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Predictive reliability and validity of hospital cost analysis with dynamic neural network and genetic algorithm

Published in Neural Computing and Applications

Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many pla...

Neural networks catching up with finite differences in solving partial differential equations in higher dimensions

Published in Neural Computing and Applications

Solving partial differential equations using neural networks is mostly a proof of concept approach. In the case of direct function approximation, a single neural network is constructed to be the solution of a particular boundary value problem. Independent variables are fed into the input layer, and a single output is considered as the solution’s va...

Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolu...

Published in Neural Computing and Applications

Now-a-days, submerged arc welding processes (SAW) are immensely being applied for joining the thick plates and surfacing application. However, the selection of optimal SAW process parameters is indeed an intricate task which aims to accomplish the desired quality of welded part at an economic way. Therefore, in the present paper, the research effor...

Towards robust voice pathology detection

Published in Neural Computing and Applications

Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking, and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system, we investigated three distinct classifiers wit...

Generative image completion with image-to-image translation

Published in Neural Computing and Applications

Though many methods have been proposed, image completion still remains challenge; besides textured patterns completion, it often requires high-level understanding of scenes and objects being completed. More recently, deep convolutional generative adversarial networks have been turned into an efficient tool for image completion. Manually specified t...

Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

Published in Neural Computing and Applications

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(λ)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(λ) is to make an adaptation of linear function approximati...

Analysis and design of genetic algorithm-based cascade control strategy for improving the dynamic performance of interle...

Published in Neural Computing and Applications

Switched-mode power supplies used for powering new generation devices like microprocessors, utility grids and electric vehicles need to operate with faster dynamic response. This paper proposes cascade control technique using genetic algorithm to obtain the optimal proportional integral outer voltage and inner current controller parameters of inter...

Emerging intelligent algorithms: challenges and applications

Published in Neural Computing and Applications

Special issue on machine learning applications for self-organized wireless sensor networks

Published in Neural Computing and Applications

Wind power ramp event detection with a hybrid neuro-evolutionary approach

Published in Neural Computing and Applications

In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning...