Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

A Q-learning Based Continuous Tuning of Fuzzy Wall Tracking

A simple easy to implement algorithm is proposed to address wall tracking task of an autonomous robot. The robot should navigate in unknown environments, find the nearest wall, and track it solely based on locally sensed data. The proposed method benefits from coupling fuzzy logic and Q-learning to meet requirements of autonomous navigations. Fuzzy if-then rules provide a reliable decision maki...

Deep Learning for Secure Mobile Edge Computing

Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we propose a deep-learning-based model to detect security threats. The model uses unsupervised learning to automate the detection process, and uses location info...

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

Learning phonetic categories by tracking movements q

We explore in this study how infants may derive phonetic categories from adult input that are highly variable. Neural networks in the form of self-organizing maps (SOMs; Kohonen, 1989, 1995) were used to simulate unsupervised learning of Mandarin tones. In Simulation 1, we trained the SOMs with syllable-sized continuous F0 contours, produced by multiple speakers in connected speech, and with th...

Optimization Task Scheduling Algorithm in Cloud Computing

Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This rese...