Fuzzy inference systems, a sub-branch of artificial intelligence, are decision support systems based on fuzzy set theory. The knowledge is transmitted to these systems through linguistic fuzzy rules. In general, the rule ...

Ever-increasing performance demand for the computer applications has resulted in shrinking the technology sizes of the CMOS circuits over the past 50 years, which made it possible to increase the number of transistors ...

Dictionaries catalog and describe the semantic information of a lexicon. WordNet provides an edge by presenting distinct concepts with the hierarchy information among them.Research in computer science has been using this ...

Accurate estimations play a significant role in the success of software projects, and companies should have sufficient number of past project data to make these estimations accurate and reliable. Some institutions gather ...

In recent years, the use of the Internet has increased in all areas of life, thus many cyber- attacks have emerged. These attacks aim to steal users' private information such as passwords, credit cards. During phishing ...

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. These algorithms have attracted much attention, particularly for solving complex problems, owing to their characteristics of ...

Designing an application in hardware under inversely competing constraints such as area, performance and power consumption with different objective functions such as reliability of the circuits is a cumbersome task. Having ...

Dense video captioning aims at detecting events in untrimmed videos and generating accurate and coherent caption for each detected event. It is one of the most challenging captioning tasks since generated sentences must ...

Authorship verification is a decision problem that tries to find out whether the text of an unknown author belongs to a suspicious author. The problem is the fundamental one of the studies in authorship analysis that has ...

Most pre-trained word embeddings are achieved from context-based learning algorithms trained over a large text corpus. This leads to learning similar vectors for words that share most of their contexts, while expressing ...