A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

The Journal of Supercomputing - Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields...     

3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the “pure” physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to “control” the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time.     

Line-of-sight task-space sensing methodology for the localization of robotic end-effectors

In this paper, the implementation of a line-of-sight (LOS) task-space sensing methodology is presented for guidance-based microlocalization of robotic end-effectors. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the end-effector's pose (position and orientation). The mobility of the localization application dictates the minimum number and the type (planar or spatial) of the LOS that would be necessary to use and, consequently, the exact configuration of the sensing system. Although the main focus of the paper is the presentation of the proposed LOS sensing system, a brief discussion of a robot-guidance method, which relies on the use of this sensing system, is also included. Extensive experiments conducted for a high-precision 3-DOF (degrees of freedom) planar robotic platform utilizing the overall guidance system validated our research.     

Influence of influent tributaries on water quality changes in Lake Mogan, Turkey

Lake Mogan, located in the steppes of central Anatolia, is a vital Turkish ecological system, in terms of its water resources, flora and fauna. The lake also is a unique recreational area for Ankara, the capital of Turkey. However, the lake is currently threatened by severe pollution, being under the risk of serious degradation because of (i) uncontrolled urbanization; (ii) point and non‐point source pollution; (iii) inefficient sewerage systems; (iv) sediment transportation and pollution via influent creeks to the lake; (v) uncontrolled soil erosion in its drainage basin; (vi) inappropriate flood control measures; and (vii) severe degradation of the natural wetlands within its drainage basin. This study evaluates the influence of its influent creeks on the water quality of Lake Mogan, mainly because the creeks are believed to be responsible for the major pollutant load reaching the lake, and because the extensive database documenting this situation has not previously been critically evaluated. Accordingly, this study reviews (i) change in the water quality of Lake Mogan between 1993 and 2002; (ii) the water quality classification of the major influent creeks feeding Lake Mogan; and (iii) the influence of the influent creeks on the water quality of the lake between 1993 and 2004.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.