Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Статья научная
The advancements of Information Technology have led to many developments that make life easier and faster with high reachability and efficiency. There is a drastic improvement in the area of distributed applications with the advent of mobile agent technology over the usual client/server framework. Applying mobile agent technology in the area of distributed applications improves the performance and quality of service. Mobile agent technologies in particular have taken a prominent place in handling effective road and vehicle traffic (VANET). This paper highlights on the integration of previously defined MATLB, PCM and MSA Agent and proposes an approach for reducing the size of the mobile agent that helps in data collection of vehicles in the VANET for effective and efficient traffic control. This paper is an advancement of agent load shedding algorithm and an attempt to optimize the size reduction process. This reduction in size of mobile agents will enhance the performance of VANET making the agents more acceptable by the hosts and correspondingly building an effective co-operative vehicular network.
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An Integrated Approach to Drive Ontological Structure from Folksonomie
Статья научная
Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.
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An Integrated CEA Approach for Color Light Source Estimation
Статья научная
Color constancy is an element of human vision framework which guarantees that the apparent color of items under fluctuating light conditions generally remains constant. It is fundamentally used to eliminate the color cast in the picture. Color Cat is a quick and precise learning-based methodology for accomplishing computational color constancy. However, despite everything it confronts a few limitations like poor brightness due to normalization used. Furthermore it doesn't promise edge preservation. So to overcome these issues a CEA strategy has been proposed which is a hybrid model based on Color Cat, Edge preservation filter and Adaptive histogram Equalization. As Adaptive histogram Equalization is exceptionally valuable for contrast improvement and edges are protected by edge preservation filter. Experimental results show that the proposed CEA approach outperforms over existing techniques.
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An Integrated Knowledge Base System Architecture for Histopathological Diagnosis of Breast Diseases
Статья научная
The histopathological diagnosis of breast diseases requires highly trained and experienced experts, and often strains pathologists’ cognitive capabilities. Accurate and timely diagnosis of breast diseases is essential for the appropriate management of the patients. The paper presents a knowledge base system that uses a combination of rule-based and case-based techniques to achieve the diagnosis. Rule-based systems handle problems with well-defined knowledge bases this limits the flexibility of such system. Case-based reasoning has been adopted to overcome this inherent weakness of rule-based systems by incorporating previous cases in the generation of new cases to improve the performance of the system. The result of this research shows that the system is capable of assisting pathologists in making accurate, consistent and timely diagnoses. The system also aid in eliminating errors of omission that have been viewed as a prominent cause of medical errors. In conclusion this paper investigated the histological features used in the diagnosis of breast diseases and proposed an integrated knowledge base system based on the features.
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An Integrated Knowledge Management Capabilities Framework for Assessing Organizational Performance
Статья научная
In the present aggressive world of competition, knowledge management strategies are becoming the major vehicle for the organizations to achieve their goals; to compete and to perform well. Linking knowledge management to business performance could make a strong business case in convincing senior management of any organization about the need to adopt a knowledge management strategy. Organizational performance is, therefore, a key issue and performance measurement models provide a basis for developing a structured approach to knowledge management. In this respect, organizations need to assess their knowledge management capabilities and find ways to improve their performance. This paper takes these issues into account when study the role of knowledge management in enhancing the organizational performance and consequently, developed an integrated knowledge management capabilities framework for assessing organizational performance. The results show that there is positive correlation between knowledge management capabilities and organizational performance. The results also show that the proposed framework can be used to assess organizational performance and also can be used as decision tool to decide which knowledge management capability should be improved.
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An Investigation on the Characteristics of Mobile Applications: A Survey Study
Статья научная
Swift advances in mobile communication technology have spawned almost unlimited new mobile applications. Mobile application development is an extremely well growing industry across the globe that created new opportunities of modern businesses and pioneered new technologies in the area. In order to build high quality mobile applications, it is imperative to understand the key characteristics that define mobile applications, which if wisely considered and implemented, can facilitate the delivery of truly exceptional, valuable and user friendly mobile apps that satisfy users’ needs. Only few scientific publications can be found which specifically identify the key characteristics and what makes mobile applications different from traditional software. For this purpose, we conducted an online survey from the mobile research and development community. The survey questions covered the entire mobile application development lifecycle starting from inception to the maintenance stage. This paper presents the survey results by classifying the key characteristics that differentiate mobile applications from traditional ones into three categories: Hardware, Software (application interaction, application development, and application security) and Communication. The study contributes towards a greater understanding of mobile software and the current trends in the mobile application development. It also highlights various features and attributes that assist in developing high quality mobile software applications.
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An Iterated Function System based Method to Generate Hilbert-type Space-filling Curves
Статья научная
Iterated function system has been found to be an important method to generate fractal sets. Hilbert space-filling curve is one kind of fractal sets which has been applied widely in digital image processing, such as image encoding, image clustering, image encryption, image storing/retrieving, and pattern recognition. In this paper, we will explore the generation of Hilbert-type space-filling curves via iterated function system based approach systematically. Cooperating a recursive calling of the common Hilbert's original space-filling curve at resolution n-1 and an IFS consisting of four affine transformations, one can generate the vertices for Hilbert-type space-filling curves at any resolution n. The merit is that the recursive algorithm is easy to implement and can be generalized to produce any other Hilbert-type space-filling curves and their variation versions.
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An Optimization Model and DPSO-EDA for Document Summarization
Статья научная
We model document summarization as a nonlinear 0-1 programming problem where an objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity. The proposed model implemented on a multi-document summarization task. Experiments on DUC2001 and DUC2002 datasets showed that the proposed model outperforms the other summarization methods.
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An Optimization of Feature Selection for Classification using Modified Bat Algorithm
Статья научная
Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on University of California Irvine datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly. The significance of this research is it will show a great impact in selecting the best features out of all the existing features which gives best accuracy rates which helps in extracting the information from raw data in Data Mining Domain. The Value of this research is it will manage main fields like medical and banking which gives exact and proper results in their respective field. The best quality of the research is to optimize the selection of features to achieve maximum predictive accuracy of the data sets which solves both single variable and multi-variable functions through the generation of binary structuring of features in the dataset and to increase the performance of classification by using nature inspired and Meta Heuristic algorithms.
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An Optimization-Based Framework for Feature Selection and Parameters Determination of SVMs
Статья научная
In this paper, feature selection and parameters determination in SVM are cast as an energy minimization procedure. The problem of feature selection and parameters determination is a very difficult problem where the number of feature is very large and where the features are highly correlated. We define the problem of feature selection and parameters determination in SVM as a combinatorial problem and we use a stochastic method that, theoretically, guarantees to reach the global optimum. Several public datasets are employed to evaluate the performance of our approach. Also, we propose to use the DNA Microarray Datasets which are characterized by the large number of features. To validate our approach, we apply it to image classification. The feature descriptors of the images were extracted by using the Pyramid Histogram of Oriented Gradients. The proposed approach was compared with twenty feature selection methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of other approaches.
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An Optimized Convolutional Neural Network Model for Detecting Depressive Symptoms from Image Posts
Статья научная
This paper presents an optimized model that uses an optimized CNN to detect depressive symptoms from image posts. This is with a view to detecting depression symptoms in individuals. Visual data were collected in their raw form and assessed as having or not having a mental condition. The images were processed, and the relevant features retrieved from them. An optimized convolutional neural network (CNN) was used to simulate the defined classification model of the image posts. The model was implemented using Python Programming Language. Precision, recall, accuracy, and the area under the Receiver Operating Characteristics (ROC) curve were used as performance indicators to assess the model's efficacy. The collected findings indicate that 77% accuracy is achieved by the optimized model. As a result, 77% of the cases were accurately predicted by the model, suggesting that the model is generally accurate in its predictions. The research will contribute to a decrease in the incidence, prevalence, and recurrence of mental health illnesses as well as the disabilities they cause.
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An Overview of Automatic Audio Segmentation
Статья
In this report we present an overview of the approaches and techniques that are used in the task of automatic audio segmentation. Audio segmentation aims to find changing points in the audio content of an audio stream. Initially, we present the basic steps in an automatic audio segmentation procedure. Afterwards, the basic categories of segmentation algorithms, and more specific the unsupervised, the data-driven and the mixed algorithms, are presented. For each of the categorizations the segmentation analysis is followed by details about proposed architectural parameters, such us the audio descriptor set, the mathematical functions in unsupervised algorithms and the machine learning algorithms of data-driven modules. Finally a review of proposed architectures in the automatic audio segmentation literature appears, along with details about the experimenting audio environment (heading of database and list of audio events of interest), the basic modules of the procedure (categorization of the algorithm, audio descriptor set, architectural parameters and potential optional modules) along with the maximum achieved accuracy.
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An analysis of the Intelligent Predictive String Search Algorithm: A Probabilistic Approach
Статья научная
Due to the huge surge of digital information and the task of mining valuable information from huge amount of data, text processing tasks like string search has gained importance. Earlier techniques for text processing relied on following some predetermined sequence of steps or some hard coded rules. However, these techniques might soon prove to be inefficient as the amount of data generated by modern computer systems in increasing more and more. One solution to this problem lies in the development of intelligent algorithms that incorporate a certain degree of intelligence and unlike traditional algorithm are able to cope up with changing scenarios. This paper presents a string searching algorithm that incorporates a certain degree of intelligence to search for a string in a text. In the search of a string, the algorithm relies on a chance process and a certain probability at each step. An analysis of the algorithm based on the approach suggested by A. A. Markov is also presented in the paper. The expected number of average comparisons required for searching a string in a text is computed. Based on the varieties of applications that are coming up in the area of text processing and the related fields, this new algorithm aims to find its use.
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An association prediction model: GECOL as a case study
Статья научная
Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.
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An astute SNA with OWA operator to compare the social networks
Статья научная
This paper mainly focuses on the development of quantitative approach based algorithm for comparing the social networks. Firstly, comparison of social networks can be done on different parameters at all the three levels – network, group and node level characteristics. Secondly, for getting more accurate results, the paper has incorporated weights to these parameters according to their importance. For addressing these two, the paper has taken an advantage from the Ordered Weighted Averaging (OWA) operator in the proposed algorithm. This algorithm outputs one quantitative value for each of the social network, on which the comparison has to be made. This paper has also employed the Gephi tool, in order to accomplish the quantitative and graphical comparison between the social networks. The analysis has been done on multiple varied social network data sets. This paper has made an effort to analyze, which among them is better in terms of connectivity and coherency factors. The paper takes into account six vital metrics of the social networks so that there will be low complexity with high accuracy. They are average degree, network diameter, graph density, modularity, clustering coefficient and average path length. The proposed SNA approach is very advantageous for finding the potential group suited for a particular task in different areas like identification of criminal activities, and more fields like economics, cyber security, medicine etc.
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An automated parking guidance system for megacities
Статья научная
Enormous increase for vehicles in the megacities, with limited parking creates a serious issue. In order to handle the issue, many cities have adopted the guided parking as a part of Intelligent Transportation System (ITS). The current ITS is continuously evolving to incorporate the required issues. ITS communicates among vehicles and parking facilities and shares the information of interest. Thereafter ITS employs dynamic information obtained from vehicles for guiding the parking. In the current work, authors have suggested two functions for parking guidance in this study. Using these functions, central server uses this dynamic information obtained from sensory networks and uses the same to suggest parking to the driver. The driver, upon receiving the suggestion, in turn may reserve the suggested parking or may choose to decline the suggestion based on his personal experience. The proposed approach considers various parameters to evaluate effectiveness of the guided parking. During simulation, these parameters have been demonstrated and it is observed that the proposed system outperforms the existing system in literature.
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An edge based clustering technique with self-organizing maps
Статья научная
Recently, artificial neural networks are fund to be efficiently used in clustering algorithms. So, the present paper focuses on the development of a novel clustering method based on artificial neural networks. The present paper uses an enhancement filter to enhance the segments in the input image. After this, the various sub images are generated and features are computed for each sub and edge image. Finally, the Self Organizing Map (SOM) is used for clustering process. The proposed novel method is evaluated with a database of 795 leaf images. Further various Probability Distributed Functions (PDFs) are used to evaluate the efficacy of the proposed method. The performance measures of the proposed method indicate the efficiency of the extended clustering method with SOM.
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An efficient data analysis based flood forecasting system (EDAFFS)
Статья научная
Among natural disasters observed each year, flood represents 40% and remains one of the most important problems that many governments want to solve. Each year flood is responsible for many damages that cost a lot of money and even lot of people’s life. To reduce these damages caused, flood forecasting and warning systems which are able to alert people when a flood occurs have been built. However, most of these flood forecasting systems(FFS) are usually designed for specific regions and mostly for developed countries and are not suitable for developing countries because of climatological and environmental parameters difference. The problem of flood forecasting in developing countries could be explained in one part by the lack of meteorological stations and hydraulic stations necessary for flood forecasting systems to make predictions. Moreover, existing flood forecasting systems, have forecast accuracy problem because of constant changes of the environment and climate usually caused by anthropic factors. To face these problems, this work proposes an auto-adaptive flood forecasting system based on hydraulic models and data analysis techniques on meteorological and wireless sensors networks data to realize reliable forecast. The large number of experiments conducted show that the solutions proposed in this work performed well.
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An empirical comparison of missing value imputation techniques on APS failure prediction
Статья научная
The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking and gear changing. The APS failure dataset consists of the daily operational sensor data from failed Scania trucks. The dataset is crucial to the manufacturer as it allows to isolate components which caused the failure. However, missing values and imbalanced class problems are the two most challenging limitations of this dataset to predict the cause of the failure. The prediction results can be affected by the way of handling these missing values and imbalanced class problem. In this paper, we have examined and presented the impact of five different missing value imputation techniques namely: Expectation Maximization, Mean Imputation, Soft Impute, MICE, and Iterative SVD in producing significantly better results. We have also performed an empirical comparison of their performance by applying five different classifiers namely: Naive Bayes, KNN, SVM, Random Forest, and Gradient Boosted Tree on this highly imbalanced dataset. The primary aim of this study is to observe the impact of the mentioned missing value imputation techniques in the enhancement of the prediction results, performing an empirical comparison to figure out the best classification model and imputation technique. We found that the MICE imputation and the random under-sampling techniques are the highest influential techniques for improving the prediction performance and false negative rate.
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Статья научная
In a prior study we found out that trust is an effective factor in the acceptance and adoption of cloud computing using the UTAUT. However, various relationships from the original UTAUT were not confirmed in the extended model for cloud computing. Therefore, we present here a study aimed at investigating the mediation effect of trust on users’ attitude toward the adoption of cloud computing using UTAUT. It is also aimed to examine the role of five moderating factors which are gender, age, education, managerial level and job domain on subjects’ behavioral intention to use cloud computing services. Data were collected from 219 subjects in order to test the modified model and were analyzed using Partial Least Square (PLS) algorithm. Experimental results demonstrated that Performance Expectancy and Facilitating Conditions are strongly mediated by trust for the behavioral intention to adopt cloud computing. Statistical results, on the other hand, indicated that the majority of the moderating factors did not have a significant impact on the acceptance of cloud computing. The paper finally concludes with the limitations of current study and directions for future work.
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