In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Baye...

This paper describes and evaluates the types of machine learning techniques which are appropriate for the special needs of automatic target recognition. Artiicial neural networks has been the most popular choice. However, they have several shortcomings , including that they do not readily allow the learned domain theory to be revised in response to new conditions, new targets, or new sensors. R...

Many practical applications benefit from machine learning and artificial intelligence technologies, but their security needs to be studied in more depth. We discuss the risk of poisoning attacks against training stage challenges defending them.

Investors collect information from trading market and make investing decision based on collected information, i.e. belief of future trend of security’s price. Therefore, several mainstream trend analysis methodology come into being and develop gradually. However, precise trend predicting has long been a difficult problem because of overwhelming market information. Although traditional time seri...

The last five years have seen an explosion in machine-learning research. This explosion has many causes: First, separate research communities in symbolic machine learning, computational learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine-learning techniques are being applied to new kinds of problem, includ...

Semi-supervised learning is by no means an unfamiliar concept to natural language processing researchers. Labeled data has been used to improve unsupervised parameter estimation procedures such as the EM algorithm and its variants since the beginning of the statistical revolution in NLP (e.g., Pereira and Schabes 1992). Unlabeled data has also been used to improve supervised learning procedures...

a multi objective honey bee mating optimization (hbmo) designed by online learning mechanism is proposed in this paper to optimize the double fuzzy-lead-lag (fll) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. the proposed double fll stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the ...

dna sequence, containing all genetic traits is not a functional entity. instead, it transfers to protein sequences by transcription and translation processes. this protein sequence takes on a 3d structure later, which is a functional unit and can manage biological interactions using the information encoded in dna. every life process one can figure is undertaken by proteins with specific functio...