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Parvaneh Shabanzadeh
Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia

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Journal article
Published: 11 April 2021 in Energies
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Five major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.

ACS Style

Leila Ezzatzadegan; Rubiyah Yusof; Noor Morad; Parvaneh Shabanzadeh; Nur Muda; Tohid Borhani. Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation. Energies 2021, 14, 2137 .

AMA Style

Leila Ezzatzadegan, Rubiyah Yusof, Noor Morad, Parvaneh Shabanzadeh, Nur Muda, Tohid Borhani. Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation. Energies. 2021; 14 (8):2137.

Chicago/Turabian Style

Leila Ezzatzadegan; Rubiyah Yusof; Noor Morad; Parvaneh Shabanzadeh; Nur Muda; Tohid Borhani. 2021. "Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation." Energies 14, no. 8: 2137.

Research article
Published: 14 January 2019 in Journal of Nanomaterials
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In this research, gold nanoparticles (Au-NPs) are biosynthesized from tetrachloroaurate (AuCl4−) aqueous solution through a simple and ecofriendly route using water extract of black Camellia sinensis leaf (C. sinensis L.) which acted as a reductant and stabilizer simultaneously. The prepared gold nanoparticles are characterized using UV-visible spectroscopy, X-ray diffraction (XRD), and transmission electron microscopy (TEM). Also, determination of the accurate predictor model for chemical reactions is particularly important because of high cost of the chemical materials and measurement devices. While the artificial neural networks (ANNs) are one of the appropriate tools to forecast any phenomena, due to the low number of data set related to chemical experimental was caused to provide appropriate model is a time-consuming iterative process. With the aim to improve the accuracy of the ANN model and overcome the local convergence of this problem, a global search technique, biogeography-based optimization (BBO) method which integrated by chaotic map is employed. The improved model showed minimum mean squared error (MSE) of 0.0134 and maximum coefficient of determination (R2) equal to 0.9822 compared with several other famous ANN training algorithm, utilizing output experimental data obtained from biosynthesis proceeding.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli; Abdollah Hajalilou; Shidrokh Goudarzi. Computational Modeling of Biosynthesized Gold Nanoparticles in Black Camellia sinensis Leaf Extract. Journal of Nanomaterials 2019, 2019, 1 -11.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof, Kamyar Shameli, Abdollah Hajalilou, Shidrokh Goudarzi. Computational Modeling of Biosynthesized Gold Nanoparticles in Black Camellia sinensis Leaf Extract. Journal of Nanomaterials. 2019; 2019 ():1-11.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli; Abdollah Hajalilou; Shidrokh Goudarzi. 2019. "Computational Modeling of Biosynthesized Gold Nanoparticles in Black Camellia sinensis Leaf Extract." Journal of Nanomaterials 2019, no. : 1-11.

Conference paper
Published: 18 October 2018 in Communications in Computer and Information Science
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Quantifying dissimilarities between two trajectories is a challenging yet fundamental task in many trajectory analysis systems. Existing methods are computationally expensive to calculate. We proposed a dissimilarity measure estimate for trajectory data by using deep learning methodology. One advantage of the proposed method is that it can get executed on GPU, which can significantly reduce the execution time for processing large number of data. The proposed network is trained using synthetic data. A simulator to generate synthetic trajectories is proposed. We used a publicly available dataset to evaluate the proposed method for the task of trajectory clustering. Our experiments show the performance of our proposed method is comparable with other well-known dissimilarity measures while it is substantially faster to compute.

ACS Style

Reza Arfa; Rubiyah Yusof; Parvaneh Shabanzadeh. Deep Dissimilarity Measure for Trajectory Analysis. Communications in Computer and Information Science 2018, 129 -139.

AMA Style

Reza Arfa, Rubiyah Yusof, Parvaneh Shabanzadeh. Deep Dissimilarity Measure for Trajectory Analysis. Communications in Computer and Information Science. 2018; ():129-139.

Chicago/Turabian Style

Reza Arfa; Rubiyah Yusof; Parvaneh Shabanzadeh. 2018. "Deep Dissimilarity Measure for Trajectory Analysis." Communications in Computer and Information Science , no. : 129-139.

Journal article
Published: 15 July 2016 in Journal of Macromolecular Science, Part A
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ACS Style

Mohamed Mahmoud Nasef; Seyedeh Sara Alinezhad; Ramli Mat; Parvaneh Shabanzadeh; Rubiyah Yusof; Masoumeh Zakeri; Hamdy Farag. Preparation of alkaline polymer catalyst by radiation induced grafting for transesterification of triacetin under neural network optimized conditions. Journal of Macromolecular Science, Part A 2016, 53, 557 -565.

AMA Style

Mohamed Mahmoud Nasef, Seyedeh Sara Alinezhad, Ramli Mat, Parvaneh Shabanzadeh, Rubiyah Yusof, Masoumeh Zakeri, Hamdy Farag. Preparation of alkaline polymer catalyst by radiation induced grafting for transesterification of triacetin under neural network optimized conditions. Journal of Macromolecular Science, Part A. 2016; 53 (9):557-565.

Chicago/Turabian Style

Mohamed Mahmoud Nasef; Seyedeh Sara Alinezhad; Ramli Mat; Parvaneh Shabanzadeh; Rubiyah Yusof; Masoumeh Zakeri; Hamdy Farag. 2016. "Preparation of alkaline polymer catalyst by radiation induced grafting for transesterification of triacetin under neural network optimized conditions." Journal of Macromolecular Science, Part A 53, no. 9: 557-565.

Research article
Published: 30 December 2015 in Mathematical Problems in Engineering
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The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.1. IntroductionAn efficient algorithm to determine the best network among the available ones is significant for wireless networks. The merits of each available network should be realized to discover the best network. Several multicriteria schemes based on artificial intelligence methods such as fuzzy logic, neural networks, and genetic algorithms by [1, 2] have weaknesses on scalability and modularity issues. They simply cannot manage based on the high numbers of radio access technologies (RATs) and the criteria of the heterogeneous wireless networks (HWN). These algorithms have several weaknesses in terms of scalability and complexity because they enter all inputs from the different RATs to one fuzzy logic block simultaneously rather than an exponential increase based on the number of inference rules.The field of future wireless networks is one of the most attractive areas among researchers [3–6]. The proposed algorithms in this area of research are classified into different groups based on the analyses, studies, and tutorials found in the related literature [3–6]. These algorithms are classified into different groups based on the expended decision technique. Reference [7] proposed a novel method for RAT selection, namely, the hopfield neural network RAT selection mechanism (HRM), that utilizes the hopfield neural networks as a strong decision-making tool. A new approach using information about data rate, monetary cost, and received signal strength as different parameters to make a handover decision has been reported by [8, 9]. The main weaknesses are related to the computation of the error function and the Jacobian inversion for acquiring a matrix in which the sizes are equal to the whole of all the weights in the neural network (NN). Therefore, the necessity for memory is very high. Existing algorithms [8, 9] consider the service fee, the received signal strength information (RSSI), user preference, and so forth. The proposed algorithm in comparison with the traditional RSSI-based algorithm enhances the outcomes mainly for both the user and the network as a result of the offered fuzzy based handover systems. In terms of hybrid categories, [10] proposed a PSO-FNN-based vertical handover decision scheme that could make an intelligent handover decision based on the analysis of the network’s position. The authors of [11] mainly dealt with a novel vertical handover decision algorithm built on fuzzy logic with the assistance of the Grey theory and the dynamic weights adaptation. A neurofuzzy multiparameter-based vertical handover decision algorithm (VHDA) was proposed by [12]. The results of the performance evaluation carried out by the handover quality indicator (used to quantify QoS), which is related to the “Ping-Pong” effect, ESA, and throughput, proved that the proposed VHDA offered better QoS than the existing vertical handover methods. Pahlavan et al. [13] method is a good representation of applying a fuzzy logic-based normalized quantitative decision algorithm and a differential prediction algorithm with a high level of accuracy.The vertical handover schemes stated above have their own benefits, but they do not consider the complexity of the network selection, and the allowance of lower computation cost function is unreasonable. Clearly, the decision process should focus on a steadfast, intelligent algorithm to execute an accurate decision and to shift to the best network candidate quickly. The goal of this study is to propose a novel network selection optimization algorithm that takes advantage of the prediction model using the CF-PSO to meet the requirements stated above. The usage of the CF-PSO algorithm for prediction with the PSO algorithm for selection has three purposes: (1) to serve as a validation algorithm for the outcome of the MOPSO, (2) to decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect, and (3) to select the best candidate access point among various access technologies. This paper provides a comparison based on two prediction methods, namely, the CF-PSO and the RBF neural network, to predict the RSSI. More importantly, this comparison evaluates the two methods from different aspects, for example, time, coefficient determination, and mean squared error. The results show that the CF-PSO has better performance, which will be presented in-depth in the following. The proposed method is a step towards future computer-based optimization methods where huge uncertainties by the optimization algorithm must be avoided. To do this, the prediction algorithm is combined with the particle swarm optimization. The proposed MOPSO-based vertical handover decision algorithm can make an intelligent handover decision based on the network position. The proposed network selection model is presented in Figure 1.Figure 1: Proposed network selection model based on the Multiobjective Particle Swarm Optimization (MOPSO) in a wireless heterogeneous environment.The outline of this paper is as follows. First, the related works are described in Section 2. In Section 3, the proposed prediction methods based on CF-PSO and RBF network are introduced. Then, the results of comparison based on the proposed models are illustrated. Section 4 illustrates the MOPSO-based vertical handover decision algorithm. Section 5 analyzes the performance of the algorithm through the simulation results. Finally, Section 6 concludes the paper.2. Related WorkMany network selection algorithms have been proposed in literature, in which PSO has been used for handover decisions. It is initialized with a set of random particles (solutions) that finds an optimal result by updating the generations. The optimum results are called particles, which fly throughout the problem space by following the current optimal particles. In addition, a GSM-like [14] hard handover algorithm has been proposed where countermeasures prevent the Ping-Pong effect by providing a baseline handover threshold against power fluctuations due to channel variations. The authors defined four rules where two rules were for preventing unnecessary handovers (Ping-Pong effects) and one rule was aimed at helping the loaded eNB j by delaying handovers from eNB i. Moreover, the other rule was aimed at alleviating the loaded eNB i by advancing handovers towards eNB j. The PSO algorithm denoted that the neighbor with which the eNB has the largest handover traffic exchange was the best neighbor. Prior knowledge is required on the optimization process that provided the parameterized form of the controller, which was optimized by the MOPSO. It has been shown that the dynamic optimization outperformed the static optimization and produced a better mobility load balancing self-organizing network controller, which improved the throughput and access probability by a few percents with respect to the planning solution.Wang et al. [15] proposed an always best connected (ABC) maintained QoS handover decision scheme based on the niche PSO algorithm. This literature reported the access network in terms of its current load and considered the terminal in terms of its current velocity and residual electric capacity. The authors also studied the application of QoS requirement, preference of the user over the access network coding system, and preference of the user over the access network provider, and so forth.In addition, Venkatachalaiah et al. [16] presented a technique for forecasting the signal strength value that aids in offering efficient handovers in wireless networks and the PSO was expended to fine-tune the weighting function of the handover decision. The proposed technique minimized the number of handovers and was shown to have a very short calculation time and better prediction accuracy compared to hysteresis based decisions. In this paper, they described the use of the Grey model in combination with the fuzzy logic and PSO algorithms. Since prediction error is inevitable, the output from the Grey model could be compensated with the use of a fuzzy controller and then fine-tuned using PSO algorithms.The method by Liu and Jiang [11] is a good symbolic example of using a fuzzy logic-based normalized quantitative decision algorithm and a differential prediction algorithm with a high level of accuracy. This scheme tried to control handovers between WLANs and UMTS. A predecision section was used in this algorithm. The forward differential prediction algorithm was used to acquire the predictive RSS, which could trigger a handover in advance. Moreover, the predecision method was applied before the handover decision module, which is able

ACS Style

Shidrokh Goudarzi; Wan Haslina Hassan; Mohammad Hossein Anisi; Seyed Ahmad Soleymani; Parvaneh Shabanzadeh. A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks. Mathematical Problems in Engineering 2015, 2015, 1 -16.

AMA Style

Shidrokh Goudarzi, Wan Haslina Hassan, Mohammad Hossein Anisi, Seyed Ahmad Soleymani, Parvaneh Shabanzadeh. A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks. Mathematical Problems in Engineering. 2015; 2015 ():1-16.

Chicago/Turabian Style

Shidrokh Goudarzi; Wan Haslina Hassan; Mohammad Hossein Anisi; Seyed Ahmad Soleymani; Parvaneh Shabanzadeh. 2015. "A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks." Mathematical Problems in Engineering 2015, no. : 1-16.

Journal article
Published: 07 October 2015 in RSC Advances
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In this study silver nanoparticles (Ag-NPs) are biosynthesized from silver nitrate aqueous solution through a simple and eco-friendly route using water extract ofVitex negundoL. (V. negundo) which acted as a reductant and stabilizer simultaneously.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli. Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network. RSC Advances 2015, 5, 87277 -87285.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof, Kamyar Shameli. Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network. RSC Advances. 2015; 5 (106):87277-87285.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli. 2015. "Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network." RSC Advances 5, no. 106: 87277-87285.

Journal article
Published: 09 August 2015 in Research on Chemical Intermediates
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This research was to apply the combination of the particle swarm optimization method and artificial neural network training with the aim of building a quantitative model to forecast the size of copper nanoparticles (Cu-NPs) prepared in sodium alginate. Sodium alginate, sodium hydroxide, copper sulfate, hydrazinium hydroxide, and ascorbic acid were used as stabilizer, pH moderator, copper precursor, reducing agent, and antioxidant, respectively. The results showed that the different sizes of Cu-NPs were obtained by changing these functions. Meaning that by increasing the amount of sodium alginate and or increase the volume of hydrazine hydrate, particle sizes of Cu-NPs were reduced. Other variables had the opposite effects due to the increase of the size of the Cu-NPs. The prediction results were remarkably in agreement with the experimental data with a correlation coefficient of 0.99 and a mean square error of 0.0058.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli; Hajar Khanehzaei. Simulation and modeling of synthesis Cu nanoparticles in sodium alginate media by means of expert systems. Research on Chemical Intermediates 2015, 42, 2831 -2843.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof, Kamyar Shameli, Hajar Khanehzaei. Simulation and modeling of synthesis Cu nanoparticles in sodium alginate media by means of expert systems. Research on Chemical Intermediates. 2015; 42 (4):2831-2843.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli; Hajar Khanehzaei. 2015. "Simulation and modeling of synthesis Cu nanoparticles in sodium alginate media by means of expert systems." Research on Chemical Intermediates 42, no. 4: 2831-2843.

Evaluation study
Published: 29 July 2015 in Computational and Mathematical Methods in Medicine
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Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. Computational and Mathematical Methods in Medicine 2015, 2015, 1 -9.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. Computational and Mathematical Methods in Medicine. 2015; 2015 ():1-9.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof. 2015. "An Efficient Optimization Method for Solving Unsupervised Data Classification Problems." Computational and Mathematical Methods in Medicine 2015, no. : 1-9.

Conference paper
Published: 01 May 2015 in 2015 10th Asian Control Conference (ASCC)
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Unsupervised classification allows us to divide the dataset into several groups without knowing how the records should relate to each other. It is one of an interesting data mining topics that can be applied in many fields. A new method for solving this optimization problem is utilized. The method is based on the so-called Mesh Adaptive Direct Search method. This method does not explicitly use derivatives, and is particularly appropriate when functions are non-smooth, an important feature that has not been addressed in previous clustering studies. Results of computational experiments on real data sets present the robustness and advantage of the new method.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof. Solving unsupervised classification problems by new method. 2015 10th Asian Control Conference (ASCC) 2015, 1 -5.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof. Solving unsupervised classification problems by new method. 2015 10th Asian Control Conference (ASCC). 2015; ():1-5.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof. 2015. "Solving unsupervised classification problems by new method." 2015 10th Asian Control Conference (ASCC) , no. : 1-5.

Journal article
Published: 01 April 2015 in Journal of Industrial and Engineering Chemistry
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ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli. Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites. Journal of Industrial and Engineering Chemistry 2015, 24, 42 -50.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof, Kamyar Shameli. Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites. Journal of Industrial and Engineering Chemistry. 2015; 24 ():42-50.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof; Kamyar Shameli. 2015. "Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites." Journal of Industrial and Engineering Chemistry 24, no. : 42-50.

Research article
Published: 27 November 2014 in Abstract and Applied Analysis
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Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

ACS Style

Parvaneh Shabanzadeh; Rubiyah Yusof. A New Method for Solving Supervised Data Classification Problems. Abstract and Applied Analysis 2014, 2014, 1 -9.

AMA Style

Parvaneh Shabanzadeh, Rubiyah Yusof. A New Method for Solving Supervised Data Classification Problems. Abstract and Applied Analysis. 2014; 2014 ():1-9.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Rubiyah Yusof. 2014. "A New Method for Solving Supervised Data Classification Problems." Abstract and Applied Analysis 2014, no. : 1-9.

Journal article
Published: 22 October 2013 in Research on Chemical Intermediates
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Artificial neural networks (ANNs) are computational tools that have found comprehensive utilization in solving many complex real world problems. Major benefits in using ANNs are their remarkable information-processing characteristics pertinent mainly to high parallelism, nonlinearity, fault and noise tolerance, and learning and generalization capabilities. An ANN approach is used to model the size of silver nanoparticles (Ag-NPs) in montmorillonite/chitosan bionanocomposites layers as a function of the silver nitrate concentration, reaction of temperature, chitosan percentage, and d-spacing of clay layers. The best ANN model is found and this final model is capable of predicting the size of nanosilver for a wide range of conditions with a mean absolute error of less than 0.004 and a regression error of about 1. Results obtained showed good ability predictive of neural network model for the prediction of the size of Ag-NPs in chemical reduction methods.

ACS Style

Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Fudziah Ismail; Ali Zamanian; Maryam Mohagheghtabar. Prediction of silver nanoparticles’ diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks. Research on Chemical Intermediates 2013, 41, 3275 -3287.

AMA Style

Parvaneh Shabanzadeh, Norazak Senu, Kamyar Shameli, Fudziah Ismail, Ali Zamanian, Maryam Mohagheghtabar. Prediction of silver nanoparticles’ diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks. Research on Chemical Intermediates. 2013; 41 (5):3275-3287.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Fudziah Ismail; Ali Zamanian; Maryam Mohagheghtabar. 2013. "Prediction of silver nanoparticles’ diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks." Research on Chemical Intermediates 41, no. 5: 3275-3287.

Journal article
Published: 23 April 2013 in Research on Chemical Intermediates
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Silver nanoparticles (Ag NPs) have been synthesized by using a chemical reducing method in the external space of talc layers as a solid support at room temperature. NaBH4 and AgNO3 were used as a reducing agent and silver precursor, respectively. The interlamellar space limits were without many changes and, therefore, Ag NPs formed on the exterior surface of talc composites with mean diameters 7.60–13.11 nm for different silver nitrate concentrations. The antibacterial effects of different sizes of Ag NPs in talc were investigated against Gram-positive (i.e. Staphylococcus aureus and methicillin-resistant S. aureus) and Gram-negative (i.e. Escherichia coli) bacteria by the disk diffusion method using Mueller–Hinton Agar. The AgNO3 was found to have significant antibacterial activity against Ag NPs that did not indicate any antibacterial effects. These results showed that Ag NPs entrapped in the surface of talc layers cannot be used as effective growth inhibitors in different biological systems.

ACS Style

Kamyar Shameli; Mansor Bin Ahmad; Emad Al-Mulla; Parvaneh Shabanzadeh; Samira Bagheri. Antibacterial effect of silver nanoparticles on talc composites. Research on Chemical Intermediates 2013, 41, 251 -263.

AMA Style

Kamyar Shameli, Mansor Bin Ahmad, Emad Al-Mulla, Parvaneh Shabanzadeh, Samira Bagheri. Antibacterial effect of silver nanoparticles on talc composites. Research on Chemical Intermediates. 2013; 41 (1):251-263.

Chicago/Turabian Style

Kamyar Shameli; Mansor Bin Ahmad; Emad Al-Mulla; Parvaneh Shabanzadeh; Samira Bagheri. 2013. "Antibacterial effect of silver nanoparticles on talc composites." Research on Chemical Intermediates 41, no. 1: 251-263.

Research article
Published: 23 April 2013 in Journal of Chemistry
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Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

ACS Style

Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Maryam Mohaghegh Tabar. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space. Journal of Chemistry 2013, 2013, 1 -8.

AMA Style

Parvaneh Shabanzadeh, Norazak Senu, Kamyar Shameli, Maryam Mohaghegh Tabar. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space. Journal of Chemistry. 2013; 2013 (6):1-8.

Chicago/Turabian Style

Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Maryam Mohaghegh Tabar. 2013. "Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space." Journal of Chemistry 2013, no. 6: 1-8.

Journal article
Published: 23 January 2013 in Research on Chemical Intermediates
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Biosynthesis of noble metal nanoparticles is a vast developing area of research. In the present study, silver nanoparticles (Ag-NPs) were synthesized from aqueous silver nitrate through a simple and biosynthetic route using water extract of Curcuma longa (C. longa) tuber powder, which acted simultaneousl as a reductant and stabilizery. The as-prepared samples are characterized using UV–Visible, XRD, TEM, SEM, EDXF, and FT-IR techniques. The formation of Ag-NPs is evidenced by the appearance of the signatory brown color of the solution and UV–vis spectra. Formation of Ag/C. longa was determined by UV–Vis spectroscopy where surface plasmon absorption maxima can be observed at 457–415 nm from the UV–Vis spectrum. The XRD analysis shows that the Ag-NPs are of a face-centered cubic structure. Well-dispersed Ag-NPs with anisotropic and isotropic morphology for 5, 10, and 20 mL of C. longa water extract having a size less than 10 nm are seen in TEM images. The optimum volume extraction to synthesize smallest particle size was 20 mL with mean diameter and standard division 4.90 ± 1.42 nm. FT-IR spectrum indicates the presence of different functional groups in capping the nanoparticles with C. longa. The zeta potential analysis results indicated that the charge of C. longa was negative and increased in Ag/C. longa emulsion with increasing of volumes of extract used (10–20 mL). The most needed outcome of this work will be the development of value-added products from C. longa for biomedical and nanotechnology-based industries.

ACS Style

Kamyar Shameli; Mansor Bin Ahmad; Parvaneh Shabanzadeh; Emad A. Jaffar Al-Mulla; Ali Zamanian; Yadollah Abdollahi; Seyed Davoud Jazayeri; Mahboobeh Eili; Farid Azizi Jalilian; Rafiuz Zaman Haroun. Effect of Curcuma longa tuber powder extract on size of silver nanoparticles prepared by green method. Research on Chemical Intermediates 2013, 40, 1313 -1325.

AMA Style

Kamyar Shameli, Mansor Bin Ahmad, Parvaneh Shabanzadeh, Emad A. Jaffar Al-Mulla, Ali Zamanian, Yadollah Abdollahi, Seyed Davoud Jazayeri, Mahboobeh Eili, Farid Azizi Jalilian, Rafiuz Zaman Haroun. Effect of Curcuma longa tuber powder extract on size of silver nanoparticles prepared by green method. Research on Chemical Intermediates. 2013; 40 (3):1313-1325.

Chicago/Turabian Style

Kamyar Shameli; Mansor Bin Ahmad; Parvaneh Shabanzadeh; Emad A. Jaffar Al-Mulla; Ali Zamanian; Yadollah Abdollahi; Seyed Davoud Jazayeri; Mahboobeh Eili; Farid Azizi Jalilian; Rafiuz Zaman Haroun. 2013. "Effect of Curcuma longa tuber powder extract on size of silver nanoparticles prepared by green method." Research on Chemical Intermediates 40, no. 3: 1313-1325.

Journal article
Published: 01 October 2012 in International Journal of Nanomedicine
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Peer reviewed article authored by (Shameli K, Ahmad MB, Zamanian A, Sangpour P, Shabanzadeh P, Abdollahi Y, Zargar M). Read article or submit your manuscript for publishing.

ACS Style

Kamyar Shameli; Mansor Ahmad; Parvaneh Shabanzadeh; Ali Zamanian; Parvaneh Sangpour; Yadollah Abdollahi; Zargar Mohsen. Green biosynthesis of silver nanoparticles using Curcuma longa tuber powder. International Journal of Nanomedicine 2012, 7, 5603 -10.

AMA Style

Kamyar Shameli, Mansor Ahmad, Parvaneh Shabanzadeh, Ali Zamanian, Parvaneh Sangpour, Yadollah Abdollahi, Zargar Mohsen. Green biosynthesis of silver nanoparticles using Curcuma longa tuber powder. International Journal of Nanomedicine. 2012; 7 ():5603-10.

Chicago/Turabian Style

Kamyar Shameli; Mansor Ahmad; Parvaneh Shabanzadeh; Ali Zamanian; Parvaneh Sangpour; Yadollah Abdollahi; Zargar Mohsen. 2012. "Green biosynthesis of silver nanoparticles using Curcuma longa tuber powder." International Journal of Nanomedicine 7, no. : 5603-10.

Journal article
Published: 27 July 2012 in Chemistry Central Journal
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This study aims to investigate the influence of different stirring times on antibacterial activity of silver nanoparticles in polyethylene glycol (PEG) suspension. The silver nanoparticles (Ag-NPs) were prepared by green synthesis method using green agents, polyethylene glycol (PEG) under moderate temperature at different stirring times. Silver nitrate (AgNO3) was taken as the metal precursor while PEG was used as the solid support and polymeric stabilizer. The antibacterial activity of different sizes of nanosilver was investigated against Gram-positive [Staphylococcus aureus] and Gram-negative bacteria [Salmonella typhimurium SL1344] by the disk diffusion method using Müeller-Hinton Agar. Formation of Ag-NPs was determined by UV-vis spectroscopy where surface plasmon absorption maxima can be observed at 412-437 nm from the UV-vis spectrum. The synthesized nanoparticles were also characterized by X-ray diffraction (XRD). The peaks in the XRD pattern confirmed that the Ag-NPs possessed a face-centered cubic and peaks of contaminated crystalline phases were unable to be located. Transmission electron microscopy (TEM) revealed that Ag-NPs synthesized were in spherical shape. The optimum stirring time to synthesize smallest particle size was 6 hours with mean diameter of 11.23 nm. Zeta potential results indicate that the stability of the Ag-NPs is increases at the 6 h stirring time of reaction. The Fourier transform infrared (FT-IR) spectrum suggested the complexation present between PEG and Ag-NPs. The Ag-NPs in PEG were effective against all bacteria tested. Higher antibacterial activity was observed for Ag-NPs with smaller size. These suggest that Ag-NPs can be employed as an effective bacteria inhibitor and can be applied in medical field. Ag-NPs were successfully synthesized in PEG suspension under moderate temperature at different stirring times. The study clearly showed that the Ag-NPs with different stirring times exhibit inhibition towards the tested gram-positive and gram-negative bacteria.

ACS Style

Kamyar Shameli; Mansor Bin Ahmad; Seyed Davoud Jazayeri; Parvaneh Shabanzadeh; Parvanh Sangpour; Hossein Jahangirian; Yadollah Gharayebi. Investigation of antibacterial properties silver nanoparticles prepared via green method. Chemistry Central Journal 2012, 6, 73 -73.

AMA Style

Kamyar Shameli, Mansor Bin Ahmad, Seyed Davoud Jazayeri, Parvaneh Shabanzadeh, Parvanh Sangpour, Hossein Jahangirian, Yadollah Gharayebi. Investigation of antibacterial properties silver nanoparticles prepared via green method. Chemistry Central Journal. 2012; 6 (1):73-73.

Chicago/Turabian Style

Kamyar Shameli; Mansor Bin Ahmad; Seyed Davoud Jazayeri; Parvaneh Shabanzadeh; Parvanh Sangpour; Hossein Jahangirian; Yadollah Gharayebi. 2012. "Investigation of antibacterial properties silver nanoparticles prepared via green method." Chemistry Central Journal 6, no. 1: 73-73.

Journal article
Published: 16 July 2012 in Molecules
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Different biological methods are gaining recognition for the production of silver nanoparticles (Ag-NPs) due to their multiple applications. The use of plants in the green synthesis of nanoparticles emerges as a cost effective and eco-friendly approach. In this study the green biosynthesis of silver nanoparticles using Callicarpa maingayi stem bark extract has been reported. Characterizations of nanoparticles were done using different methods, which include; ultraviolet-visible spectroscopy (UV-Vis), powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive X-ray fluorescence (EDXF) spectrometry, zeta potential measurements and Fourier transform infrared (FT-IR) spectroscopy. UV-visible spectrum of the aqueous medium containing silver nanoparticles showed absorption peak at around 456 nm. The TEM study showed that mean diameter and standard deviation for the formation of silver nanoparticles were 12.40 ± 3.27 nm. The XRD study showed that the particles are crystalline in nature, with a face centered cubic (fcc) structure. The most needed outcome of this work will be the development of value added products from Callicarpa maingayi for biomedical and nanotechnology based industries.

ACS Style

Kamyar Shameli; Mansor Bin Ahmad; Emad A. Jaffar Al-Mulla; Nor Azowa Ibrahim; Parvaneh Shabanzadeh; Abdolhossein Rustaiyan; Yadollah Abdollahi; Samira Bagheri; Sanaz Abdolmohammadi; Muhammad Sani Usman; Mohammed Zidan. Green Biosynthesis of Silver Nanoparticles Using Callicarpa maingayi Stem Bark Extraction. Molecules 2012, 17, 8506 -8517.

AMA Style

Kamyar Shameli, Mansor Bin Ahmad, Emad A. Jaffar Al-Mulla, Nor Azowa Ibrahim, Parvaneh Shabanzadeh, Abdolhossein Rustaiyan, Yadollah Abdollahi, Samira Bagheri, Sanaz Abdolmohammadi, Muhammad Sani Usman, Mohammed Zidan. Green Biosynthesis of Silver Nanoparticles Using Callicarpa maingayi Stem Bark Extraction. Molecules. 2012; 17 (7):8506-8517.

Chicago/Turabian Style

Kamyar Shameli; Mansor Bin Ahmad; Emad A. Jaffar Al-Mulla; Nor Azowa Ibrahim; Parvaneh Shabanzadeh; Abdolhossein Rustaiyan; Yadollah Abdollahi; Samira Bagheri; Sanaz Abdolmohammadi; Muhammad Sani Usman; Mohammed Zidan. 2012. "Green Biosynthesis of Silver Nanoparticles Using Callicarpa maingayi Stem Bark Extraction." Molecules 17, no. 7: 8506-8517.

Journal article
Published: 09 July 2012 in International Journal of Molecular Sciences
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The sol-gel method was carried out to synthesize nanosized Yttrium Iron Garnet (YIG). The nanomaterials with ferrite structure were heat-treated at different temperatures from 500 to 1000 °C. The phase identification, morphology and functional groups of the prepared samples were characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), respectively. The YIG ferrite nanopowder was composited with polyvinylidene fluoride (PVDF) by a solution casting method. The magnitudes of reflection and transmission coefficients of PVDF/YIG containing 6, 10 and 13% YIG, respectively, were measured using rectangular waveguide in conjunction with a microwave vector network analyzer (VNA) in X-band frequencies. The results indicate that the presence of YIG in polymer composites causes an increase in reflection coefficient and decrease in transmission coefficient of the polymer.

ACS Style

Hassan Soleimani; Zulkifly Abbas; Noorhana Yahya; Kamyar Shameli; Hojjatollah Soleimani; Parvaneh Shabanzadeh. Reflection and Transmission Coefficient of Yttrium Iron Garnet Filled Polyvinylidene Fluoride Composite Using Rectangular Waveguide at Microwave Frequencies. International Journal of Molecular Sciences 2012, 13, 8540 -8548.

AMA Style

Hassan Soleimani, Zulkifly Abbas, Noorhana Yahya, Kamyar Shameli, Hojjatollah Soleimani, Parvaneh Shabanzadeh. Reflection and Transmission Coefficient of Yttrium Iron Garnet Filled Polyvinylidene Fluoride Composite Using Rectangular Waveguide at Microwave Frequencies. International Journal of Molecular Sciences. 2012; 13 (7):8540-8548.

Chicago/Turabian Style

Hassan Soleimani; Zulkifly Abbas; Noorhana Yahya; Kamyar Shameli; Hojjatollah Soleimani; Parvaneh Shabanzadeh. 2012. "Reflection and Transmission Coefficient of Yttrium Iron Garnet Filled Polyvinylidene Fluoride Composite Using Rectangular Waveguide at Microwave Frequencies." International Journal of Molecular Sciences 13, no. 7: 8540-8548.

Journal article
Published: 30 May 2012 in International Journal of Molecular Sciences
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The roles of green chemistry in nanotechnology and nanoscience fields are very significant in the synthesis of diverse nanomaterials. Herein, we report a green chemistry method for synthesized colloidal silver nanoparticles (Ag NPs) in polymeric media. The colloidal Ag NPs were synthesized in an aqueous solution using silver nitrate, polyethylene glycol (PEG), and β-d-glucose as a silver precursor, stabilizer, and reducing agent, respectively. The properties of synthesized colloidal Ag NPs were studied at different reaction times. The ultraviolet-visible spectra were in excellent agreement with the obtained nanostructure studies performed by transmission electron microscopy (TEM) and their size distributions. The Ag NPs were characterized by utilizing X-ray diffraction (XRD), zeta potential measurements and Fourier transform infrared (FT-IR). The use of green chemistry reagents, such as glucose, provides green and economic features to this work.

ACS Style

Kamyar Shameli; Mansor Bin Ahmad; Seyed Davoud Jazayeri; Sajjad Sedaghat; Parvaneh Shabanzadeh; Hossein Jahangirian; Mahnaz Mahdavi; Yadollah Abdollahi. Synthesis and Characterization of Polyethylene Glycol Mediated Silver Nanoparticles by the Green Method. International Journal of Molecular Sciences 2012, 13, 6639 -6650.

AMA Style

Kamyar Shameli, Mansor Bin Ahmad, Seyed Davoud Jazayeri, Sajjad Sedaghat, Parvaneh Shabanzadeh, Hossein Jahangirian, Mahnaz Mahdavi, Yadollah Abdollahi. Synthesis and Characterization of Polyethylene Glycol Mediated Silver Nanoparticles by the Green Method. International Journal of Molecular Sciences. 2012; 13 (6):6639-6650.

Chicago/Turabian Style

Kamyar Shameli; Mansor Bin Ahmad; Seyed Davoud Jazayeri; Sajjad Sedaghat; Parvaneh Shabanzadeh; Hossein Jahangirian; Mahnaz Mahdavi; Yadollah Abdollahi. 2012. "Synthesis and Characterization of Polyethylene Glycol Mediated Silver Nanoparticles by the Green Method." International Journal of Molecular Sciences 13, no. 6: 6639-6650.