Mustafa Borahan Tumer

• Ph.D.
• Professor at Marmara University

Markov chains are simple yet powerful mathematical structures to model temporally dependent processes. They generally assume stationary data, i.e., fixed transition probabilities between observations/states. However, live, real-world processes, like in the context of activity tracking, biological time series, or industrial monitoring, often switch...

With the growing state/action space, learning a satisfactory policy for regular Reinforcement Learning (RL) algorithms such as flat Q-learning becomes quickly infeasible. One possible solution to handle such cases is to employ hierarchical RL (HRL). In this work, we present two methods to autonomously construct (1) skills (ASKA) and (2) arbitrarily...

Online anomaly detection and identification is a major task of many Industry 4.0 applications. Electric motors, being one of the most crucial parts of many products, are subjected to end-of-line tests to pick up faulty ones before being mounted to other devices. With this study, we propose a Syntactic Pattern Recognition based approach to online an...

Dynamic systems are highly complex and hard to deal with due to their subject- and time-varying nature. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly useful. One promising estimation method that is capable of unlearning past information to deal with...

The Quality Assurance (QA) team verifies software for months before its release decisions. Nevertheless, some crucial bugs remain undetected in manual testing. These bugs would make the system unusable on field, thus merchant loses money then manufacturer loses its customers. Thus, automatic software testing methods have become inevitable to catch...

A “marketplace” is an e-commerce medium where product and inventory information is provided by varying third parties, whereas catalog service is hosted, and payments are processed by the marketplace operator. As a result of increasing use of marketplaces, e-commerce capabilities can now be accessed by everyone. Consequently, both the number of merc...

Multivariate Time Series (MTS) data obtained from large scale systems carry resourceful information about the internal system status. Multivariate Time Series Clustering is one of the exploratory methods that can enable one to discover the different types of behavior that is manifested in different working periods of a system. This knowledge can th...

Two essential properties of a signal compression method are the compression rate and the distance between the original signal and the reconstruction from the compressed signal. These two properties are used to assess the performance and quality of the method. In a recent work [B. Tümer, B. Demiröz, Lecture Notes in Computer Science-Computer and Inf...

This paper investigates the problem of cycle detection in periodic noisy data sequences. Our approach is based on reinforcement learning principles. A constructive approach is used to devise a variable structure learning automaton (VSLA) that becomes capable of recognizing the potential cycles of the noisy input sequence. The constructive approach...

Accurate classification of data sets is an important phenomenon for many applications. While multi-dimensionality to a certain point contributes to the classification performance, after a point, incorporating more attributes degrades the quality of the classification. In a pattern classification problem, by determining and excluding the least effec...

In this paper, we present a methodology for automatic diagnosis of systems characterized by continuous signals. For each condition considered, the methodology requires the development of an alphabet of signal primitives, and a set of hierarchical fuzzy automatons (HFAs). Each alphabet is adaptively obtained by training an adaptive resonance theory...

We present an adaptive compression system (ACS) that compresses signals using signal primitives obtained by the self organizing neural architecture growing cell structures (GCS) [6]. We determine the length w max of the primitive that maximizes the compression. We decompose the signal into w max -long segments. Then GCS is trained to adaptively c...

We present an adaptive compression system (ACS) that compresses signals using signal primitives obtained by the self organizing neural architecture growing cell structures (GCS) [6]. We determine the length w(max) of the primitive that maximizes the compression. We decompose the signal into w(max)-long segments. Then GCS is trained to adaptively co...

A methodology for automated diagnosis of systems characterized by continuous signals is presented. The methodology requires the definition and construction of several fuzzy automatons each capable of identifying a particular condition. When the diagnostic system is in operation, the time sampled system measurements are presented to all automatons s...

Hierarchical fuzzy automatons (HFAs) are employed to perform automatic diagnosis on a signal represented as set of discrete time measurements. The HFA incorporates two levels of hierarchy with the lower level identifying structures within the signal and the top level integrating the results from lower level automatons. An adaptive resonance theory...

This work presents the FSDS, a diagnostic tool which follows a fuzzy syntactic approach to fault diagnostics by analysis of time-sampled signals. The FSDS uses the signals generated by the target system to achieve the diagnosis. First, the FSDS transforms the signal into a string of sets of elementary structures, templates. Then, examining the cons...