Modeling batch annealing process using data mining techniques for cold rolled steel sheets

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained

Latent dirichlet markov allocation for sentiment analysis

In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model

FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

STRUCTURAL STUDY AND INVESTIGATION OF NMR TENSORS IN INTERACTION OF DOPAMINE WITH ADENINE AND GUANINE

The interaction of dopamine with adenine and guanine were studied at the Hartree-Fock level theory. The structural and vibrational properties of dopamine-4-N7GUA and dopamine-4-N3ADE were studied at level of HF/6-31G*. Interaction energies (ΔE) were calculated to be -11.49 and -11.92 kcal/mol, respectively. Some of bond lengths, angels and tortions are compared. NBO studies were performed to the second-order and perturbative estimates of donor-acceptor interaction have been done. The procedures of gauge-invariant atomic orbital (GIAO) and continuous-set-of-gauge-transformation (CSGT) were employed to calculate isotropic shielding, chemical shifts anisotropy and chemical shifts anisotropy asymmetry and effective anisotropy using 6-31G* basis set. These calculations yielded molecular geometries in good agreement with available experimental data. KEY WORDS: Ab initio, Dopamine, GIAO, CSGT, DNA, Hartree Fock Bull. Chem. Soc. Ethiop. 2007, 21(3), 427-435

Feature selection methods in Persian sentiment analysis

With the enormous growth of digital content in internet, various types of online reviews such as product and movie reviews present a wealth of subjective information that can be very helpful for potential users. Sentiment analysis aims to use automated tools to detect subjective information from reviews. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on stemming and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a collection of cellphone reviews, where the results show the effectiveness of the proposed approache

Stigma variability in saffron (Crocus sativus L.)

The obstacle to improving Crocus sativus is its sterility caused by being triploid. Thus, the discovery of the new variant of saffron with increased number of stigmas was welcomed as a reason for improvingits yield. The study of development and the process of budding of the corm of saffron showed that these flowers occur by fusion of two or more flowering buds on one corm. Cytological and morphological studies showed that this characteristic is unstable and is not genetically controlled

Considering DG in Expansion Planning of Subtransmission System

Deregulation has been obtained new options in the design and planning of the power system. One of these options is the integration of Distributed Generation (DG) into the power system. In this paper, the presence of distributed generation is regarded as another alternative for supplying the load of subtransmission system. The effects of DG on expansion planning of subtransmission system have been modeled as an  optimization problem where the Genetic Algorithm (GA) and Linear Programming (LP) are employed to solve it. The proposed approach is applied to a realistic subtransmission system and the results are evaluated

Free convection of hybrid nanofluids in a C-shaped chamber under variable heat flux and magnetic field: simulation, sensitivity analysis, and artificial neural networks

In the present investigation, the free convection energFree convection of hybrid nanofluids in a C-shaped chamber under variable heat flux and magnetic field: simulation, sensitivity analysis, and artificial neural networksy transport was studied in a C-shaped tilted chamber with the inclination angle α that was filled with the MWCNT (MultiWall Carbon Nanotubes)-Fe3O4-H2O hybrid nanofluid and it is affected by the magnetic field and thermal flux. The control equations were numerically resolved by the finite element method (FEM). Then, using the artificial neural network (ANN) combined with the particles swarm optimization algorithm (PSO), the Nusselt number was predicted, followed by investigating the effect of parameters including the Rayleigh number (Ra), the Hartmann number (Ha), the nanoparticles concentration (ϕ), the inclination angle of the chamber (α), and the aspect ratio (AR) on the heat transfer rate. The results showed the high accuracy of the ANN optimized by the PSO algorithm in the prediction of the Nusselt number such that the mean squared error in the ANN model is 0.35, while in the ANN model, it was optimized using the PSO algorithm (ANN-PSO) is 0.22, suggesting the higher accuracy of the latter. It was also found that, among the studied parameters with an effect on the heat transfer rate, the Rayleigh number and aspect ratio have the greatest impact on the thermal transmission intensification. The obtained data also showed that a growth of the Hartmann number illustrates a reduction of the Nusselt number for high Rayleigh numbers and the heat transfer rate is almost constant for low Rayleigh number values