Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Re

Mathematical integration techniques rely on mathematical relationships such as addition, subtraction, division, and subtraction to merge images with different resolutions to achieve the best effect of the merger. In this study, a simulation is adopted to correct the geometric and radiometric distortion of satellite images based on mathematical integration techniques, including Brovey Transform (BT), Color Normalization Transform (CNT), and Multiplicative Model (MM). Also, interpolation methods, namely the nearest neighborhood, Bi-linear, and Bi-cubic were adapted to the images captured by an optical camera. The evaluation of images resulting from the integration process was performed using several types of measures; the first type depend

Embracing digital technological advancements in media and communication has led government entities to adopt communication practices fully aligned with the digital and networked system in government communication. Traditional media practices within the government environment increasingly rely on the ability to utilize digital tools and systems for content creation, communication, evaluation, and the management of the entire communication process within an electronic and intelligent framework for government services. Naturally, this transformation has caught the attention of communication and public relations researchers worldwide, as the digital and networked aspects of government communication now form an intelle

The process of self-assessment plays a key role in achieving quality educational institutions (college, department or program academic particular), because the assessment process provides reviews of the effectiveness of the criteria used in the enterprise, especially in the field of teaching and learning, and is result self-assessment providing self-assessment report. The self- assessment can be performed at different levels (college, academic department, Master, Ph.D. program, or courses). The importance of the research focused on to provide a measure of self-assessment helps profile officials in the implementation of the assessment process are clear and precise and fast, and to provide them to measure the availability requireme

The redevelopment of brownfields participate to sustainable urban development, it can make cities more valuable worth for community and more attractive for companies to settle down their projects through investment, and it can help to reduce expenditures for the construction of infrastructures and other services. Recent studies in brownfield redevelopment and investment have shown great interest in urban planning studies because of their negative effects on cities, they promoted sprawl, pollution, social and economic problems. There is a general agreement among researchers and experts that brownfield sites can be reused as green spaces, commerce centers, and residential projects. To promote sustainable urban development the role of the c

Lifestyle Medicine is the application of evidence-based lifestyle approaches for the prevention, treatment, and even the reversal of lifestyle-related chronic diseases such as diabetes, hypertension, heart disease, obesity, polycystic ovarian diseases, dementia, arthritis, and cancers

In the United States, the pharmaceutical industry is actively devising strategies to improve the diversity of clinical trial participants. These efforts stem from a plethora of evidence indicating that various ethnic groups respond differently to a given treatment. Thus, increasing the diversity of trial participants would not only provide more robust and representative trial data but also lead to safer and more effective therapies. Further diversifying trial participants appear straightforward, but it is a complex process requiring feedback from multiple stakeholders such as pharmaceutical sponsors, regulators, community leaders, and research sites. Therefore, the objective of this paper is to describe three viable strategies that can p

Growth of Penicillium expansum, an ubiquitous mould found in stored fruit globallyt, was significantly restricted by exposure to 48 h cell-free supernatant of two strains of Lactobacillus plantarum (p < 0.001). In addition, the biotransformation of patulin, a toxic secondary metabolite formed by P. expansum, on exposure to L. plantarum cells and cell-free supernatant highlights the potential of this GRAS microbe as a biocontrol agent. Up to 80% of patulin was biotransformed following a 4 h incubation with 1010 cells ml−1 (37 °C) forming E- and Z-ascladiol. The formation of these products was more pronounced at elevated pH and cell density. Exposure to cell free supernatant or sonicated cells resulted in complete patulin biotransformation

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and