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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Khamforoush, M. Rahi, M. -J. Hatami, T. Rahimzadeh, K. Univ Kurdistan Fac Engn Dept Chem Engn Sanandaj Iran Natl Iranian Oil Prod Distribut Co Kurdistan Branch Sanandaj Iran

ISBN: (纸本)9781457707391

In this study two models for remediation of diesel contaminated soil by composting process were used: mathematical modeling and artificial neural network (ANN) modeling. The mathematical model was solved iteratively and validated with experimental data. Then, a three-layer back propagation ANN was trained, tested and validated to predict the decomposition of diesel in contaminated soil according to 3600 data sets which were obtained from mathematical model. The Best neural network result has been obtained with one hidden layer network, with 14 neurons. "tansig" for hidden layers and "purelin" for the output layer gave the best performance compared to other activation functions. The ANN architecture contains six inputs. Diesel decomposition percent is the only output of ANN. ANN predicted results are very close to the target data. The high correlation coefficient, 0.9995, between the network prediction and the corresponding data proves that ANN modeling is a satisfactory method for remediation process.

关键词： artificial neural network modeling mathematical modeling diesel contaminated soil remediation composting process

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Relationship between intraocular pressure lowering effect and chemical structure of imidazo[1,2-a]benzimidazole and pyrimido [1,2-a]benzimidazole derivatives

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DATA IN BRIEF 2018年 18卷 340-347页

作者： Vassiliev, Pavel Iezhitsa, Igor Agarwal, Renu Marcus, Adrian Julian Spasov, Alexander Zhukovskaya, Olga Anisimova, Vera Volgograd State Med Univ Res Inst Pharmacol Volgograd Russia Univ Teknol MARA Fac Med Ctr Neurosci Res Shah Alam Malaysia Southern Fed Univ Res Inst Phys & Organ Chem Rostov Na Donu Russia

This article contains data that relate to the study carried out in the work of Marcus et al. (2018) [1]. Data represent an information about pharmacophore analysis of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives and results of construction of the relationship between intraocular pressure (IOP) lowering activity and hypotensive activity of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives using a multilayer perceptron artificial neural network. In particular, they include the ones listed in this article: 1) table of all pharmacophores of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole derivatives that showed LOP lowering activity;2) table of all pharmacophores of the compounds that showed absence of LOP lowering activity;3) table of initial data for artificial neural network analysis of relationship between LOP activity and hypotensive activity of this chemical series;4) graphical representation of the best neural network model of this dependence;5) original txt-file of results of pharmacophore analysis;6) xls-file of initial data for neural network modeling;7) original stw-file of results of neural network modeling;8) original xml-file of the best neural network model of dependence between LOP lowering activity and hypotensive activity of these azole derivatives. The data may be useful for researchers interested in designing new drug substances and will contribute to understanding of the mechanisms of LOP lowering activity. (C) 2018 The Authors. Published by Elsevier Inc.

关键词： QSAR In silico drug design Intraocular pressure Intraocular pressure lowering activity Hypotensive activity Imidazo[1,2-a]benzimidazoles Pyrimido[1,2-a]benzimidazoles Pharmacophore analysis artificial neural network modeling

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Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2015年第5期52卷 3065-3071页

作者： Soltani, Mahmoud Omid, Mahmoud Alimardani, Reza ETKA Org Ctr Res & Dev Tehran Iran Univ Tehran Fac Agr Engn & Technol Dept Agr Machinery Engn Karaj Iran

Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, artificial neural network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R-2, Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R-2 (test) and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R-2 (test) and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

关键词： Egg Machine vision system Volume prediction Math modeling artificial neural network modeling

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On Analysis and Evaluation of Cocktail Party Effect on Applied Educational Practice Theory Using neural networks modeling

On Analysis and Evaluation of Cocktail Party Effect on Appli...

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The 10th International Conference on Arts, Social Sciences, Humanities and Interdisciplinary Studies (ASSHIS-17);the 11th International Conference on Law, Education, Business and Management (LEBM-17);the 6th International Conference on Advances in Science, Engineering and Technology (ICASET-17);the 9th International Conference on Chemical, Agricultural, Biological and Environmental Sciences (CABES-17);the 7th International Conference on Civil, Architecture, Ecology and Waste Management (CAEWM-17)

作者： Hassan M.H.Mustafa Fadhel Ben Tourkia Comp. Eng. Dept. Al-Baha Private College of Sciences

This piece of research deals with an interestingly observed interdisciplinary challenging educational issue associated with children's learning performance phenomenon in classrooms. more precisely, it addresses an answer for the critically challenging educational question : how any of the students could focus on teachers' interactive speaking in noisy environmentally overcrowd class?. more precisely. More specifically, it deals with the critical educational issue of What is the effect of the school environment on students considering Cocktail Party Effect(CPE) on learning achievement and its relation to the Educational Practice Theory. Moreover, this work presents a systematic approach for evaluation of an interdisciplinary phenomenal problem of human's selectivity auditory scene analysis. Interestingly, in the educational field practice this Cocktail Party Effect results in the Cocktail Party Problem(CPP) dealing with an auditory perception phenomenon. Additionally, it is noticed that introduced proposal for active audition modeling is motivated by analogous active vision processes, such as that observed during Optical Character Recognition(OCR). In nature, observed OCR as well as pattern recognition processes have to be carried out under non ideal environmental learning condition(under effect of noisy data). More particularly, CPP is motivated to be solved by adopting selective auditory attention or equivalently selective hearing which is a type of selective attention and involves the auditory system of the nervous system. Accordingly, children's Selective hearing is characterized as the action in which considerable children's ability to focus their attention inside classroom on a specific source of teacher's spoken wording signals. Commonly, this process experienced as following one speaker(teachers' speech) in the presence of another noisy overcrowded signals resulting in CPE at classroom. By the end of this paper, some interesting simulation results presented a

关键词： artificial neural network modeling noisy crowded learning environment signal to noise ratio cocktail party problem

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On Analysis and Evaluation of Cocktail Party Effect on Applied Educational Practice Theory Using neural networks modeling

On Analysis and Evaluation of Cocktail Party Effect on Appli...

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The 6th International Conference on Advances in Science, Engineering and Technology (ICASET-17)

作者： Hassan M.H.Mustafa Fadhel Ben Tourkia Comp.Eng.Dept. Al-Baha Private College of Sciences

This piece of research deals with an interestingly observed interdisciplinary challenging educational issue associated with children's learning performance phenomenon in *** precisely,it addresses an answer for the critically challenging educational question : how any of the students could focus on teachers' interactive speaking in noisy environmentally overcrowd class?.more *** specifically,it deals with the critical educational issue of What is the effect of the school environment on students considering Cocktail Party Effect(CPE) on learning achievement and its relation to the Educational Practice ***,this work presents a systematic approach for evaluation of an interdisciplinary phenomenal problem of human's selectivity auditory scene ***,in the educational field practice this Cocktail Party Effect results in the Cocktail Party Problem(CPP) dealing with an auditory perception ***,it is noticed that introduced proposal for active audition modeling is motivated by analogous active vision processes,such as that observed during Optical Character Recognition(OCR).In nature,observed OCR as well as pattern recognition processes have to be carried out under non ideal environmental learning condition(under effect of noisy data).More particularly,CPP is motivated to be solved by adopting selective auditory attention or equivalently selective hearing which is a type of selective attention and involves the auditory system of the nervous ***,children's Selective hearing is characterized as the action in which considerable children's ability to focus their attention inside classroom on a specific source of teacher's spoken wording ***,this process experienced as following one speaker(teachers' speech) in the presence of another noisy overcrowded signals resulting in CPE at *** the end of this paper,some interesting simulation results presented after taking into accou

关键词： artificial neural network modeling noisy crowded learning environment signal to noise ratio cocktail party problem

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modeling and simulation of the thermodynamic cycle of the Diesel Engine using neural networks

Modeling and simulation of the thermodynamic cycle of the Di...

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14th IFAC Symposium on Control in Transportation Systems (CTS)

作者： Rida, Ali Nahim, Hassan Moussa Younes, Rafic Shraim, Hassan Ouladsine, Mustapha Lebanease Univ FOE Beyrouth Hadath Lebanon Aix Marseille Univ LSIS Marseille France

In this paper, a unique single zone combustion model is proposed to predict Diesel engines performance, pressure, and temperature based on the conservation of mass and energy. In order to simulate all phases of combustion, the proposed model takes in consideration the dynamics of the intake and exhaust gas through the valves, the ignition delay, the instantaneous change in gas properties, the properties of the burned fuel, and the heat losses by the walls. Validation of this model has been realized by experimental data. Important issue has been recognized that the physical model takes too much time in calculation. For this purpose, a Feed-Forward neural network (FFNN) model is developed and validated experimentally to predict the pressure and temperature in the cylinder in nominal and faulty operations. Finally, the influence of some possible faults that may be produced on the diesel engine cycle during the operation has been analyzed. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Diesel Engine Thermodynamic cycle artificial neural network modeling Faulty operation mode

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Efficient Reconfigurable Microstrip Patch Antenna modeling Exploiting Knowledge Based artificial neural networks 1

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Conference on Simulation-Driven modeling and Optimization

作者： Simsek, Murat Aoad, Ashrf Istanbul Tech Univ Fac Aeronaut & Astronaut TR-80626 Istanbul Turkey

ISBN: (数字)9783319275178

ISBN: (纸本)9783319275178;9783319275154

artificial neural network (ANN) is widely used for modeling and optimization in antenna design problems. It is a very convenient alternative for using computationally intensive 3D-Electromagnetic (EM) simulation in design. The reconfigurable microstrip patch antennas have been considered to ensure operational frequencies for different kind of purposes. ANN is used for modeling of antenna design problems to obtain a surrogate based model instead of a computationally intensive 3D-EM simulation. Further improvement in modeling, a prior knowledge about the problem such as an empirical formula, an equivalent circuit model, and a semi-analytical equation is directly embedded in ANN structure through a knowledge based modeling technique. Knowledge based techniques are developed to improve some properties of conventional ANN modeling such as accuracy and data requirement. All these improvements ensure better accuracy compared to conventional ANN modeling. The necessary knowledge can be obtained by the coarse model which is a complex 3D-EM simulation in terms of grid size selection. Knowledge based techniques can improve the performance of conventional ANN through the guidance of the coarse model. As long as the coarse model approximates to the computationally intensive 3D-EM simulation, the performance of the knowledge based surrogate model can converge to the design targets. The efficiency of modeling strategies is demonstrated by a reconfigurable 5-fingers microstrip patch antenna. The antenna has four modes of operation, which are controlled by two PIN diode switches with ON/OFF states, and it resonates at multiple frequencies between 1 and 7 GHz. The number of training data is changed in terms of selected parameters from the design space. Three different sets are used to show modeling performance according to the size of training data. The simulation results show that knowledge based neural networks ensure considerable savings in computational costs as compared to the c

关键词： artificial neural network modeling Knowledge based modeling Antenna design Reconfigurable microstrip patch antenna

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A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2014年第1期45卷 255-266页

作者： Wang, Y. S. Shen, G. Q. Xing, Y. F. Shanghai Univ Engn Sci Sch Automot Engn Shanghai 201620 Peoples R China

Based on the artificial neural network (ANN) technique, an objective sound quality evaluation (SQE) model for synthesis annoyance of vehicle interior noises is presented in this paper. According to the standard named GB/T18697, firstly, the interior noises under different working conditions of a sample vehicle are measured and saved in a noise database. Some mathematical models for loudness, sharpness and roughness of the measured vehicle noises are established and performed by Matlab programming. Sound qualities of the vehicle interior noises are also estimated by jury tests following the anchored semantic differential (ASD) procedure. Using the objective and subjective evaluation results, furthermore, an ANN-based model for synthetical annoyance evaluation of vehicle noises, so-called ANN-SAE, is developed. Finally, the ANN-SAE model is proved by some verification tests with the leave-one-out algorithm. The results suggest that the proposed ANN-SAE model is accurate and effective and can be directly used to estimate sound quality of the vehicle interior noises, which is very helpful for vehicle acoustical designs and improvements. The ANN-SAE approach may be extended to deal with other sound-related fields for product quality evaluations in SQE engineering. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Sound quality Vehicle interior noise Synthetical annoyance evaluation artificial neural network modeling Jury test

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Performance Measurement in Banking & Software Firm: An Empirical Research

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Global Journal of Flexible Systems Management 2015年第1期16卷 3-18页

作者： Behera, Ajay Kumar Nayak, Narayan C. Das, Harish C. Mechanical Engineering Department SOA University Bhubaneswar India Mechanical Engineering Department IGIT Sarang India

This research develops principles for an evaluation criterion of the performance of service firm. The relationship between Information Technology (IT) adoption and the performance of firms is not well characterized. The aim of this research is to provide an analysis on the relationship between IT adoption with its usage and IT performance in Indian banking and software firms. IT performance was measured with the help of three important variables: efficiency, effectiveness, and productivity. Each one of the above has been described with a set of pretested questionnaires that were designed for the respondents from the selected firm. Empirical data was collected in the year 2013. A scientific sampling scheme was implemented using the probability sampling technique. Perceptions of branch managers, staff members and customers were collected using survey methodology. Hypothesized relationships between IT adoption and IT performance are empirically tested using survey data from service firms. The relationships between the two, as tested through correlation analyses, are found to be dependent on the IT usage and IT literacy level. IT performance was found to have a positive correlation with factors such as efficiency, effectiveness as well as profitability of service firm. Tenure of IT adoption and utility of IT contributes much towards increase in performance of firms. The analysis suggested that IT usage has a positive linear relationship with IT performance. Overall, this research highlights the complexity of the relationship between IT adoption and IT performance. This research calls for caution in defending IT as universally efficient solutions, and argues that their productivity is linked to the complementary introduction of organizational and technological innovations. This study is conducted in a specific sector, so the results cannot be generalized. © 2014, Global Institute of Flexible Systems Management.

关键词： artificial neural network modeling Effectiveness Efficiency IT literacy level IT performance IT usage

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Dynamical Evaluation Of Academic Performance In E-learning Systems Using neural networks modeling (Time Response Approach)

Dynamical Evaluation Of Academic Performance In E-learning S...

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IEEE Global Engineering Education Conference

作者： Mustafa, Hassan M. H. Al-Hamadi, Ayoub Al-Shenawy, Nada M. Dladlu, Nosipho El-Qwasmeh, Eyas Al Baha Univ Fac Engn Dept Comp Engn Al Baha Saudi Arabia Otto Von Guericke Univ Inst Elect Signal Proc & Commun IESK Magdeburg Germany Tanta Univ Fac Engn Comp & Control Dept Tanta Egypt North West Univ Fac Agr Sci & Technol Dept Comp Sci Mmabatho South Africa King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

ISBN: (纸本)9781479931903

This paper explores a relatively new methodological approach for the field integrating learning and education, with other research areas, such as neurobiological, cognitive, and computational sciences. Specifically, presented work is an interdisciplinary piece of research aiming to simulate appropriately a challenging and critical issue concerned with academic performance in e-learning systems. Namely, considering face to face tutoring phenomenon observed while an interactive e-learning process is performed. Referring to strong interest announced by educationalists to know how neurons' synapses inside the brain are interconnected. Together to perform communication processing among brain regions. Herein, a special attention has been developed towards dynamical academic evaluation of timely based brain learning via face to face (FTF) interactive tutoring. In other words, this piece of research presents an interdisciplinary realistic dynamic investigation. For academic performance phenomenon associated with e-learners' contribution as time response performed human's brain neuronal function. Accordingly, artificial neural networks (ANN((s) under bar)) have been adopted for realistic modeling of academic performance evaluation based on timely dependant student's response till attaining learning convergence (desired output). After running of designed realistic simulation program, some interesting results have been presented. Interestingly, individual differences' phenomenon observed via after statistical analysis of obtained simulation results.

关键词： artificial neural network modeling E-Learning Performance Evaluation Synaptic Connectivity Multiple choice questions

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