Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
A comparative analysis of firefly and Fuzzy-Firefly based kernelized hybrid C-Means algorithms
Статья научная
In most of the clustering algorithms, the assignment of initial centroids is performed randomly, which affects both the final outcome and the number of iterations required. Another aspect of the approaches in clustering algorithms is the use of Euclidean distance as the measure of similarity between data points, which is handicapped by linear separability of input data. The purpose of this paper is to combine suitable techniques so that both the above problems can be handled suitably leading to efficient algorithms. For the initial assignment of centroids we use Firefly and Fuzzy Firefly algorithms. We replace the Euclidean distance by Kernels (Gaussian and Hyper-tangent) leading to hybridized versions. For experimental analysis we use five different images from different domains as input. Two efficiency measures; Davis Bouldin index (DB) and Dunn index (D) are used for comparison. The tabular values, their graphical representations and output images are generated to support the claims. The analysis proves the superiority of the optimized algorithms over their existing counterparts. We also find that Hyper-tangent kernel with Rough Intuitionistic Fuzzy C-Means algorithm using Fuzzy Firefly algorithm produces the best results and has a much faster convergence rate. The analysis of medical, satellite or geographical images can be done more efficiently using the proposed optimized algorithms. It is supposed to play an important role in image segmentation and analysis.
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A cyclic attribution technique feature selection method for human activity recognition
Статья научная
Feature selection is a technique of selecting the most important features for predictive model construction. It is a key component in machine learning for many pattern recognition applications. The primary objective of this paper is to create a more precise system for Human Activity Recognition (HAR) by identifying the most appropriate features. We propose a Cyclic Attribution Technique (CAT) feature selection technique for recognition of human activity based on group theory and the fundamental properties of the cyclic group. We tested our model on UCI-HAR dataset focusing on six activities. With the proposed method, 561 features could be reduced to 63. Using an Artificial Neural Network (ANN), we compared performances of our new dataset with selected features and the original dataset classifier. Results showed that the model could provide an excellent overall accuracy of 96.7%. The proposed CAT technique can specify high-quality features to the success of HAR with ANN classifier. Two benefits support this technique by reducing classification overfitting and training time.
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A formal model for legacy system understanding
Статья научная
Migration of legacy system is not a single step activity but a process that comprises of several phases of which Legacy System Understanding (LSU) is the first step. The intent of this work is to carry out a detailed study on the Legacy System Understanding in terms of Techniques and Tools used and to identify the potential gaps in them. The understanding of the legacy system has to be at the perspective of system level rather than the code level which has a narrow perspective, because the migration at code level may have a cascading impact to different aspects of the Legacy System. These findings have enabled us to formulate a process for building up an artefact repository and artefact dependency repository. These repositories along with the legacy system have aided us in understanding the legacy system in a comprehensive manner in terms of migrating artefacts in the context of migration of legacy systems. A formal mathematical model for representing the status of LSU and application of the same on a case study has been presented.
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A fuzzy-based adaptive energy efficient load distribution scheme in ad-hoc networks
Статья научная
The traditional energy aware routing policies are not capable enough to keep up with dynamic properties of mobile ad-hoc network (e.g., mobility, quick topology changes, link-layer contentions etc.) and do not offer adequate performance in high congested situations. In past decades, authors have expressed their concerns towards smart routing paradigms concerning lesser energy consumption. However, many of these proposals are not able to offer significant performance concerning the quality of service. Consequently, the pattern of interest shifts towards cross-layer energy optimization schemes. These proposals did use of lower layers’ special information and provide significant performance enhancements. Still, many of the issues are associated with these proposals. Moreover, many of the proposals consider idle and sleep power consumption which too causes a considerable amount of energy consumption. Nevertheless, these methods require complex synchronization and efficient coordination which is too inefficient for extremely variable networks (MANETs). To address these issues, we propose an effective fuzzy- based energy efficient load distribution scheme which takes care of energy consumption considering congestion as a parameter. In comparison with some of the existing energy aware routing strategies, proposed method offers substantial improvements in terms of total energy consumption, network lifetime, total number of dead nodes, and average throughput.
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A genetic algorithm based fractional fuzzy PID controller for integer and fractional order systems
Статья научная
This work aims at designing a fractional Proportional-Integral-Derivative controller wherein we hybridize a genetic algorithm based fractional Proportional-Integral-Derivative controller with a fuzzy logic Proportional-Integral-Derivative controller. We attempt at optimizing the fractional order Proportional-Integral-Derivative controller parameters by incorporating a Genetic Algorithm based mechanism. Thereafter, the optimized genetic algorithm based fractional Proportional-Integral-Derivative control is further fine tuned and hybridized to a fuzzy Proportional-Integral-Derivative control. Here, fuzzy logic based inference mechanism is used to tackle system uncertainties and use of rule firing strengths produces an adaptive control. Genetic Algorithm has been used to generate the most optimal controller by a natural selection of the fittest. Amalgamating Genetic Algorithm and fuzzy control approaches on fractional order systems produces a highly efficient and noise tolerant control regime. We give simulation results and compare our hybrid approach against conventional and fractional Proportional-Integral-Derivative approaches on various integer and fractional order systems (with dead time) to elucidate its superiority and effectiveness.
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Статья научная
Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset. One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).
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A hybrid approach for requirements prioritization using LFPP and ANN
Статья научная
Requirements prioritization is a most important activity to rank the requirements as per their priority of order .It is a crucial phase of requirement engineering in software development process. In this research introduced a MCDM model for requirements prioritization. To select a best supplier firm of washing machine three important criteria are used. In this proposed model investigation for requirements prioritization, a case study adopted from Ozcan et al using LOG FAHP (Logarithmic fuzzy analytic hierarchy process) and ANN (Artificial Neural Network) based model to choose the best supplier firm granting the highest client satisfaction among all technical aspects. The test was conducted on MATLAB software and result evaluated on fuzzy comparison matrix with three supplier selection criteria based on FAHP and LOGANFIS that shows the decision making outcome for requirements prioritization is better than existing approaches with higher priority.
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Статья научная
The paper presents the results of the research concerning the development of the hybrid model of 1-D signal adaptive filter based on the complex use of both the empirical mode decomposition and the wavelet analysis. Implementation of the proposed model involves three stages. Firstly, the initial signal is decomposed to the empirical modes by the Huang transform with allocation the components, which contain the noise. Then the wavelet filtering is performed to remove the noise component. The optimal parameters of the wavelet filter are determined based on the minimal value of ratio of Shannon entropy for the filtered data and the allocated noise component and these parameters are determined depending on type of the studied component of the signal. Finally, the signal is reconstructed with the use of the processed modes. The results of the simulation with the use of the test data have shown higher effectiveness of the proposed method in comparison with standard method of the signal denoising based on wavelet analysis.
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A link and Content Hybrid Approach for Arabic Web Spam Detection
Статья научная
Some Web sites developers act as spammers and try to mislead the search engines by using illegal Search Engine Optimizations (SEO) tips to increase the rank of their Web documents, to be more visible at the top 10 SERP. This is since gaining more visitors for marketing and commercial goals. This study is a continuation of a series of Arabic Web spam studies conducted by the authors, where this study is dedicated to build the first Arabic content/link Web spam detection system. This Novel system is capable to extract the set of content and link features of Web pages, in order to build the largest Arabic Web spam dataset. The constructed dataset contains three groups with the following three percentages of spam contents: 2%, 30%, and 40%. These three groups with varying percentages of spam contents were collected through the embedded crawler in the proposed system. The automated classification of spam Web pages used based on the features in the benchmark dataset. The proposed system used the rules of Decision Tree; which is considered as the best classifier to detect Arabic content/link Web spam. The proposed system helps to clean the SERP from all URLs referring to Arabic spam Web pages. It produces accuracy of 90.1099% for Arabic content-based, 93.1034% for Arabic link-based, and 89.011% in detecting both Arabic content and link Web spam, based on the collected dataset and conducted analysis.
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A method of A-BAT algorithm based query optimization for crowd sourcing system
Статья научная
In the field of database administration query optimization is one of the refinement processes. In recent years, huge volumes of data are flooded from different resources, which make query optimization, a difficult task for the researchers. In the crowd sourcing, environment query optimization is the biggest problem. The client is simply required to post an SQL-like subject, and the framework assumes the main issue of organizing the inquiry; execution setup is generated and in the crowd sourcing market places the evaluation plan evaluated. In order to retrieve data fast and reduce query processing time, Query optimization is badly required. In order to optimize the queries, Meta heuristic techniques are used. In this proposed paper, preprocessing method is used to mine the information from the Crowd. The Original population based ABC algorithm has low convergence speed. In this paper a novel A-BAT algorithm is proposed, which highly improve convergence speed, accuracy and Latency. This algorithm uses a Random walk phase. The proposed algorithm had better optimization accuracy, convergence rate, and robustness.
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A model for estimating firmware execution time taking into account peripheral behavior
Статья научная
The paper deals with the problem of estimating the execution time of firmware. Any firmware is bound to wait for a response from peripheral devices such as external memory chips, displays, analog-to-digital converters, etc. The firmware’s execution is frozen until the expected response is obtained. Thus, any firmware’s execution time depends not only on the computational resources of the embedded system being inspected but also on peripheral devices each of which is able to perform a set of operations during some random time period residing, however, within a known interval. The paper introduces a model of a computer application for evaluation of microcontroller-based embedded systems’ firmware’s execution time that takes into consideration the type of the microcontroller, the total duration of all the assembler-like instructions for a specific microcontroller, all the occasions of waiting for a response from hardware components, and the possible time periods for all the responses being waited for. Besides, we proposed the architecture of the computer application that assumes a reusable database retaining data on microcontrollers’ instructions.
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A multi-level parallel system for laws masks abnormality lung detection
Статья научная
Lung is a vital organ that plays a pivotal role in every second of our lives. Lungs may be affected by a number of diseases, including pulmonary edema and cancer. These diseases deemed life-sustained diseases, so they possess high preferences in detection, diagnosis, and possible treatments. In this paper, we presented a textural feature analysis framework that is capable of detecting lung abnormalities (edema or cancer) using Laws masks texture features. Laws masks are conventional texture feature extractor, and considered as one of the best methods for texture analysis in image processing. However, computing and extracting the texture features through various masks are very time consuming, whereas lung diseases demand rapid yet accurate diagnosis. Today, increased efficiency is being achieved through parallelism, and this trend is believed to continue in the future, with all computing devices likely to have many processors. Therefore, our objective is to investigate a multi-level parallel algorithm on Laws masks to describe structural variations of lung abnormalities. To our knowledge, there are no published researches that employed parallel strategies for lung abnormalities detection using Laws method. The proposed system has been experimented on real CT lung images. The results indicate that Laws texture features are capable of discriminating among normal, edema and cancerous lungs. Furthermore, applying parallel processing approaches improves significantly the overall system performance.
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Статья научная
This research aims to test the feasibility of Programmable Logic Controller implementation of an Artificial Neural Network based bearing fault diagnosis using vibration datasets. The main drawback of using a Programmable Logic Controller along with an Artificial Neural Network is that it does not support the parallel nature of neural networks. This drawback is not significant for relatively small applications like bearing diagnosis that involve very short execution time. In this paper, a three layer multilayer perceptron backpropagation neural network is trained using Levenberg-Marquardt training algorithm with vibration dataset consisting of four bearing status classes: normal, outer race way fault, inner race way fault and rolling element (ball) fault. Time-frequency domain and time domain input features were considered in this research. Both approaches have performed well during simulation phase. But the time-frequency feature extraction approach was observed to take too long scan cycle time to be implemented in real-time. This is due to the computationally intensive nature of Fast Fourier Transform algorithm involved during feature extraction. The time domain approach is proved to be feasible for Programmable Logic Controller implementation. The time domain input features used for neural network training were root mean square, variance, kurtosis and negative log likelihood values. The average performance obtained during simulation with 10-fold cross validation performance estimator was an error of 7.9 x10-4. The performance tests of Programmable Logic Controller implementation resulted in 100% bearing fault detection rate.
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A new approach for dynamic parametrization of ant system algorithms
Статья научная
This paper proposes a learning approach for dynamic parameterization of ant colony optimization algorithms. In fact, the specific optimal configuration for each optimization problem using these algorithms, whether at the level of preferences, the level of evaporation of the pheromone, or the number of ants, makes the dynamic approach an interested one. The new idea suggests the addition of a knowledge center shared by the colony members, combining the optimal evaluation of the configuration parameters proposed by the colony members during the experiments. This evaluation is based on qualitative criteria explained in detail in the article. Our approach indicates an evolution in the quality of the results over the course of the experiments and consequently the approval of the concept of machine learning.
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Статья научная
In this paper a new Quantum Tunneling Particle Swarm Optimization (QTPSO) algorithm is proposed and applied to the training of feedforward Artificial Neural Networks (ANNs). In the classical Particle Swarm Optimization (PSO) algorithm the value of the cost function at the location of the personal best solution found by each particle cannot increase. This can significantly reduce the explorative ability of the entire swarm. In this paper a new PSO algorithm in which the personal best solution of each particle is allowed to tunnel through hills in the cost function analogous to the Tunneling effect in Quantum Physics is proposed. In quantum tunneling a particle which has insufficient energy to cross a potential barrier can still cross the barrier with a small probability that exponentially decreases with the barrier length. The introduction of the quantum tunneling effect allows particles in the PSO algorithm to escape from local minima thereby increasing the explorative ability of the PSO algorithm and preventing premature convergence to local minima. The proposed algorithm significantly outperforms three state-of-the-art PSO variants on a majority of benchmark neural network training problems.
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A new robust and imperceptible image watermarking scheme based on hybrid transform and PSO
Статья научная
In this paper, a new robust and imperceptible digital image watermarking scheme that can overcome the limitation of traditional wavelet-based image watermarking schemes is proposed using hybrid transforms viz. Lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scheme uses canny edge detector to select blocks with higher edge pixels. Two reference sub-images, which are used as the point of reference for watermark embedding and extraction, have been formed from selected blocks based on the number of edges. To achieve a better trade-off between imperceptibility and robustness, multiple scaling factors (MSF) have been employed to modulate different ranges of singular value coefficients during watermark embedding process. Particle swarm optimization (PSO) algorithm has been adopted to obtain optimized MSF. The performance of the proposed scheme has been assessed under different conditions and the experimental results, which are obtained from computer simulation, verifies that the proposed scheme achieves enhanced robustness against various attacks performed. Moreover, the performance of the proposed scheme is compared with the other existing schemes and the results of comparison confirm that our proposed scheme outperforms previous existing schemes in terms of robustness and imperceptibility.
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A novel approach for regression testing of web applications
Статья научная
Software testing is one of the most arduous and challenging phase which is to be implemented with the intention of finding faults with the execution of minimum number of test cases to increase the overall quality of the product at the time of delivery or during maintenance phase. With the ever increasing demand of web applications and to meet never ending customer expectations, updations are to incorporate which will be validated through testing process. The structure of the web applications (dynamic website) can be modeled using weighted directed graph which consists of numerous paths starting from homepage (index page) of the website. For thorough testing of the website each and every path of the graph should be tested but due to various constraints like time, money and human resources it becomes very much impractical. This scenario ultimately gives rise to the motivation for the development of technique which reduces the number of paths to be tested so that tester community can test only these numbers of path instead of all possible paths so that satisfactory number of faults can be exposed. In this proposed approach assignment of weights on the edges of the directed graph takes place on the basis of the organization of the website, changes in the structure of the website at page level, experience of the coder and the behaviour of the users who have visited the website earlier. The most fault prone paths are identified using random, greedy, Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) algorithms. Two small size websites and one company’s website, and their two versions, were considered for experimentation. Results obtained through ACO and ABCO are promising in nature. This approach will support testing process to be completed in time and delivery of the updated version within given hard deadlines.
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A novel evolutionary automatic data clustering algorithm using teaching-learning-based optimization
Статья научная
Teaching-Learning-Based Optimization (TLBO) is a contemporary algorithm being used as a novel, trustworthy, precise and robust optimization technique for global optimization over continuous spaces both constrained and unconstrained tribulations. TLBO works on the beliefs of teaching and learning and clearly justifies this pedagogy by highlighting the effect of power of a teacher on the output of learners in a class. This paper, explores the applicability of k-means unsupervised learning into TLBO with two endeavors, i.e. to automatically find the optimal number of naturally classified partition in the data without any prior information, and the other is to inspect the naturally classified partitions with cluster validity indices (CVIs) and endorse the goodness of clusters. The proposed automatic clustering algorithm using TLBO (AutoTLBO) pursues a novel evolutionary approach by incorporating the simple k-means algorithm and CVIs into TLBO to configure and validate automatic natural partition in datasets. This algorithm retains the core ideology of clustering to minimize the inter cluster distances and maximize the intra cluster distances among the data. Experimental analysis substantiates the openness of the anticipated method after inspecting suavest panoramic rendering over artificial and benchmark datasets.
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A novel handoff necessity estimation approach based on travelling distance
Статья научная
Mobility management is one of the most important challenges in Next Generation Wireless Networks (NGWNs) as it enables users to move across geographic boundaries of wireless networks. Nowadays, mobile communications has heterogeneous wireless networks offering variable coverage and Quality of Service (QoS). The availability of alternatives generates a problem of occurrence of unnecessary handoff that results in wastage of network resources. To avoid this, an efficient algorithm needs to be developed to minimize the unnecessary handoffs. Conventionally, whenever a Wireless Local Area Network (WLAN) connectivity is available, the mobile node switch from the cellular network to wireless local area network to gain maximum use of high bandwidth and low cost of wireless local area network as much as possible. But to maintain call quality and minimum number of call failure, a considerable proportion of these handovers should be determined. Our algorithm makes the handoff to wireless local area network only when the Predicted Received Signal Strength (PRSS) falls below a threshold value and travelling distance inside the wireless local area networkis larger than a threshold distance. Through MATLAB simulation, we show that our algorithm minimizes the probability of unnecessary handoff, and probability of handoff failure. Hence, the proposed algorithm is able to improve handover performance.
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A novel intelligent ARX-Laguerre distillation column estimation technique
Статья научная
In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introduced for data-driven identification of the distillation column. The Laguerre method is used for the removal of input/output noise and decreases the system complexity. The fuzzy logic method is proposed to reduce the system’s estimation error and to accurately optimize the ARX-Laguerre parameters. The proposed method outperforms the ARX and ARX-Laguerre technique by achieving average estimation accuracy improvements of 16% and 9%, respectively.
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