Gökalp, Osman
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Gokalp, Osman
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Email Address
osmangokalp@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
Status
Current Staff
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Sustainable Development Goals
1NO POVERTY
0
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Documents
14
Citations
199
h-index
6
Documents
12
Citations
155
Scholarly Output
4
Articles
3
Views / Downloads
39/72
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
0
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0
Patents
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0
WoS Citations per Publication
0.00
Scopus Citations per Publication
0.00
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| 33rd Conference on Signal Processing and Communications Applications-SIU-Annual -- Jun 25-28, 2025 -- Istanbul, Turkiye | 1 |
| Journal of Building Engineering | 1 |
| Journal of Scientific Reports-A (Online) | 1 |
Current Page: 1 / 1
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Scholarly Output Search Results
Now showing 1 - 4 of 4
Article Ensemble Machine Learning Algorithms for Thermal Comfort Prediction in HVAC Systems of Smart Buildings(Golden Light Publishing, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, GulbenPredicting the thermal comfort of building occupants is of paramount importance in the operation of smart buildings, providing a data-driven approach to control Heating, Ventilation, and Air Conditioning (HVAC) systems for managing occupant thermal comfort and energy use, which aligns with modern sustainability and efficiency goals. Recently, ensemble machine learning (ML)-based thermal comfort prediction models have been proposed to provide more accurate estimation of thermal comfort; however, these efforts often lack a systematic and comprehensive evaluation across a wide range of ML models within a single study. To address this gap, this study presents a systematic comparative analysis of four ensemble ML frameworks (bagging, boosting, stacking, and voting) with six basic ML algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Multinomial Na & iuml;ve Bayes) and six advanced ensemble ML algorithms (Random Forest, Rotation Forest, Extra Trees, Gradient Boosting Classifier, Histogram Gradient Boosting Classifier, and Extreme Gradient Boosting). The analysis is conducted using the widely recognized ASHRAE Global Thermal Comfort Database II, providing both 3-point and 7-point Thermal Sensation Vote (TSV) predictions. Accuracy, precision, recall and F1 metrics are used for evaluation and 10-fold cross validation is applied for further comparison. The results demonstrate the Histogram Gradient Boosting (HGB) algorithm achieved the highest F1 score (0.638) for 7-point TSV prediction whereas the Random Forest (RF) algorithm provided the highest F1 score (0.549) for 7-point TSV prediction. In practice, these findings suggest that integrating RF and HGB models into Building Management Systems or IoT-based HVAC platforms can support real-time adaptive control, helping practitioners to reduce energy use while maintaining occupant comfort.Conference Object Performance Evaluation of Filter-Based Gene Selection Methods in Cancer Classification(IEEE, 2025) Gokalp, OsmanWith the advances in microarray technology, gene expression levels can be measured efficiently, and this data can be used to solve important problems such as cancer classification. However, microarray data suffers from the high-dimensionality problem and requires dimensionality reduction techniques such as feature selection. This study addresses the cancer classification problem using microarray datasets and comparatively evaluates the performance of different filter-based gene (feature) selection methods. To this end, 11 microarray datasets have been evaluated using 6 different filter methods, and experimental results are presented. According to the findings, the gene selection methods used can improve classification performance by 5% to 30%. Using 5-fold cross-validation, the highest accuracy rates were achieved with 32 genes selected by the gain ratio filter for the Breast and Colon datasets, and with 8 genes selected by the information gain filter for the CNS dataset.Article An Energy Efficient Cluster Head Selection in Priority Region Aware Wireless Sensor Networks Using Metaheuristic Algorithms(2025) Ugur, Aybars; Aydın, Doğan; Gökalp, OsmanThe cluster head (CH) selection problem is one of the challenges posed by wireless sensor network (WSN) design, where nodes assume leadership roles. The primary objective of this problem is energy conservation, as becoming a CH requires high energy consumption. Therefore, optimizing the CH selection process is crucial. Despite numerous attempts to solve this problem, existing algorithms do not consider area prioritization, where certain regions such as industrial facilities with hazardous zones and military surveillance areas require special attention. This work first describes the standard CH selection problem in non-priority environments and then introduces priority region-aware WSNs. It then presents how energy-efficient CH selection using metaheuristics, with a priority- and energy-aware fitness function developed in this study, can be performed in such networks for the first time in the literature. The findings from comprehensive simulation-based experiments demonstrate the superior performance of both classical and state-of-the-art metaheuristic-driven approaches compared to the baseline Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Specifically, the Adaptive Differential Evolution with Optional External Archive (JADE) algorithm improves the performance of LEACH by up to 16% in terms of the total priority of transferred packets. Additionally, it can extend the lifetime of nodes in high-priority regions by up to 27% to 44%.Article Prediction of HVAC Operational Variables Using Recurrent Neural Networks for Advanced Control Strategies(Elsevier, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, GulbenHeating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, making accurate prediction of their operational variables essential for improving energy efficiency and occupant comfort. This study compares three recurrent neural network (RNN) architectures, namely standard RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for multi-input, multi-output prediction of nine HVAC variables using high-resolution data from a real office building (2018-2020). A two-stage hyperparameter optimization varying network depth, width, learning rate, and batch size was applied. Results show that a shallow architecture with a single hidden layer of 16 nodes, learning rate of 0.001, and batch size of 64 offers the best balance between accuracy and generalization. While the standard RNN showed higher errors and lower stability, both LSTM and GRU performed well. The GRU achieved the lowest average rank (4.2) and mean validation loss (0.0049) across cross-validation folds, demonstrating superior consistency and reliability, and was selected for final evaluation. On the holdout test set, GRU consistently outperformed the other models, achieving R-2 > 0.81 for seven of nine variables, with a peak R-2 of 0.99 for mean indoor air temperature and an overall average R-2 of 0.80. These results highlight GRU's advantages in accuracy, stability, and efficiency, suggesting its suitability for intelligent building control. The study's scope is limited to three RNNbased architectures with a constrained hyperparameter search; future work should extend comparisons to broader model families and datasets.