Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Статья научная
This paper exploits the feature extraction capabilities of the discrete cosine transform (DCT) together with an illumination normalization approach in the logarithm domain that increase its robustness to variations in facial geometry and illumination. Secondly in the same domain the entropy measures are applied on the DCT coefficients so that maximum entropy preserving pixels can be extracted as the feature vector. Thus the informative features of a face can be extracted in a low dimensional space. Finally, the kernel entropy component analysis (KECA) with an extension of arc cosine kernels is applied on the extracted DCT coefficients that contribute most to the entropy estimate to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having high positive entropy contribution. The resulting system was successfully tested on real image sequences and is robust to significant partial occlusion and illumination changes, validated with the experiments on the FERET, AR, FRAV2D and ORL face databases. Experimental comparison is demonstrated to prove the superiority of the proposed approach in respect to recognition accuracy. Using specificity and sensitivity we find that the best is achieved when Renyi entropy is applied on the DCT coefficients. Extensive experimental comparison is demonstrated to prove the superiority of the proposed approach in respect to recognition accuracy. Moreover, the proposed approach is very simple, computationally fast and can be implemented in any real-time face recognition system.
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A Fast Topological Parallel Algorithm for Traversing Large Datasets
Статья научная
This work presents a parallel implementation of a graph-generating algorithm designed to be straightforwardly adapted to traverse large datasets. This new approach has been validated in a correlated scenario known as the word ladder problem. The new parallel algorithm induces the same topological structure proposed by its serial version and also builds the shortest path between any pair of words to be connected by a ladder of words. The implemented parallelism paradigm is the Multiple Instruction Stream - Multiple Data Stream (MIMD) and the test suite embraces 23-word ladder instances whose intermediate words were extracted from a dictionary of 183,719 words (dataset). The word morph quality (the shortest path between two input words) and the word morph performance (CPU time) were evaluated against a serial implementation of the original algorithm. The proposed parallel algorithm generated the optimal solution for each pair of words tested, that is, the minimum word ladder connecting an initial word to a final word was found. Thus, there was no negative impact on the quality of the solutions comparing them with those obtained through the serial ANG algorithm. However, there was an outstanding improvement considering the CPU time required to build the word ladder solutions. In fact, the time improvement was up to 99.85%, and speedups greater than 2.0X were achieved with the parallel algorithm.
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A Formal Description of Problem Frames
Статья научная
Michael Jackson defines a Problem Frame as a mean to describe and classify software development problems. The initial description of problem Frames is essentially graphical. A weakness of this proposal is the lack of formal specification allowing efficient reasoning tools. This paper deals with Problem Frames’ formal specification with Description Logics. We first propose a formal terminology of Problem Frames leading to the specification of a Problem Frames’ TBOX and a specific problem’s ABOX. The Description Logics inference tools can then be used to decompose multi frame problems or to fix a particular problem into a Problem Frame.
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Статья научная
Software reusability is very important and crucial attribute to evaluate the system software. Due to incremental growth of software development, the software reusability comes under attention of many researcher and practitioner. It is pretty easier to reuse the software than developing the new software. Software reusability reduces the development time, cost and effort of software product. Software reusability define the depth to which a module can be reused again with very little or no modification. However the prediction of this quality attribute is cumbersome process. Aspect oriented software development is new approach that introduce the concerns to overcome the issues with modular programming and object oriented programming. However many researcher worked on accessing the software reusability on object oriented system but the software reusability of aspect oriented system is not completely explored. This paper explores the various metric that affects the reusability of aspect oriented software and estimate it using fuzzy logic approach.
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A Framework for Effective Object-Oriented Software Change Impact Analysis
Статья научная
Object-oriented (OO) software have complex dependencies and different change types which frequently affect their maintenance in terms of ripple-effects identification or may likely introduce some faults which are hard to detect. As change is both important and risky, change impact analysis (CIA) is a technique used to preserve the quality of the software system. Several CIA techniques exist but they provide little or no clear information on OO software system representation for effective change impact prediction. Additionally, OO classes are not faults or failures-free and their fault-proneness is not considered during CIA. There is no known CIA approach that incorporates both change impact and fault prediction. Consequently, making changes to software components while neglecting their dependencies and fault-proneness may have some unexpected effects on their quality or may increase their failure risks. Therefore, this paper proposes a novel framework for OO software CIA that allows for impact and fault predictions. Moreover, an intermediate OO program representation that explicitly represents the software and allows its structural complexity to be quantified using complex networks is proposed. The objective is to enhance static CIA and facilitate program comprehension. To assess its effectiveness, a controlled experiment was conducted using students’ project with respect to maintenance duration and correctness. The results obtained were promising, indicating its importance for impact analysis.
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A Framework for Multi-Tenant Database Adoption based on the Influencing Factors
Статья научная
Multi-tenant databases (MTD) are aspect of computing that has become one of the revolutionary technologies of recent years. This technology has helped to discard the large-scale investments in hardware and software resources, in upgrading them regularly and also in expensive licences of application software used on in-house hosted database systems. A MTD is a way of deploying a Database as a Service (DaaS) for the convenience and benefits of tenants. This concept is good for higher scalability and flexibility but it involves a lot of technicalities. The adoption of a MTD is based on some salient factors which can be grouped into four categories for easy understanding. A survey is presented in this research that involves a focus group of thirty respondents. The result shows the degree of impact each factor has on the decision to adopt a MTD. This paper also considers these factors and develops a framework that will help prospective tenants to take an informed decision about the adoption of the concept.
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Статья научная
Today’s technology prediction of a heart disease using intelligent system is a real challenge to modern technology. In this paper different membership functions using a fuzzy rule based system for the diagnosis of the heart disease has been presented. The system has seven inputs .These are Chest pain type, resting blood pressure in mm(Trestbps),Serum cholesterol in mg(Chol),numbers of years as a smoker(years), fasting of blood sugar(fbs), maximum heart rate achieved(thalrest), resting blood rate(tpeakbps). The angiographic disease status of heart of patients has been recorded as an output. It is to mention that the diagnosis of heart disease by angiographic disease status is assigned by a number between 0 to 1,that number indicates whether the heart attack is mild or massive. Here an effort has been made to decide suitable membership function for proper diagnosis of heart disease.Different membership functions used are triangular, trapezoidal, Gaussian ,Z shaped, bell shaped ,sigmoid based ,Gaussians combination membership functions. Based on the minimum value of absolute residual the particular membership function can be decided using the fuzzy rule base system for the proper diagnosis heart disease status of a patient.
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Статья научная
In an e-learning environment, learn in a collaborative way is not always so easy because one of the difficulties when arranging e-learning contents can be that these contents and learning paths are not adapted to this type of learning. Online courses are constructed in a way that does not stimulate interaction, cooperation and collaborative learning. This is why the e-learning often is seen as individual and lonely. In this sense, one way to reduce these problems and promote collaborative e-learning and interaction learner-learner and learner-teacher is to model these contents in the form of collaborative educational games. The primary aim of this work is to exploit the potential of educational games to improve students' collaboration in e-learning environments. Thus, this paper presents a framework for designing, implementing and building collaborative educational games targeted to e-learning. The proposed framework is composed of two main phases: game design phase that consists to propose a collaborative design process of educational games; and game development phase that consists to implement, package, describe and deliver the games using the IMS-LD standard. The paper describes the steps followed for modeling games, the framework architecture and adopted technical choices. The final framework supports the creation and the use of such games using one of the most popular tools of learning in the web era: the LMS (Learning Management System).
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A Frequency Based Approach to Multi-Class Text Classification
Статья научная
Text classification is a method which involves managing and processing important information that can be categorized into predefined classes within a collection of text data. This method plays a vital role in the field of information processing and information retrieval. Different approaches to text classification specifically based on machine learning algorithms have been discussed and proposed in various research works. This paper discusses a classification approach based on the frequencies of some important text parameters and classifies a given text accordingly into one among multiple categories. Using a newly defined parameter called wf-icf, classification accuracy obtained in a previous work was significantly improved upon.
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A Fuzzy Preference Relation Based Method for Face Recognition by Gabor Filters
Статья научная
In this paper we have applied Gabor filter for fiducial point localization. After obtaining the fiducial points the number of fiducial points are reduced using a distance formula. The distance of each of this fiducial point is then calculated by the distance formula and stored in the database of the system. The same methodology is also applied on the input face which is to be matched with the faces available in the database. Then a fuzzy preference relation matrix is obtained . the largest eigen value of this matrix is then determined by algebraic method or numerical method depending on the order of the matrix. To apply the numerical method which is more easier for large order matrices we have used the C programming of this method . Once the largest eigen value is determined the corresponding priority vector can easily be obtained from which we can easily match the input face with the database.
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Статья научная
A wireless sensor network's sensor nodes have scarce resources, are exposed to the open environment, and use wireless communication. These features make the network vulnerable to physical capture and security attacks, therefore adversaries attempt various attacks such as false report injection attacks. A false report injection attack generates a false alarm by forwarding a false report to the base station. It confuses a user and lowers the reliability of the system. In addition, it leads to depletion of the node energy in the process of delivering a false report. A dynamic en-route filtering scheme performs detection in the data transfer process, but it incurs unnecessary energy loss in a continuous attack situation. In this paper, in order to solve this problem, a scheme is proposed for determining whether or not to redistribute keys at execution. The proposed scheme saves energy by detecting false reports at an earlier hop than the existing scheme by using fuzzy logic and the feature of a loaded secret key of each node in the key pre-distribution phase. Furthermore, it improves the detection performance with an appropriate re-distribution of the key. Experimental results show up to 52.33% energy savings and an improved detection performance of up to 18.57% compared to the existing scheme.
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A Gender Recognition Approach with an Embedded Preprocessing
Статья научная
Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.
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A Genetic Programming Framework for Topic Discovery from Online Digital Library
Статья научная
Various topic extraction techniques for digital libraries have been proposed over the past decade. Generally the topic extraction system requires a large number of features and complicated lexical analysis. While these features and analysis are effective to represent the statistical characteristics of the document, they didn't capture the high level semantics. In this paper, we present a new approach for topic extraction. Our approach combines user's click stream data with traditional lexical analysis. From our point of view, the user's click stream directly reflects human understanding of the high-level semantics in the document. Furthermore, a simple, yet effective, piece-wise linear model for topic evolution is proposed. We apply genetic algorithm to estimate the model and extract topics. Experiments on the set of US congress digital library documents demonstrate that our approach achieves better accuracy for the topic extraction than traditional methods.
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A Goal-Directed Orchestration Approach for Agile Service Composition
Статья научная
Composition of services provides value added service by combining existing services and is essential to meet the varying users’ requests. The need for on-demand, automated, on-the fly and failure resilient service composition led to various dynamic and adaptive service composition approaches. An overview of several existing composition approaches is provided and the limitations in these approaches are identified and depicted as research opportunities. It has been found that all these approaches behave in a rigid way to respond to the changing services environment. They are bridged by proposing a Goal-Directed Orchestration approach which employs an orchestration engine to provide flexibility in responding to the changes in dynamic services environment. To illustrate how our approach could work better than the other existing approaches, we discussed with a usage scenario in travel trip planning domain. Our proposed model is compared with the existing models based on a set of defined features.
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A Hybrid Algorithm for Classification of Compressed ECG
Статья научная
Efficient compression reduces memory requirement in long term recording and reduces power and time requirement in transmission. A new compression algorithm combining Linear Predictive coding (LPC) and Discrete Wavelet transform is proposed in this study. Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database. The performance of algorithm is quantified by computing distortion measures like percentage root mean square difference (PRD), wavelet-based weighted PRD (WWPRD) and Wavelet energy based diagnostic distortion (WEDD). The PRD is found to be below 6 %, values of WWPRD and WEDD are less than 0.03. Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.
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A Hybrid Approach for Blur Detection Using Naïve Bayes Nearest Neighbor Classifier
Статья научная
Blur detection of the partially blurred image is challenging because in this case blur varies spatially. In this paper, we propose a blurred-image detection framework for automaticallQy detecting blurred and non-blurred regions of the image. We propose a new feature vector that consists of the information of an image patch as well as blur kernel. That is why it is called kernel-specific feature vector. The information extracted about an image patch is based on blurred pixel behavior on local power spectrum slope, gradient histogram span, and maximum saturation methods. To make the features vector useful for real applications, kernels consisting of motion-blur kernels, defocus-blur kernels, and their combinations are used. Gaussian filters are used for filtering process of extracted features and kernels. Construction of kernel-specific feature vector is followed by the proposed Naïve Bayes Classifier based on Nearest Neighbor classification method (NBNN). The proposed algorithm outperforms the up-to-date blur detection method. Because blur detection is an initial step for the de-blurring process of partially blurred images, our results also demonstrate the effectiveness of the proposed method in deblurring process.
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A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques
Статья научная
Money laundering is a criminal activity to disguise black money as white money. It is a process by which illegal funds and assets are converted into legitimate funds and assets. Money Laundering occurs in three stages: Placement, Layering, and Integration. It leads to various criminal activities like Political corruption, smuggling, financial frauds, etc. In India there is no successful Anti Money laundering techniques which are available. The Reserve Bank of India (RBI), has issued guidelines to identify the suspicious transactions and send it to Financial Intelligence Unit (FIU). FIU verifies if the transaction is actually suspicious or not. This process is time consuming and not suitable to identify the illegal transactions that occurs in the system. To overcome this problem we propose an efficient Anti Money Laundering technique which can able to identify the traversal path of the Laundered money using Hash based Association approach and successful in identifying agent and integrator in the layering stage of Money Laundering by Graph Theoretic Approach.
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A Hybrid Multi-sensor Multi-target Tracking Scheme with MLE and ANFIS
Статья научная
The Joint Probabilistic Data Association (JPDA) solves single sensor multi-target tracking in clutter, but it cannot be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the number of targets and the number of returns. This paper presents a hybrid method to implement MMT by combing Maximum Likelihood Estimation (MLE) with Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLE is applied to classify the same source observations at one time into the same set, then the cheap JPDA(CJPDA) approach is used to calculate the data association probability, and ANFIS is used to realize the MMT. The computer simulations indicate that this scheme achieves MMT perfectly with higher precision and easy realization.
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A Hybrid P2P Approach to Service Discovery in the Cloud
Статья научная
Highly scalable techniques for service discovery are key to the efficient use of Cloud resources, since the Cloud computing appears to be part of the mainstream computing in a few years. We embarked on a preliminary study on Cloud service discovery by adopting an unstructured P2P paradigm. We developed an efficient mechanism for routing of service requests by coupling a number of components: one-hop replication, semanticaware message routing, topology reorganization, and supernodes. A number of experiments were carried out that demonstrated the expected performance of the proposed P2P search scheme.
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A Knowledge-Based System for Life Insurance Underwriting
Статья научная
The purpose of this work is to enhance the life insurance underwriting process by building a knowledge-based system for life insurance underwriting. The knowledge-based system would be useful for organizations, which want to serve their clients better, promote expertise capture, retention, and reuse in the organization. The paper identifies the main input factors and output decisions that life insurance practitioners considered and made on a daily basis. Life underwriting knowledge was extracted through interviews in a leading insurance company in Kenya. The knowledge is incorporated into a knowledge-based system prototype designed and implemented, built to demonstrate the potential of this technology in life insurance industry. Unified modelling language and visual prolog language was used in the design and development of the prototype respectively. The system's knowledge base was populated with sample knowledge obtained from the life insurance company and results were generated to illustrate how the system is expected to function.
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