Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1126
NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents
Статья научная
Mining negations from electronic narrative medical documents is one of the prominent data mining applications. Since medical documents are freely written, it is impossible to consider all possible sentence structures in advance and so frequent update of mining algorithms is inevitable. Unfortunately most of the proposed algorithms in the literature are too complex to be easily updated. Besides, most of them cannot be easily ported to other natural languages. The simple NegEx algorithm utilizes only two regular expressions and sets of terms to mine negations from narrative medical documents and so does not suffer from these shortcomings. Meanwhile, it has shown impressive mining results and so it is the most widely adopted algorithm. This paper proposes the Negation Mining (NegMiner) tool to address some of the shortcomings of the NegEx algorithm. The NegMiner exploits some basic syntactic and semantic information to deal with contiguous and multiple negations. It is a user-friendly tool that facilitates the task of knowledge base update and the task of document analysis through the use of PDF files. This also makes it able to deal with the existence of a medical finding several times in a single sentence. Experimental results have shown the superiority of the mining results of the NegMiner in comparison to the simulated NegEx algorithm.
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Neural Network based Modeling and Simulation of Transformer Inrush Current
Статья научная
Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN) model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM) algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.
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Neural network modeling and correlation analysis of brain plasticity mechanisms in stroke patients
Статья научная
The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.
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Neural-Based Cuckoo Search of Employee Health and Safety (HS)
Статья научная
A study using the cuckoo search algorithm to evaluate the effects of using computer-aided workstations on employee health and safety (HS) is conducted. We collected data for HS risk on employees at their workplaces, analyzed the data and proposed corrective measures applying our methodology. It includes a checklist with nine HS dimensions: work organization, displays, input devices, furniture, work space, environment, software, health hazards and satisfaction. By the checklist, data on HS risk factors are collected. For the calculation of an HS risk index a neural-swarm cuckoo search (NSCS) algorithm has been employed. Based on the HS risk index, IHS four groups of HS risk severity are determined: low, moderate, high and extreme HS risk. By this index HS problems are allocated and corrective measures can be applied. This approach is illustrated and validated by a case study. An important advantage of the approach is its easy use and HS index methodology speedily pointing out individual employee specific HS risk.
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Статья научная
This paper deals with the stabilization of Takagi-Sugeno fuzzy models. Using non-quadratic Lyapunov function, new sufficient stabilization criteria with PDC controller are established in terms of Linear Matrix Inequality. Finally, a stabilization condition for uncertain system is given.
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New Trending Events Detection based on the Multi-Representation Index Tree Clustering
Статья научная
Traditional Clustering is a powerful technique for revealing the hot topics among Web information. However, it failed to discover the trending events coming out gradually. In this paper, we propose a novel method to address this problem which is modeled as detecting the new cluster from time-streaming documents. Our approach concludes three parts: the cluster definition based on Multi-Representation Index Tree (MI-Tree), the new cluster detecting process and the metrics for measuring a new cluster. Compared with the traditional method, we process the newly coming data first and merge the old clustering tree into the new one. Our algorithm can avoid that the documents owning high similarity were assigned to different clusters. We designed and implemented a system for practical application, the experimental results on a variety of domains demonstrate that our algorithm can recognize new valuable cluster during the iteration process, and produce quality clusters.
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New delay-based fast retransmission policy for CMT-SCTP
Статья научная
Concurrent Multipath Transfer (CMT) uses multi-homing feature of Stream Control Transmission Protocol (SCTP) to transfer data concurrently over the multiple paths. CMT provides bandwidth aggregation, fault tolerance, and reliability in multipath data transfer. In multipath data transmission, each path has different delay and bandwidth. Therefore, destination receives unordered data which causes receiver buffer blocking and unwanted congestion window (cwnd) reduction. Both the problem degrades the CMT performance significantly. Thus, this paper proposes a new delay-based fast retransmission policy to adjust the transmission rate of each path according to path delay. Simulation results show that the proposed approach achieves better throughput, reduces the number of the timeout and improves the cwnd growth. The proposed approach improved throughput up to 16% in variable packet loss and 18% in variable network delay environment.
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Non-Functional Requirements Classification Using Machine Learning Algorithms
Статья научная
Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.
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Non-isothermal Flow through a Curved Channel with Strong Curvature
Статья научная
Non-isothermal flow through a curved square channel with strong curvature is investigated numerically by using the spectral method and covering a wide range of the Dean number, Dn, 100≤Dn≤6000 for the curvature δ=0.5. A temperature difference is applied across the vertical sidewalls for the Grashof number , where the outer wall is heated and the inner one cooled. After a compressive survey over the parametric ranges, two branches of asymmetric steady solutions with two- and four-vortex solutions are obtained by the Newton-Raphson iteration method. Then, in order to investigate the non-linear behavior of the unsteady solutions, time evolution calculations as well as power spectrum of the solutions are obtained, and it is found that in the unsteady flow undergoes in the scenario “steady → periodic → multi-periodic → chaotic”, if Dn is increased.
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Non-linear model of the damping process in a system with a two-mass pendulum absorber
Статья научная
In this paper, the dynamic behavior of the damping system is analyzed with a two-mass pendulum absorber, the equations of motion of non-linear mechanical systems are built accordingly. AFC equation systems have been identified in the non-linear formulation. To obtain the frequency response, the Ritz averaging method is used. A new numerical method of determining the parameters of optimal tuning two-mass pendulum absorber in the non-linear formulation has been Proposed and implemented.
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Nonlinear Evaluation of Electroencephalogram Signals in Different Sleep Stages in Apnea Episodes
Статья научная
Distinct sleep phases are related to different dynamical patterns in electroencephalogram (EEG) signals. In this article, the relationship between the sleep stages and nonlinear behavior of sleep EEG is explored. In particular, analysis of approximate entropy (ApEn) and the largest Lyapunov exponent is evaluated in patients with sleep apnea, which is defined as respiratory flow that is suspended or decreased for more than 10 s. The pathological sleep EEG signals for analysis were obtained from the MIT-BIH polysomnography database available online at the PhysioBank. The results show that for the both normal and apneic sleep epochs, ApEn decreased significantly as the sleep goes into deeper stages. Therefore, it indicated that as sleep becomes deeper, the brain function becomes less activated. Compared with normal sleep, the mean value of largest lyapunov exponents was also significantly lower than that of normal epochs during deep sleep stages. The results also show that the average largest lyapunov exponents of EEG signals increased in the REM state. Because during this stage of sleep, the cortex becomes more active and more neurons incorporate in the information processing. In conclusion, the nonlinear dynamical measures obtained from the nonlinear dynamical analysis such as the approximate entropy and largest lyapunov exponents can be useful for characterizing the physiological or pathological states of the brain.
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Nonlinear Fuzzy Model-base Technique to Compensate Highly Nonlinear Continuum Robot Manipulator
Статья научная
Refer to this research, a gradient descent optimization methodology for position fuzzy- model based computed torque controller (GDFCTC) is proposed for highly nonlinear continuum robot manipulator. The main problem of the pure computed torque controller (CTC) was equivalent problem in uncertain systems. The simulation results exhibit that the CTC works well in certain system. To eliminate the continuum robot manipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on applied fuzzy logic in equivalent nonlinear dynamic part to estimate unknown parameters. This relatively controller is more plausible to implement in an actual real-time when compared to other techniques of nonlinear controller methodology of continuum arms. Based on the gradient descent optimization method, the PD-gain updating factor has been developed in certain and partly uncertain continuum robots. The new techniques proposed and methodologies adopted in this paper supported by MATLAB/SIMULINK results represent a significant contribution to the field of design an optimized nonlinear computed torque controller for continuum robots.
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Nonlinear Gust Response Analysis of Free Flexible Aircraft
Статья научная
Gust response analysis plays a very important role in large aircraft design. This paper presents a methodology for calculating the flight dynamic characteristics and gust response of free flexible aircraft. A multidisciplinary coupled numerical tool is developed to simulate detailed aircraft models undergoing arbitrary free flight motion in the time domain, by Computational Fluid Dynamics (CFD), Computational Structure Dynamics (CSD) and Computational Flight Mechanics (CFM) coupling. To achieve this objective, a structured, time-accurate flow-solver is coupled with a computational module solving the flight mechanics equations of motion and a structural mechanics code determining the structural deformations. A novel method to determine the trim state of flexible aircraft is also stated. First, the field velocity approach is validated, after the trim state is attained, gust responses for the one-minus-cosine gust profile are analyzed for the longitudinal motion of a slender-wing aircraft configuration with and without the consideration of structural deformation.
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Nonlinear Time Series Predication of Slope Displacement based on Smoothing Filtered Data
Статья научная
According to the slope in geotechnical engineering, many displacement monitoring points are usually set to obtain the displacement data to ensure slope stability, these data are typical nonlinear time series, and it has high value about how to make use of displacement monitoring data to do the next step forecast analysis. Due to a certain degree of error, smoothing filter method is used to pretreat the displacement data, eliminate the influence of the error on the results and ensure the rationality. Based on smoothing filter data, these two methods are proposed to predict the displacement of the slope: exponential smoothing and chaos neural network. Both methods are used to make predictive analysis of the displacement monitoring data of outer monitoring point TP/BM27 in high slope of Three Gorges Ship-Lock, forecasting results show that: predictive values are close to measured values, chaos neural network prediction method is better than exponential smoothing method. At the same time, the displacement data with higher reliability and smoothing filter processing are used to make predictive analysis, the results can be more reasonable, so smoothing filter processing plays an important role in the analysis of displacement prediction.
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Normalized Statistical Algorithm for Afaan Oromo Word Sense Disambiguation
Статья научная
Language is the main means of communication used by human. In various situations, the same word can mean differently based on the usage of the word in a particular sentence which is challenging for a computer to understand as level of human. Word Sense Disambiguation (WSD), which aims to identify correct sense of a given ambiguity word, is a long-standing problem in natural language processing (NLP). As the major aim of WSD is to accurately understand the sense of a word in particular context, can be used for the correct labeling of words in natural language applications. In this paper, I propose a normalized statistical algorithm that performs the task of WSD for Afaan Oromo language despite morphological analysis The propose algorithm has the power to discriminate ambiguous word’s sense without windows size consideration, without predefined rule and without utilize annotated dataset for training which minimize a challenge of under resource languages. The proposed system tested on 249 sentences with precision, recall, and F-measure. The overall effectiveness of the system is 80.76% in F-measure, which implies that the proposed system is promising on Afaan Oromo that is one of under resource languages spoken in East Africa. The algorithm can be extended for semantic text similarity without modification or with a bit modification. Furthermore, the forwarded direction can improve the performance of the proposed algorithm.
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Статья научная
Reduction of computational complexity of digital hardware has drawn the special attention of researchers in recent past. Proper emphasis is needed in this regard towards the settlement of computationally efficient as well as functionally competent design of digital systems. In this communication, we have made one novel attempt for designing multiplier-free Finite duration Impulse Response (FIR) digital filter using one robust evolutionary optimization technique, called Differential Evolution (DE). The search has been directed through two sequentially opposite paths which include quantization and optimization as fundamental operations. Besides performing a detailed comparative analysis between these two proposed approaches; the performance evaluation of the designed filter with other existing discrete coefficient FIR models has also been carried out. Finally, the optimum search method for realizing the required set of specifications has been suggested.
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Novel Feature Selection Algorithms Based on Crowding Distance and Pearson Correlation Coefficient
Статья научная
Feature Selection is an important phase in classification models. Feature Selection is an effective task used to decrease the dimensionality and eliminate redundant and unrelated features. In this paper, three novel algorithms for feature selection problem are proposed. The first one is a filter method, the second one is a wrapper method, and the last one is a hybrid filter method. Both the proposed algorithms use the crowding distance used in the multiobjective optimization as a new metric to assess the importance of the features. The idea behind the use of the crowding distance is that the less crowded features have great impacts on the target attribute (class), and the crowded features have generally the same impact on the class attribute. To enhance the crowded distance, a combination with other metrics will give good results. In this work, the hybrid method combines between the crowding distance and Pearson correlation coefficient to well order the importance of features. Experiments on well-known benchmark datasets including large microarray datasets have shown the effectiveness and the robustness of the proposed algorithms.
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Статья научная
The purpose of this paper is to study the numerical oscillations of Runge-Kutta methods for the solution of alternately advanced and retarded differential equations with piecewise constant arguments. The conditions of oscillations for the Runge-Kutta methods are obtained. It is proven that the Runge-Kutta methods preserve the oscillations of the analytic solution. In addition, the relationship between stability and oscillations are shown. Some numerical examples are given to confirm the theoretical results.
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Numerical Simulation of Transient Gauss pulse Coupling through Small Apertures
Статья научная
Transient electromagnetic pulse (EMP) can easily couple into equipments through small apertures in its shells. To study the coupling effects of transient Gauss pulse to a cubic cavity with openings, coupling course is simulated using sub-gridding finite difference in time domain (FDTD) algorithm in this paper. A new grid partition approach is provided to simulate each kind of apertures with complex shapes. With this approach, the whole calculation space is modeled, and six kinds of aperture with different shapes are simulated. Coupling course is simulate in the whole time domain using sub-gridding FDTD approach. Selecting apertures with dimension of several millimeters to research, coupled electric field waveform, power density and coupling coefficient are calculated. The affect on coupling effects by varied incident angle and varied pulse width are also analyzed. The main conclusion includes interior resonance phenomenon, increase effect around rectangle aperture and several distributing rules of coupled electric field in the cavity. The correctness of these results is validated by comparing with other scholars’ results. These numerical results can help us to understand coupling mechanism of the transient Gauss pulse.
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Object Oriented Software Effort Estimate with Adaptive Neuro Fuzzy use Case Size Point (ANFUSP)
Статья научная
Use case size point (USP) method has been proposed to estimate object oriented software development effort in early phase of software project and used in a lot of software organizations. Intuitively, USP is measured by counting the number of actors, preconditions, post conditions, scenarios included in use case models. In this paper have presented a Adaptive fuzzy Neural Network model to estimate the effort of object oriented software using Use Case size Point approach. In our proposed system adaptive neural network fuzzy use case size point has less error and system worked more accurate and appropriative than prior methods.
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