This paper proposes a novel method for aligning multiple genomic or proteomic sequences using a fuzzyfied Hidden Markov Model (HMM). HMMs are known to provide compelling performance among multiple sequence alignment (MSA) algorithms, yet their stochastic nature does not help them cope with the existing dependence among the sequence elements. Fuzzy HMMs are a novel type of HMMs based on fuzzy se...

Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein s...

A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multipaths and inherently stocha...

The development of autonomous vehicle systems has progressed rapidly in recent years. One challenge that persists is the capability system to respond human drivers. Human behavior an integral part driving; thus, driver determines changing lanes and speed adjustments. However, unpredictable immeasurable. Some traffic accidents are caused due erratic driver. Although, laws, such as Indonesia, reg...

BACKGROUND Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psy...

In this paper, we present a fuzzy logic integration to the Hidden Markov Models (HMM). We have replaced the basic arithmetic operators by some adequate k z y operators. Using fuzzy operators permits us to relax the additivity constraint of probability measures. So we will need only the monotonicity with respect to set inclusion. Some results show the interest of our approach.