Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
An Assessment of Extreme Programming Based Requirement Engineering Process
Статья научная
Comprehensive requirement engineering (RE) process acts as a backbone of any successful project. RE processes are very complex because most of the requirement engineering documentation is written in natural languages, which are less formal and often distract the designers and developers of the system. To streamline different phases of the software lifecycle, first we need to model the requirement document so that we can analyze and integrate the software artifacts. Designers can ensure completeness and consistency of the system by generating models using the requirement documents. In this paper, we have made an attempt to analyze extreme programming based RE approach to understand its utility in the requirement elicitation phase. In this study, different RE process models are evaluated and a comparison of the extreme programming technique is drawn to highlight the merits of the latter technique over the conventional RE techniques.
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Статья научная
Learning is based on collaborative learning theory. Collaborative learning theory has interaction, individual accountability, teamwork and personalized guidance. All these aspects can be performed in web 2.0 using social networking sites. So e-learning 2.0 on web 2.0 is not a new class of learning management system or a pedagogy but which promotes the user to collaborate and build the information and not just a mere spectator/consumer of information. In this paper the researchers have made an assessment of 23 e-learning systems, a survey on some of the popular tools/sites which will be useful and augment e-learning 2.0 and have discussed the features of an experimental web solution to have a look and feel of these tools.
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An Automatic Approach to Detect Software Anomalies in Cloud Computing Using Pragmatic Bayes Approach
Статья научная
Software detection of anomalies is a vital element of operations in data centers and service clouds. Statistical Process Control (SPC) cloud charts sense routine anomalies and their root causes are identified based on the differential profiling strategy. By automating the tasks, most of the manual overhead incurred in detecting the software anomalies and the analysis time are reduced to a larger extent but detailed analysis of profiling data are not performed in most of the cases. On the other hand, the cloud scheduler judges both the requirements of the user and the available infrastructure to equivalent their requirements. OpenStack prototype works on cloud trust management which provides the scheduler but complexity occurs when hosting the cloud system. At the same time, Trusted Computing Base (TCB) of a computing node does not achieve the scalability measure. This unique paradigm brings about many software anomalies, which have not been well studied. This work, a Pragmatic Bayes approach studies the problem of detecting software anomalies and ensures scalability by comparing information at the current time to historical data. In particular, PB approach uses the two component Gaussian mixture to deviations at current time in cloud environment. The introduction of Gaussian mixture in PB approach achieves higher scalability measure which involves supervising massive number of cells and fast enough to be potentially useful in many streaming scenarios. Wherein previous works has been ensured for scheduling often lacks of scalability, this paper shows the superiority of the method using a Bayes per section error rate procedure through simulation, and provides the detailed analysis of profiling data in the marginal distributions using the Amazon EC2 dataset. Extensive performance analysis shows that the PB approach is highly efficient in terms of runtime, scalability, software anomaly detection ratio, CPU utilization, density rate, and computational complexity.
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An Efficient Algorithm for Finding a Fuzzy Rough Set Reduct Using an Improved Harmony Search
Статья научная
To increase learning accuracy, it is important to remove misleading, redundant, and irrelevant features. Fuzzy rough set offers formal mathematical tools to reduce the number of attributes and determine the minimal subset. Unfortunately, using the formal approach is time consuming, particularly if a large dataset is used. In this paper, an efficient algorithm for finding a reduct is introduced. Several techniques are proposed and combined with the harmony search, such as using a balanced fitness function, fusing the classical ranking methods with the fuzzy-rough method, and applying binary operations to speed up implementation. Comprehensive experiments on 18 datasets demonstrate the efficiency of using the suggested algorithm and show that the new algorithm outperforms several well-known algorithms.
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An Efficient Approach of Power Consumption in Cloud using Scheduling of Resources
Статья научная
Cloud computing is the stage for a choice of services like software, infrastructure as a cloud service and each person wants to have the benefit of that cloud services using the cloud computing concept, which ultimately increases the data size and loaded records on cloud servers. Due to increased number of files on the cloud database the retrieval of files becomes much more time consuming and complex. Also this file retrieval doesn't ensure the exact retrieval of files from the storage. Besides, the privacy apprehensions affect to the appropriate documents regained by the cloud user in the afterward phase in view of the fact that they may also enclose sensitive data and make known information about sensitive exploration words or phrase. Here in this paper an efficient approach of power consumption using scheduling of resources is implemented.
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Статья научная
In Project management, team's motivation is one of the most important factors in the success of a project. Project Managersmanage the whole project by assuring that all the members are performing the duties they are assigned. Emotional Intelligence is the ability to identify the attitude of a team member towards a project. In developing countrieslack of emotional intelligence factors may cost people their jobs. In this paper conflict management, technical management, interest development and stress management problems are discussed with reference to developing countries and an efficient approach to resolve these issues is also proposed. Therefore project managers should raise their EI (emotional intelligence) and also help members to raise their EI. This paper explores the related work in EI, and then finds the useful Emotional Intelligence factors which are appropriate for the project manager. Then this paper also determinesa framework which is useful for project managers.
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Статья научная
Biggest internet security threat is the rise of Botnets having modular and flexible structures. The combined power of thousands of remotely controlled computers increases the speed and severity of attacks. In this paper, we provide a comparative analysis of machine-learning based classification of botnet command & control(C&C) traffic for proactive detection of Peer-to-Peer (P2P) botnets. We combine some of selected botnet C&C traffic flow features with that of carefully selected botnet behavioral characteristic features for better classification using machine learning algorithms. Our simulation results show that our method is very effective having very good test accuracy and very little training time. We compare the performances of Decision Tree (C4.5), Bayesian Network and Linear Support Vector Machines using performance metrics like accuracy, sensitivity, positive predictive value(PPV) and F-Measure. We also provide a comparative analysis of our predictive models using AUC (area under ROC curve). Finally, we propose a rule induction algorithm from original C4.5 algorithm of Quinlan. Our proposed algorithm produces better accuracy than the original decision tree classifier.
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An Efficient Technique for Optimality Measurement of Approximation Algorithms
Статья научная
Many algorithms have been proposed for the solution of the minimum vertex cover (MVC) problem, but the researchers are unable to find the optimality of an approximation algorithm. In this paper, we have proposed a method to evaluate that either the result returned by an approximation algorithm for the minimum vertex cover problem is optimal or not. The proposed method is tested on three algorithms, i.e., maximum degree greedy (MDG) algorithm, modified vertex support algorithm (MVSA) and clever steady strategy algorithm (CSSA). The proposed method provides an opportunity to test the optimality of an approximation algorithm for MVC problem with low computation complexity. The proposed method has performed well during experimentation, and its results brighten the path of successful implementation of the method for the evaluation of approximation algorithms for the minimum vertex cover (MVC) problem. The testing of the proposed method was carried out on small graph instances. The proposed method has resolved the problem to test the optimality of the approximation algorithm for the minimum vertex cover problem. This technique has digitized the process of finding out the accuracy of the optimal solution returned by approximation algorithms for MVC.
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An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment
Статья научная
Cloud is a collection of heterogeneous resources and requirements of these resources can change dynamically. Cloud providers are always interested in maximizing the resources utilization and the associated revenues, by trimming down energy consumption and operational expenses, while on the other hand cloud users are interested in minimizing response time and optimizing overall application throughput. In cloud environment to allocate the resources with minimum overhead time along with efficient utilization of available resources is very challenging task. The resources in cloud datacenter are allocated using a virtual machine (VM) scheduling technique. So there is a need of an efficient VM scheduling technique to maximize system performance and cost saving. In this paper two dynamic virtual machine scheduling techniques i.e. Best fit and Worst fit are proposed for reducing the response time along with efficient and balanced resource utilization. The proposed algorithms removes the limitations of the previously proposed Novel Vector based algorithm and minimizes the response time complexity in order of O(logn) and O(1) using Best Fit and Worst Fit strategies respectively.
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An Efficient and Effective New Generation Objective Quality Model for Mobile Applications
Статья научная
Recent proliferation of mobile market has swiftly increased the competition in mobile software market, new technology and new devices are emerging at phenomenal speed. As the number of mobile applications is increasing at daily rate, quality is becoming major issue. So mobile software organization need some quality model as guideline to improve and maintain quality of application under development. Mobile application mainly depends on user response and user acceptance so they need maintainability, usability, suitability etc. This research paper presents mobile application quality model focusing on key quality characteristics mainly extracted from ISO 9126 quality model, which effect quality of mobile applications. Furthermore some responsibilities of QA team in mobile application development are also discussed and lastly focused on the issue of ‘tracking the quality of mobile applications after deployment’.
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An Efficient and Objective Generalized Comparison technique for Software Quality Models
Статья научная
To scrutinize the uniqueness of software quality model it is crucial to compare it with existing ones. Quality is generally apprehended in a model that illustrates the features and their interactions. Numerous models for measuring quality of software processes have been recommended to assess particular type of software products. Numerous methodologies and practices have been suggested to perform the specific or general scope based comparisons among eminent models. These comparisons are leak. The Suggested comparison lacks the clear differentiation and in depth analysis. Consequently, a prescribed method of comparison among software quality models has been defined. The suggested technique is applied on an inclusive comparison among renowned software quality models. The consequence of suggested technique demonstrates the power and faintness of quality models.
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Статья научная
Learning styles vary according to the individual and this diversity is fundamental in terms of teaching as curricula must respond adaptively to the various learning styles of pupils. This study conducts an analysis of an Arabic form of the Index of Learning Styles (ILS), a 44-item questionnaire designed to determine learning styles using the Felder-Silverman learning style model. This study focuses on the interpretation of data derived from the Arabic form of the Index of Learning Styles (ILS) to establish correlations between the learning styles of 1024 female students drawn from two specific departments at the King Abdul-Aziz University in Saudi Arabia. The findings, generated by Multiple Correspondence Analysis and cross-validated by correlation analysis, demonstrate a definite link between certain learning styles from opposing dimensions that are considered to be contradictory within the same dimension of learning. The validity and reliability of the Arabic scale was established and compared to the examples reported in the literature. Findings show comparable reliability and factor analysis supports the interdependencies between dimensions and perhaps the constructs they intend to assess. The results of this paper have implications for the design of e-learning tools, materials and sessions in order to adapt to the relationships between learning styles and have a positive impact on the learners themselves and their learning experience.
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An Energy-Efficient and Robust Voltage Level Converter for Nanoelectronics
Статья научная
Low-power design has recently become very important especially in nanoelectronic VLSI circuits and systems. Functioning of circuits at ultra-low voltages leads to lower power consumption per operation. An efficient method is to separate the logic blocks based on their performance requirement and applying a specific supply voltage for each block. In order to prevent an enormous static current in these multi-VDD circuits, voltage level converters are essential. This study presents an energy-efficient and robust single-supply level converter (SSLC) based on multi-threshold carbon nanotube FETs (CNTFETs). Unique characteristics of the CNTFET device and transistor stacking are utilized suitably to reduce the power and energy consumption of the proposed LC. The results of the extensive simulations, conducted using 32nm CNTFET technology of Stanford University indicate the superiority of the proposed design in terms energy-efficiency and robustness to process, voltage and temperature variations, as compared to the other conventional and state-of-the-art LC circuits, previously presented in the literature. The results demonstrate almost on average 35%, 55%, 90% and 68% improvements in terms of delay, total power, static power and energy consumption, respectively.
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An Enhanced Action Research Approach for Managing Risks in Software Process Improvement
Статья научная
Managing risks in Software Process Improvement (SPI) is a key point of software success. A software risk is considered as an essential characteristic of software development process which if ignored will increase the chance of project failure. For this purpose different risk management approaches are developed. These approaches lead to the identification, assessment and control of risk occurrence in software projects. Collaborative Practice Research (CPR) is one of the action research approaches for managing risk in SPI. In this approach the focus is on gathering information regarding SPI and acknowledging risk management in process development by developing risk assessment strategies and models. The main challenge of this action research approach is to validate the developed risk approach. This paper has a critical review on the existing research approach i.e. CPR. It also provides an enhanced form of CPR which modifies the current CPR approach by including a risk validation activity.
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An Enhanced Digital Image Watermarking Scheme for Medical Images using Neural Network, DWT and RSA
Статья научная
Image watermarking is the process of the hiding the one image into other image for the copyright protection. The process of watermarking must be done in this way that the pixels of the original image must remain in its original HD form. A lot of work has been done in this context in previous years but some techniques have their own applications, drawbacks as well as advantages. So, this paper will utilize three techniques i.e. Discreet Wavelet Transform (DWT), Neural Network (NN) and RSA encryption for image watermarking. In the end the performance of the proposed technique will be measured on the basis of PSNR, MSE, BCR, BER and NCC in MATLAB R2010a environment.
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An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Статья научная
A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders' blurriness. Clusters' quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.
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An Evaluation of the Critical Factors Affecting the Efficiency of Some Sorting Techniques
Статья научная
Sorting allows information or data to be put into a meaningful order. As efficiency is a major concern of computing, data are sorted in order to gain the efficiency in retrieving or searching tasks. The factors affecting the efficiency of shell, Heap, Bubble, Quick and Merge sorting techniques in terms of running time, memory usage and the number of exchanges were investigated. Experiment was conducted for the decision variables generated from algorithms implemented in Java programming and factor analysis by principal components of the obtained experimental data was carried out in order to estimate the contribution of each factor to the success of the sorting algorithms. Further statistical analysis was carried out to generate eigenvalue of the extracted factor and hence, a system of linear equations which was used to estimate the assessment of each factor of the sorting techniques was proposed. The study revealed that the main factor affecting these sorting techniques was time taken to sort. It contributed 97.842%, 97.693%, 89.351%, 98.336% and 90.480% for Bubble sort, Heap sort, Merge sort, Quick sort and Shell sort respectively. The number of swap came second contributing 1.587% for Bubble sort, 2.305% for Heap sort, 10.63% for Merge sort, 1.643% for Quick sort and 9.514% for Shell sort. The memory used was the least of the factors contributing negligible percentage for the five sorting techniques. It contributed 0.571% for Bubble sort, 0.002% for Heap sort, 0.011% for Merge sort, 0.021% for Quick sort and 0.006% for Shell sort.
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An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
Статья научная
A new neuro-fuzzy system's architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers' location of membership functions and the receptive field's parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results proves the effectiveness of the developed architecture and the learning procedure.
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An Experimental Study on College Teacher's Adoption of Instructional Technology
Статья научная
Instructional technology can make teachers do their jobs easier, better, faster and more effectively. Students can also benefit from its application. However, some college teachers do not adopt instructional technologies in their teaching as we expected. They like to teach the way they were taught as students before. Why and what factors really influence their adoption of instructional technology? This study offered a model suggestiong instructional technology adoption by college teachers depends on: the student, the teacher, the technology and the surroundings. An experiment was designed to verify the model. Samples were selected from teachers at a mid-sized university. Experimental data was collected by interviewing fifteen teachers (samples). Those interviewed represented five high-level users, five medium-level users, and five low-level users of instructional technology. Quantitative methods such as frequency counting were used to analyze and sort the data. Finally, conclusions can be drawn that different components in the model had different influential degree to the different levels of users of instructional technology.
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An Identification of Better Engineering College with Conflicting Criteria using Adaptive TOPSIS
Статья научная
Students like to find better engineering college for their higher education. It is very challenging to find the better engineering college with conflicting criteria. In this research, the criterion such as academic reputation and achievements, infrastructure, fees structure, location, quality of the faculty, research facilities and other criterion are considered to find the better engineering college. Multi Criteria Decision Making (MCDM) is the most well known branch of decision making under the presence of conflicting criteria. TOPSIS is one of the MCDM technique widely applied to solve the problems which involves many number of criteria. In this research, TOPSIS is Adaptive and applied to find better engineering college. To evaluate the proposed methodology the parameters such as time complexity, space complexity, sensitivity analysis and rank reversal are considered. In this comparative analysis, better results are obtained for Adaptive TOPSIS compared to COPRAS.
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