Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Citation - Scopus: 2
Turcoins: Turkish Republic Coin Dataset
(IEEE, 2021) Gökberk, Berk; Akarun, Lale; Temiz, Hüseyin
In this paper, we present a novel and comprehensive dataset which contains Turkish Republic coins minted since 1924 and present a deep learning based system that can automatically classify coins. The proposed dataset consists of 11080 coin images from 138 different classes. To classify coins, we utilize a pre-trained neural network (ResNet50) which is pre-trained on ImageNet. We train the pre-trained neural networks on our dataset by transfer learning. The imbalanced nature of the dataset causes the classifier to show lower performance in classes with fewer samples. To alleviate the imbalance problem, we propose a StyleGAN2-based augmentation method providing realisticfake coins for rare classes. The dataset will be published in http://turcoins.
Citation - WoS: 3
Citation - Scopus: 4
A Novel Graph Transformation Strategy for Optimizing Sptrsv on Cpus
(Wiley, 2023) Yılmaz, Buse
Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present a novel graph transformation strategy to increase the parallelism degree of a sparse matrix and compare it to our previous strategy. It is seen that our transformation strategy can provide a speedup as high as 1.42x$$ 1.42x $$.
Optimizing Collective Building Management Through a Machine Learning-Based Decision Support System
(IEEE, 2023) Güvençli, Mert; Dağ, Hasan; Doğan, Erkan; Çakar, Tuna; Özyürüyen, Burcu; Kiran, Halil
This study presents the design, implementation, and evaluation of a Decision Support System (DSS) developed for Collective Building Management. Given the potential advantages of machine learning techniques in this domain, the research explores how these techniques can be used to improve collective building management. The dataset consists of 824,932 records and 15 attributes, after preprocessing the data to fill in missing values with the median. The random forest algorithm was chosen for model training and achieved a performance rate of 71.2%. This model can be used to optimize decision processes in collective building management. The proposed prototype is notable for its ability to automatically generate operational plans. In conclusion, machine learning-based DSSs are effective tools for collective building management.
Citation - WoS: 2
Citation - Scopus: 2
Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods
(IEEE, 2023) Erözden, Ozan; Şahin, Türkay; Akyürek, Güçlü; Filiz, Gözde; Çakar, Tuna
Altruistic punishment (third-party punishment) is important in terms of maintaining social norms and promoting prosocial behavior. This study examined data obtained using the near infrared spectroscopy (fNIRS) method to predict altruistic punishment decisions. It was found that specific neural activity patterns were significantly related to decisions regarding the punishment of the perpetrator. This research contributes to the development of social decision-making models and helps advance our understanding of the cognitive and neural processes involved in third-party punishments.
Özyinelemeli Sinir Ağları ile Türkçe Doğal Dil Üretimi
(TÜBİTAK, 2018) Demir, Şeniz; Gökmen, Muhittin
İnsanlar arasındaki iletişimi sağlayan doğal diller, zaman içinde insanlarla etkin ve kullanıcı dostu etkileşim kurabilmek amacıyla sistemler ve yazılımlar tarafından kullanılmaya başlanmıştır. Tıpkı insanlar gibi sesli veya yazılı doğal dil ifadelerini anlayabilen ve sonrasında kullanıcıların beklentilerini karşılayabilen dil tabanlı teknolojiler (örn. arama motorları, bilgisayar destekli eğitici sistemler ve diyalog sistemleri) bu motivasyonla ortaya çıkmıştır. Bu çalışmalarda, problemin doğası ve hedef dilin yapısındaki zorluklara ek olarak insanların doğal dilleri nasıl öğrendiğini ve kullandığını modellemedeki kısıtlar başarım oranlarını etkilemiştir. Günümüzde, dil tabanlı teknolojiler insanlar tarafından yaygın şekilde kullanılıyor olsalar da (örn. Google Arama Motoru ve Apple Siri), ulaşılan teknolojik seviye hedef dile göre çeşitlilik göstermektedir. Sondan eklemeli ve zengin dil yapısı ile Türkçe geliştirilen teknolojik çözümler ve üretilen veri kaynakları açısından pek çok doğal dilin gerisinde kalmaktadır. Ayrıca, bugüne kadar Türkçe dil teknolojileri konusunda yapılan çalışmaların ağırlıklı olarak dili işleme, anlama ve analiz etmeye dönük (örn. kelimelerin morfolojik analizi, özel isim tespiti, bağlılık çözümlemesi, metin sınıflandırma ve metin özetleme) olduğu gözlemlenmektedir. Türkçe dil üretimi konusunda sınırlı yeteneklere sahip ve akademik seviyede kalarak devamı getirilmemiş birkaç çalışma mevcuttur. Fakat bu çalışmalar karmaşık sayılabilecek dilbilimi teorileri ile ifade edilen içerik ifadelerini cümlelere dönüştürmekten öteye geçmemiştir ve başka uygulamalarla entegre olarak test edilmemiştir. Bu çalışmada, Türkçe dilinin derin öğrenme tabanlı bir sistem (dil aracı) ile otomatik olarak üretimi hedeflenmektedir. Bu sistemin, girdi olarak verilen içerik ifadelerini Türkçe dili kurallarına uygun ve anlaşılır cümlelere dönüştüreceği öngörülmektedir. Literatürdeki en kapsamlı Türkçe dil üretimi sistemi olması planlanan bu çalışmada son yıllarda pek çok dil teknolojisinde başarımı ispat edilmiş diziden diziye öğrenebilen (örn. kelime dizisinden başka bir kelime dizisi) özyinelemeli sinir ağı yapıları kullanılacaktır. Bu ağların sağladığı dinamiklik ile farklı çeşitler (örn. uzun kısa süreli bellek ve girişli özyinelemeli birim) ve genişlemeler (örn. dikkat mekanizması) denenecektir ve başarımı en yüksek sinir ağı mimarisi belirlenecektir. Buna ek olarak, sinir ağlarının kullanımı bazı faktörlerin (örn. bağlam bilgisi ve kullanıcı tercihleri) sisteme entegrasyonuna ve üretim aşamasına olan etkilerinin incelenmesine imkân sağlayacaktır.
Citation - WoS: 3
Citation - Scopus: 2
A Benchmark Dataset for Turkish Data-To Generation
(Elsevier, 2022) Demir, Şeniz; Öktem, Seza
In the last decades, data-to-text (D2T) systems that directly learn from data have gained a lot of attention in natural language generation. These systems need data with high quality and large volume, but unfortunately some natural languages suffer from the lack of readily available generation datasets. This article describes our efforts to create a new Turkish dataset (Tr-D2T) that consists of meaning representation and reference sentence pairs without fine-grained word alignments. We utilize Turkish web resources and existing datasets in other languages for producing meaning representations and collect reference sentences by crowdsourcing native speakers. We particularly focus on the generation of single-sentence biographies and dining venue descriptions. In order to motivate future Turkish D2T studies, we present detailed benchmarking results of different sequence-to-sequence neural models trained on this dataset. To the best of our knowledge, this work is the first of its kind that provides preliminary findings and lessons learned from the creation of a new Turkish D2T dataset. Moreover, our work is the first extensive study that presents generation performances of transformer and recurrent neural network models from meaning representations in this morphologically-rich language.
Citation - Scopus: 1
Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods
(IEEE, 2022) Koksal, Mehmet Yigit; Çakar, Tuna; Demircioğlu, Esin Tuna; Girisken, Yener
The fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, near infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether people like a stimulus or not, using machine learning various algorithms. A comparison was made between feature extraction methods, which was the main focus while developing the model.
Citation - WoS: 1
Citation - Scopus: 1
Fuzzy Elephant Herding Optimization and DBSCAN for Emergency Transportation: A Case Study for the 2023Turkiye Earthquake
(Springer international Publishing Ag, 2024) Drias, Yassine; Drias, Habiba
In recent times, our planet has experienced numerous natural disasters across all continents. The damage caused by these disasters has been so extensive that Emergency Medical Services (EMS) proved incapable of handling the situation. In this article, we present a novel approach for urgent disaster transport with the aim of minimizing loss of life. In this context, we are investigating the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) to cluster the large geographic zone affected by the 2023 earthquake in Turkiye. The clustering is done based on hospitals' capacity on one hand and damages on the other hand. The ambulance dispatching task is then tackled using a new fuzzy version of Elephant Herding Optimization called FEHO. This approach addresses the challenge of dispatching ambulances to cover emergency locations effectively and optimally in the clustered regions. Experiments conducted on real data demonstrate the effectiveness of our approach in managing emergency transportation and highlight its potential to minimize the number of casualties.
Citation - WoS: 1
Citation - Scopus: 1
A Novel Genetic Algorithm-Based Improvement Model for Online Communities and Trust Networks
(IOS Press, 2020) Bekmezci, ilker; Cimen, Egemen Berkic; Ermiş, Murat
Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.
Power Savings in Cold Storage
(2016) Arslan, Şuayb Şefik; Göker, Turguy
Methods and apparatus associated with data cold storage are described. Example apparatus include an array of data storage devices arranged in rows and columns. Columns of the array are orthogonal to rows. A row has an associated row-centric power supply, and a column has an associated column-centric local electronics module (LEM) that controls a data storage device in the column independently of other data storage devices in the array. Example apparatus include logics that control a power mode of a data storage device independently of other data storage devices in the array, that control a power mode of an LEM, that adaptively regulate the level of data stored in a buffer, and that determine whether a data object will be stored in the buffer or stored on a data storage device in the array, based on the probability the data object will be accessed within a threshold period of time.
Citation - WoS: 2
Citation - Scopus: 5
Mojette Transform Based Ldpc Erasure Correction Codes for Distributed Storage Systems
(2017) Arslan, Şuayb Şefik; Normand, Nicolas; Parrein, Benoit
Mojette Transform (MT) based erasure correction coding possesses extremely efficient encoding/decoding algorithms and demonstrate promising burst erasure recovery performance. MT codes are based on discrete geometry and provide redundancy through creating projections. Projections are made of smaller data structures called bins and are generated from a two dimensional convex-shaped data. For exact data recovery, only a subset of projections are needed by the decoder. We realize that the discrete geometry definition of MT erasure codes corresponds to creating structured/deterministic generator matrices. In this study, we show an alternative Low Density Parity Check (LDPC) code construction methodology through investigating parity check matrices of MT codes which shows sparseness as the blocklength of the code gets large. In a distributed storage setting, we also quantify the repair bandwidth and show that this novel interpretation can be used to facilitate bin-level local repairs.
Citation - WoS: 2
Citation - Scopus: 2
Distributed Matrix Multiplication With Mds Array Bp-Xor Codes for Scaling Clusters
(IEEE, 2019) Arslan, Şuayb Şefik
This study presents a novel coded computation technique for distributed matrix-matrix product computation at a massive scale that outperforms well known previous strategies in terms of total execution time. Our method achieves this performance by distributing the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. The product computation is performed using MDS array Belief Propagation (BP)-decodable codes based on pure XOR operations. In addition, our scheme is configurable and suited for modern compute node architectures equipped with multiple processing units organized in a hierarchical manner. Assuming the number of backup nodes being sublinear in the size of the product, we shall demonstrate that the proposed scheme achieves order-optimal computation from an end-to-end latency perspective while ensuring acceptable communication requirements that can be addressed by today's high speed network link infrastructures.
Citation - WoS: 6
Citation - Scopus: 6
Efficient Strategy for Multi-Uav Path Planning in Target Coverage Problems
(IEEE, 2022) Bekmezci, İlker; Pehlivanoğlu, Perihan; Pehlivanoğlu, Y. Volkan
In recent years, multi unmanned aerial vehicles (UAVs) are used in the same system to accomplish more complex missions. In many multi-UAV system applications, the main objective is to visit some predetermined checkpoints in operational area. If the number of check points and constraints increases, finding a feasible solution takes up too much time. In this paper, a checkpoint based multi-UAV path planning problem is solved by using improved genetic algorithm. The main contributions of this paper are: (1) the introducing revisit time interval concept, (2) the investigating of the effect of objective function description, and (3) looking into an outcome of using multiple runways on optimal multi-UAV path planning. The proposed strategy-based optimization methodology is performed for checkpoint based multi-UAV path planning problems in two-dimensional (2D) environment. Performance results show that the proposed strategy provides effective and feasible paths for each UAV.
A New Benchmark Dataset for P300 Erp-Based Bci Applications
(Academic Press Inc Elsevier Science, 2023) Çakar, Tuna; Özkan, Hüseyin; Musellim, Serkan; Arslan, Suayb S.; Yağan, Mehmet; Alp, Nihan
Because of its non-invasive nature, one of the most commonly used event-related potentials in brain -computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target (6 x 6) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely-and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40 -target (5 x 8) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data .mendeley.com /datasets /vyczny2r4w.(c) 2023 Elsevier Inc. All rights reserved.
Next-Generation Data Storage: Transistor and Dna
(Institute of Electrical and Electronics Engineers Inc., 2018) Pusane, Ali E.; Arslan, Şuayb Şefik; Ashrafi, Reza A.
With the generation of diverse data growing at exponential rates, investigating better digital storage media is inevitable. Currently, one solution is the utilization of solid-state based memory devices, which offer several desirable characteristics, including very fast write/read operations, scalability, and reduced fabrication costs. However, with the increased need for long term and large storage space, their data retention capabilities drastically decline. Another emerging storage technology on the horizon is the biotechnological based DNA storage, which renders a phenomenal storage capacities. In this paper, basics of these two promising storage technologies are reviewed and their potential future trends are discussed. © 2018 IEEE.
A Multiobjective Evolutionary Algorithm Approach for Map Sketch Generation
(2018) Topcu, Şafak; Etaner-Uyar, A. Sima
In this paper, we present a method to generate map sketches for strategy games using a state of the art many-objective evolutionary algorithm, namely NSGAIII. The map sketch generator proposed in this study outputs a three objective Pareto-front in which all the points are fair and strong in different aspects. The generated map sketch can be used by level designers to create real time strategy maps effectively and/or help them see multiple aspects of a game map simultaneously. The algorithm can also be utilised as a benchmark generator to be used in tests for various cases such as shortest path algorithms and strategy game bots. The results reported in this paper are very promising and promote further study.
Citation - WoS: 2
Citation - Scopus: 2
Facial Expression Recognition From Still Images
(2017) Gökmen, Muhittin; Gazioglu, Bilge Suheyla Akkoca
With the development of technology, Facial Expression Recognition (FER) become one of the important research areas in Human Computer Interaction. Changes in the movement of some muscles in face create the facial expressions. By defining these changes, facial expressions can be recognized. In this study, a cascaded structure consists of Local Zernike Moments (LZM), Local XOR Patterns (LXP) and Global Zernike Moments (GZM) methods is proposed for the FER problem. The generally used database is the Extended Chon - Kanade (CK +) in FER problems. The database consists of image sequences of 327 expressions of 118 people. Most FER system includes recognition of 7 classes of emotions happiness, sadness, surprise, anger, disgust, fear and contempt, and we use Library of Support Vector Machines (LIBSVM) classifier for multi class classification with the leave one out cross-validation method. Our overall system performance is measured as 90.34% for FER.
Fault-Tolerant Strassen-Like Matrix Multiplication
(IEEE, 2020) Arslan, Şuayb Şefik; Güney, Osman B.; Oblokulov, Muhtasham
In this study, we propose a simple method for fault-tolerant Strassen-like matrix multiplications. The proposed method is based on using two distinct Strassen-like algorithms instead of replicating a given one. We have realized that using two different algorithms, new check relations arise resulting in more local computations. These local computations are found using computer aided search. To improve performance, special parity (extra) sub-matrix multiplications (PSMMs) are generated (two of them) at the expense of increasing communication/computation cost of the system. Our preliminary results demonstrate that the proposed method outperforms a Strassen-like algorithm with two copies and secures a very close performance to three copy version using only 2 PSMMs, reducing the total number of compute nodes by around 24% i.e., from 21 to 16.
Citation - WoS: 3
Citation - Scopus: 3
A Joint Dedupe-Fountain Coded Archival Storage
(2017) Arslan, Şuayb Şefik; Göker, Turguy; Wideman, Rod
An erasure-coded archival file storage system is presented using a chunk-based deduplication mechanism and fountain codes for space/time efficient operation. Unlike traditional archival storage, this proposal considers the deduplication operation together with correction coding in order to provide a reliable storage solution. The building blocks of deduplication and fountain coding processes are judiciously interleaved to present two novel ideas, reducing memory footprint with weaker hashing and dealing with the increased collisions using correction coding, and applying unequal error protection to deduplicated chunks for increased availability. The combination of these two novel ideas made the performance of the proposed system stand out. For example, it is shown to outperform one of the replication-based as well as RAID data protection schemes. The proposed system also addresses some of the fundamental challenges of today's low-cost deduplicated data storage systems such as hash collisions, disk bottleneck and RAM overflow problems, securing savings up to 90% regular RAM use.
Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning
(Ieee, 2024) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, Yassine
This research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.

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