Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
Cloud Policy Model in the Desktop Management System
Статья научная
By studying the policy and desktop management systems theories, referencing the Internet Engineering Task Force (IETF) policy model and theories of cloud computing, this paper proposed a cloud policy model that can be applied in specific desktop management system. It mainly explains the whole system framework and its implementation mechanisms, and it discusses the problems and solutions that the cloud policy model uses in the desktop management system.
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Cloud computing: a paradigm shift in the way of computing
Статья научная
Cloud computing has been emerging out as a new and evolving paradigm with tremendous momentum. It is one of the most acceptable information technology based service which drew the attention of the people not only from the academia, industry but also registered its popularity among the general people. Features like scalability, elasticity, less entry cost, easy to access and subscription and pay per use etc. compel the businesses and end users to migrate themselves from the traditional platform to the cloud based platform. With the wide acceptability of cloud computing based services in the society, people have various myths like some think it as a new name of internet, as it shares many features of the internet while others feel it as another name of existing technology like distributed system, grid computing, and parallel computing etc.. This paper will help in making people aware of this technology by highlighting the points of difference with the existing technology and focusing on the various advantages and area of application which presents the evidence of its popularity and continual growth. The work in the paper will end with the discussion on the status of various issues and shortcomings from which it is suffering along with the present and future scope in this popular area.
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Clustering Students According to their Academic Achievement Using Fuzzy Logic
Статья научная
The software for clustering students according to their educational achievements using fuzzy logic was developed in Python using the Google Colab cloud service. In the process of analyzing educational data, the problems of Data Mining are solved, since only some characteristics of the educational process are obtained from a large sample of data. Data clustering was performed using the classic K-Means method, which is characterized by simplicity and high speed. Cluster analysis was performed in the space of two features using the machine learning library scikit-learn (Python). The obtained clusters are described by fuzzy triangular membership functions, which allowed to correctly determine the membership of each student to a certain cluster. Creation of fuzzy membership functions is done using the scikit-fuzzy library. The development of fuzzy functions of objects belonging to clusters is also useful for educational purposes, as it allows a better understanding of the principles of using fuzzy logic. As a result of processing test educational data using the developed software, correct results were obtained. It is shown that the use of fuzzy membership functions makes it possible to correctly determine the belonging of students to certain clusters, even if such clusters are not clearly separated. Due to this, it is possible to more accurately determine the recommended level of difficulty of tasks for each student, depending on his previous evaluations.
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Clustering Techniques in Bioinformatics
Статья научная
Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with data in such a manner that it can help researchers identify the required information. As a step forward to address this challenge, experts have utilized clustering techniques as a mean of exploring hidden structure and patterns in underlying data. Improved stability, robustness and accuracy of unsupervised data classification in many fields including pattern recognition, machine learning, information retrieval, image analysis and bioinformatics, clustering has proven itself as a reliable tool. To identify the clusters in datasets algorithm are utilized to partition data set into several groups based on the similarity within a group. There is no specific clustering algorithm, but various algorithms are utilized based on domain of data that constitutes a cluster and the level of efficiency required. Clustering techniques are categorized based upon different approaches. This paper is a survey of few clustering techniques out of many in data mining. For the purpose five of the most common clustering techniques out of many have been discussed. The clustering techniques which have been surveyed are: K-medoids, K-means, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Self-Organizing Map (SOM) clustering.
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Clustering based architecture for software component selection
Статья научная
The component-based software engineering (CBSE) consists of component selection, qualification, adaptation, assembly and updating of components according to the requirements. The focus of this paper is software component selection only. Now-a-days many selection processes, techniques and algorithms are proposed for this task. This paper presents generalized software component selection architecture using clustering. The architecture is divided into four tiers namely Component Requirements and Component Selection Tier, Query and Decision Tier, Application logic tier with Clustering and Component Cluster Tier. The architecture offers manifold advantages like i) presenting a generalized architecture where the existing techniques can be applied, reducing the search space for the component selection. ii) It also illustrates the usage of clustering in the software component selection without the need for pre- specification of number of clusters and considering more than two features while clustering. iii)The cluster validation is performed to check the correctness of the clusters. This complete selection process is validated on a representative instance of set of components.
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Статья научная
The paper deals with a problem (studied by the authors for several years) of the cognitive barriers (difficulties) related to the third component of a peda-gogical triad "how to learn, what to learn, how to study". At the first stage, methodical approaches to the control of students knowledge in mathematical and natural-science disciplines were worked our. At the next stage, the cognitive barriers of the students arising in the course of studying the above-mentioned disciplines were elucidated. The results obtained during the performance of the two specified stages allowed methodological rec-ommendations related to "The concept of modern natu-ral sciences" discipline to be developed.
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Collaborative Anti-jamming in Cognitive Radio Networks Using Minimax-Q Learning
Статья научная
Cognitive radio is an efficient technique for realization of dynamic spectrum access. Since in the cognitive radio network (CRN) environment, the secondary users (SUs) are susceptible to the random jammers, the security issue of the SU's channel access becomes crucial for the CRN framework. The rapidly varying spectrum dynamics of CRN along with the jammer's actions leads to challenging scenario. Stochastic zero-sum game and Markov decision process (MDP) are generally used to model the scenario concerned. To learn the channel dynamics and the jammer's strategy the SUs use reinforcement learning (RL) algorithms, like Minimax-Q learning. In this paper, we have proposed the multi-agent multi-band collaborative anti-jamming among the SUs to combat single jammer using the Minimax-Q learning algorithm. The SUs collaborate via sharing the policies or episodes. Here, we have shown that the sharing of the learned policies or episodes enhances the learning probability of SUs about the jammer's strategies but reward reduces as the cost of communication increases. Simulation results show improvement in learning probability of SU by using collaborative anti-jamming using Minimax-Q learning over single SU fighting the jammer scenario.
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Collaborative Question Answering System Using Domain Knowledge and Answer Quality Predictor
Статья научная
With the rapid development of E-Learning, collaborative learning is important for teaching, learning methods and strategies. Studies over the years shown that students had actively and interactively involved in a classroom discussion to gain their knowledge. Collaborative learning is able to accommodate the situation, where student can exploit and share their resources and skills by asking for information, evaluating, monitoring one another’s information and idea. Therein, the activity allowing one question has many answer or information that should be selected. Every answer has a weighting and very subjective to select. In this paper, we introduce question answering for collaborative learning with domain knowledge and answer quality predictor. By using answer quality predictor, the quality of answers could be determined. On the other side, domain knowledge could be used as knowledge about the environment in which the target information operates as a reference. Through the process of collaborative learning, the usage knowledge base will be enriched for future question answering. Further, not only the student could get answers form others but also provided by the system.
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Colleges Require ICT Facilities to Enhance Educational and Employment Prospects
Статья научная
ICT infrastructure and its effective application in the college have been a topic of conflict. Many studies clearly state that the usage of ICT in college has never been regulated by the students. If students are not trained properly in ICT in college, a large segment of society will always be unemployed, implying that sufficient attention and direction should be provided to the college by the college administration. Students can be educated via online and offline courses if ICT is properly managed. This research examined at the state of ICT at the college. The survey included administrative and teaching employees from seven institutions. We only selected institutions that previously have a thorough comprehension of the survey and understood how to use ICT effectively. However, several findings did not meet our expectations. Some colleges did not grasp the survey well enough. They were utterly unaware of how to use ICT in the present and future while keeping the interests of students and institutions in mind. Some college surveys had some variances, although they were minimal in comparison to the overall survey. Our data suggest that majority of the colleges do not understand about the correct usage of ICT. They are unaware that with ICT, learners can be made employable, and financially disadvantaged students can receive education at a low cost. They were uncertain about how to use ICT and how to advance ICT at the institution so that online and offline courses could begin. Our findings also imply that by interacting with other institutions throughout the world, ICT incompetence can be overcome. Government and college administration should work together to alleviate the ICT scarcity to some extent.
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Статья научная
Customer Relations Management (CRM) is an essential marketing approach which telecommunication companies use to interact with current and prospective customers. In recent years, researchers and practitioners have investigated customer churn prediction (CCP) as a CRM approach to differentiate churn from non-churn customers. CCP helps businesses to design better retention measures to retain and attract customers. However, a review of the telecommunication sector revealed little to no research works on appetency (i.e. customers likely to purchase new product) and up-selling (i.e. customers likely to buy upgrades) customers. In this paper, a novel up-selling and appetency prediction scheme is presented based on support vector machine (SVM) algorithm using linear and polynomial kernel functions. This study also investigated how using different sample sizes (i.e. training to test sets) impacted the classification performance. Our findings demonstrated that the polynomial kernel function obtained the highest accuracy and the least minimum error in the first three sample sizes (i.e. 80:20, 77:23, 75:25) %. The proposed model is effective in predicting appetency and up-sell customers from a publicly available dataset.
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Статья научная
The purpose of this paper is to prove new common fixed point theorem in Intuitionistic fuzzy metric space. While proving our result, we utilize the idea of occasionally weakly compatible maps due to Al-Thagafi and N. Shahzad. Our result substantially generalize and improve a multitude of relevant common fixed point theorems of the existing literature in fuzzy metric and Intuitionistic fuzzy metric space.
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Статья научная
In this paper, we prove two common fixed point theorems. In first theorem, we prove common fixed point theorem for two weakly compatible self maps of type (A) satisfying an integral type contractive condition in intuitionistic fuzzy metric space. In the second theorem, we prove common fixed point theorem for two weakly compatible maps satisfying an integral type contractive condition in intuitionistic fuzzy metric space. These results are proved without exploiting the notion of continuity and without imposing any condition of t-norm and t-conorm.
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Статья научная
The article is devoted to the question of communicative competence formation, represented in all spheres of professional application in higher education and states that the degree of its formation depends on a person's approach to behave in different social situations. This study examines the essence and structure of communicative competence, as well as the system of its formation while teaching a foreign language to higher education students of economics. Evaluation of the characteristics of developing communicative competence when working with economic texts is carried out as the main communicative unit on the example of the use of specific material in speech. The methodology of the formation of communicative competence among future economists is theoretically determined and experimentally tested through interaction with economic texts in English for professional purposes (49 students aged 17–20 years participated in the research). Analysis of linguistic, psychological, psycholinguistic and methodological bases of communicative competence formation, questionnaires of students and survey results gave grounds for the development of experimental methods of these competences formation by future economists in the process of studying modern foreign language. The interactive methods of learning from economic texts were developed under a new concept of teaching foreign language for the formation of communicative competence and introduced in experimental groups of learners. The data indicated a significant increase in intermediate and high levels of future economists’ communicative competences formation in groups with interactive classes.
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Communicative Tasks as a Means of Developing the Emotional Intelligence of Students
Статья научная
Emotional intelligence (EI), the ability to manage emotions, empathize, and regulate one's behavior, is essential for every member of society nowadays. Various social institutions, including these at the educational institutions, influence its development. The paper examines the relationship between solving communicative tasks and developing students' emotional intelligence and describes the features of students' native language training. Here, we are providing examples of analytical, associative, and research communicative tasks. The tasks involve communicative activities, emotional and sensory perception of the text, and the involvement of analytical and creative abilities. A pedagogical experiment was conducted to confirm the development of EI in the process of solving communicative tasks. Hall's methodology determined the level of EI formation. The total number of respondents was 156 (control group – 77 people, experimental group – 79 people). Three control surveys were conducted in March (at the beginning of the experiment), June (in the middle of the experiment), and December (at the end of the experiment) of 2022 to track changes in the development of EI. The empirical results were subjected to statistical analysis. Pearson's criterion confirmed the normality of the distributions, and Student's criterion showed the statistical significance of changes at the end of the experiment for all indicators and for the indicators "emotional awareness," "managing one's emotions," and "empathy" in the middle of the experiment. The study confirmed a new method of developing EI - the use of communicative tasks in teaching the native language, which extends the existing research results on the development of EI in learning foreign languages. The study lays grounds for several conclusions, including that solving communicative tasks may model "life situations" and indirectly form models of the future behavior of young people. It also identifies an urgent need for special courses for teachers on developing skills to create communicative tasks; it is essential to modernize teacher training programs, which should include the development of skills to model situational emotionally colored tasks that do not have an unambiguous answer and require analysis, comparisons, and evaluations.
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Статья научная
Compression is a way to compress data to produce a file with a size smaller than its original size. Compression techniques can be performed on text data or binary, image (JPEG, PNG, ....), Audio (MP3, AAC, RMA, WMA, ... ..) and video (MPEG, H261, H263, ....). Compression Data is a way to process information using bits or other information units lower than the representation of data that is not encoded with a particular encoding system. Data compression has a function to condense, shrink data to its size becomes smaller. With the smaller size of storage space required then less to make it a more efficient storing process, but it also can shorten the time of the data exchange. Data compression using the run-length encoding (RLE) is a technique used to compress the data contains recurring characters. Run-length encoding (RLE) is a very simple form of data. In RLE running data (sequence data value is the same with many of the data elements in a row) is stored as the value of a single data and calculated the length of the data. This method is useful for data that contains a lot of data, such as simple graphic images (icons, line drawings, and animation). Data compression can be realized in various ways. Data compression can be designed using the VHDL language and can also use a microcontroller. Every realization of data compression has different performances. In this research, the performance was analyzed at the speed of compression. From the experiments conducted, the results of compression speed using VHDL implementation are 6.95 KB / s and microcontroller implementation is 5.34 KB/s. Based on the experimental results from the implementation of data compression using VHDL proposed in this study has a speed of 30.11% better.
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Comparative Studies of Self-organizing Algorithms for Forecasting Economic Parameters
Статья научная
This manuscript presents the economic research results based on their input-output characteristics and functional description with inductive modeling methods and tools. There are a wide plethora of methods to be used for solving this type of problem, including various neural network models, linear and nonlinear regressions, reference vectors’ methods, fuzzy models, etc. The main disadvantage of these methods is that the obtained models cannot always interpret and obtain a model of optimal complexity. Unlike the mentioned methods and tools, the group method of data handling (GMDH) allows building models directly from a data sample without the attraction of additional a priori information. This algorithm admits finding internal dependencies in the data and determining optimal model complexity. There is a broad range of iterative GMDH algorithms that have been developed and studied. Oversampling algorithms are applicable for solving the structural identification problems for a limited number of arguments. Iteration algorithms are suitable for solving tasks with many arguments, but they do not guarantee proper structure development. Multi-row GMDH iteration algorithms are the most popular ones. However, they have several sufficient defects, such as informative argument loss or non-informative argument inclusion, as well as a polynomial degree of exponential growth. In this context, the applicability of the GMDH-based iterative and combined architectures for solving the model's interrelation problems between a volume of capital investments and GDP by activity types in the transport branch is considered. The determination coefficient is utilized for the estimation of the obtained models based on a complicated evaluation procedure. The Kolmogorov-Smirnov criterion estimates the model’s adequacy. The F-criterion Fisher assesses the significance of polynomial models. The demonstrated results proved that the combined iterative and combinatorial algorithms turned out to be the most effective solution for all evaluation criteria.
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Comparative Study of High Speed Back- Propagation Learning Algorithms
Статья научная
Back propagation is one of the well known training algorithms for multilayer perceptron. However the rate of convergence in back propagation learning tends to be relatively slow, which in turn makes it computationally excruciating. Over the last years many modifications have been proposed to improve the efficiency and convergence speed of the back propagation algorithm. The main emphasis of this paper is on investigating the performance of improved versions of back propagation algorithm in training the neural network. All of them are assessed on different training sets and a comparative analysis is made. Results of computer simulations with standard benchmark problems such as XOR, 3 BIT PARITY, MODIFIED XOR and IRIS are presented. The training performance of these algorithms is evaluated in terms of percentage of accuracy, and convergence speed.
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Comparative Study of Supervised Algorithms for Prediction of Students’ Performance
Статья научная
Predicting academic performance of the student is crucial task as it depends on various factors. To perform such predictions the machine learning and data mining algorithms are useful. This paper presents investigation of application of C5.0, J48, CART, Naïve Bayes (NB), K-Nearest Neighbour (KNN), Random Forest and Support Vector Machine for prediction of students’ performance. Three datasets from school level, college level and e-learning platform with varying input parameters are considered for comparison between C5.0, NB, J48, Multilayer Perceptron (MLP), PART, Random Forest, BayesNet, and Artificial Neural Network (ANN). Paper presents comparative results of C5.0, J48, CART, NB, KNN, Random forest and SVM on changing tuning parameters. The performance of these techniques is tested on three different datasets. Results show that the performances of Random forest and C5.0 are better than J48, CART, NB, KNN, and SVM.
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Comparative analysis of stemming algorithms for web text mining
Статья научная
As the massive data is increasing exponentially on web and information retrieval systems and the data retrieval has now become challenging. Stemming is used to produce meaningful terms by stemming characters which finally result in accurate and most relevant results. The core purpose of stemming algorithm is to get useful terms and to reduce grammatical forms in morphological structure of some language. This paper describes the different types of stemming algorithms which work differently in different types of corpus and explains the comparative study of stemming algorithms on the basis of stem production, efficiency and effectiveness in information retrieval systems.
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Статья научная
Though University enrolment in Nigeria is on the increase, more males than females are still being enrolled today, which varies according to discipline as well as from one geopolitical zone of the country to another. This is more pronounced in Science, Technology, Engineering and Mathematics fields, as female enrolment seems to be higher in the commercial and arts courses than the sciences and engineering. Secondary data were obtained from the Nigerian Bureau of Statistics, based on the Joint Admission and Matriculation Board registrations for a period of 5 years, spanning 2011 to 2015. The data were classified based on the six geopolitical zones in Nigeria, and multivariate data analysis, supported by Multivariate Analysis of Variance was carried out on the data. The results obtained revealed that there is still a wide variance in male and female enrolment in these fields, with male enrolment being significantly higher than that of female candidates. It also revealed that female enrolment varies depending on the geopolitical zone, with female enrolment in Science, Technology, Engineering and Mathematics being generally higher in geographical zones in southern Nigeria compared with those in northern Nigeria. The results obtained were further compared with data obtained from previous researches and the comparison was discussed. In addition, this study offers recommendations on how to encourage more female participation in Science, Technology, Engineering, and Mathematics.
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