Demir, Alper
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Demir, Alper
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alper.demir@ieu.edu.tr
Main Affiliation
05.05. Computer Engineering
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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
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
1
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7AFFORDABLE AND CLEAN ENERGY
1
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
3
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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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
1
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Documents
13
Citations
35
h-index
4
Documents
8
Citations
38
Scholarly Output
11
Articles
3
Views / Downloads
7/18
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
8
Scopus Citation Count
9
Patents
0
Projects
5
WoS Citations per Publication
0.73
Scopus Citations per Publication
0.82
Open Access Source
3
Supervised Theses
0
| Journal | Count |
|---|---|
| 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562 | 2 |
| 3rd International Informatics and Software Engineering Conference, IISEC 2022 | 1 |
| Applıed Intellıgence | 1 |
| Computers in Biology and Medicine | 1 |
| International Journal of Machine Learning and Cybernetics | 1 |
Current Page: 1 / 2
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11 results
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Now showing 1 - 10 of 11
Article Citation - WoS: 2Citation - Scopus: 3Learning What To Memorize: Using Intrinsic Motivation To Form Useful Memory in Partially Observable Reinforcement Learning(Springer, 2023) Demir, AlperReinforcement Learning faces an important challenge in partially observable environments with long-term dependencies. In order to learn in an ambiguous environment, an agent has to keep previous perceptions in a memory. Earlier memory-based approaches use a fixed method to determine what to keep in the memory, which limits them to certain problems. In this study, we follow the idea of giving the control of the memory to the agent by allowing it to take memory-changing actions. Thus, the agent becomes more adaptive to the dynamics of an environment. Further, we formalize an intrinsic motivation to support this learning mechanism, which guides the agent to memorize distinctive events and enable it to disambiguate its state in the environment. Our overall approach is tested and analyzed on several partial observable tasks with long-term dependencies. The experiments show a clear improvement in terms of learning performance compared to other memory based methods.Research Project Kısmi Gözlemlenebilir Pekiştirmeli Öğrenmede Faydalı Bellek Oluşturma(2023) Demirbilek, Burak Han; Demir, AlperPekiştirmeli öğrenme, gerçek hayattaki bir öğrenme ortamını modellemeyi amaçlayan önemli bir makine öğrenme tekniğidir. Konuyla ilgili son araştırmalar, araştırmacılar arasında onu çok popüler hale getirmiş ve birçok gerçek hayat senaryosuna daha uygulanabilir olmasını sağlamıştır. Kısmi gözlemlenebilirlik altında pekiştirmeli öğrenme, çevreden toplanan sınırlı bilgi nedeniyle özellikle zorlu bir alandır. Bu gibi hallerde, etmen, verilen görev için bir hareket tarzı oluşturabilmesi için ortamın durumunu tahmin etmek için deneyimlerden oluşan bir bellek tutmak zorundadır. Bazı çalışmalar, etmenin mevcut andan önceki bir dizi gözlemi ve eylemi bellekte tutmasını sağlayan basit bir pencere tabanlı bellek yaklaşımı kullanır, ancak bu tür yöntemler, bir bilgiyi uzun süre boyunca bellekte tutmayı gerektirdiği problemlere genellenemez. Diğer çalışmalar, gerekli bilgileri yapı içinde tutan karmaşık bir model kullanır, ancak bu tür modeller, probleme çok özel olmak ve analiz için çok kapalı olmaktan dolayı yetersiz kalmaktadırlar. Bu tür problemlerde, bir eyleme karar verecek ilgili bilgi zamansal olarak uzaktır, bu nedenle etmen neyi bellekte tutacağı konusunda seçici olmak zorundadır. Yararlı bir bellek tutma problemini ele alan çalışma azdır ve uygulanabilirlikleri sınırlıdır. Bu sebeple bu alan keşfedilmemiş kalmıştır. Bu çalışmada, belleği değiştiren eylemler yapmasına izin vererek bellek kontrolünü etmene verme fikrini takip ediyoruz. Böylece etmen, bir ortamın dinamiklerine daha uyumlu hale gelir. Ayrıca, bu öğrenme mekanizmasını desteklemek için, etmenin ayırt edici olayları hatırlamasında yol gösterici olan ve ortamdaki durumunu netleştirmesini sağlayan bir içsel motivasyon yapısı oluşturduk. Genel yaklaşımımız, birkaç pekiştirmeli öğrenme yöntemine uygulanmış, uzun süreli bellek gerektiren birkaç kısmi gözlemlenebilir problem üzerinde test edilmiş ve analiz edilmiştir. Deneyler, diğer bellek tabanlı yöntemlere kıyasla öğrenme performansı açısından net bir gelişme göstermektedir.Conference Object A Reinforcement Learning Based Approach to Solve Voltage Issues in Distribution Networks(IEEE, 2025) Cakir, Muhammed Turhan; Nayir, Hasan; Demir, Alper; Kaya, Huseyin; Ceylan, OguzhanThis paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve over-voltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.Conference Object External Visual Memory with Autoencoder-Based Intrinsic Motivation for Reinforcement Learning under Partial Observability(IEEE Computer Society, 2025) Demir, Alper; Demirbilek, Burak Han; Polat, FarukArticle Peptide-Nanoparticle Platforms for Antisense Therapeutics: A Coarse-Grained Modeling Approach to Brain Delivery(Elsevier Ltd, 2026) Uner, B.Y.; Demir, A.; Zhou, P.; Taşkiran, E.Z.; Wassenaar, T.Traumatic brain injury (TBI) is a leading cause of long-term neurological deficits, often resulting in complex, unresolved molecular and cellular dysfunctions. Among these, gene–circuit disruptions—particularly those affecting neuroinflammation, oxidative stress, and mitochondrial dynamics—have emerged as critical mediators of post-traumatic neuropathology. In this study, we utilized artificial intelligence (AI)-driven proteomics and RNA sequence integration to map altered signaling pathways following TBI. Computational predictions identified specific gene–circuit nodes susceptible to therapeutic intervention, including redox-sensitive mitochondrial regulators and genes involved in the neuroimmune interface. Importantly, although our analyses are derived from rodent models, the conserved signaling pathways and regulatory circuits identified here provide a translational window with strong relevance to human TBI pathophysiology, thereby bridging preclinical findings with potential therapeutic application. Based on these insights, we designed a suite of responsive nanoparticle formulations optimized in silico for targeted delivery to dysregulated brain regions. These carriers incorporated ligands targeting disrupted circuits and incorporated redox-sensitive release mechanisms. Our platform demonstrates the feasibility of a closed-loop, data-guided strategy that integrates AI-based gene network profiling with rational nanocarrier design. This approach provides a scalable framework for precision neurotherapeutics, particularly for complex disorders such as TBI where conventional monotherapies have proven inadequate. © 2026 Elsevier Ltd.Conference Object Analyzing Traffic Patterns in Izmir: a Study on Busy Hours and Congestion(Institute of Electrical and Electronics Engineers Inc., 2024) Başoğul, Ali Ozan; Çekirdek, Hamza; Nakipoğlu, K.; Yağcı, Semih; Demir, AlperDeveloping strategies for transportation is one of the main tasks for smart cities. With the traffic data on key arteries of Izmir, this project aims to estimate the time intervals and routes where traffic jam may occur in Izmir and uncover relationships between various factors affecting traffic. The study demonstrates that accurate models can be developed to predict the number of passing vehicles and reveals interesting correlations within the data. © 2024 IEEE.Correction Landmark Based Guidance for Reinforcement Learning Agents Under Partial Observability (nov 2022, 10.1007/S13042-022-01713-5)(Springer Heidelberg, 2023) Demir, Alper; Çilden, Erkin; Polat, Faruk[No Abstract Available]Conference Object Water Consumption Dynamics in Izmir: Analyzing Influences of District, Seasonality, and External Events(Institute of Electrical and Electronics Engineers Inc., 2024) Arslan, A.; Titiz, I.E.; Gürcan, E.C.; Demir, A.This study investigates the patterns of water consumption in Izmir from January 2015 to January 2024, using a dataset provided by Izmir's Open Data Portal. The research aims to understand how different factors such as district, seasons, and external events like the COVID-19 pandemic influence water usage across the city. By employing statistical analysis and machine learning models including Decision Trees and K-means, this project identifies significant spatial and temporal variations in water consumption. This analysis not only aids in the efficient management of water resources but also serves as a foundation for future predictive modeling and sustainability efforts in urban settings. © 2024 IEEE.Article Citation - WoS: 6Citation - Scopus: 6Landmark Based Guidance for Reinforcement Learning Agents Under Partial Observability(Springer Heidelberg, 2023) Demir, Alper; Cilden, Erkin; Polat, FarukUnder partial observability, a reinforcement learning agent needs to estimate its true state by solely using its observation semantics. However, this interpretation has a drawback, which is called perceptual aliasing, avoiding the convergence guarantee of the learning algorithm. To overcome this issue, the state estimates are formed by the recent experiences of the agent, which can be formulated as a form of memory. Although the state estimates may still yield ambiguous action mappings due to aliasing, some estimates exist that naturally disambiguate the present situation of the agent in the domain. This paper introduces an algorithm that incorporates a guidance mechanism to accelerate reinforcement learning for partially observable problems with hidden states. The algorithm makes use of the landmarks of the problem, namely the distinctive and reliable experiences in the state estimates context within an ambiguous environment. The proposed algorithm constructs an abstract transition model by utilizing the landmarks observed, calculates their potentials throughout learning -as a mechanism borrowed from reward shaping-, and concurrently applies the potentials to provide guiding rewards for the agent. Additionally, we employ a known multiple instance learning method, diverse density, for automatically discovering landmarks before learning, and combine both algorithms to form a unified framework. The effectiveness of the algorithms is empirically shown via extensive experimentation. The results show that the proposed framework not only accelerates the underlying reinforcement learning methods, but also finds better policies for representative benchmark problems.Conference Object Multi-Dimensional Autoscaling in Microservices Using Reinforcement Learning(IEEE Computer Society, 2025) Nayir, Hasan; Demir, Alper; Kaya, Huseyin
