International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1187
Ensemble feature selection algorithm
Статья научная
In this paper, we propose a new feature selection algorithm based on ensemble selection. In order to generate the library of models, each model is trained using just one feature. This means each model in the library represents a feature. Ensemble construction returns a well performing subset of features associated to the well performing subset of models. Our proposed approaches are evaluated using eight benchmark datasets. The results show the effectiveness of our ensemble selection approaches.
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Entailment and spectral clustering based single and multiple document summarization
Статья научная
Text connectedness is an important feature for content selection in text summarization methods. Recently, Textual Entailment (TE) has been successfully employed to measure sentence connectedness in order to determine sentence salience in single document text summarization. In literature, Analog Textual Entailment and Spectral Clustering (ATESC) is one such method which has used TE to compute inter-sentence connectedness scores. These scores are used to compute salience of sentences and are further utilized by Spectral Clustering algorithm to create segments of sentences. Finally, the most salient sentences are extracted from the most salient segments for inclusion in the final summary. The method has shown good performance earlier. But the authors observe that TE has never been employed for the task of multi-document summarization. Therefore, this paper has proposed ATESC based new methods for the same task. The experiments conducted on DUC 2003 and 2004 datasets reveal that the notion of Textual Entailment along with Spectral Clustering algorithm proves to be an effective duo for redundancy removal and generating informative summaries in multi-document summarization. Moreover, the proposed methods have exhibited faster execution times.
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Estimation and Approximation Using Neuro-Fuzzy Systems
Статья научная
Estimation and Approximation plays an important role in planning for future. People especially the business leaders, who understand the significance of estimation, practice it very often. The act of estimation or approximation involves analyzing historical data pertaining to domain, current trends and expectations of people connected to it. Exercising estimation is not only complicated due to technological change in the world around, but also due to complexity of the problems. Traditional numerical based techniques for solution of ill-defined non-linear real world problems are not sufficient. Hence, there is a need of some robust methodologies which can deal with dynamic environment, imprecise facts and uncertainty in the available data to achieve practical applicability at low cost. Soft computing seeks to solve class of problems not suited for traditional algorithmic approaches. To address the common problems in business of inexactness, some models are put forward for servicing, support and monitoring by approximating and estimating important outcomes. This work illustrates some very general yet widespread problems which are of interest to common people. The suggested approaches can overcome the fuzziness in traditional methods by predicting some future events and getting better control on business. This includes study of various neuro-fuzzy architectures and their possible applications in various areas, where decision-making using classical methods fail.
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Статья научная
Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (λ ) and linear inner loop gain (K). Since the sliding surface gain (λ) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new λ and new K are obtained by the previous λ and K multiple gains updating factor(α). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
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Evaluation of Feature Selection Approaches for Urdu Text Categorization
Статья научная
Efficient feature selection is an important phase of designing an effective text categorization system. Various feature selection methods have been proposed for selecting dissimilar feature sets. It is often essential to evaluate that which method is more effective for a given task and what size of feature set is an effective model selection choice. Aim of this paper is to answer these questions for designing Urdu text categorization system. Five widely used feature selection methods were examined using six well-known classification algorithms: naive Bays (NB), k-nearest neighbor (KNN), support vector machines (SVM) with linear, polynomial and radial basis kernels and decision tree (i.e. J48). The study was conducted over two test collections: EMILLE collection and a naive collection. We have observed that three feature selection methods i.e. information gain, Chi statistics, and symmetrical uncertain, have performed uniformly in most of the cases if not all. Moreover, we have found that no single feature selection method is best for all classifiers. While gain ratio out-performed others for naive Bays and J48, information gain has shown top performance for KNN and SVM with polynomial and radial basis kernels. Overall, linear SVM with any of feature selection methods including information gain, Chi statistics or symmetric uncertain methods is turned-out to be first choice across other combinations of classifiers and feature selection methods on moderate size naive collection. On the other hand, naive Bays with any of feature selection method have shown its advantage for a small sized EMILLE corpus.
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Evaluation of Interest Point Detectors in Presence of Noise
Статья научная
Detection of repeatable keypoints is often one of the first steps leading to obtain a solution able to recognise objects on images. Such objects are characterised by content of image patches indicated by keypoints. A given image patch is worth being described and processed in further steps, if the interest point inside of it can be found despite different image transformations or distortions. Therefore, it is important to compare keypoint detection techniques using image datasets that contain transformed or noisy images. Since most of detector evaluations rely on small datasets or are focused on a specific application of compared techniques, in this paper two large datasets which cover typical transformations, as well as challenging distortions that can occur while image processing, are used. The first dataset contains 200,000 transformed images, and it has been prepared for the purpose of this study. The second dataset, TID2013, is widely used for perceptual image quality assessment; it contains 3,000 images with 24 distortions. Finally, interest point detectors are evaluated on four datasets, and repeatability score and time of detection are used as measures of their performance.
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Статья научная
Type-1 diabetes is a disease characterized by high blood-glucose level. Using a fully automated closed loop control system improves the quality of life for type1 diabetic patients. In this paper, a scalable closed loop blood glucose regulation system which is tuned to each patient is presented. This control system doesn't need any data entry from the patient. A recurrent neural network (RNN) is used as a nonlinear predictor and a fuzzy logic controller (FLC) is used to determine the insulin dosage which is required to regulate the blood glucose level. The insulin infusion is restricted by calculation of insulin on board (IOB) which avoids overdosing of insulin. The performance of the proposed NMPC is evaluated by applying full day meal regime to each patient. The evaluation includes testing in relation to specific life style condition, i.e. fasting, postprandial, fault meal estimation, and exercise as a metabolic disturbance. Our simulation results indicate that, the use of a RNN along with a FLC can decrease the postprandial glucose concentration. The proposed controller can be used in fasting and can avoid severe hypo or hyper-glycemia during fasting. It can also decrease the postprandial glucose concentration and can dynamically respond to different glycemic challenges.
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Evaluation of Websites by Many Criteria Using the Algorithm for Pairwise Comparison of Alternatives
Статья научная
The article proposes a comprehensive method for the multicriteria evaluation of websites. The essence of this method is that using this method we can not only evaluate a website traditionally, but also obtain the following useful results: importance coefficient of each of the criteria, the evaluation of the website for each criterion individually. Moreover, we can also compare the sampled websites and then rank them. It is noted that in order to get the precise result, the sampled websites must be referred to the same category, that is, have the same set of criteria for evaluation.
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Evolution of Knowledge Representation and Retrieval Techniques
Статья научная
Existing knowledge systems incorporate knowledge retrieval techniques that represent knowledge as rules, facts or a hierarchical classification of objects. Knowledge representation techniques govern validity and precision of knowledge retrieved. There is a vital need to bring intelligence as part of knowledge retrieval techniques to improve existing knowledge systems. Researchers have been putting tremendous efforts to develop knowledge-based system that can support functionalities of the human brain. The intention of this paper is to provide a reference for further research into the field of knowledge representation to provide improved techniques for knowledge retrieval. This review paper attempts to provide a broad overview of early knowledge representation and retrieval techniques along with discussion on prime challenges and issues faced by those systems. Also, state-of-the-art technique is discussed to gather advantages and the constraints leading to further research work. Finally, an emerging knowledge system that deals with constraints of existing knowledge systems and incorporates intelligence at nodes, as well as links, is proposed.
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Evolutionary Optimized Neural Network (EONN) Based Motion Control of Manipulator
Статья научная
In this paper, an Evolutionary Optimized Neural Network (EONN) based control scheme is proposed. This control scheme is based on the fact that optimizing values of a few parameters of neural network can enhance its control performance. Radial Biased Neural Network (RBNN) is chosen here and PSO, one of the most emerging global optimizing techniques, is used to optimize the parameters of a RBNN. From hidden to output layer RBNN uses Gaussian function for mapping. Spread factor (s) of this intelligent RBNN is then optimized by a modified PSO to improvise its performance. The proposed controller has been verified by implementing it for position control of a robotic manipulator. For comparison purpose, proposed scheme has been verified with RBNN and the classical PD controller. MATLAB environment has been chosen for simulation study carried out. Robustness of the proposed controller has been checked by applying it to the manipulator for three different paths.
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Experimental validation of contextual variables for research resources recommender system
Статья научная
Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.
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Exploring Feature Selection and Machine Learning Algorithms for Predicting Diabetes Disease
Статья научная
One of the most common diseases in the world is the chronic diabetes. Diabetes has a direct impact on the lives of millions of people worldwide. Diabetes can be controlled and improved with early diagnosis, but the majority of patients continue to live with it. There is a dispirit need to a system to anticipate and select the people who are most likely to be diabetes in the future. Diagnosing the future diseased person without taking any blood or glucose screening tests, is the main goal of this study. This paper proposed a deep-learning model for diabetes disease prediction. The proposed model consists of three main phases, data pre-processing, feature selection and finally different classifiers. Initially, during the data pre-processing stage, missing values are handled, and data normalization is applied to the data. Then, three techniques are used to select the most important features which are mutual information, chi-squared and Pearson correlation. After that, multiple machine learning classifiers are used. Four experiments are then conducted to test our models. Additionally, the effectiveness of the proposed model is evaluated against that of other well-known machine learning techniques. The accuracy, AUC, sensitivity, and F-measure of the linear regression classifier are higher than those of the other methods, according to experimental data, which show that it performs better. The suggested model worked better than traditional methods and had a high accuracy rate for predicting diabetic disease.
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Extracting a linguistic summary from a medical database
Статья научная
In general, medical clustering concerns a big database. The present paper aims at extracting a fuzzy linguistic summary from a large medical database. A linguistic summary is used to reduce large volumes of data to simple sentences. It is worth noting that with the increase of the amount of medical data, different techniques of machine learning have been developed recently. In this article, an attempt is made to build a medical linguistic summary template. Our linguistic summary model is based on the calculated fuzzy cardinality. It deals with semantic queries in natural language. Our proposal is to develop a classification system based on the linguistic summary of two medical databases in which the calculation of similarity between different sets of linguistic summaries is used; the patient’s class is then identified by calculating the Sugeno integral. The present study was successful in developing a classification system that is based on the linguistic summary of two datasets from the UCI Machine Learning Repository, i.e. Pima Indians Diabetes dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The results obtained were then employed for a benchmark test.
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Extraction of Hidden Social Networks from Wiki-Environment Involved in Information Conflict
Статья научная
Social network analysis is a widely used technique to analyze relationships among wiki-users in Wikipedia. In this paper the method to identify hidden social networks participating in information conflicts in wiki-environment is proposed. In particular, we describe how text clustering techniques can be used for extraction of hidden social networks of wiki-users caused information conflict. By clustering unstructured text articles caused information conflict we create social network of wiki-users. For clustering of the conflict articles a hybrid weighted fuzzy-c-means method is proposed.
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Eye Gaze Relevance Feedback Indicators for Information Retrieval
Статья научная
There is a growing interest in the research on interactive information retrieval, particularly in the study of eye gaze-enhanced interaction. Feedback generated from user gaze features is important for developing an interactive information retrieval system. Generating these gaze features have become less difficult with the advancement of the eye tracker system over the years. In this work, eye movement as a source of relevant feedback was examined. A controlled user experiment was carried out and a set of documents were given to users to read before an eye tracker and rate the documents according to how relevant they are to a given task. Gaze features such as fixation duration, fixation count and heat maps were captured. The result showed a medium linear relationship between fixation count and user explicit ratings. Further analysis was carried out and three classifiers were compared in terms of predicting document relevance based on gaze features. It was found that the J48 decision tree classifier produced the highest accuracy.
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FPGA Fuzzy Controller Design for Magnetic Ball Levitation
Статья научная
This paper presents a fuzzy controller design for nonlinear system using FPGA. A magnetic levitation system is considered as a case study and the fuzzy controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy tools to implement the fuzzy logic controller into HDL code. This paper, advocates a novel approach to implement the fuzzy logic controller for magnetic ball levitation system by using FPGA.
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Face Recognition System based on SURF and LDA Technique
Статья научная
In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of imag-es to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimization of human face recognition and detection is an important problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in hu-man faces due to the illumination and pose variations. In this paper we proposed a novel method in face recogni-tion to improve the quality parameters using speed up robust feature and linear discriminant analysis for opti-mize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.
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Face Veins Based MCMT Technique for Personal Identification
Статья научная
Face veins based personal identification is a challenging task in the field of identity verification of a person. It is because many other techniques are not identifying the uniqueness of a person in the universe. This research paper finds the uniqueness of a person on the basis of face veins based technique. In this paper five different persons face veins images have been used with different rotation angles (left/right 900 to 2700 and 3150). For each person, eight different images at different rotations were used and for each of these images the same minimum cost minutiae tree (MCMT) is obtained. Here, Prim’s or Kruskal’s algorithm is used for finding the MCMT from a minutiae graph. The MCMT is traversed in pre-order to generate the unique string of vertices and edge lengths. We deviated the edge lengths of each MCMT by five pixels in positive and negative directions for robustness testing. It is observed in our experiments that the traversed string which consists of vertices and edge lengths of MCMT is unique for each person and this unique sequence is correctly identifying a person with an accuracy of above 95%. Further, we have compared the performance of our proposed technique with other standard techniques and it is observed that the proposed technique is giving the promising result.
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Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks
Статья научная
This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.
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Fault Tolerant Multi-Criteria Multi-Path Routing in Wireless Sensor Networks
Статья научная
The Ad Hoc On-Demand Multi-Path Distance Vector (AOMDV) routing protocol allows the transport of data along one or more paths in wireless sensor networks (WSNs). The path chosen is based on a single shortest path hop count metric. The data on some WSNs is mission critical, for example, military and health care applications. Hence, fault tolerance in WSNs is becoming increasingly important. To improve the fault tolerance of WSNs in lossy environments, this work adds to the AOMDV routing protocol as it incorporates an additional packet loss metric. This Multi-criteria AOMDV or M-AOMDV is evaluated using the ns2 simulator. Simulations show that M-AOMDV maintains relatively low packet loss rates when the WSN is experiencing loss.
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