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Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems. [ABSTRACT FROM AUTHOR]

Smart health-care assistants are designed to improve the comfort of the patient where smart refers to the ability to imitate the human intelligence to facilitate his life without, or with limited, human intervention. As a part of this, we are proposing a new Intelligent Communication Assistant capable of detecting physiological needs by following a new efficient Inverse Reinforcement learning algorithm designed to be able to deal with new time-recorded states. The latter processes the patient's environment data, learns from the patient previous choices and becomes capable of suggesting the right action at the right time. In this paper, we took the case study of Locked-in Syndrome patients, studied their actual communication methods and tried to enhance the existing solutions by adding an intelligent layer. We showed that by using Deep Inverse Reinforcement Learning using Maximum Entropy, we can learn how to regress the reward amount of new states from the ambient environment recorded states. After that, we can suggest the highly rewarded need to the target patient. Also, we proposed a full architecture of the system by describing the pipeline of the information from the ambient environment to the different actors. [ABSTRACT FROM AUTHOR]

Sleep is a fundamental restorative process for human mental and physical health. Considering the risks that sleep disorders can present, sleep analysis is considered as a primordial task to identify the different abnormalities. Sleep scoring is the gold standard for human sleep analysis. The manual sleep scoring task is considered exhausting, subjective, time-consuming and error prone. Moreover, sleep scoring is based on fixed epoch lengths usually of 30 seconds, which leads to an information loss problem. In this paper, we propose an automatic unsupervised sleep scoring model. The aim of our work is to consider different epoch's durations to classify sleep stages. Therefore, we developed a model based on Hierarchical Multi-Agent Systems (HMASs) that presents different layers where each layer contains a number of adaptive agents working with a specific time epoch. The effectiveness of our approach was investigated using real electroencephalography (EEG) data. Good results were reached according to a comparative study realized with the often used machine learning techniques for sleep stages classification problems. [ABSTRACT FROM AUTHOR]

In counter-terrorism actions, commanders are confronted with difficult and important challenges. Their decision-making processes follow military instructions and must consider the humanitarian aspect of the mission. In this paper, we aim to respond to the question: What would the casualties be if governmental forces reacted in a given way with given resources? Within a similar context, decision-support systems are required due to the variety and complexity of modern attacks as well as the enormous quantity of information that must be treated in real time. The majority of mathematical models are not suitable for real-time events. Therefore, we propose an analytical model for a time-dependent prediction of terrorist attacks (ATiPreTA). The output of our model is consistent with casualty data from two important terrorist events known in Tunisia: Bardo and Sousse attacks. The sensitivity and experimental analyses show that the results are significant. Some operational insights are also discussed. [ABSTRACT FROM AUTHOR]

This work addresses the multi‐objective route planning and charging problem for Battery Electric Vehicles (BEVs). The proposed solution is based on the NSGA‐II algorithm to derive the Pareto‐optimal set of eco‐routes with the minimum travel and charging time, distance, energy consumption and charging costs while complying with the constraints related to the battery State of Charge (SoC) and the status of charging stations. Influencing factors, such as, vehicle and battery parameters, weather conditions, driver behaviour, road grade, Time Of Use (TOU) electricity cost, and Charging Stations (CS) status and type, are taken into account. The final results of the proposed method are compared with the well‐established simulator SUMO. [ABSTRACT FROM AUTHOR]