Vahid Rezaei Tabar
[ 1 ] - A generalization of Profile Hidden Markov Model (PHMM) using one-by-one dependency between sequences
The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on th...
[ 2 ] - ارزیابی روابط بین شاخص‌های بهره‌وری نیروی انسانی و دستمزد، در کارگاه‌های بزرگ صنعتی مبتنی بر الگوریتم‌های فرا ابتکاری و شبکه‌های بیزی
برای اینکه سازمانی بتواند در جهت رشد و بهبود بهره‌وری خود اقدام نماید، لازم است که عوامل موثر در زمینه بهبود بهره‌وری را شناسایی کرده و سپس بر اساس اهمیت آنها، اقدامات مناسب را به عمل آورد. پژوهش حاضر با هدف تعیین عواملی که به صورت مستقیم و غیر مستقیم بر روی بهره‌وری نیروی انسانی و حقوق و دستمزد کارکنان به صورت هم‌زمان تاثیر می‌‌گذارند و همچنین بررسی چگونگی تاثیر این دو عامل بر روی یکدیگر، صورت...
[ 3 ] - Generalized Baum-Welch and Viterbi Algorithms Based on the Direct Dependency among Observations
The parameters of a Hidden Markov Model (HMM) are transition and emission probabilities‎. ‎Both can be estimated using the Baum-Welch algorithm‎. ‎The process of discovering the sequence of hidden states‎, ‎given the sequence of observations‎, ‎is performed by the Viterbi algorithm‎. ‎In both Baum-Welch and Viterbi algorithms‎, ‎it is assumed that...
[ 4 ] - Learning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis
‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...
[ 5 ] - Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
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