In food sector, there is a huge demand for rapid, reliable, user & eco-friendly biosensors to analyse the quality and safety of food products. Biosensor based methodology depends upon the recognition of a specific antigens or receptors by corresponding antibodies, aptamers or high-affinity ligands. The first scientifically commercialised sensors were the electrochemical sensors used for the ana...

In food sector, there is a huge demand for rapid, reliable, user & eco-friendly biosensors to analyse the quality and safety of food products. Biosensor based methodology depends upon the recognition of a specific antigens or receptors by corresponding antibodies, aptamers or high-affinity ligands. The first scientifically commercialised sensors were the electrochemical sensors used for the ana...

In this paper, we relate the problem of finding a maximum clique to the intersection number of the input graph (i.e. the minimum number of cliques needed to edge cover the graph). In particular, we consider the maximum clique problem for graphs with small intersection number and random intersection graphs (a model in which each one of m labels is chosen independently with probability p by each ...

In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-exis...

Multi-label classification, or the same example can belong to more than one class label, happens in many applications. To name a few, image and video annotation, functional genomics, social network annotation and text categorization are some typical applications. Existing methods have limited performance in both efficiency and accuracy. In this paper, we propose an extension over decision tree ...

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. One way of learning underlying structures over labels is to project both instances and labels into the same space where an instance and its relevant labels tend to have similar representations. In this paper, we present a novel method to learn a joint sp...

Label Assignment and Failure Recovery Approaches for IP Multicast Communication in MPLS Networks Omar Qasem Banimelhem, Ph.D. Concordia University, 2005 Multiprotocol Label Switching (MPLS) is an Internet Engineering Task Force (IETF) framework that provides for the efficient designation, routing, forwarding, and switching of traffic flows through the network. MPLS is widely used for multicast ...

Gene tree correction with respect to a given species tree is a problem that has been recently proposed in order to better understand the evolution of gene families. One of the combinatorial methods proposed to tackle with this problem aims to correct a gene tree by removing the minimum number of leaves/labels (Minimum Leaf Removal and Minimum Label Removal, respectively). The two problems have ...

Multi-label learning is an extension of the standard supervised learning setting. In contrast to standard supervised learning where one training example is associated with a single class label, in multi-label learning one training example is associated with multiple class labels simultaneously. The multi-label learner induces a function that is able to assign multiple proper labels (from a give...