Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    Predicting Diabetes Using Machine Learning Techniques
    (2022) Kirgil, Elif Nur Haner; Erkal, Begum; Ayyildiz, Tulin Ercelebi; 0000-0002-7372-0223; JBI-6492-2023
    Early diagnosis of diabetes, which can cause death, is very important for the health of the person. In the literature, machine learning techniques are frequently used in diagnosis of many diseases, including diabetes. The aim of the study is to predict diabetes with high accuracy by using machine learning and preprocessing techniques. Pima Indian Diabetes dataset was used in the study. J48 (Decision Tree), Naive Bayes, Support Vector Machine, Logistic Regression, Multilayer Perceptron, K Nearest Neighbor, Logistic Model Tree, and Random Forest were used for classification. Of the preprocessing methods, feature selection, imputing missing values, normalization and standardization are performed. According to the results obtained, the highest accuracy value got with the Random Forest algorithm as 80.869.
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    Texture of Activities: Exploiting Local Binary Patterns for Accelerometer Data Analysis
    (2016) Asuroglu, Tunc; Acici, Koray; Erdas, Cagatay Berke; Ogul, Hasan; 0000-0003-4153-0764; 0000-0002-3821-6419; 0000-0003-3467-9923; AAC-7834-2020; HDM-9910-2022
    Recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. The problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary Pattern (LBP) approach, which is frequently used in identifying textures in images, in one-dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. The experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.
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    Use of Acoustic and Vibration Sensor Data to Detect Objects in Surveillance Wireless Sensor Networks
    (2017) Kucukbay, Selver Ezgi; Sert, Mustafa; Yazici, Adnan; 0000-0002-7056-4245; AAB-8673-2019
    Nowadays, people are using stealth sensors to detect intruders due to their low power consumption and wide coverage. It is very important to use lightweight sensors for detecting real time events and taking actions accordingly. In this paper, we focus on the design and implementation of wireless surveillance sensor network with acoustic and seismic vibration sensors to detect objects and/or events for area security in real time. To this end, we introduce a new environmental sensing based system for event triggering and action. In our system, we first design an appropriate hardware as a part of multimedia surveillance sensor node and use proper classification technique to classify acoustic and vibration data that are collected by sensors in real-time. According to the type of acoustic data, our proposed system triggers a camera event as an action for detecting intruder (human or vehicle). We use Mel Frequency Cepstral Coefficients (MFCC) feature extraction method for acoustic sounds and Support Vector Machines (SVM) as classification method for both acoustic and vibration data. We have also run some experiments to test the performance of our classification approach. We show that our proposed approach is efficient enough to be used in real life.
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    Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones
    (2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484
    Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.