International Journal on Artificial Intelligence Tools

Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods.

The rapid evolution of social networks in recent years has focused the attention of researchers to find adequate solutions for the management of these networks. For this purpose, several efficient algorithms dedicated to the tracking and the rapid detection of the community structure have been proposed. In this paper, we propose a novel density-based approach with dual optimization for tracking community structure of increasing social networks. These networks are part of dynamic networks evolving by adding nodes with their links. The local optimization of the density makes it possible to reduce the resolution limit problem generated by the optimization of the modularity. The presented algorithm is incremental with a relatively low algorithmic complexity, making it efficient and faster. To demonstrate the effectiveness of our method, we test it on social networks of the real world. The experimental results show the performance and efficiency of our algorithm measured in terms of modularity density, modularity, normalized mutual information, number of communities discovered, running time and stability of communities.

The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different variations of PDP with different number of objectives and constraints have been considered. Depending on the number of objectives, multi and many-objective evolutionary algorithms have been applied to solve the problem and to study the conflicts between objectives. In this paper, PDP is formulated as a many-objective pickup and delivery problem (MaOPDP) with delay time of vehicle having six criteria to be optimized. To the best of our knowledge, this variation of PDP has not been considered in the literature. To solve the problem, this paper proposes a memetic I-DBEA (Improved Decomposition Based Evolutionary Algorithm), which is basically the modification of an existing many-objective evolutionary algorithm called I-DBEA. To demonstrate the superiority of our approach, a set of experiments have been conducted on a variety of small, medium and large-scale problems. The quality of the results obtained by the proposed approach is compared with five existing multi and many-objective evolutionary algorithms using three different multi-objective evaluation measures such as hypervolume (HV), inverted generational distance (IGD) and generational distance (GD). The experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the obtained solutions.

Taking videos as nodes in a graph, graph matching is an effective technique for unsupervised video-based person re-identification (re-ID). However, most of existing methods are sensitive to noisy training data and mainly only focus on visual content relations between query and gallery videos, which may introduce large amount of false positives. To enhance the robustness to training data and alleviate the visual ambiguity, a Dynamic Hybrid Graph Matching (DHGM) method is proposed, which jointly considers both content and context information for person re-ID in an iterative manner. The content relations between video nodes are obtained by metric learning, based on which the context relation is acquired by encoding the bidirectional feature of each probe node relative to its graph neighbors. The model is iteratively updated during the process of graph construction for promoted distance measurement and further better matching performance. Experimental results on the PRID 2011 and iLIDS-VID datasets demonstrate the superiority of the DHGM.