International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1112
Статья научная
In this paper, using fractional differential and integral operators, constructed are two-dimensional mathematical models of viscoelastic deformation, which are characterized by memory effects, spatial non-locality, and self-organization. The fractal rheological models by Maxwell, Kelvin and Voigt, their structural properties and the influence of the fractional integro-differential operator on the process of viscoelasticity are investigated. Using the Laplace transform method and taking into account the properties of the fractional differential apparatus, analytical relations are obtained in the integral form for describing the stresses of generalized two-dimensional fractional-differential rheological models by Maxwell, Kelvin, and Voigt. Since the fractional-differential parameters of fractal models allow describing deformation-relaxation processes more perfectly than traditional methods, algorithmic aspects of identification of structural and fractal parameters of models are presented in the work. Explicit expressions have been obtained to describe the deformation process for one-dimensional fractional-differential models by Voigt, Kelvin, and Maxwell. The results of identification of structural and fractal parameters of the Maxwell and Voigt models are presented. The estimates of the accuracy of the obtained identification results were found using the statistical criterion based on the correlation coefficient. The influence of fractional-differential parameters on deformation-relaxation processes is investigated.
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Статья научная
In the present paper a new cryptographic method called UES Version-III has been introduced. Nath et al recently developed few efficient encryption methods such as UES version-I, Modified UES-I, UES version-II, TTJSA, DJMNA Nath et. al showed that TTJSA and DJMNA is most suitable methods to encrypt password or any small message. The name of the present method is Ultra Encryption Standard Version-III. It is a Symmetric key Cryptosystem which includes multiple encryption, bit-wise randomization, new advanced bit-wise encryption technique with feedback. In this paper, the authors have performed encryption entirely at the bit-level to achieve greater strength of encryption. In the result section the authors have shown the spectral analysis of encrypted text as well as plain text. The spectral analysis shows that UES-III is free from standard cryptography attack such as brute force attack, known plain text attack and differential attack.
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Статья научная
In the present paper a new combined cryptographic method called Modified UES Version-I has been introduced. Nath et al. have already developed several symmetric key methods. It combines three different methods namely, Generalized Modified Vernam Cipher method, Permutation method and Columnar Transposition method. Nath et al recently developed few efficient combined encryption methods such as TTJSA, DJMNA where they have used generalized MSA method, NJJSAA method and DJSA methods. Each of the methods can be applied independently to encrypt any message. Nath et. al showed that TTJSA and DJMNA is most suitable methods to encrypt password or any small message. The name of this method is Ultra Encryption Standard modified (UES) version-I since it is based on UES version-I developed by Roy et. al. In this method an encryption key pad in Vernam Cipher Method also the feedback has been used which is considered to make the encryption process stronger. Modified UES Version-I may be applied to encrypt data in any office, corporate sectors etc. The method is most suitable to encrypt any type of file such as text, audio, video, image and databases etc.
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Undergraduate Program in Network Engineering and Security – A Feasibility Study
Статья научная
In this article, a feasibility study for initiating a new undergraduate program in network engineering and security is presented. The study was based on surveying and analyzing the current and projected future market demand for specialized network engineering graduates. The results of the study concluded that the demand for such a specialty in the work place is rapidly growing as the networking and telecommunication technologies are becoming essential and integral parts of about any organization around the world. As a result of the study, a pioneering program of network engineering and security was established at the Jordan University of Science and Technology.
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Understandings of Graduate Students on Nature of Science
Статья научная
Knowing about nature of science (NOS) and its products is a basic requirement of all graduate students and researchers due to being both members of society and experts on different scientific disciplines. As the first step, determining NOS understandings of graduate students has importance to go further in developing current situation. Therefore, this study aimed to determine NOS understandings of graduate students from different disciplines. The study included seven graduate students who were enrolled in universities as researchers. As the data collection way, face-to-face interview was utilized. The data of the study was analyzed by assigning the participants to four categories; expert, naive, mixed and not applicable. The results showed that majority of the participants were expert on social and cultural embeddedness of science and role of creativity and imagination in science while majority of the participants were naive on the aspects of “hierarchy between theories and laws”. Majority of them had mixed understandings on the aspects of existence of only one method in science, subjectivity, tentativeness. Interestingly, all of the participants were naive in terms of definition of science. The results and implications of the study will be discussed..
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Статья научная
Federated Learning (FL) is an emerging machine learning approach with promising applications. In this paper, FL is comprehensively examined in relation to teacher performance evaluation. Through FL, teachers can be evaluated based on data-driven metrics while preserving data privacy. There are several benefits, including data privacy preservation, collaborative learning, scalability, and privacy-preserving insights. Additionally, it faces problems related to communication efficiency, system heterogeneity, and statistical heterogeneity. To address these issues, we propose a novel clustering-based technique in federated learning. The technique aims to overcome the challenges of system heterogeneity and improve communication efficiency. We provide a comprehensive review of existing research on clustering techniques in the context of federated learning, offering insights into the state of the art in this field. In addition, we emphasize the need for advanced compression methods, enhanced privacy-preserving mechanisms, and robust aggregation algorithms for future federated learning research. To address these challenges, we present a clustering-based approach to address the merits and challenges of federated learning The clustering-based approach we propose in this research demonstrates promising results in terms of reducing communication overhead and improving model convergence in federated learning. These findings suggest that incorporating clustering techniques can significantly enhance the efficiency and effectiveness of federated learning algorithms, paving the way for more scalable and privacy-preserving distributed machine learning systems. The findings of this study suggest that clustering techniques can improve the efficiency and scalability of federated learning.
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Статья научная
The role of teachers in facilitating learning is undoubtedly important as they are responsible for selecting appropriate instructional approaches. Project-Based learning (PrBL) has gained recognition as an effective teaching approach as it encourages students to think critically, collaboratively, and systematically. PrBL refers to an educational approach that emphasizes student engagement and active learning through the completion of real-world projects. Students are required to acquire information, search, and experiment to solve a specific problem. PrBL is largely adopted by the higher educational level. Limited use in primary schools has been highlighted by much research. The decision to adopt such a method depends on several factors. The main drivers to make such a decision are the teachers’ preference and the readiness for support from the school. The location of the school largely contributes to the readiness, facilities, support, and quality of education. This paper examines the teachers’ point of view on the utilization of PrBL. Comparing the points of view of the teachers who taught in different locations allows us to observe the factors that should be carefully addressed in order to promote the use of PrBL in primary schools. By using both qualitative and quantitative data, this study aims to understand the potential drawbacks preventing from using the PrBL. The data mining techniques were used to discover insights from both types of data including Epistemic Network Analysis (ENA) and sequence mining. ENA employs various mathematical and statistical techniques to analyze and visualize the network structure and dynamics. It can measure the strength of connections, identify central key concepts, and compare the differences in the structure between groups. Sequence mining allows us to observe the pattern of PrBL utilization. The results showed that even though teachers viewed PrBL as a useful approach, not many of them are using it. Also, there are some inconsistencies of knowledge on the steps in the PrBL process. Additionally, teachers often mentioned several problems they faced when using the PrBL. Hence, extra support and knowledge provision are needed, especially for schools located in suburban and rural areas.
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Статья научная
Intelligent agents have been used in collaborative learning. However, they are rarely used to facilitate group interactions in collaborative m-learning environments. In view of this, the paper discusses the use of intelligent agents in facilitating collaborative learning in mobile learning environments. The paper demonstrates how to design intelligent agents and integrate them in collaborative mobile learning environments to allow group learners to improve their levels of group knowledge construction. The design was implemented in a collaborative mobile learning system running on Modular Object-Oriented Dynamic Learning Environment (Moodle) platform. The application was used in some experiments to investigate the effects of those facilitated interactions on the level of group knowledge construction. The results showed improved levels of group knowledge construction in instances where the facilitations were enabled compared to where they were disabled. The paper concludes that the use of intelligent agents in facilitating learner group interactions in collaborative mobile learning environments improves the levels of group knowledge construction. For future work, the use of intelligent agents can be tested in other areas of group interactions to enhance group learning.
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Using Components of Corpus Linguistics and Annotation Tools in Sign Language Teaching
Статья научная
As an interdisciplinary group of people working on automatic sign language recognition, authors of the article developed concept how to facilitate the process of understanding Sign Language (SL) utterances by hearing learners. Concept is based on Polish SL, and was created in response to signals from the students who indicate understanding of SL messages as the hardest part of learning process, but can be easily adapted to other SLs. In comparison to speech corpora, SL databases constitute only a small fraction. From the other side thanks to the Internet there are available video recordings with native-signers, as well as tools which enable their analysis. Developed concept is based on using one of the available annotation tools used by professionals all over the world to describe SL corpora in SL classes. Making annotations helps students in understanding foreign language, corpora analysis helps to find objective rules governing SL, computer assisted language learning can be attractive way of study and Internet – great source of materials.
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Using Computer Aided Assessment System to Assess College Students Writing Skill
Статья научная
College students are facing challenges to present their ideas by writing a paper because they rely more on information from computer and web. The purpose of this paper is to present a novel computer aided assessment system, to assess college students’ writing ability. The CAAS system comprises of an expert team, a set of achievement standard for assessment, and software systems to conduct data analysis and store related information. It has been used by institute in U.S. It yields face validity and consistency reliability. Objective evaluation results will be provided by randomly-assigned multiple reviewers. CAAS can assist college students improve writing skills, can describ a brief picture and detailed information for both school administrators and policy makers as well.
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Using Extra Credit to Facilitate Extra Learning in Students
Статья научная
Giving students extra credit work is a hotly debated pedagogical issue. This paper shares experience of using extra credit quizzes to push students to think critically and beyond the boundaries. This particular type of quizzes are not announced before and presented to students as a surprise quiz. A certain percentage of the grade earned in these quizzes was included in student's final grade calculations. With a well-developed model of questions, quiz structure and grade calculation, the presented model of extra credit eliminates negativity related to extra credit work and also motivates students into course work. Our findings showed that by relieving students from the mental pressure of testtaking andby making those tests/quizzes as extra credit; students actually performs better in solving harder problems and eventually learns more of the advanced course topics.
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Using FAHP in the educational and vocational guidance
Статья научная
This paper presents the use of the FAHPmethod (Fuzzy Analytic Hierarchy Process) to help young people choose the most appropriate activity sectors for their profile. This choice is based on three criteria: Professional interests, professional sub-interests and personality traits. This work is a part of a global context aiming to apply the Multi-criteria Decision-Making (MCDM) methods in the vocational guidance according to the process schematized in Figure 3.
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Статья научная
The urge to progressively motivate e-counselling in schools is somewhat dependent on students’ behavioural intention towards the use of counselling technologies. This paper presents an empirical approach of using Unified Theory of Acceptance and Use of Technology model to ascertain students’ behavioural intention to adopt and use e-counselling in Ghana. Questionnaires were used to collect data from two hundred and fifty (N=250) randomly selected students from Ghana. Cronbach alpha (α) was first employed to validate and ascertain the reliability of the data. Subsequently, Multiple Linear Regression (MLR) was employed in analysing the data. After that, a follow-up interview was conducted to explore the variance in our findings from the collected data through the questionnaires. In the end, the reliability of the test items contained in the questionnaire yielded strongly at 87.6%. Also, whereas the outcome of the research suggests Performance Expectancy and Social Influence as the influencing constructs (factors) towards students’ behavioural intention to adopt and use e-counselling, Facilitation Condition and Effort Expectancy had no effect on the behavioural intention of students to adopt and use e-counselling in Ghana. The findings shall be considered in developing e-counselling system for counselling delivery.
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Using Virtual Security Lab in Teaching Cryptography
Статья научная
Teaching Information Security for undergraduate students requires a safe hand-on environment for practicing. This paper focuses on using a virtual lab for two modules in cryptography concepts. At the end, a survey was conducted on a group of students at the Information Systems Department at the King Abdulaziz University to measure the performance of the students’ outcomes in the lab comparing with other students from a previous semester. The result of the survey shows a significant feedback on the system.
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Using Wavelet-Based Contourlet Transform Illumination Normalization for Face Recognition
Статья научная
Evidently, the results of a face recognition system can be influenced by image illumination conditions. Regarding this, the authors proposed a system using wavelet-based contourlet transform normalization as an efficient method to enhance the lighting conditions of a face image. Particularly, this method can sharpen a face image and enhance its contrast simultaneously in the frequency domain to facilitate the recognition. The achieved results in face recognition tasks experimentally performed on Yale Face Database B have demonstrated that face recognition system with wavelet-based contourlet transform can perform better than any other systems using histogram equalization for its efficiency under varying illumination conditions.
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Статья научная
The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability.
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Статья научная
The accelerating pace of industrial transformation necessitates a strategic reconfiguration of higher education curriculum to ensure alignment with dynamic labour market demands. This study introduces a hybrid decision-making framework that integrates Machine Learning with Multi-Criteria Decision Making techniques to evaluate and classify the readiness and relevance of academic programs. The methodological core includes the Step-wise Weight Assessment Ratio Analysis, Linguistic q-Rung Orthopair Fuzzy Numbers, and the Multi-Attributive Border Approximation Area Comparison method for criteria weighting, coupled with a classification model based on Support Vector Machine optimized using the Salp Swarm Optimization algorithm. The results demonstrate the framework's efficacy in identifying curriculum gaps and recommending adaptive enhancements, especially for programs categorized as “Needs Improvement” Beyond classification, the system facilitates strategic curriculum planning, fosters pedagogical innovation, and promotes industry-responsive learning pathways. This study highlights the transformative potential of machine learning in higher education, equipping students with the skills required to navigate an increasingly dynamic professional landscape, while offering actionable insights into instructional redesign, competency-based delivery, and industry-informed pedagogy. Future research will explore longitudinal impact assessment and broader stakeholder integration to enhance the framework’s scalability and contextual adaptability.
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Статья научная
The growing field of educational data mining seeks to analyse educational data in order to develop models for improving education and the effectiveness of educational institutions. Educational data mining is utilised to develop novel approaches for extracting information from educational databases, enabling improved decision-making within the educational system. The main objective of this research paper is to investigate recent advancements in data mining techniques within the field of educational research, while also analysing the methodologies employed by previous researchers in this area. The predictive capabilities of various machine learning algorithms, namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Random Forest, K-Nearest Neighbour, and XGBoost Classifier, were evaluated and compared for their effectiveness in determining students' academic performance. The utilisation of Random Forest and XGBoost classifiers in analysing scholastic, behavioural, and additional student features has demonstrated superior accuracy compared to other algorithms. The training and testing of these classification models achieved an impressive accuracy rate of approximately (96.46% & 87.50%) and (95.05% & 84.38%), respectively. Employing this technique can provide educators with valuable insights into students' motivations and behaviours, ultimately leading to more effective instruction and reduced student failure rates. Students' achievements significantly influence the delivery of education.
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VR IGLOOTM: A VR-Based Teaching-Learning Model for Distance Education
Статья научная
Traditional distance learning has been widely adopted for its capacity to provide educational access to a broad and diverse audience, overcoming spatial and temporal limitations. However, it needs to deliver the same immersion and learning effectiveness as face-to-face instruction, particularly in courses requiring hands-on practice, where these limitations become more pronounced. To address this, virtual reality (VR)-based distance learning has gained attention as a potential solution. Previous studies have confirmed that VR-enhanced distance learning can improve educational outcomes; however, a standardized teaching-learning model designed explicitly for VR-based distance learning has yet to be established. Consequently, instructors have often relied on conventional models, leading to variability in instructional quality. This paper proposes the VR IGLOO model, a structured VR-based teaching-learning framework tailored for distance education. For this purpose, analysis of the conventional studies and focus group interview (FGI) of the expert group were conducted. And the validity of the proposed model was verified through Delphi validation.
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Various Types of Image Noise and De-noising Algorithm
Статья научная
Image de-noising is a procedure that used to upgrade the picture quality after corrupted by the noise. There are a few techniques have been proposed for picture de-noising. Noise lessening and reclamation of image is relied upon to enhance the subjective review of a picture and the execution criteria of quantitative picture examination systems Digital picture is slanted to an assortment of commotion which influences the nature of picture. The criteria of the commotion expulsion issue rely on upon the noise sort by which the picture is defiling. To diminish the image commotion a few sorts of direct and non strategies separating methods and de-noising calculation have been proposed. Straight channels are not ready to successfully take out motivation commotion as they tend to obscure the edges of a picture. Then again non straight channels are suited for managing drive commotion. Diverse methodologies for decrease of commotion and image upgrade have been viewed as, each of which has their own restriction and favorable circumstances.
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