Search

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation. [ABSTRACT FROM AUTHOR]

Clustering is still one of the most common unsupervised learning techniques in data mining since it allows the discovery of meaningful and interesting patterns, knowledge, rules and associations from large-scale datasets. K-medoids, a variant of K-means, is a popular clustering method that attempts to find the optimal combination of K medoids from among a set of potential combinations. It has been successfully applied to solve various real-life problems owing to its simplicity and effectiveness. Nevertheless, due to the exponential number of possible combinations of K medoids, it is extremely challenging to produce the optimal one within a reasonable amount of time. Therefore, in this work, we propose to formulate the problem of K-medoids clustering as an optimization problem and then combine two effective and powerful Swarm Intelligence (SI) algorithms, namely Firefly Algorithm (FA) and Particle Swarm Optimization (PSO), to select the appropriate combination of K medoids. We extensively evaluate the proposed FA-PSO for K-medoids-based clustering, abbreviated as FA-PSO-KMED, using 10 UCI datasets. We first use the Iterated F-Race (I/F-Race) algorithm to determine the optimal parameter settings for FA and PSO. Then, we compare the results of the proposed FA-PSO-KMED with those obtained using the well-known state-of-the-art K-medoids-based clustering algorithms: PAM, CLARA and CLARANS. We also compare the results with 11 popular swarm intelligence algorithms: PSO, ABC, CS, FA, BA, APSO, EHO, HHO, SMA, AO and RSA. Experimental results and statistical analysis show that the proposed FA-PSO-KMED is very promising and demonstrates a significant improvement over the other clustering algorithms. [ABSTRACT FROM AUTHOR]

The present work is related to Web intelligence and more precisely to medical information foraging. We present here a novel approach based on agents technology for information foraging. An architecture is proposed, in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and the dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The whole system offers a tool to help the user undertaking information foraging. We implemented the system using a group of cooperative reactive agents and more precisely a colony of artificial bees. In order to validate our proposal, experiments were conducted on MedlinePlus, a benchmark dedicated for research in the domain of Health. The results are promising either for those related to Web regularities and for the response time, which is very short and hence complies the real time constraint. [ABSTRACT FROM AUTHOR]

We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint. [ABSTRACT FROM AUTHOR]