classification to achieve our goal. It has been determined also that deep learning-based technique CNN with Fully convolutional learning calls for easier results because it eliminates the possible future modelling error caused by an imprecise knowledge of bird species and works well on coding in cohesion with the spectral analysis kernel using the librosa library. We have concluded. After obtaining the dataset from the open-source repository, it is then processed locally. For training, testing, and validation we used a subset of the dataset of 8000 sound samples. We offered a method relying on a CNN reset learned that proved to be very quick and optimum because it was first needing the spectrogram analytic kernel to learn what to class in bird species, and then it gets the system trained on features extracted. In a novel 9-step implementation, a bird species spectrogram can be detected from an audio sample. There was a loss of less than 0.0063, and the conditioning workouts accuracy is 0.9895 for the system, 0.9 as precision, and training and validation use 50 epochs in system.
Cloud authentication based face recognition technique
(Altınbaş Üniversitesi, 2019) Alrahlawee, Anfal Thaer; Duru, Adil Deniz; Bayat, Oğuz; Uçan, Osman Nuri
The recognition process for a single face can be completed in relatively less time. However, large scale implementation that involves recognition of several faces would make the procedure a lengthy one. Cloud computing service has been employed in this paper to provide a solution for scalability, where cloud computing increases the essential resources when larger data is to be processed. The programming and training of the developed system has been done in order to detect and recognize faces through cloud computing. Viola and Jones algorithm is employed for detecting faces that used integral image, cascaded classifiers, five sorts of Haar-like features, and Adaboost learning method. Face recognition has been done using Linear Discriminant Analysis (LDA), as it is more efficient compared to Principal Component Analysis (PCA) algorithm. Several MUCT database images have been used for assessing the performance of system.
Comparision of ant colony and genetic algorithms for the solution of travel salesman problem
(Altınbaş Üniversitesi, 2018) Alashheb, Waled Milad Abulsasem; Duru, Adil Deniz; Uçan, Osman Nuri; Bayat, Oğuz
The Theory of computational complexity is an essential branch of study in the science of theoretical computing and mathematics. The resolution of Polynomial and Non Polynomial problems is one of the main problems that have open solutions, for which no famous efficient algorithm exist. The Problem of Traveling Salesman (TSP) is an example of these problems. Such a problem include, a count of specified cities must be visited by a traveling salesman where both start and end points will be the same city and getting a tour of all cities so that the complete distance or time is minimized will be the aim. The application of Optimization algorithms is one of the famous methods of the solution regarding to the TSP. These algorithms usually simulate the occurring phenomena in nature. Currently there exist several of such algorithms; for example, Genetic Algorithm (GA) and Optimization of Ant Colony (ACO). This paper aimed to compare two approaches, GA and ACO for solution of TSP. The results obtained from our experiments showed that the ACO is better than GA since it requires less execution time for solving the same problem.
Covid-19 ultrasound image classification using svm based on kernels deduced from convolutional neural network
(2021) Al-jumaili, Saif; Duru, Adil Deniz
Abstract— Millions of people are infected daily with Coronavirus to this day, which increases deaths daily, that has made the virus an epidemic. Based on the current crisis, the availability of tool kits for test plays a significant role in fighting against Covid-19. According to less of availability tools and time consume by using traditional medical tools kit, that provide motivation for researchers to use the advantages of artificial intelligence (AI) techniques. Due to the ability of integrated with medical imaging, AI is very useful for precise diagnosis and classification for different types of diseases. However, in this study, we introduce an idea that combines a set of pre-trained deep learning convolutional neural network models with a supervised machine learning classifier, Supporting Vector Machines (SVM). The dataset used in this study was Lung ultrasound (LUS). To extract features from images, we utilized four types of CNN models namely (Resnet18, Resnet50, GoogleNet, and NASNet-Mobile). Depending on the experimental outcomes, our proposed method show outperform compared to the other latest papers published. Our results achieved based on the four types of evaluation metrics which are Accuracy, Precision, Recall, and F1-Score, where all evaluations achieved exceeded of 99%.
Covid-19 X-ray image classification using SVM based on Local Binary Pattern
(IEEE, 2021) Al-jumaili, Saif; Al-azzawi, Athar; Duru, Adil Deniz
Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.
Deep transfer learning methods for classification colorectal cancer based on histology images
(Institute of Electrical and Electronics Engineers Inc., 2022) Alhanaf, Ahmed Sami; Al-Jumaili, Saif; Bilgin, Gökhan; Duru, Adil Deniz; Alyassri, Salam; Balık, Hasan Hüseyin
Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.
Dikkat seviyesinin gerçek zamanda izlenmesi; bir web uygulaması
(Altınbaş Üniversitesi, 2017) Çobas, Yunus; Duru, Adil Deniz; Uçan, Osman Nuri; Bayat, Oğuz
Dikkat seviyesinin ölçüldüğü ve izlendiği bu bildiri kapsamında; Emotiv Epoc cihazı ile yapılan ölçümler gerçek zamanlı olarak değerlendirilerek bir web uygulama arayüzü sayesinde geri bildirim alınmıştır. Alınan geri bildirimler, web uygulamasının sahip olduğu filtre sayesinde duygu tipi ve stress düzeyi bilimsel normlara göre anlamlandırılabilmiş, bu bilgilerden yola çıkılarak yapılan analizler ile kişinin dikkat seviyesi gerçek zamanlı olarak ölçülebilir hale getirilmiştir.
Dynamic time warping based connectivity classification of event-related potentials
(Ieee, 2019) Al-rubaye, Kadhum Kareem; Bayat, Oğuz; Uçan, Osman Nuri; Duru, Dilek Goksel; Duru, Adil Deniz
Human brain electrical responses measured as Electroencephalogram epochs have different characteristics by means of amplitude and frequency content depending on the conditions and stimuli. Event-related potentials are the responses given to the stimuli and can be measured using the EEG. The average of these epochs are computed to remove the background activity and helps to exhibit the response to stimuli solely. In the concept of this study, dynamic time warping based connectivity features are used to classify the single-trial ERP epochs. Color Stroop test was implemented and ERP data are collected from 10 subjects. Support vector machine and K-NN classifiers are used and accurate classification results are achieved with the use of DTW metrics.
Emotion recognition based on spatially smooth spectral features of the EEG
(Ieee, 2013) Ballı, Tuğçe; Deniz, Sencer M.; Cebeci, Bora; Erbey, Miray; Duru, Adil Deniz; Demiralp, Tamer
The primary aim of this study was to select the optimal feature subset for discrimination of three dimensions of emotions (arousal, valence, liking) from subjects using electroencephalogram (EEG) signals. The EEG signals were collected from 25 channels on 21 healthy subjects whilst they were watching movie segments with emotional content. The band power values extracted from eleven frequency bands, namely delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (30-50 Hz), low theta (4-6 Hz), high theta (6-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) bands, were used as EEG features. The most discriminative features for classification of EEG feature sets were selected using sequential floating forward search (SFFS) algorithm and a modified version of SFFS algorithm, which imposes the topographical smoothness of spectral features, along with linear discriminant analysis (LDA) classifier. The best classification accuracies for three emotional dimensions were obtained for liking (72.22%) followed by arousal (67.50%) and valence (66.67%). SFFS-LDA and modified SFFS-LDA algorithms produced slightly different classification accuracies. However, the findings suggested that the use of modified SFFS-LDA algorithm provides more robust feature subsets for understanding of underlying functional neuroanatomic mechanisms corresponding to distinct emotional states.
Evaluation and measuring classifiers of diabetes diseases
(Ieee, 2017) Jasim, Ihsan Salman; Duru, Adil Deniz; Shaker, Khalid; Abed, Baraa M.; Saleh, Hadeel M.
Classification plays tremendous role in data mining process, especially for huge amount of data and it is suitable for predict new knowledge and discover patterns. This process can work with different types of data whether it was nominal or continuous. In this paper classification will be performs on diseases diagnoses by choosing to work with (k-nearest neighborhood algorithm KNN) measure and evaluate the method with (Artificial Neural Network ANN). These two classification methods have been chosen to classify (Pima-Indian-Diabetes PID) using spiral spinning technique. Classification done by taking 1 to 50 values of (K) in KNN versus 1 to 50 values of hidden layers for ANN in single iteration checking the accuracy as measuring to evaluate performance. T-test used to validate choosing two different factors (K in KNN and number of hidden layers in ANN), t-test results shows that the method is extremely statically significant. After performing classification by changing architecture, ANN proves better results than KNN in this disease classification.
Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images
(International Information and Engineering Technology Association, 2023) Al-Azzawi, Athar; Al-Jumaili, Saif; Duru, Adil Deniz; Duru, Dilek Göksel; Uçan, Osman Nuri
In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew's correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID- 19.
Göz hareketlerine dayalı beyin bilgisayar arayüzü tasarımı
(2020) Koç, Engin; Bayat, Oğuz; Duru, Dilek Göksel; Duru, Adil Deniz
Modern teknoloji ile birlikte insanların göz hareketlerini inceleyerek uyaranlara karşı vermiş oldukları tepkiler takip edilebilir hale gelmiştir. Bu takip yöntemlerden biri de Göz İzleme (Eye-Tracking) tekniğidir. Bu teknikteki gelişmeler sayesinde araştırmacılar, sağlık, savaş sanayi, sivil havacılık, web tasarımı, dijital medya vb. birçok alanda hayatı daha kolay hale getirilebilecek sistemler hakkında çalışmalar yapabilmektedir. Bu çalışma kapsamında, göz izleme teknolojisinin temel özelliklerinden faydalanılarak beyin bilgisayar arayüzü (BBA) uygulaması geliştirilmiştir. Katılımcıların göz sabitlenme bilgisi, tarafımızca hazırlanan deneysel paradigma yazılımları bünyesinde göz-izleme cihazı ile ölçülerek, kişilerden verilen ödevleri gerçekleştirmeleri istenmiştir. Bu kapsamda iki farklı uyaran yazılımı üretilmiştir. Birinci yazılımda, kişilerin ekranda çeşitli bölgelerde görülen butonlara odaklanarak, gözlerinin sabitlenmesi ile butonlara basmaları sağlanmıştır. İkinci yazılımda ise, katılımcının harflere odaklanması istenerek, kelimeler ve cümleleri yazdırmayı sağlayan sanal bir klavye uygulaması geliştirilmiştir. Ayrıca göz fiksasyonları ısı haritası ile görselleştirilmiştir. Tüm aşamalarda kullanılan yazılım ve analiz tarafımızca geliştirilmiştir. Sonuç olarak, hareket etmeden göz hareketleri ile bildirim yapmayı sağlayan hibrid bir sistem geliştirilmiştir. Göz hareketlerine dayalı önerilen BBA sistemi test edilmiş ve yüksek komut/dakika sonuçlarına ulaşılmıştır. Deneysel bulgular önerilen hibrid BBA’nın güçlü ve öne çıkacak bir teknoloji olduğunu göstermektedir.
Gözbebeği hareketleri temelli duygu durumu sınıflandırılması
(2020) Mete, Samet; Çakır, Oğuz; Bayat, Oğuz; Duru, Dilek Göksel; Duru, Adil Deniz
İnsanlardaki duygu durumu otonom sinir sistemi tarafından kontrol edilmektedir. Bu sebeple, olumlu veya olumsuz bir uyaran ile karşılaştığında otonom sinir sistemi çok kısa bir süre içerisinde, uyaran çeşidinin bireyde tetiklediği duygu türüne göre çeşitli bedensel farklılıklara sebebiyet vermektedir. Bedensel bu farklılıklardan bir tanesi de kişilerin gözbebeğinin uyaran çeşitine göre gösterdiği fizyolojik farklılıklardır. Yapılan araştırmalar göz bebeği hareketlerinin ve boyutunun ölçülmesinin yararlı bir girdi sinyali olabileceğini göstermektedir. İnsanlar olumsuz bir uyaran gördüğünde gözdeki pupil boyutunda genişleme, olumlu bir uyaran gördüğünde ise pupil boyutunda daralma oluşmaktadır. Bu bilgiler ışığında çalışma kapsamında, erkek ve kadın katılımcılara uygulanan, türlü kategorilerden oluşan büyük bir fotoğraflar dizisi olan IAPS içerisinden, katılımcılarda fazlasıyla zıt duygulanımlar meydana getiren uyaran sınıfları değerlik puanlarına göre tercih edilmiş ve uyaranlar olumlu, olumsuz ve nötr olmak üzere üç sınıfa ayrılmıştır. Çalışma sırasında IAPS’ten seçilmiş olan toplamda 60 adet fotoğraf kullanılmış ve 13 adet katılımcıya sunulmuş ve göz takip cihazı kullanılarak katılımcıların göz verileri veri tabanına kaydedilmiştir. Sol ve sağ pupil büyüklükleri ve fiksasyon süresi sınıflama için girdi olarak kullanılmıştır. Üç sınıf kullanılararak, kNN, Naive Bayes, Destek Vektör Makinaları, Doğrusal Diskriminant analizi, karar ağacı ve lojistik regresyon teknikleri uygulanmıştır. Düşük sınıflandırma başarısından ötürü, işlem sadece pozitif ve negatif sınıflar için tekrar hesaplanmıştır. Bu iki emosyonel durum için %68’lik bir oran ile kNN sınıflandırma yönteminde en yüksek sınıflandırma başarısına ulaşılmıştır. Naive Bayesçi Sınıflandırıcı ve DVM %55, LDA %50, karar ağaçları ve lojistik regresyon %48’lik başarıya ulaşmıştır. Kişilerin çeşitli uyaranlara verdiği emosyonel yanıtların göz hareketlerine yansıyabileceği ve göz hareketlerinden kişinin emosyonel uyarılma düzeyi hakkında fikir sahibi olunabileceği düşünülmektedir.
Intelligent database interface techniques using semantic coordination
(Institute of Electrical and Electronics Engineers Inc., 2018) Mohammed, Tareq Abed; Alhayli, Shaymaa; Albawi, Saad; Duru, Adil Deniz
More and more the use of artificial intelligence and data mining techniques established in many fields to solve the problem of classification. This paper consider most new database applications request smart interface to improve effective collaborations in the middle of database and the clients. The most open interfaces for databases must be clever and ready to comprehend characteristic dialect expressions. The overall aim of this study is to look at the importance of using data mining techniques with artificial intelligence in algorithms and applications. We propose a general design for an intelligent database interface. Furthermore, a genuine usage of such a framework which can be connected to any database. One of the fundamental attributes of this interface is space, freedom, which implies that this interface can be utilized with any database. Another aspect of this framework is that it is easy setup. The intelligent interface utilizes semantic coordinating procedure to change natural language query to Structured Query Language (SQL) by depending lexicon and set of creation guidelines. The lexicon comprises semantics sets for tables and sections. The query model is executed and the outcomes are introduced to the client. This interface was initially tested utilizing Supplier-Parts database by using JAVA and the result proves the efficiency of the proposed method in intelligent database system. © 2018 IEEE.

Yazar "Duru, Adil Deniz" seçeneğine göre listele

Sayfa Başına Sonuç

Sıralama seçenekleri