Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
Comparative study of inspired algorithms for trajectory-following control in mobile robot
Статья научная
This paper is devoted to the design of a trajectory-following control for a differentiation nonholonomic wheeled mobile robot. It suggests a kinematic nonlinear controller steer a National Instrument mobile robot. The suggested trajectory-following control structure includes two parts; the first part is a nonlinear feedback acceleration control equation based on back-stepping control that controls the mobile robot to follow the predetermined suitable path; the second part is an optimization algorithm, that is performed depending on the Crossoved Firefly algorithm (CFA) to tune the parameters of the controller to obtain the optimum trajectory. The simulation is achieved based on MATLAB R2017b and the results present that the kinematic nonlinear controller with CFA is more effective and robust than the original firefly learning algorithm; this is shown by the minimized tracking-following error to equal or less than (0.8 cm) and getting smoothness of the linear velocity less than (0.1 m/sec), and all trajectory- following results with predetermined suitable are taken into account. Stability analysis of the suggested controller is proven using the Lyapunov method.
Бесплатно
Статья научная
Dynamics of Malaria parasite diagnosis is complex and been widely studied. Research is on-going on the effects of climatic variations on symptomatic malaria infection. Malaria diagnosis can be asymptomatically or symptomatically low, mild and high. An analytical program is needed to detect individual malaria parasite counts from complex network of several infection counts. This study adopted the experimental malaria parasite counts collected from selected hospitals in Minna Metropolis, Niger State, Nigeria and Climatic data collected at the time the experiment was conducted from NECOP, Bosso, FUT Minna, Niger State, Nigeria. One thousand and two hundred (1,200) experimental data were collected and two classifiers Support Vector Machine (SVM), Artificial Neural Network (ANN) do the prediction. Experimental results indicated that SVM produced Accuracy 85.60%, Sensitivity 84.06%, Specificity 86.49%, False Positive Rate(FPr) 0.1351% and False Negative Rate(FNr) 0.1594% than Neural Network model of Accuracy 48.33%, Sensitivity 60.61%, Specificity 45.48%, low False Positive Rate (FPr) 0.5442% and False Negative Rate(FNr) 0.3939% as depicted in their respective confusion matrix.
Бесплатно
Comparing Elementary Students' Programming Success based on Programming Environment
Статья научная
In the Republic of Croatia Informatics is an elective course in elementary school for students from 5th to 8th grade. When it comes to programming language teachers can choose between BASIC and Logo. There are a lot of new programming environments for learning and teaching programming like Scratch and also new ways of teaching programming like game based learning. This study compares attitudinal and learning outcomes of 7th-grade students programming in Logo and Scratch. The classes were normal classes, non-extracurricular activities. The questionnaire is used to measure the attitude towards programming and programming languages. The test is constructed to measure learned programming concepts in both compared programming languages Logo and Scratch. Results showed that learning Scratch first can provide a better understanding of basic programming concept for novices in elementary school than Logo.
Бесплатно
Статья научная
This study aimed to compare 5th graders' scores obtained from Scratch projects developed in the framework of Information Technologies and Software classes via Dr Scratch web tool with the scores obtained from Computational Thinking Levels Scale and to examine this comparison in terms of different variables. Correlational research model was utilized in the study that 31 students participated in. Students were taught basic programming by using Scratch during a 6-week period. At the end of training, students' programming skills were measured via Dr. Scratch web tool. Computational thinking skills were measured using Computational Thinking Levels Scale which includes 5 factors: creativity, problem solving, algorithmic thinking, collaboration and critical thinking. Data were analyzed for internal reliability to calculate scale reliability. Cronbach Alpha reliability coefficient was found to be 0.809. It was found that scores obtained by students by using any of the measurement tools did not differ according to gender or period of computer use, however, a high level significant relationship was observed between students' programming skills with Scratch and their computational thinking skills.
Бесплатно
Статья научная
This article discusses the findings of a study that elucidated users’ acceptance of two management systems for key performance indicators (KPIs) in terms of their usefulness and ease of use scores at a higher education institution in Malaysia. The two management systems were Key Performance Indicators Monitoring System (KPI-MS) and Excel Spreadsheet System (ESS). ESS is a system developed using Microsoft Excel and has been in used since the year 2008 in the institution to calculate KPIs marks. The ESS system, however, has several shortcomings, and the KPI-MS system was developed with the intention to replace ESS. KPI-MS is an online KPI performance monitoring system which allows users to access the system wherever and whenever they want using a web browser. In addition, KPI-MS is designed as an intelligent system that is able to process raw data automatically to produce results that can be easily visualized in a graphical manner. A survey questionnaire was used to collect data on the acceptance of both systems. A total of 78 participants who were involved in KPI data processing from all 42 schools and centres in the higher education institution in Malaysia participated in this study. The instrument of this study was adapted and modified from Davis’ Technology Acceptance Model (TAM). This instrument was content-validated by three experts in the related field and the reliability index computed with Cronbach alpha was 0.955. A descriptive analysis was conducted to compare the mean scores of both KPI-MS and ESS rated by the users. The results showed that the users rated KPI-MS as a very useful system in monitoring KPI performance of their schools or centres compared to ESS. Also, users rated the KPI-MS to be significantly easier (p≤0.01) and more enjoyable to use. In conclusion, it is recommended that KPI-MS should replace the ESS system in managing KPIs data.
Бесплатно
