Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
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
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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 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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An ensemble model using a BabelNet enriched document space for twitter sentiment classification
Статья научная
With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.
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Статья научная
In today’s world tremendous amount of unstructured data, especially text, is being generated through various sources. This massive amount of data has lead the researchers to focus on employing data mining techniques to analyse and cluster them for an efficient browsing and searching mechanisms. The clustering methods like k-means algorithm perform through measuring the relationship between the data objects. Accurate clustering is based on the similarity or dissimilarity measure that is defined to evaluate the homogeneity of the documents. A variety of measures have been proposed up to this date. However, all of them are not suitable to be used in the k-means algorithm. In this paper, an extensive study is done to compare and analyse the performance of eight well-known similarity and dissimilarity measures that are applicable to the k-means clustering approach. For experiment purpose, four text document data sets are used and the results are reported.
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Статья научная
The transport or advection-diffusion-reaction equation is a well-known partial differential equation employed to model several types of flux problems. The cooling fin problem is a particular case of such an equation. This work presents a straightforward model for the rectangular cooling fin in a problem. The model was based on the finite differences numerical method and an efficient implementation was developed in a high-level mathematical programming language. The accuracy was evaluated with different granularity levels of meshes, and two distinct boundary conditions are compared. In the first one, only prescribed temperatures are assumed at the four tips of the domain. For the second scenario, it is assumed a heat flux at one tip of a fin with the same geometrical shape. The achieved solutions produced by the algorithm were able to depict the temperature along the whole fin surface accurately. Furthermore, the algorithm reaches relevant performance for meshes up to 4257 points where the CPU time was about 33 seconds.
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Статья научная
Collaborative team formation in a social network is an important aspect for solving a real-world problem that requires different expert skills to achieve it. In this paper, we propose an improved African Buffalo Optimization algorithm integrated with discrete crossover operator conjointly with swap sequence for efficient team formation whose members can assist in solving a given problem with minimum communication cost. The proposed algorithm is called Improved African Buffalo Optimization algorithm (IABO). In IABO, a new concept of swap sequence applied to improve the performance by generating better team members that cover all the required skills. To the best of our knowledge, this is the first work that considers the African Buffalo Optimization algorithm for collaborative team formation in a social network of experts. A set of experiments have been done on two popular real-world benchmark datasets (i.e., DBLP and Stack Overflow) to determine the efficiency of the proposed algorithm in team formation. The results demonstrate the effectiveness of the IABO algorithm in comparison with GA, PSO and standard African Buffalo Optimization algorithm (ABO). 10.5815/ijitcs.2018.05.02
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Статья научная
Significant work has already been done for complex quadratics. However, the dynamics of rational functions and their properties are equally interesting. In this paper we have generated computer images from a C++ computer program. We have then developed an artificial neural network model using predictive modeling software based on RMS type of error out of two samples of points obtained from the generated images. The imaginary part of sample II was predicted by applied the real parts of sample I and sample II to the artificial neural network. The real part of sample II was more important than the real part of sample I in predicting the imaginary part of sample II. The predicted imaginary part of sample II was then imported to Matlab Signal Processing Tool (SPTool) via Matlab workspace. We have applied a stable band pass filter to the model to eliminate noise from it for its analysis. A modulated signal produced reveals that the methodology used shall be applied to explore properties of computer generated images from the generated wavelet. We have further imported the predicted imaginary part of sample II to autoSIGNAL software for time and frequency range analysis of the continuous wavelet transform.
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Analogue Wavelet Transform Based the Solution of the Parabolic Equation
Статья научная
In this paper we have proved that the solution of parabolic equation and its Fast Fourier Transform generate continuous wavelet transforms. Indeed, we have solved the parabolic equation using PDETool, exported its solution and coefficients to Matlab workspace. We have then imported the solution from workspace to signal processing tool. We have sampled the imported solution with the sampling frequency of 8192Hz and applied the band pass filter with that frequency. The convolution of the sampled PDE solution with the impulse response of the band pass filter has generated wavelet transform. This algorithm computes the wavelet transform either directly of via Faster Fourier Transform. The computation of the FFT of the PDE solution has produced complex wavelet.
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