Traditional machine learning, mainly supervised learning, follows the assumptions of closed-
world learning, ie, for each testing class, a training class is available. However, such …

Traditional fuzzy set theory has recently demonstrated its inefficiency in handling linguistic
terms directly, including the degree of neutrality in medical diagnoses. Most decision support …

A significant part of the current research in the field of Artificial Intelligence is devoted to
knowledge bases. New techniques and methodologies are emerging every day for the …

Deep learning methods predicated on convolutional neural networks and graph neural
networks have enabled significant improvement in node classification and prediction when …

The picture fuzzy set is an extension of the fuzzy and intuitionistic fuzzy set for solving real-
world problems. Entropy and distance measures play significant roles in measures for …

Attributing a piece of malware to its creator typically requires threat intelligence. Binary
attribution increases the level of difficulty as it mostly relies upon the ability to disassemble …

Historically, the utilization of context, the range and scope of context-aware systems, and the
levels of computational intelligence in such systems have been very limited. While the …

In conventional monitoring of human behavior in forest protection, deep learning
approaches can be detected human behavior significantly since thousands of visitors' forest …

Human behaviour demonstrates environmental awareness and self-awareness which is
used to arrive at decisions and actions or reach conclusions based on reasoning and …

Coverage path planning describes the process of finding an effective path robots can take to
traverse a defined dynamic operating environment where there are static (fixed) and …