International Journal of Computational Intelligence and Applications

Malaria is a kind of disease that greatly threatens human health. Nearly half of the world’s population is at risk of malaria. Anti-malarial drugs which are sought, developed and synthesized keep malaria under control, having received increasing attention in drug discovery field. Machine learning techniques have been used widely in drug research and development. On the basis of semi-supervised machine learning for molecular descriptions, this research develops a multilayer deep belief network (DBN) that can be used to identify whether compounds have the anti-malarial activity. Firstly, the influence of feature dimensions on predicting accuracy is discussed. Furthermore, the proposed model is applied to contrast shallow machine learning and supervised machine learning with the similar deep architecture. The research results show that the proposed model can predict anti-malarial activity accurately. The stable performance on the evaluation metrics confirms the practicability of our model. The proposed DBN model performs better than other shallow supervised models and deep supervised models. Moreover, it could be applied to reduce the cost and the time of drug discovery.

Radial basis function (RBF) is combined with fuzzy C-means algorithms and its learning process made by projection-based learning (PBL) has been proposed in this paper, which is pointed out as PBL-fuzzy radial basis function (PBL-FRBF). The proposed method PBL-FRBF is producing good performances by selecting appropriate center and its width in order to achieve it by unsupervised classification algorithms instead of random selection. The PBL decreases the learning time, finds optimum output weight by its energy function and prefers smallest amount of samples for testing. Performance analysis is evaluated by benchmark datasets for classification problem taken from the UCI machine learning repository. The performance of the proposed PBL-FRBF has produced superior results when compared with FRBF and RBF for classification problems.

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with $O(N^2)$ complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with $O(N)$ complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset.

The paper proposes a novel signal reconstruction algorithm through substituting the gradient descent method in the iterative hard thresholding algorithm with a faster sparse randomized Kaczmarz method. By designing a series of gradually attenuated weights for the matrix rows whose indexes lie outside of the support set of the original sparse signal, we can focus the iterations on the effective support rows of the measurement matrix. The experiment results show that the proposed algorithm presents a faster convergence rate and more accurate reconstruction accuracy than the state-of-the-art algorithms. Meanwhile, the successful reconstruction probability of the proposed algorithm is higher than that of other algorithms. Moreover, the characteristics of the proposed signal reconstruction algorithm are also analyzed in detail through numerical experiments.

This paper aims to develop a hybrid grey wolf optimization algorithm (HGWO) for solving the job shop scheduling problem (JSP) with the objective of minimizing the makespan. Firstly, to make the GWO suitable for the discrete nature of JSP, an encoding mechanism is proposed to implement the continuous encoding of the discrete scheduling problem, and a ranked-order value (ROV) rule is used to conduct the conversion between individual position and operation permutation. Secondly, a heuristic algorithm and the random rule are combined to implement the population initialization in order to ensure the quality and diversity of initial solutions. Thirdly, a variable neighborhood search algorithm is embedded to improve the local search ability of our algorithm. In addition, to further improve the solution quality, genetic operators (crossover and mutation) are introduced to balance the exploitation and exploration ability. Finally, experimental results demonstrate the effectiveness of the proposed algorithm based on 23 benchmark instances.

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.