Adoption of human metabolic processes as Data Quality Based Models

The Journal of Supercomputing - The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and...     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...     

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly...     

State estimation and experimental verification of a robotic vehicle with six in-wheel drives using Kalman filter

Microsystem Technologies - In this research we present an algorithm for a six-wheeled robotic vehicle with articulated suspension (RVAS) to estimate the vehicle velocity and acceleration states,...     

Modeling and control of a finger-like mechanism using bending shape memory alloys

Microsystem Technologies - In this research a biologically inspired finger-like mechanism similar to human musculoskeletal system is developed based on Shape Memory Alloys (SMAs). SMA actuators are...     

Statistically correlated multi-task learning for autonomous driving

Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

RFID-based interactive multimedia system for the children

In this paper, we present an interactive edutainment system for the children that leverages multimedia and RFID technologies in a seamless manner. The proposed system allows children to learn about new objects/entities by tapping on physical objects through a specially designed RFID-Bluetooth based Tangible User Interface (TUI) tool. The output of the system is delivered as a set of appropriate multimedia representations related to the objects being tapped. The TUI uses RFID technology for object identification and Bluetooth communication to transmit data to the computer where the system??s software is running. We incorporated our system in three games that allow children of different ages to benefit from the system??s functionalities and encourage them to interact with it.     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.