Tander, Baran

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Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, B
Job Title
Dr. Öğr. Üyesi
Email Address
Tander@khas.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
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ORCID ID
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Scholarly Output

12

Articles

3

Citation Count

0

Supervised Theses

1

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2020 - 20232
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End date: 2023Start date: 2020

Scholarly Output Search Results

Now showing 1 - 2 of 2
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    Conference Object
    Citation - WoS: 0
    Citation - Scopus: 3
    Smart Stethoscope
    (IEEE, 2020) Türker, Mehmet Nasuhcan; Çevik, Mesut; Çagan, Yagiz Can; Özmen, Atilla; Yıldırım, Batuhan; Tander, Baran; Demirel, Mücahit; Özmen, Atilla; Tander, Baran; Çevik, Mesut
    In this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is developed to help the health workers to make accurate diagnoses. Furthermore, the respiratory diseases are classified by using Deep Learning and Long Short-Term Memory (LSTM) techniques whereas the probability of these diseases are obtained.
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    Master Thesis
    Estimation of Energy Production in Biogas Plants
    (Kadir Has Üniversitesi, 2023) YURTEKİN, ŞEVVAL AYŞE; Tander, Baran; Özmen, Atilla; Tander, Baran
    The importance of renewable energy sources is getting more and more significant day by day. Renewable energies are required since the world's energy consumption is rising in tandem with the human population. The gas produced from wastes such as biogas, agricultural waste, and animal dung is an example of biomass energy, a renewable energy source. Machine learning is a branch of computer science that tries to improve its performance with the data it accumulates over time by simulating a human learning mechanism. This research begins with a discussion of biogas generation and investigations. The discussion then shifts to artificial intelligence, machine learning, and neural networks. In the application portion, an application of feature selection utilizing data, wastes, and biogas production from the Pales biogas plant is developed, as well as an application that forecasts biogas output using regression and an artificial neural network model. In the Python-based model, machine learning and deep learning libraries are utilized, and the data is preprocessed to make it compatible with the model. In this 22-featured model, the elements that contribute to the model, specifically biogas generation, were chosen using feature selection algorithms. The regression model and neural network model were created with the selected features as Dairy cow manure, Wheat Juice, Potato peel, Potato whole, Mixed vegie, Weak vinasse and Poultry manure. There are three distinct digesters included in the data. Three separate analyses were performed on three distinct digesters, and the findings were compared to the cumulative data. In the study, 20% of the data were set aside as test data and 80% were used for training. In terms of model performance, the data feature selection approach was effective for regression models, but negatively effect of variable reduction in neural networks. The R2 score was 52% in the neural network model trained without variable selection, and the mean of regression models trained with feature selection was 49%.