Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 7 Articles
Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic\nchemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through\ndifferent batches. Our aimis to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity.\nMethods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS\ncompliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial\nactivity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium\nperfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial\nactivities within a 10mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same\ntime.The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and\nthe nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity\nof EOs thus improving their use in CAM....
Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation\nof basic linguistic units or phonemes, constructing words from phonemes and contextually\nanalyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying\nthe possibility of designing a software system using one of the techniques of artificial intelligence\napplications neuron networks where this system is able to distinguish the sound signals and\nneural networks of irregular users. Fixed weights are trained on those forms first and then the\nsystem gives the output match for each of these formats and high speed. The proposed neural\nnetwork study is based on solutions of speech recognition tasks, detecting signals using angular\nmodulation and detection of modulated techniques....
Software effort estimation plays an important role in the software development process: inaccurate estimation leads to poor\nutilization of resources and possibly to software project failure. Many software effort estimation techniques have been tried in\nan effort to develop models that generate optimal estimation accuracy, one of which is machine learning. It is crucial in machine\nlearning to use a model that will maximize accuracy and minimize uncertainty for the purposes of software effort estimation.\nHowever, the process of selecting the best algorithm for estimation is complex and expert-dependent. This paper proposes an\napproach to analyzing datasets, automatically building estimation models with various machine learning techniques, and evaluating\nand comparing their results to find the model that produces the most accurate and surest estimates for a specific dataset.\nThe proposed approach to automated model selection combines the Bayesian information criterion, correlation coefficients, and\nPRED measures....
The purpose of this study is the study and modeling of phenomenon ââ?¬Ë?in teraction between the antenna and the human bodyââ?¬â?¢ by the Artificial Neural Network (ANN).This technique is based on mathematical formulations and a base of learning who took simulations by noted trade CST MS. An example of the interaction between a body, which the dielectric properties are given and a dipole antenna, has been studied. The results validate the new approach. The good agreement between the results of the given simulation published justifies the modeling process and validates the current approach of the analysis....
In this paper, the vehicle faults are estimated and diagnosed by introducing wavelet transform, based on the state-space method.\nIt monitors the state of railway vehicle suspension system, establishes a vertical dynamic state-space model of railway-vehicle\nsystem to identify the parameters of vehicle suspension system. The simulation results show that, in the circumstances of the\nchange of the parameters which is resulted from the gradual fault or the composite fault of the suspension system, the simulation\nresults can effectively identify the basic characteristics of its parameters, realize vehicle fault diagnosis, so as to achieve the\npurpose of monitoring the state of suspension system, and provide a method for rail vehicle online condition monitoring....
Since the early days of the information era, digital music has been becoming one of the most consumed types of media, introducing\nthe need for content-based tools that can search, browse, and retrieve music. Here we describe a method that can\nquantify similarities between musical genres in an unsupervised fashion, and computes networks of similarities between different\nmusicians or musical styles. The method works by converting each song to its 2D spectrogram, and then extracting a large\nset of 2883 2D numerical content descriptors. The descriptors are weighted by their informativeness, and then the similarities\nbetween the musical styles are measured using the weighted distances between the musical pieces of each pair of musicians or\ngenres. The similarities between all pairs provide a similarity matrix, which is visualized by a phylogeny. Experiments using\n23 well known musicians representing seven musical genres show that the algorithm was able to separate the artists into groups\nthat are in agreement with their respective musical genres. The analysis was done in an unsupervised fashion, and without any\nhuman definition or annotation of the musical styles....
A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm\nhas been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for\nnonlinear system identification may lead to building models for the identified plant with inconsistency responses from control\nperspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM\nalgorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system\nidentification aimed at getting better generalization performances by reducing the effect of a random initialization procedure....
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