Purpose The purpose of the paper is to predict mapping areas vulnerable flooding in Ourika watershed High Atlas Morocco with aim providing a useful tool capable helping mitigation and management floods associated region, as well whole. Design/methodology/approach Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) x-gradient b...

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top that, the ensemble model that synthesizes advantages different ML models deserves more attention. In this study, based...

By using an in-house data set of small-molecule structures, encoded by Ghose-Crippen parameters, several machine learning techniques were applied to distinguish between kinase inhibitors and other molecules with no reported activity on any protein kinase. All four approaches pursued--support-vector machines (SVM), artificial neural networks (ANN), k nearest neighbor classification with GA-optim...

Given activity training data from Hight-Throughput Screening (HTS) experiments, virtual High-Throughput Screening (vHTS) methods aim to predict in silico the activity of untested chemicals. We present a novel method, the Influence Relevance Voter (IRV), specifically tailored for the vHTS task. The IRV is a low-parameter neural network which refines a k-nearest neighbor classifier by non-linearl...

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distr...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

In this poster, we firstly put forward to an effective anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm to fulfill DDoS attacks detection task towards ensuring the QoS of web server. The method is good at detecting network anomalies with high detection rate, high confidence and low false positives than traditional methods, because it...

The ever-growing popularity of online map services and their wide deployment in smartphones and car-navigation systems have led to extensive use of location-based services. One of the most popular classes of such services is k-nearest neighbor (kNN) queries where users search for geographical points of interests (e.g., restaurants) and the corresponding directions and travel times to these loca...