Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
Software Testing Resource Allocation and Release Time Problem: A Review
Статья научная
Software testing Resource allocation and release time decisions are vital for the software systems. The objective behind such critical decisions may differ from firm to firm. The motive of the firm may be maximization of software reliability or maximization of number of faults to be removed from each module or it may be minimization of number of faults remaining in the software or minimization of testing resources. Taking into consideration these different aims, various authors have investigated the problem of resource allocation and release time problem. In this paper we investigate various software release policies and resource allocation problem, for example, policies based on the dual constraints of cost and reliability.
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Solution and Level Identification of Sudoku Using Harmony Search
Статья научная
Different optimization techniques have been used to solve Sudoku. Zong Woo Geem have applied harmony search in Sudoku to get better result. He has taken a Sudoku and time complexity has been optimized by different values of parameters. But, he has not given way of solution in details. He has also not given any idea to recognize the level of Sudoku. In this paper, an algorithm has been proposed based on harmony search to solve and identify the Sudoku efficiently. It has been observed that time complexity i.e. the maximum number of iteration has been reduced by choosing appropriate parameter values. The level of Sudoku has also been identified using probability metric. Finally, the number of iterations has been calculated with different values of parameters and the level of different Sudoku has been identified.
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Solution for Using FEMM in Electrostatic Problems with Discrete Distribution Electric Charge
Статья научная
Finite Element Method Magnetics (FEMM) is an open source software package for solving electromagnetic problems based on the finite element method. The application can numerically solve linear electrostatic problems and magnetostatic 2D problems, respectively low frequency magnetic, linear harmonic and nonlinear. FEMM is a product much used in science and engineering that, in the last 15 years, has begun to be used more and more in the academic environment. Despite the fact that FEMM can be used to solve complex problems in science and engineering, electrostatic FEMM cannot work directly with discrete electric charge distributions, that is, point electric charge. This work presents a FEMM model for simulating point electric charge that can be used in case of electrostatic problems with discrete charge distributions. The numerical solution for the electrostatic field is compared with the analytical solution. This model can be used in the case of an assembly of point electric charges with axial symmetry.
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Spam Mail Detection through Data Mining – A Comparative Performance Analysis
Статья научная
As web is expanding day by day and people generally rely on web for communication so e-mails are the fastest way to send information from one place to another. Now a day's all the transactions all the communication whether general or of business taking place through e-mails. E-mail is an effective tool for communication as it saves a lot of time and cost. But e-mails are also affected by attacks which include Spam Mails. Spam is the use of electronic messaging systems to send bulk data. Spam is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. In this study, we analyze various data mining approach to spam dataset in order to find out the best classifier for email classification. In this paper we analyze the performance of various classifiers with feature selection algorithm and without feature selection algorithm. Initially we experiment with the entire dataset without selecting the features and apply classifiers one by one and check the results. Then we apply Best-First feature selection algorithm in order to select the desired features and then apply various classifiers for classification. In this study it has been found that results are improved in terms of accuracy when we embed feature selection process in the experiment. Finally we found Random Tree as best classifier for spam mail classification with accuracy = 99.72%. Still none of the algorithm achieves 100% accuracy in classifying spam emails but Random Tree is very nearby to that.
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Статья научная
This study employed geographic information systems (GIS) to analyze the spatial factors related to dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) epidemics. Chachoengsao province, Thailand, was chosen as the study area. This study examines the diffusion pattern of disease. Clinical data including gender and age of patients with disease were analyzed. The hotspot zonation of disease was carried out during the outbreaks for years 2001 and 2007 by using local spatial autocorrelation statistics (LSAS) and kernel-density estimation (KDE) methods. The mean center locations and movement patterns of the disease were found. A risk zone map was generated for the incidence. Data for spatio-temporal analysis and risk zonation of DF/DHF/DSS were employed for years 2000 to 2007. Results found that the age distribution of the cases was different from the general population’s age distribution. Taking into account that the quite high incidence of DF/DHF/DSS cases was in the age group of 13-24 years old and the percentage rate of incidence was 42.9%, a DF/DHF/DSS virus transmission out of village is suspected. An epidemic period of 20 weeks, starting on 1st May and ending on 31st September, was analyzed. Approximately 25% of the cases occurred between Weeks 6-8. A pattern was found using mean centers of the data in critical months, especially during rainy season. Finally, it can be identified that from the total number of villages affected (821), the highest risk zone covered 7 villages (0.85%); the moderate risk zone comprised 39 villages (4.75%); for the low risk zone 22 villages (2.68%) were found; the very low risk zone consisted of 120 villages (14.62%); and no case occurred in 633 villages (77.10%). The zones most at risk were shown in districts Mueang Chachoengsao, Bang Pakong, and Phanom Sarakham. This research presents useful information relating to the DF/DHF/DSS. To analyze the dynamic pattern of DF/DHF/DSS outbreaks, all cases were positioned in space and time by addressing the respective villages. Not only is it applicable in an epidemic, but this methodology is general and can be applied in other application fields such as dengue outbreak or other diseases during natural disasters.
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Spatial and Transform Domain Filtering Method for Image De-noising: A Review
Статья научная
Present investigation reveals the quantum of work carried in the filtering methods for image de-noising. An image is often gets corrupted by various noises that are visible or invisible while being gathered, coded, acquired and transmitted. Noise influences various process parameters that may cause a quality problem for further image processing. De-noising of natural images is appears to be very simple however when considered under practical situations becomes complex. It has been cited by various author that parameter such as type and quantum of noise, image etc. through single algorithm or approach becomes cumbersome when results are optimized. In order to improve the quality of an image noise must be removed when the image is pre-processed and the important signal features like edge details should be retained as much as possible. The search on efficient image de-noising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper reviews significant de-noising methods (spatial and transform domain method) and their salient features and applications. One filter in each category has been taken in consideration to understand the characteristics of both spatial and transform domain filters.
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Specialization Impact on Internet Resource Usage: Omani Undergraduate Learner’s Perspectives
Статья научная
The current educational processes need various tools and technological supports in order to attain the required level of knowledge. The learning processes have been simplified with the help of different resources including internet resources. The usage of internet resources usage depends on the learner’s requirements in the field of study. This research had identified the significant impacts of the specialization of the learners on the internet resource usage. Also, the paper identified some specializations that have major influences in using such internet resources in learning processes. The study had been conducted in Omani undergraduate student’s environment with respect to selected specializations. The specialization impacts on the frequency of using internet resources, places of searching and purpose of using internet resources in the learning processes were analyzed using conditional probabilities and impacts had been identified with the help of decision tree diagram. The results showed that the students studying Information Technology specialization had greater impact in using internet resources in their learning processes compared to others at undergraduate levels.
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Specific queries optimization using Jaya approach
Статья научная
The Fast query engine is a requirement as a supporting tool for the semantic web technology application such as Electronic Commerce environ. As the large data is represented using the effective data representation called RDF. The focus of this paper is to optimize the specific type of the query called Cyclic query and star query on main-memory RDF data model using ARQ query engine of Jena. For the considered problem, we ruminate a Jaya algorithm for rearrangement of the order of triple pattern and also compare the results with an already proposed approach in the literature. The evaluation result shows that Jaya performs better in terms of execution time in comparison to Ant Colony Optimization.
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Speed Learning: Maximizing Student Learning and Engagement in a Limited Amount of Time
Статья научная
Active learning has warranted great promise in improving student engagement and learning. It is not a new thought and has been promoted and encouraged as early as the 1980s. Due to the many benefits of active learning it is being practiced by many faculty in their classrooms. Faculty are urged to self-reflect on their teaching styles and work on improving the pedagogies to capture and maintain student interest by increasing student engagement. Although active learning has been used as an instrument to engage students and ultimately increase learning, it has seldom been implemented to directly impact learning relative to time. This paper explores the application of active learning pedagogy to help achieve maximum learning in a limited period of time. The active learning method employed in this study is grounded in classic pedagogies that have been developed based on various psychological theories of learning, motivation and engagement. After the employment of a series of this active learning technique a survey of the students revealed an increase in student learning.
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Stabilitty of Anti-periodic Solutions for Certain Shunting Inhibitory Cellular Neural Networks
Статья научная
In this paper, the existence and exponential stability of anti-periodic solutions for shunting inhibitory cellular neural networks (SICNNs) with continuously distributed delays are considered by constructing suitable Lyapunov fuctions and applying some critial analysis techniques. Our results remove restrictive conditions of the global Lipschitz and bounded conditions of activation functions and new sufficient conditions ensuring the exist-ence and exponential stability of anti-periodic solutions for SICNNs are obtained. Moreover, an example is given to illustrate the feasibility of the conditions in our results.
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Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks
Статья научная
In this paper, we describe a novel spatial domain method for steganography in RGB images where a secret message is embedded in the blue layer of certain blocks. In this algorithm, each block first chooses a unique t1xt2 matrix of pixels as a “matrix pattern” for each keyboard character, using the bit difference of neighbourhood pixels. Next, a secret message is embedded in the remaining part of the block, those without any role in the “matrix pattern” selection procedure. In this procedure, each pattern sums up with the blue layer of the image. For increasing the security, blocks are chosen randomly using a random generator. The results show that this algorithm is highly resistant against the frequency and spatial domain attacks including RS, Sample pair, X2 and DCT based attacks. In addition, the proposed algorithm could provide more than 84.26 times of capacity comparing with a competitive method. Moreover, the results indicated that stego-image has almost 1.73 times better transparency than the competitive algorithm.
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Stereo rectification of calibrated image pairs based on geometric transformation
Статья научная
The objective of stereo rectification is to make the corresponding epipolar lines of image pairs be parallel to the horizontal direction, so that the efficiency of stereo matching is improved as the corresponding points stay in the same horizontal lines of both images. In this paper，a simple and convenient rectification method of calibrated image pairs based on geometric transformation is proposed, which can avoid the complicated calculation of many previous algorithms such as based on epipolar lines, based on fundamental matrix or directly depend on corresponding points. This method is divided into two steps including coordinate system transformation and re-projection of image points. Firstly, we establish two virtual cameras with parallel optical axis by coordinate system transformation based on the pose relationship of the two cameras from calibration result. Secondly, we re-project the points of the original image onto new image planes of the virtual cameras through geometrical method, and then realized the stereo rectification. Experiments of real stereo image pairs show that the proposed method is able to realize the rectification of stereo image pairs accurately and efficiently.
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Stochastic game with lexicographic payoffs
Статья научная
Stochastic games are discussed as a priva-te class of a general dynamic games. A certain class of lexicographic noncooperative games is studied - lexi-cographic stochastic matrix games . The problem of the existence of Nash equilibrium is studied with two analyses - standard and nonstandard way. Standard means using the same kind of mixed strategies in case of scalar games. In this case in lexi-cographic stochastic matrix game Nash equilibrium may not be existed. Its existence takes place in relevant stochastic affine matrix game to the existence of Nash equilibrium. In game a set of Nash equi-librium is given by means of relevant stochastic affine matrix game's set of equilibrium. The sufficient condi-tions of the existance such affine game is proved. In nonstandard way of analyses we use such mixed stra-tegies, they use components with lexicog-raphic probabilites. In this case the kinds of subsets of a set of equilibrium in game are described.
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Статья научная
Purpose – Achieving organizational excellence requires high-levels of commitment and coordination of multiple dimensions throughout an organization. One dimension that most organizational excellence frameworks highlight is the necessity of having a comprehensive learning system, which focuses on knowledge and training. Therefore, a proper understanding of knowledge management is important to identify the factors that drive the achievement of high organizational performance and excellence. Design/methodology/approach – A case study approach has been used, which utilized in-depth interviews with key personnel to obtain valuable insights into the use of strategic knowledge management to drive operational excellence. While the survey was conducted to assist in analyzing certain perspectives related to knowledge management within the organization. Findings – The main findings highlight how the emerging enterprise social network systems have played a major role in institutionalizing collaboration and corporate socializing within the organization, which are both important factors for strategic knowledge management to be successful. Based on the study, a framework has been proposed to assist in the successful implementation of strategic knowledge management for the achievement of organizational excellence. Research limitations/implications – Due to the research approach used some of the findings may include varying degrees of bias in the responses obtained. In addition, due to the lack of time available the proposed framework was not evaluated. Originality/value – Investigating the link and relationship between strategic knowledge management and organizational performance that lead to higher levels of organizational excellence.
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Strategies of Nonsolidary Behavior in Teaching Organization
Статья научная
A system of interpersonal relationship and its modeling in the form of finite noncooperative game is studied in this article by means of payoff functions. In such games for the main principle of optimality Nash’s Equilibrium Situation is acknowledged. The stages of development of Game Theory are analyzed including the modern situation. Two groups – nonsolidary and solidary of different behaviors characterized for the relationship are defined. The strategies of nonsolidary behavior characterized for the strategic relationships of the players are described and the strategies of solidary behavior are connected with negotiations and agreements. Teaching organization is defined as a management of system comprising a p teacher (professor) and K = {1,2,..., n} collective of pupils (students). Each participant of s system has its own interest and difference from each other. This situation gives us a ground to consider some aspects of Game Theory model for optimal management of s.
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Structural Protein Function Prediction - A Comprehensive Review
Статья научная
The large amounts of available protein structures emerges the need for computational methods for protein function prediction. Predicting protein function is mainly based on finding similarities between proteins with unknown function with already annotated proteins. This may be achieved using different protein characteristics: sequences, interactions, localization, structure and or psychochemical. A lot of review papers mainly focus on sequence and psychochemical features-based methods. This is because sequence and psychochemical data are easy to deal with and to interpret the results, and much available compared to protein structures. However, structure-based computational methods provide additional accuracy and reliability of protein function prediction. Therefore, unlike many review papers, this paper presents an up-to-date review on the structure-based protein function prediction. The aim was to provide a recent and comprehensive review of protein structure related topics: function aspects, structural classification, databases, tools and methods.
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Student Learning Ability Assessment using Rough Set and Data Mining Approaches
Статья научная
All learners are not able to learn anything and everything complete. Though the learning mode and medium are different in e-learning mode and in classroom learning, similar activities are required in both the modes for teachers to observe and assess the learner(s). Student performance varies considerably depending upon whether a task is presented as a multiple-choice question, an open-ended question, or a concrete performance task [3]. Due to the dominance of e-learning, there is a strong need for an assessment which would report the learning ability of a learner based on the learning skills under various stages. This paper focuses on assessment through multiple choice questions at the beginning and at the end of learning. The learning activities of the learner are tracked during the learning phase through a Continuous Assessment test to realize the understanding level of the learner. The scores recorded in the database is analyzed using a Rough Set Approach based Decision System. The effectiveness of teaching learning process indicates the learning ability of the learner, presented in a Graphical form. It is evident from the results that the entry behavior and the behavior while learning determine the actual learning. Students generate internal opinion as they monitor their engagement with learning activities and tasks and also assess progress towards goals. Those who are effective at self-regulation, however, produce better feedback or are able to use the self-opinion they generate to achieve their desired goals. The tool developed assists the teacher to be aware of the learning ability of learners before preparing the content and the presentation structure towards complete learning. In other words, the developed tool helps the learner to self-assess the learning ability and thereby identify and focus to gain the lacking skills.
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Student testing and monitoring system (STMS) using Nlp
Статья научная
In the domain of knowledge, there is a rising demand for such a System to provide learning support via a platform which can generate any sort of questions automatically from provided source either (PDF) books or simply any keyword against a user needs to perform a test where STMS serves the purpose. Regarding Keyword operation, the System scraps all the text from Wikipedia and converts it into multiple choice questions. Moreover, it summarizes raw text from Wikipedia and parse the text from provided content to generate Multiple-Choice Questions(MCQs). The System also finds all the Named Entities and POS (Parts of speech tags) in the content to create relevant questions. The questions include Multiple-Choice Questions(MCQs), Cloze based questions and WH- questions (why, where, when etc.). In addition, when users score standard points in the test then they qualify for earning zone where they can earn money ($ Dollars) for scoring points in each test. The Income comes from AdSense applied on the website and other Local ads, Affiliating marketing and advertisements. All in all, the System would help in educational learning by providing helping material in the lacking knowledge areas after analyzing the tests users have performed while the Web-Traffic is the key to Success for monetary benefits.
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Student-centered Role-based Case Study Model to Improve Learning in Decision Support Systems
Статья научная
One of the important learning objectives of our bachelor course on "Techniques in Decision Support Systems" is to develop understanding of core decision making process in real-life business situations. The conventional teaching methods are unable to explain complexities of real-life business. Although the classroom discussions can be effective to understand general factors, such as opportunity cost, return on investment, etc. affecting business decisions, the effects of factors like dynamic business environment, incomplete information, time pressure etc. can not be truly explained through such simple discussions. In this paper, we describe our experience of adopting student-centered, role-based, case study to deal with this situation. The interactive case-based study not only provided students with experiential learning, but also gave them liberty to test their thoughts. As a result, we observed improved students' learning as well as improved grades. In addition, this approach made classes more dynamic and interesting.
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Students Classification With Adaptive Neuro Fuzzy
Статья научная
Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and can be used as an effective alternative system for classifying students.
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