Although simple individually, artificial neurons provide state-of-the-art performance when
interconnected in deep networks. Arguably, the Tsetlin Automaton is an even simpler and …

Nature-inspired meta-heuristic algorithms possess various actions inspired by natural
phenomena, animal behaviors, chemistry or physics laws etc. The actions are utilized to …

Reinforcement learning (RL) is a new research area practical in the internet of things (IoT)
where it addresses a broad and relevant task through about making decisions. RL enables …

Hough transform has been the most common method for circle detection exhibiting
robustness but adversely demanding a considerable computational load and large storage …

This paper explores the use of the Learning Automata (LA) algorithm to compute threshold
selection for image segmentation as it is a critical preprocessing step for image analysis …

Most of the proposed algorithms to solve the dynamic clustering problem are based on
nature inspired meta-heuristic algorithms. In this paper a different reinforcement based …

Reinforcement learning (RL) is a new propitious research space that is well-known
nowadays on the internet of things (IoT), media and social sensing computing are …

Abstract The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that
constructs conjunctive clauses from data. The clauses capture frequent patterns with high …

Abstract The recently introduced Tsetlin Machine (TM) has provided competitive pattern
recognition accuracy in several benchmarks, however, requires a 3-dimensional …

In this paper, an automatic brain tumor diagnosis system is presented using a new threshold-
based segmentation method. The proposed segmentation method is based on collaboration …