The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. network from external interruptions intrusions. Because IoT devices run with range heterogeneous technologies process data over time, standard solutions may not be practical. It necessary to develop intelligent procedures that can used for m...

Machine Learning (ML) has revamped every domain of life as it provides powerful tools to build complex systems that learn and improve from experience and data. Our key insight is that to solve a machine learning problem, data scientists do not invent a new algorithm each time, but evaluate a range of existing models with different configurations and select the best one. This task is laborious, ...

Among the approaches that have been proposed to support dynamic adaptation, one can find two distinct techniques that appear to be antagonistic. On the one hand, different adaptation models have been proposed as a mean to capture, in an intelligible way, the valuable knowledge that experts have about the system behavior and how to manage it. However, expert-defined models are typically incomple...

Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems t...

bearing capacity prediction of axially loaded piles is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. machine learning methods have been reported by many contemporary researches with some degree of success in modeling geotechnical phenomena. in this study, a fairly new machine l...

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we ...

Machine learning (ML) deals with algorithms that automatically improve with experience where the experience for a ML algorithm is huge repositories of data. Machine learning methods produce a program that fits data to a model from lots of examples that specify the correct output for a given input. Formal Concept Analysis (FCA) is a successful model of learning from positive and negative example...

We investigate automatic classification of speculative language (‘hedging’), in biomedical text using weakly supervised machine learning. Our contributions include a precise description of the task with annotation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classification is feasi...