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...     

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...     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Deep neural network architectures for dysarthric speech analysis and recognition

Neural Computing and Applications - This paper investigates the ability of deep neural networks (DNNs) to improve the automatic recognition of dysarthric speech through the use of convolutional...     

Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control

Neural Computing and Applications - This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an...     

Energy inefficiency diagnosis in industrial process through one-class machine learning techniques

In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

     

Intrinsic nonlinear multiscale image decomposition: A 2D empirical mode decomposition-like tool

Many works have been achieved for analyzing images with a multiscale approach. In this paper, an intrinsic and nonlinear multiscale image decomposition is proposed, based on partial differential equations (PDEs) and the image frequency contents. Our model is inspired from the 2D empirical mode decomposition (EMD) for which a theoretical study is quite nonexistent, mainly because the algorithm is based on heuristic and ad hoc elements making its mathematical study hard. This work has three main advantages. Firstly, we prove that the 2D sifting process iterations are consistent with the resolution of a nonlinear PDE, by considering continuous morphological operators to build local upper and lower envelopes of the image extrema. In addition to the fact that now differential calculus can be performed on envelopes, the introduction of such morphological filters eliminates the interpolation dependency that also terribly suffers the method. Also, contrary to former 2D empirical modes, precise mathematical definition for a class of functions are now introduced thanks to the nonlinear PDE derived from the consistency result, and their characterization on the basis of Meyer spaces. Secondly, an intrinsic multiscale image decomposition is introduced based on the image frequency contents; the proposed approach almost captures the essence and philosophy of the 2D EMD and is linked to the well known Absolutely Minimizing Lipschitz Extension model. Lastly, the proposed multiscale decomposition allows a reconstruction of images. The filterbank capability of the new multiscale decomposition algorithm is shown both on synthetic and real images, and results show that our proposed approach improves a lot on the 2D EMD. Moreover, the complexity of the proposed multiscale decomposition is very reduced compared to the 2D EMD by avoiding the surface interpolation approach, which is the core of all 2D EMD algorithms and is very time consuming. For that purpose also, our work will then be a great benefit; especially, in higher dimension spaces.     

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.     

Determining trust in media-rich websites using semantic similarity

Significant growth of multimedia content on the World Wide Web (or simply ??Web??) has made it an essential part of peoples lives. The web provides enormous amount of information, however, it is very important for the users to be able to gauge the trustworthiness of web information. Users normally access content from the first few links provided to them by search engines such as Google or Yahoo!. This is assuming that these search engines provide factual information, which may be popular due to criteria such as page rank but may not always be trustworthy from the factual aspects. This paper presents a mechanism to determine trust of websites based on the semantic similarity of their multimedia content with already established and trusted websites. The proposed method allows for dynamic computation of the trust level of websites of different domains and hence overcomes the dependency on traditional user feedback methods for determining trust. In fact, our method attempts to emulate the evolving process of trust that takes place in a user??s mind. The experimental results have been provided to demonstrate the utility and practicality of the proposed method.