International Journal of Modern Education and Computer Science @ijmecs
Journal articles - International Journal of Modern Education and Computer Science
All articles: 1173
Enhanced Learning with Abacus and its Analysis Using BCI Technology
Scientific article
Although technology is successfully being used these days as a tool to improve education at all levels, its improper usage is curbing the imagination of the student community, leading to a diminution in their thinking capacity and ability to focus and concentrate. As attention is a vital cognitive feature of any learning process, students these days are not coping well with this process. This study attempts to analyse the focusing capacity of students from two different backgrounds; students who have undergone training in mental arithmetic and usage of the abacus and students without any formal mental arithmetic training. The analysis is done through a simple Electroencephalogram (EEG) based gaming software, which measures the time needed for the players to focus and reach a specific attention level. An EEG device measures brain invoked potentials. Due to the availability of low cost commercial grade EEG devices, usage of these devices today, is not confined only to research and clinical purposes, but is being used beyond these applications. This study is an attempt to apply Brain Computer Interface (BCI) Technology to assess cognition. The performance of the first category was found to be better than the second set of students.
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Scientific article
With the outgrowth of cloud computing, a large amount of private information is stored over cloud servers, which is in encrypted format. But searching over encrypted data is very difficult. Earlier search schemes were based on Boolean search through keywords. But don't consider relevance of files. After that ranked search comes into its role, which uses searchable symmetric encryption (SSE). To achieve more practical and efficient design method was further modified to "Order preserving symmetric encryption" (OPSE), which uses primitives and indexed metadata files used in ranked SSE. In this proposed work further enhancements are done to reduce storage space for encrypted metadata using Porter Stemming method. Improvements in retrieval time are also done by using Boyer Moore's searching algorithm.
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Enhanced Ring Signatures Schemes for Privacy Preservation in Wireless Sensor Networks
Scientific article
Advancements in the domains of low-data-rate wireless networking and micro-electro-mechanical systems enabled the inception of a new networking domain, called wireless sensor network. These ad-hoc kind of networks have diversified applications in battlefield surveillance, disaster monitoring, intrusion detection etc. These networks consist plethora of sensor nodes which are severely resource constrained. As the application of the wireless sensor network is increasing, there is an emerging need for the security and privacy scheme which makes the network secure from various attacks and hide the ongoing activities in the network from a non-network entity. Privacy in wireless sensor network is yet a challenging domain to work on. Lot of work has been done to ensure privacy in the network. These relate to provide privacy in terms of the network entity and the privacy of the sensed information. Most of the solutions till date is based upon routing in the network layer, random walk based flooding, dummy data injection and cross layer solutions. Each of the schemes induce some overhead in the network. A light weight scheme is always desired for resource constraint wireless sensor networks. In this work we will propose a scheme which assures the privacy of the nodes in the network along with the privacy of the event generated in the network through a self organizing scheme. Through various simulation results the validity of our scheme among different network scenarios will be shown. We will also prove through graphical results that our proposed scheme enhances network lifetime quite satisfactorily.
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Enhancement of energy aware hierarchical cluster-based routing protocol for WSNs
Scientific article
Wireless sensor networks are present almost everywhere because of their extensive variety of utilization. However, sensor nodes are battery constrained. Therefore, proficient utilization of power turns into testing issues. Aggregated data at the base station, by individual nodes cause a flood of information which results in greater power consumption. To avoid or minimize this issue a new technique of data aggregation has been proposed. In this paper, we proposed enhanced novel energy aware hierarchical cluster-based (ENEAHC) routing protocol with the aim to: minimizing as much as total energy consumption and to enhance the performance of the energy efficient protocol by using inter-cluster based data aggregation. LZW based data aggregation likewise connected to the Cluster head (CH) to improve more results. Performance results show ENEAHC scheme reduce the end-to-end energy consumption and prolong the lifetime of the network compared to well known clustering algorithms i.e. LEACH and NEAHC. We design the actual relay node selecting issue like a non-linear programming issue and make use of property of compress sensing to find the optimal solution. The results are evaluated at the end of this paper through simulation.
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Scientific article
This research study aims to compare the learning achievements of first-year students in an algorithm and programming course before and after participating in cooperative blended learning activities focused on variables, expressions, and control commands. By utilizing problem-based learning methods, the researchers sought to meticulously analyze the profound impact of these activities on students’ academic advancement. The research tools deployed encompassed satisfaction questionnaires and achievement tests. The research cohort encompassed seven experienced specialists within higher education institutions, each endowed with a minimum of ten years of pedagogical experience, along with twenty-five participating students. Employing rigorous statistical analysis via T-tests, the study conclusively revealed a statistically noteworthy enhancement in student achievement post the program, underscoring the affirmative influence of cooperative blended learning activities. Moreover, the overall satisfaction level among learners engaging in the proposed learning activities was remarkably elevated, evident through an average satisfaction rating of 4.54 and a standard deviation of 0.73. These empirical insights succinctly underscore the demonstrable effectiveness of assimilating cooperative blended learning methods within algorithm and programming education, thereby accentuating the pivotal role of these pedagogical approaches in shaping contemporary educational practices.
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Scientific article
Customer attrition is a major issue that affects the telecom industry as it reduces the company’s revenues and the overall customer base. Solving this problem involves the use of accurate prediction models that utilize CRM data and machine learning algorithms. Though several research papers have been written and published on CCP in the telecom industry, the existing models lack reliability and accuracy. The use of sophisticated data mining and machine learning techniques has been widely practised for improving predictive models. Churn prediction models that exist have their problems in terms of accuracy and errors. It is still important to develop more sophisticated models that can work well with large data and give accurate predictions. Therefore, this work aims to offer the OKMSVM model for multiclass cancer-type classification. The method applied for the dimensionality reduction pre-process is Kernel Principal Component Analysis (KPCA) and the feature selection pre-process is done using Ant Lion Optimization (ALO). This combination assists in improving the chance of the prediction and also the reduction of probable errors. The performance of the proposed OKMSVM model was compared with some of the most common churn prediction models such as HTLSVM, DNN, ICPCSF and other ML models. It was seen that the OKMSVM model outperformed other models with an accuracy of 91. 5%, an AUC of 85. Accurate, with a correlation coefficient of 0. 838. It further shows that this model is better than the current models in the market in estimating customer churn.
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Enhancing Efficient Study Plan for Student with Machine Learning Techniques
Scientific article
This research aims to enhance the achievement of the students on their study plan. The problem of the students in the university is that some students cannot design the efficient study plan, and this can cause the failure of studying. Machine Learning techniques are very powerful technique, and they can be adopted to solve this problem. Therefore, we developed our techniques and analyzed data from 300 samples by obtaining their grades of students from subjects in the curriculum of Computer Science, Faculty of Science and Technology, Sakon Nakhon Rajabhat University. In this research, we deployed CGPA prediction models and K-means models on 3rd-year and 4th-year students. The results of the experiment show high performance of these models. 37 students as representative samples were classified for their clusters and were predicted for CGPA. After sample classification, samples can inspect all vectors in their clusters as feasible study plans for next semesters. Samples can select a study plan and predict to achieve their desired CGPA. The result shows that the samples have significant improvement in CGPA by applying self-adaptive learning according to selected study plan.
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Enhancing Emotion Detection with Adversarial Transfer Learning in Text Classification
Scientific article
Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.
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Enhancing Information Systems Students' Soft Skill – a Case Study
Scientific article
Information Systems (IS) curricula should provide students with both technical and non-technical (soft) skills. The technical aspects are covered by various courses. However, soft skills like teamwork, interpersonal communication, presentation delivery, and others are hardly covered. Employers, who consider both technical and soft skills to be equally important, search for professional Information Systems employees possessing both sets of skills. These employers often complain that finding an IS graduate with both types of skills is quite difficult. The IS 2010 Model Curriculum refers to both types of skills, considering them an essential part of the graduate knowledge base. However, in many cases the soft skills are not sufficiently addressed, and even if they are, it is not necessarily in the context of software development projects. The Systems Analysis and Design (SAD) course provides an important foundation for the IS profession. This is especially true due to the emerging role of the programmer-analyst who is responsible not only for programming but also for some analysis work. In order to strengthen the soft skills in the context of system analysis and design, we suggest a workshop structure emphasizing these soft skills while students analyze and design a complete information system. Our SAD workshop includes some face to face lectures and team-based collaborations. The students undertake many online activities, including teamwork, interviews with simulated clients, team-based peer reviews, presentation delivery, and so forth. The workshop employs a grade difference calculation mechanism that revealed, along with the students' reflections, that the workshop structure enhanced the students' ability to cope with the workshop assignments while strengthening their soft skills and preparing them for their future analysis and design challenges.
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Scientific article
The focus of the research is on the analysis of the effectiveness of different forms of educational activities in developing youth’s information and media literacy (IML), based on the results of the Ukrainian project “MEDIA & CAPSULES”, implemented within IREX’s “Learn and Discern” initiative. The study compared the impact of webinar sessions, masterclasses, and information and media workshops on three key IML indicators: information literacy, media literacy, and digital security. An empirical pre-post design was used to assess changes in participants’ competencies before and after each type of educational intervention. Statistical analysis revealed that information and media workshops had the strongest overall impact, particularly enhancing media literacy and digital security. Masterclasses were most effective in improving information literacy, while webinars showed moderate improvements across all indicators. The findings highlight the importance of aligning instructional formats with specific educational goals and provide practical implications for educators and curriculum developers working to strengthen youth resilience against misinformation and digital threats.
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Enhancing Leakage Power in CPU Cache Using Inverted Architecture
Scientific article
Power consumption is an increasingly pressing problem in modern processor design. Since the on-chip caches usually consume a significant amount of power so power and energy consumption parameters have become one of the most important design constraint. It is one of the most attractive targets for power reduction. This paper presents an approach to enhance the dynamic power consumption of CPU cache using inverted cache architecture. Our assumption tries to reduce dynamic write power dissipation based on number of ones and zeros in the in-coming cache block data using bit to indicate is the block is mostly one or zero. This architecture reduces the dynamic write power by 17 %. We use Proteus Simulator to test that proposed circuit and performed the experiments on a modified version of the cacti6.0 simulator.
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Scientific article
There is a growing interest in integrating active learning and computer-based approaches in the teaching and learning of mathematics in elementary schools. In this study, we introduce Digital Game-Based Learning (DGBL) of Mathematics targeting students in the 5th and 6th grades following a design-to-implementation strategy. We first developed an edutainment Mathematics game and then tested it with 196 pupils from 9 public elementary schools in Morocco. The rationale of the study is to probe the effect of DGBL in lessening pupils’ mathematical anxiety and improving classroom experience. Students in our study were more engaged and less anxious towards learning Mathematics. Our designed pedagogical edutainment game made students more comfortable when dealing with numerical arithmetic assignments. The study suggests that edutainment games lead to positive individual attitudes towards mathematics and to a better math classroom experience, thus more effective teaching and learning of mathematics.
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Student engagement is a crucial aspect of higher education learning. However, it may be challenging to ensure active engagement, especially if students lack motivation to participate. This research proposes an innovative technique for increasing student participation in online sessions that incorporates real-time chat interaction, engagement reminders, and attendance tracking. Unlike traditional research, which focuses on post-session analysis, the developed Bot actively monitors student participation during session itself, providing real-time notifications to disengaged students without requiring the need for human intervention. It records attendance for each session, monitors weekly student participation, and dispatches updates via email to both instructors and students. To provide a more interesting and responsible learning environment, the Bot also utilizes AI to evaluate student responses and provide suggestions for their improvement.
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This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.
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Scientific article
By means of a thorough investigation of ensemble methodologies and feature selection approaches, this work explores enhancing predictive modelling in e-learning contexts. The setting is in the growing significance of data-driven decision-making in education and tailored learning programs. The main concern is how to fairly forecast student performance in environments of digital learning. This work intends to solve gaps by investigating new ensemble models and robust feature selection techniques based on already published research. Using cutting-edge analytical techniques including hybrid BR2-2T models and the Chi-square test, the study produces remarkable accuracy surpassing known limits. The results underline the need of feature selection and ensemble methods in improving forecast accuracy and dependability. Finally, this study marks a major step in the field of e-learning predictive modelling since it helps to improve educational results and enable data-driven interventions.
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Enhancing Student Placement Accuracy with AI Using GRU and Modified Dwarf Mongoose Optimization
Scientific article
Early prediction of students' placement outcomes is critical for aligning curricula with industry demands, optimizing academic planning, and providing focused career support. It also enhances institutional reputation, strengthens employer partnerships, and supports data-driven decision-making. However, predictive modeling in this context is challenged by data heterogeneity, evolving market factors, subjective evaluations, and bias mitigation. This study proposes an AI-driven framework that integrates Gated Recurrent Unit (GRU) networks with Modified Dwarf Mongoose Optimization (MDMO) to address these challenges. GRU effectively captures temporal patterns in academic and behavioral data, while MDMO ensures optimal hyperparameter tuning through advanced search strategies. Model performance was rigorously evaluated using multiple metrics including accuracy, false positive rate (FPR), false negative rate (FNR), sensitivity, specificity, and Matthews Correlation Coefficient (MCC). The proposed GRU-MDMO model achieved an accuracy of 98.5%, sensitivity of 97.78%, specificity of 99.09%, and MCC of 96.97%, outperforming other baseline models such as SVM, ANN, RF, and traditional GRU variants. These results demonstrate the model’s robustness, reliability, and suitability for early placement prediction. This approach empowers institutions to improve placement rates, enhance curriculum design, attract admissions, and ultimately foster better student career outcomes through AI-guided educational intelligence.
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A hybrid analytic–holistic evaluation framework is developed and validated for assessing Information Technology (IT) undergraduate capstone projects in Thai higher education. Drawing on five academic years of multi-stakeholder assessment data, the research identifies five critical dimensions influencing evaluation outcomes: student competency performance, project proposal quality, advising effectiveness, panel variability, and assessment-criteria weighting. By integrating structured analytic rubrics with holistic panel judgments, the framework addresses persistent challenges of grading inconsistency, evaluator bias, and misalignment between academic assessment and industry expectations. Statistical analyses indicate that documentation quality, project innovation, and internship performance are the strongest predictors of final panel grades, explaining 72% of evaluation variance. The principal contribution lies in providing empirical evidence of assessment variability and a validated, replicable model that improves fairness, transparency, accreditation compliance, and alignment with professional IT competencies. The framework supports outcome-based education standards (ABET, ASIIN, ETQA) and offers practical guidance for enhancing quality assurance and graduate employability.
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Enhancing the Project-Based Learning Experience Through the Use of Live Web Data
Scientific article
Project-based learning (PBL) was proven to be a very useful model to give students hands on experiences and allow them to be active learners rather than passive listeners. In this paper we introduce a tool to help enhance the project development experiences for information technology (IT) students. The main purpose of this tool is to help reuse web information to enable IT projects for students. This tool is called InetRetriever and it can be easily used by students to retrieve, in real-time, any required real information from the web and to implement, execute, and test their projects with real life data. As PBL is becoming an integral part of many information technology (IT) courses, and in many cases real data is essential for many types of projects, it becomes important to make such data available and accessible easily. In various cases, students focus gets shifted from the real objectives of a project when they spend a lot of their time trying to find data or create methods to get them this data. Furthermore, many projects fail and cannot demonstrate their real capabilities when they are tested and demonstrated using small sets of sample data or some fabricated data sets because the students could not include real data available on the Internet. Another possibility here is that students could not even complete their projects because of the difficulties in retrieving the required data and the excess amount of time they spend on that tasks rather than on the real tasks in the project. Therefore, InetRetriever was developed to overcome this obstacle. It was tested in different information technology courses to enable effective and realistic project-based learning. Using this tool we observed increased student interest in the project development and higher levels of interactions and learning.
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Engineering education plays a pivotal role in the development of technologies, society, nation, economy and employment. It is well evident that in the information age, technology is developing very fast and correspondingly the demand for highly skilled and qualified professionals is also increasing. One of the most critical issues in engineering education is handling placement of young technocrats from an industrial perspective to prepare the required workforce in the 21st century. The trajectory of development of Electronics Engineering (EE) has intersected every walk of human life. The last decade has also witnessed an assorted increase in Electronics Engineering Education in India. Infact, most of the engineering institutes imparting EE education countrywide focus only on domain knowledge and a mere 25% of the engineering professionals are actually employable. Along with domain knowledge, there are non-technical skills and competencies which play a significant part in contributing to an individual's effective and successful participation in the workplace. This article throws light on career opportunities that will go a large way towards ensuring successful career planning and handling placement prospects. The paper addresses the career prospects in a broader domain of electronics engineering and other allied fields. The study will also focus on ongoing activities and initiatives at BPSMV State University in Haryana.
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Error Measurement & its Impact on Bilateral -Canny Edge Detector-A Hybrid Filter
Scientific article
Image Processing, a subset of Computer Vision, is an important branch in modern technology. Edge detection is a subset of segmentation to detect object of interest. Different image edge detection filters and their evaluating parameters are introducing rapidly. But the performance of an edge detector is an open problem. In this paper different performance measures of edge detection have been discussed in details and their application on a hybrid filter using Bilateral and Canny is proposed. Its parametric performance has been evaluated and other well established or classical existing edge detecting filters have been compared with it to measure its efficiency.
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