End-to-End Multiview Gesture Recognition for Autonomous Car Parking System

The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods....     

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.     

Influence of Copper Nanoparticles Concentration on the Properties of Poly(vinylidene fluoride)/Cu Nanoparticles Nanocomposite Films

In this study, the nanocomposite films of polyvinylidene fluoride/copper nanoparticles were prepared by mixing of copper nanoparticles in a solution of dimethylformamide and polyvinylidene fluoride. The prepared nanocomposites were investigated by fourier transform infrared spectroscopy and X-ray diffraction techniques, showed an obvious α- to β-phase transformation compared to pure PVDF. Scanning electron microscope micrographs showed spherulitic crystal structure of PVDF. The spherulitic morphology of the pure PVDF is maintained for the PVDF nanocomposites; the size of the spherulites decreased by increasing weight fraction of copper nanoparticles. The optical band gap values deduced from the UV–Visible absorption spectra were found to reduce from 4.77 eV in pure PVDF to 3.2 eV after embedding 1 wt% of copper nanoparticles. The surface resistivity values were decreased with increasing copper nanoparticles content. Thermal stability of the nanocomposites was studied by thermogravimetric analysis (TGA). TGA curves showed that nanocomposite films have higher resistance to thermal degradation compared to pure PVDF.     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

Preparation of Au nanoparticles by Q switched laser ablation and their application in 4-nitrophenol reduction

Clean Technologies and Environmental Policy - This work reports the fabrication of Au nanoparticles (NPs) by laser ablation of a gold metal plate immersed in water in the absence of stabilizing...     

The Effect of Methylcellulose,Temperature, and Microwave Pretreatment on Kinetic of Mass Transfer During Deep Fat Frying of Chicken Nuggets

The aim of this study was to determine the effect of microwave pretreatment, usage of methylcellulose, oil temperature, and frying time on mass transfer during deep fat frying of chicken nuggets. Methylcellulose was used in batter and as a coating on product. Microwave with two power densities namely 3.7 and 7.4 W/g was used for reduction of initial moisture content of samples before frying. Frying was performed at three temperatures (150 °C, 170 °C, and 190 °C) and five intervals (0.5, 1, 2, 3, and 4 min) in the sunflower oil. The least oil content was observed when MC was used as a coating layer on non-precooked samples fried at 190 °C. Oil absorption of samples with MC in batter was partially higher compared to control samples. This could be attributed to the rheology of batters. The first-order kinetic model was fitted to moisture and oil content. For determining the correlation between temperature and moisture diffusivity, Arrhenius equation was used. The constant rate for moisture and oil transfer was in the range of 2.2–5 and 0.023–2.67 s^?1, respectively. Effective moisture diffusivity values were between 1.43?×?10^?8 and 3.24?×?10^?8 m²/s. Activation energy ranged between 0.71 and 1.71 kJ/mol.     

Editorial: Mobile Networks in the Era of Big Data

Mobile Networks and Applications -