Institution
Islamic Azad University
Education•Tehran, Iran•
About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.
Topics: Population, Adsorption, Fuzzy logic, Catalysis, Nanofluid
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a general analysis of one-dimensional steady-state thermal stresses in a hollow thick cylinder made of functionally graded material is developed, where the temperature distribution is assumed to be a function of radius, with general thermal and mechanical boundary conditions along the inside and outside surfaces.
361 citations
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TL;DR: The antioxidant activity of the aerial parts of Ferula assafoetida was determined by employing various in vitro assay systems as discussed by the authors, and the extracts showed good nitric oxide-scavenging activity (IC 50 was 270 ± 3) and Fe 2+ chelating ability, IC 50 was 0.57 ± 0.02 mg ml -1 ).
Abstract: The antioxidant activity of the aerial parts of Ferula assafoetida was determined by employing various in vitro assay systems. IC 50 for DPPH radical-scavenging activity was 380 ± 12 mg ml -1 . The extracts showed good nitric oxide-scavenging activity (IC 50 was 270 ± 3) and Fe 2+ chelating ability (IC 50 was 0.57 ± 0.02 mg ml -1 ). The peroxidation inhibition (antioxidant activity) of the extracts exhibited values from 82% (at 24 hrs) and 88% (at 72 hrs). The extract exhibited a fairy weak reducing power at 25-800 μg ml -1 of extracts which was not comparable with Vitamin C (p 3 method) was 90.9 ± 6.3 mg quercetin equivalent/g of extract powder.
360 citations
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University of West Bohemia1, Macquarie University2, Tehran University of Medical Sciences3, Razi University4, Islamic Azad University5, Edinburgh Napier University6, University of Wisconsin-Madison7, Louisiana State University8, Texas A&M University–Kingsville9, University of Toronto10, Babol University of Medical Sciences11
TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers.
Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
358 citations
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TL;DR: A hybrid prediction algorithm comprised of Support Vector Regression and Modified Firefly Algorithm is proposed to provide the short term electrical load forecast and the experimental results affirm that the proposed algorithm outperforms other techniques.
Abstract: Precise forecast of the electrical load plays a highly significant role in the electricity industry and market. It provides economic operations and effective future plans for the utilities and power system operators. Due to the intermittent and uncertain characteristic of the electrical load, many research studies have been directed to nonlinear prediction methods. In this paper, a hybrid prediction algorithm comprised of Support Vector Regression (SVR) and Modified Firefly Algorithm (MFA) is proposed to provide the short term electrical load forecast. The SVR models utilize the nonlinear mapping feature to deal with nonlinear regressions. However, such models suffer from a methodical algorithm for obtaining the appropriate model parameters. Therefore, in the proposed method the MFA is employed to obtain the SVR parameters accurately and effectively. In order to evaluate the efficiency of the proposed methodology, it is applied to the electrical load demand in Fars, Iran. The obtained results are compared with those obtained from the ARMA model, ANN, SVR-GA, SVR-HBMO, SVR-PSO and SVR-FA. The experimental results affirm that the proposed algorithm outperforms other techniques.
358 citations
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TL;DR: A cross-layer-based channel access and routing solution for sensing and actuating is proposed for monitoring and controlling agriculture and farms in rural areas and reduces network latency up to a certain extent.
Abstract: Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.
356 citations
Authors
Showing all 83704 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ajit Kumar Mohanty | 141 | 1124 | 93062 |
Pierluigi Paolucci | 138 | 1965 | 105050 |
Eric Conte | 132 | 1206 | 84593 |
Patrizia Azzi | 132 | 1275 | 83686 |
D. Del Re | 131 | 1406 | 87230 |
Jean-Laurent Agram | 128 | 1221 | 84423 |
Seyed Mohsen Etesami | 128 | 1101 | 76488 |
Jean-Charles Fontaine | 128 | 1190 | 84011 |
Roberta Arcidiacono | 128 | 1322 | 80917 |
Tejinder Virdee | 128 | 1208 | 74372 |
Frank Hartmann | 127 | 1116 | 81455 |
Paolo Azzurri | 126 | 1058 | 81651 |
Achim Stahl | 124 | 1248 | 111121 |
Federica Primavera | 120 | 876 | 63895 |
Riccardo Andrea Manzoni | 120 | 946 | 67897 |