International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1096
Статья научная
Student academic performance (SAP) prediction is a key issue in education data analysis. Also, the assessment of students’ performance is used to enhance the efficiency of educational institutions. With the development in educational institutions and modern technology, focusing on the academic performance prediction of the student based on access to the smartphone is the need of the hour. To improve the accuracy of student academic performance prediction, the Canberra Match Normalization-based Generalized Canonical Correlative Decision Stump Classifier (CMN-GCCDSC) is introduced. Initially, student data are collected from the dataset. After the data collection process, the proposed CMN-GCCDSC technique is applied in two phases namely data preprocessing and classification respectively. In the first phase, data preprocessing is carried out to eliminate duplicate data using the Canberra Match Data Normalization technique to minimize space and time consumption. In the second phase, data classification is performed with preprocessed output to classify student academic performance using a generalized canonical correlative decision stump classifier based on Smartphone addiction prediction. The generalized canonical correlation analysis is used for decision-making. Based on analysis, student academic performance is classified and results are obtained. An experimental assessment of the proposed CMN-GCCDSC technique and existing methods is carried out with metrics such as accuracy, sensitivity, specificity, space complexity, and time complexity. The CMN-GCCDSC technique is an effective solution that addresses the limitations of Genetic Algorithm (GA)-based decision tree classifiers. By combining the Decision Stump Classifier (DSC) approach with Generalized Canonical Correlation (GCC), the most important feature to consider for academic prediction among students can be selected, ultimately reducing the dimensionality of the dataset, and improving classifier performance. With higher accuracy rates achieved, this technique can help identify at-risk students early and discover hidden trends and patterns in student performance, leading to improved academic outcomes with additional support from institutions and faculties.
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Categorization in Unsupervised Generative Self-learning Systems
Статья научная
In this study the authors investigated the connections between the training processes of unsupervised neural network models with self-encoding and regeneration and the information structure in the representations created by such models. We propose theoretical arguments leading to conclusions, confirmed by previously published experimental results that unsupervised representations obtained under certain constraints in training compliant with Bayesian inference principle, favor configurations with better categorization of hidden concepts in the observable data. The results provide an important connection between training of unsupervised machine learning models and the structure of representations created by them and can be used in developing new methods and approaches in self-learning as well as provide insights into common principles underlying the emergence of intelligence in machine and biologic systems.
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Статья научная
Application of Education Management Information System for administering school academic activities is widely recognized as an essential tool of improving quality of education for sustainable development. However, in developing countries including Tanzania, most secondary schools use manual system for collecting, storing and disseminating education information. The Manual system limits schools to have accurately, timely and reliable dissemination of education information. Moreover, when parents want to monitor student’s academic progress, the manual system requires them to visit schools physically and sometimes to wait until the end of the terminal and annual examination to get student academic report. Social and economic activities are one of the factors which limit parents to monitor student’s academic progress effectively. Poor parental involvement for monitoring and tracking student’s academic progress leads to poor student academic achievement. To address the solution, the study used structured interview and questionnaires to collect data from secondary schools education stakeholder. The collected data was analyzed using Pandas Python data analysis package. Findings from the study revealed that, poor student academic achievement in Tanzanian secondary schools is being caused by poor parental involvement in monitoring and tracking student’s academic progress. However, the study developed and implemented a centralized Education Management Information System for enhancing parental involvement in monitoring and tracking student’s academic progress. The significance of this study was to enhance parental involvement for student academic achievement by improving delivery of quality education for sustainable development.
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Child based Level-Wise List Scheduling Algorithm
Статья научная
Cloud is the Latest concept in IT. Users use the resources or services which are provided & managed by the service providers. Users need not to buy the hardware or software which now can be used on rental basis. Workflow represents the cloud application which has different tasks to be executed in an order. Scheduling algorithms are used to assign these tasks to processors and these algorithms decide the cost and time of execution. In this paper, a simple scheduling algorithm has been proposed named Child Based Level-Wise List Scheduling (CBLWLS) algorithm. According to the dependencies CBLWSL calculate priorities of tasks and finds the sequence of task execution and then maps the selected task to the available processors. We perform experiments on Epigenomics workflow structure graphs used in some real applications and their analysis shows that CBLWLS algorithm performed better than the HEFT (Heterogeneous Earliest Finish Time) algorithm, on the parameters of time of execution, execution cost and schedule length ratio.
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Classification Model of Prediction for Placement of Students
Статья научная
Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students’ activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In this study, we collected the student’s data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student’s academics performance for Training and placement. This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.
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Classification of ECG Using Chaotic Models
Статья научная
Chaotic analysis has been shown to be useful in a variety of medical applications, particularly in cardiology. Chaotic parameters have shown potential in the identification of diseases, especially in the analysis of biomedical signals like electrocardiogram (ECG). In this work, underlying chaos in ECG signals has been analyzed using various non-linear techniques. First, the ECG signal is processed through a series of steps to extract the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and instantaneous heart rate (IHR) have been calculated. Then some nonlinear parameters like standard deviation, and coefficient of variation and nonlinear techniques like central tendency measure (CTM), and phase space portrait have been determined from both the BBI and IHR. Standard database of MIT-BIH is used as the reference data where each ECG record contains 650000 samples. CTM is calculated for both BBI and IHR for each ECG record of the database. A much higher value of CTM for IHR is observed for eleven patients with normal beats with a mean of 0.7737 and SD of 0.0946. On the contrary, the CTM for IHR of eleven patients with abnormal rhythm shows low value with a mean of 0.0833 and SD 0.0748. CTM for BBI of the same eleven normal rhythm records also shows high values with a mean of 0.6172 and SD 0.1472. CTM for BBI of eleven abnormal rhythm records show low values with a mean of 0.0478 and SD 0.0308. Phase space portrait also demonstrates visible attractor with little dispersion for a healthy person’s ECG and a widely dispersed plot in 2-D plane for the ailing person’s ECG. These results indicate that ECG can be classified based on this chaotic modeling which works on the nonlinear dynamics of the system.
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Статья научная
Attention Deficit Hyperactivity Disorder (ADHD) is the most frequent brain disorders in children. Brain is the greatest complicated data processing part in human body. ADHD can begin in childhood age and may extend till adolescent too. ADHD patients activities/actions/behaviour are totally different from non ADHD patients. To solve the problem in early stage is more precious contribution for children life. Otherwise the disorder may cause further destruction in child brain. An activity of ADHD child is: carelessness, impulsive, and feverish. These activities may be common in other children too but for ADHD patients these activities are more severe and more often occurs. ADHD can arise problems at school, home, it may affect children learning ability, and child may not join with others. ADHD is one among many childhood syndromes. The paper summarises the different ADHD diagnosis methods and suggested treatments for the disorder.
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Cloud Computing ensembles Agile Development Methodologies for Successful Project Development
Статья научная
In today's IT world combination of AD (Agile Development) and CC (Cloud Computing) is a good recipe for the user needs fulfillment in efficient manners. This combination brings superiority for both worlds, Agile and Cloud. CC opportunities are optimized by AD processes for iterative software releases and getting more frequent user feedback while reducing cost. This paper analyzes the AM (Agile Methodology) processes and its benefits, issues with CC. ACD (Agile Cloud Development) approach helps a lot in overwhelming the challenges of both practices, encourages higher degree of innovation, and allows finding discovery and validation in requirements.
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Cloud Library Framework for Ethiopian Public Higher Learning Institutions
Статья научная
The ever increasing users' information need of electronic resources forced librarians to increase their effort of collecting, organizing, preserving and disseminating huge amount of electronic materials, which require state-of-the-art infrastructures so that the electronic resources be deployed easily, quickly and economically. Cloud library is the best option for libraries; especially where electronic library services divide is highly visible, like the Ethiopian higher learning institutions. Such library system allows the establishment of information technology infrastructure on demand and lowers the difficulty of control mechanism. The integration of existing library services can be implemented by clustering current library environment. The methodology employed for this work include a rigorous analysis of a recent research on one point cloud library service as an alternative to e-service provision and management. In addition, to designing a final framework a researchers conduct a survey research which helps to identify the stakeholders view on cloud library services, the model required and services needed. Questionnaire was used to collect data from purposively selected academic libraries. The selection was centered on the generation of universities namely 1st, 2nd and 3rd generation.
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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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