The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

Active Shape Models (ASMs), a knowledge-based segmentation algorithm developed by Cootes and Taylor [1, 2], have become a standard and popular method for detecting structures in medical images. In ASMs – and various comparable approaches – the model of the object’s shape and of its gray-level variations is based the assumption of linear distributions. In this work, we explore a new way to model...

awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. therefore, the present study two hybrid models, wavelet- adaptive neural fuzzy interference system (wanfis) and wavelet- artificial neural network (wann) are used for flow prediction of gamasyab river (nahavand, hamedan, iran...

Predicting the impact of input process variables on chemical processes is key to assess their performance latter. Models explaining this through a mechanistic approach are rarely readily available, complex in nature and/or require long development time. With increased automation industries and availability high-throughput experimental data, data-driven machine learning models gaining popularity...

In this paper, a new classification method is presented which uses clustering techniques to augment the performance of K-Nearest Neighbor algorithm. This new method is called Nearest Cluster approach, NC. In this algorithm the neighbor samples are automatically determined using clustering techniques. After partitioning the train set, the labels of cluster centers are determined. For specifying ...

Continuous monitoring of spatial queries has received significant research attention in the past few years. In this paper, we propose two efficient algorithms for the continuous monitoring of the constrained k nearest neighbor (kNN) queries. In contrast to the conventional k nearest neighbors (kNN) queries, a constrained kNN query considers only the objects that lie within a region specified by...

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored each user. Identifying whether temperature, humidity, lighting, CO2 levels used as efficient predictors of room accuracy is needed help designers better utilize readings data collected in order improve design, effort suit users. It also ...

The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers ...

The multiple-instance learning model has received much attention recently with a primary application area being that of drug activity prediction. Most prior work on multiple-instance learning has been for concept learning, yet for drug activity prediction, the label is a real-valued affinity measurement giving the binding strength. We present extensions of k-nearest neighbors (k-NN), Citation-k...