Hekimoğlu, Mustafa
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Name Variants
Hekimoğlu, Mustafa
M.,Hekimoğlu
M. Hekimoğlu
Mustafa, Hekimoğlu
Hekimoglu, Mustafa
M.,Hekimoglu
M. Hekimoglu
Mustafa, Hekimoglu
Hekimoglu,M.
Hekimoglu, M.
Hekimoğlu, M.
M.,Hekimoğlu
M. Hekimoğlu
Mustafa, Hekimoğlu
Hekimoglu, Mustafa
M.,Hekimoglu
M. Hekimoglu
Mustafa, Hekimoglu
Hekimoglu,M.
Hekimoglu, M.
Hekimoğlu, M.
Job Title
Doç. Dr.
Email Address
Mustafa.hekımoglu@khas.edu.tr
Main Affiliation
Industrial Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
30
Articles
21
Citation Count
0
Supervised Theses
3
4 results
Scholarly Output Search Results
Now showing 1 - 4 of 4
Article Citation - WoS: 3Citation - Scopus: 3Residual Lstm Neural Network for Time Dependent Consecutive Pitch String Recognition From Spectrograms: a Study on Turkish Classical Music Makams(Springer, 2023) Mirza, Fuat Kaan; Baykaş, Tunçer; Gursoy, Ahmet Fazil; Hekimoğlu, Mustafa; Baykas, Tuncer; Pekcan, Mehmet Önder; Hekimoglu, Mustafa; Pekcan, OnderTurkish classical music, characterized by 'makam', specific melodic configurations delineated by sequential pitches and intervals, is rich in cultural significance and poses a considerable challenge in identifying a musical piece's particular makam. This identification complexity remains an issue even for experienced musical experts, emphasizing the need for automated and accurate classification techniques. In response, we introduce a residual LSTM neural network model that classifies makams by leveraging the distinct sequential pitch patterns discerned within various audio segments over spectrogram-based inputs. This model's design uniquely merges the spatial capabilities of two-dimensional convolutional layers with the temporal understanding of one-dimensional convolutional and LSTM mechanisms embedded within a residual framework. Such an integrated approach allows for detailed temporal analysis of shifting frequencies, as revealed in logarithmically scaled spectrograms, and is adept at recognizing consecutive pitch patterns within segments. Employing stratified cross-validation on a comprehensive dataset encompassing 1154 pieces spanning 15 unique makams, we found that our model demonstrated an accuracy of 95.60% for a subset of 9 makams and 89.09% for all 15 makams. Our approach demonstrated consistent precision even when distinguishing makam pairs known for their closely related pitch sequences. To further validate our model's prowess, we conducted benchmark tests against established methodologies found in current literature, providing a comparative assessment of our proposed workflow's abilities.Article Citation - WoS: 11Optimization of Wastewater Treatment Systems for Growing Industrial Parks(Elsevier, 2023) Savun-Hekimoglu, Basak; Hekimoğlu, Mustafa; Isler, Zulal; Yücekaya, Ahmet Deniz; Hekimoglu, Mustafa; Ediger, Şevket Volkan; Burak, Selmin; Karli, Deniz; Yucekaya, Ahmet; Ediger, Volkan S.Wastewater treatment is one of the crucial functions of industrial parks as wastewater from industrial facilities usually contains toxic compounds that can cause damage to the environment. To control their environmental loads, industrial parks make investment decisions for wastewater treatment plants. For this, they need to consider technical and economic factors as well as future growth projections as substantial construction and operational costs of wastewater treatment plants have to be shared by all companies in an industrial park. In this paper, we consider the long-term capacity planning problem for wastewater treatment facilities of a stochastically growing industrial park. By explicitly modeling randomness in the arrival of new tenants and their random wastewater discharges, our model calculates the future mean and variance of wastewater flow in the industrial park. Mean and variance are used in a Mixed Integer Programming Model to optimize wastewater treatment plant selection over a long planning horizon (30 years). By fitting our first model to empirical data from an industrial park in Turkey, we find that considering the variance of wastewater load is critical for long-term planning. Also, we quantify the economic significance of lowering wastewater discharges which can be achieved by water recycling or interplant water exchange.Article Citation - WoS: 0Citation - Scopus: 0Decoding Compositional Complexity: Identifying Composers Using a Model Fusion-Based Approach With Nonlinear Signal Processing and Chaotic Dynamics(Pergamon-elsevier Science Ltd, 2024) Mirza, Fuat Kaan; Baykaş, Tunçer; Baykas, Tuncer; Hekimoğlu, Mustafa; Hekimoglu, Mustafa; Pekcan, Mehmet Önder; Pekcan, Onder; Tuncay, Gonul PacaciMusic, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.Article Citation - WoS: 0Citation - Scopus: 0On spare parts demand and the installed base concept: A theoretical approach(Elsevier, 2023) Hekimoğlu, Mustafa; Frenk, J. B. G.; Hekimoglu, MustafaOriginal Equipment Manufacturers (OEMs) aim to design their service supply chain before the introduction of their products to maximize their aftersales business revenues, reduce waste and achieve sustainability. In this study, we develop a stochastic model that unifies the installed base, i.e., the number of products in use, spare parts demand, and the number of discarded products within a single modeling framework based on three product characteristics: sales rate, usage time, and failure rate. Our model describes the installed base and spare part demand evolution over the entire life cycle of a parent product using stochastic point processes. At the same time we propose under very general assumptions on the cdf of the usage time and the mean arrival functions of the sales and failure processes an easy bisection procedure to compute the time at which the expected installed base and rate of the expected demand for spare parts is maximal. Our numerical experiments show that the volume of aftersales services increases in the expected usage time if the products face an increasing failure rate. The same experiments also reveal a 20 percent shift of the time at which the expected installed base is maximal in case the expected usage time is increased threefold. At the same time, we observe a boosting effect of the intensity of the sales process on this point in time.
