International Journal of Education and Management Engineering @ijeme
Journal articles - International Journal of Education and Management Engineering
All articles: 694
Effective XML compressor: XMill with LZMA data compression
Scientific article
The XMill is an efficient XML compression tool which takes the advantage of awareness of XML. XMill compresses the data on the basis of three principles- separate the XML structure from the data, group related data and apply the semantic compressors. The XMill uses the gZip library to compress the XML string data for increasing the compression ratio. Here we have proposed a new method to increase the compression ratio of XMill tool. In this method we have added the 7Zip library to the XMill tool; 7Zip library uses the LZMA algorithm to compress the data. LZMA is an enhanced & improved version of LZ77 algorithm which is used in the gZip library. LZMA algorithm has following features over the LZ77 algorithm •Uses up to 4GB dictionary length instead of 32KB for removing the duplicate data. •Uses the look-a-head approach instead of greedy approach. •Uses the optimal parsing, shorter code for recently repeated matches. •Uses the context handling. Due to the above features our proposed approach achieves the best compression ratio with a comparable compression speed.
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Scientific article
Involving students actively in the learning process is one of the ways that can be used in developing the learning objectives of mathematics which is to improve students' critical thinking skills. Students can be called have mathematical critical thinking skills if they have a way of thinking systematically, have an awareness in thinking, and have the ability to distinguish a truth from truth, and can provide arguments from a problem. In fact, students do not have the knowledge independently before the learning process begins so the students only expect new information at school. The lack of learning resources that are easy for students to understand makes learning still go in one direction and create passive learners. The impact is made students having difficulty in solving problems with varying level of difficulty due to insufficient time to finish it at school. These unsolved questions become independent tasks at home, and students become more difficult to solve it and caused students have not developed the ability to think critically mathematically yet. The purpose of this study is to create a product in the form of an effective learning video to improve students' critical thinking skills. The development model used is Model Plomp which consists of three phases, namely the initial investigation phase, the prototype development phase, and the assessment phase. The subjects of this study were class XI students and a mathematics teacher. Based on the results of observations during the learning process, students look active in discussions and can solve problems that are given systematically. The results of the final test of students 'critical thinking skills, the students' mathematical critical thinking skills increased to 87.87%. It shows that students have good mathematical critical thinking skills after using flipped classroom-based mathematics learning tools. So, it can be concluded that the flipped classroom-based learning device is effective to improve students' mathematical critical thinking skills.
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Effects of Constructive Controversy Strategy and Self-efficacy on Students‟ Attitude to Genetics
Scientific article
This study determined the effect of constructive controversy strategy (CCS) and the moderator effect of self-efficacy on students’ attitude to genetics concept in Oyo State, Nigeria. The study was anchored to situated learning theory, while the pretest-posttest control group quasi-experimental design was adopted. The sample comprised senior secondary school II science students from six public-owned secondary schools purposively selected from two local government areas. An intact class of secondary school II science students from each school was randomly assigned to CCS (121) and conventional strategy (119). Data were analyzed using analysis of covariance at p<0.05. There was a significant main effects of treatment on student’ attitude (F(1;215)=4.42; partial η2=0.02) to genetics concept. This implies that the observed difference in the attitude mean scores of students to genetics after they were exposed to CCS and control groups was significant. Students taught with CCS had improved attitude (x ̅=65.00) than those in the conventional strategy (x ̅=56.63). Hence, CCS enhanced students’ attitude to genetics. In terms of interaction, treatment and genetics self-efficacy had significant interaction effect on students’ attitude to genetics (F(2; 213)=3.04; partial η2 = 0.05), indicating that self-efficacy of the students influenced the effectiveness of the strategies employed in this study and this will favour medium self-efficacious students whenever these strategies are used. The implication of this is that biology teachers should always consider students’ self-efficacy whenever they want to implement constructive controversy strategy in biology classroom.
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Efficacy of Assistive Technology for Improved Teaching and Learning in Computer Science
Scientific article
This study examined the efficacy of assistive technology (AT) for improved teaching and learning in computer science (a case study of an inclusive educational system). Two (2) hypotheses were formulated and tested for this study. A descriptive survey method was adopted for this study, the population of this study comprises all Students with special needs and all teachers teaching at Durbar Grammar School, Oyo, Oyo State, Nigeria. A purposive sampling technique was used to select twenty (20) respondents (teachers) and all the Students with special needs were involved (40). A structured questionnaire of two sections (sections A and B to be answered by the Teachers and the Students respectively) which was validated and tested for reliability was used; a reliability coefficient of 0.81 was obtained. Simple percentages and the Chi-square statistical method were used to analyze the collected data which was tested with this study’s hypotheses. The results of this study revealed that AT is capable of improving the teaching and learning of computer science for Students with special needs in an inclusive education if AT is allowed to play its role. It was also discovered that both the teacher and students with special needs were exposed to very little AT and there was no periodical training programme for both the teachers and the students with special needs on the use of AT which has affected their teaching and learning ability. This paper, therefore recommends that a periodical training programme on the use of AT be organized by all the stakeholders in inclusive education for both the students with special needs and all the teachers teaching them.
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Efficient Round Robin Scheduling Algorithm with Dynamic Time Slice
Scientific article
Round Robin (RR) scheduling algorithm is the widely used scheduling algorithm in multitasking. It ensures fairness and starvation free execution of processes. Choosing the time quantum in RR algorithm is very crucial as small time slice results in large number of context switches and large time quantum increases the response time. To overcome these problems of RR scheduling, instead of static time slice dynamic time slice can be used to get optimal performance. The objective of this paper is to modify RR algorithm by adjusting time slices of different rounds depending on the remaining CPU bursts of currently running processes and considering their waiting times until that round in respect of the other processes' waiting times. Experimental analysis reveals that the proposed algorithm produces better average turnaround time, average waiting time and fewer number of context switches than existing algorithms.
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Emerging Trends in Database and its Applications in MNC’s
Scientific article
When information is set for easy retrieval, access, updation, and management, then it is known as Database. Its major purpose is storing aggregations of records and data files that contain information regarding transactions, customer details, product details and financials. The paper deals with a briefing on DBMS which is a collection of inter-related data and set of programs to retrieve and access the data which focuses on :- a) exchange of data, b) retrieve the data, c) storage of data, and d) manipulation on data. The paper also gives a glance on elements of DBMS also describes the architecture which is consist of: a) internal level, b) conceptual level, and c) external level. The paper also mentions some of the uses of databases and will try to give an idea about the applications of Database in different MNC’s and projects in Data ware.
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Emotional Deficiency in Web-based Teaching Environment and Its Solving Strategies
Scientific article
In china, developing rapidly in Internet teaching exits serious lack of affection. This article aims to analyze the features on the lack of affection, reveal the causes of it and try to give corresponding solutions. The results indicate that lack of affection have manifested themselves mainly on lacking affinity for the resources of the network courses, being deficient in emotional information in virtual communication, being careless with emotional communication, the commercialized relations between teachers and students, and the lack of positive emotion. The main reasons that bring about the lack of affection are the impact of traditional education which pays more attention on intellectual education instead of affective education, the difficulties of design of teaching in affective domain, the inadequacy of role transition of teachers, the defects of Internet environment, the inadequacy of interaction in network teaching. At last, based on the perspective of teachers, students, and study resources, corresponding solutions on the lack of affection are given.
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Empathetic Intelligent Virtual Assistant to Support Distance Learning: Systematic Review
Scientific article
Distance learning as a modality has been growing for some time, however it received a significant boost from 2020. As teachers' activities have increased, Intelligent Virtual Assistants (IVAs) have helped to cope with the high workload and volume of requests. However, IVAs that include modules on empathy and teaching personalization are scarce. In the current work, we intend to map, through a systematic literature review, the level of maturity of the IVAs and how they can include empathy and personalization to improve results in conversations. The study considers a systematic review methodology that analyzes a series of works involving the use of IVAs and empathetic modules, and the platforms, resources, and functionalities available. We demonstrate the relevance of the topic in the scientific area, the diversity of countries involved, and the limitations and challenges that still need to be discussed.
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Empirical Study on Forewarning of Urban Sustainable Development
Scientific article
A forewarning model was set up to evaluate the process of urban sustainable development based on fuzzy matter element theory. Taking status of Nan tong city from 2005 to 2008 as an example, we set up an index system of forewarning in the process of urban sustainable development in Nan tong and established the forewarning standards, limits and degrees.
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Empirical analysis of cervical and breast cancer prediction systems using classification
Scientific article
Cancer is a life-threatening disease with high mortality rates. In the Indian subcontinent, women have a higher possibility to be diagnosed with cancer than men. The most common cancers identified in Indian women are Breast Cancer and Cervical Cancer. Both these cancers have high survival rates in case of early prediction. This paper reviews the attributes which are used in the existing datasets for prediction of these two cancers. The paper also proposes new attributes to overcome the limitations of existing ones, which will further increase the effectiveness of cancer prediction systems. The efficiency of existing and proposed attributes is compared by processing datasets through data mining algorithms using WEKA tool. The algorithms used for this study are – J48 (Decision Tree), Na?ve Bayes, Random Forest, Random Tree, KStar and Bagging Algorithm. The empirical analysis done in the paper reported improvement in the efficiency of cancer prediction over existing prediction systems.
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Employee attendance monitoring system by applying the concept of Enterprise Resource Planning (ERP)
Scientific article
Enterprise Resource Planning (ERP) is a business process management system that integrates and automates the activities of an organisation in terms of its technology, human resources and services. ERP provides an integrated environment that links the business processes of different departments of an organisation into one unit so that departments benefit from each other through their joint transactions. This study applies the ERP concept to create an employee attendance monitoring system (EAMS). Three departments, HR, Finance and the Director of Administration, were linked using the EAMS. The traditional system of attendance monitoring was time consuming and required greater effort. This is because the attendance report needed to be printed from the HR department and then sent to the Finance Department for any necessary actions (i.e. salary deduction). The EAMS will automate the whole process, thus resulting in fair decisions in less time. In the traditional system, the any delay in calculations of attendance can be unwarranted or maintaining an accurate time record can be difficult as it is manually updated. To solve these issues, a computer based monitoring system is required to establish accuracy and fairness. For that purpose, we designed and developed the EAMS. The EAMS automatically calculates any delay in employee attendance using the concept of ERP systems. More precisely, this system will calculate the delay in the attendance within set rules, as defined and applied by the different departments in the organisation. The attendance times will be checked automatically by the system: if there is any delay which violates the set rules of the organisation, necessary action will be taken automatically against the employee in terms of salary deduction or other notifications. To apply the EAMS, we constructed a case study at the “Faculty of Economics and Business Administration at King Abdulaziz University, (Female Section)”. Later, it is hoped, that this system can be used in other organisations based on their needs and enhancement to the existing framework. The expected results of this system are that it will save time and effort for all employees at the Faculty of Economics and Administration. In addition, there will be a reduction of errors in the attendance reports.
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Scientific article
Recent technological advancements have fueled a notable increase in credit card usage, consequently amplifying the prevalence of credit card fraud in both offline and online transactions. Although measures such as PIN codes, embedded chips, and supplementary keys like tokens have enhanced credit card security, financial institutions are compelled to bolster their usage controls and deploy real-time monitoring systems to promptly identify and mitigate suspicious activities. This study explores the utilization of ensemble methods, incorporating the k-nearest neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) models, along with the Isolation Forest (iForest) algorithm, to enhance the efficacy of credit card fraud detection. Additionally, automated parameter optimization using GridSearchCV is employed to fine-tune the iForest model parameters. By integrating multiple classifiers into an ensemble approach and automating parameter tuning for the iForest model, our research aims to provide a robust solution capable of adapting to varying datasets and improving fraud detection accuracy. Through empirical analysis and comparison of individual models with the ensemble approach, we underscore the significance of ensemble learning and parameter optimization in enhancing fraud detection capabilities, thereby contributing to the advancement of financial security measures in the realm of credit card transactions.
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Scientific article
One of the effects of the rapid adoption of the cashless policy in Nigeria and the introduction of new naira notes is operational difficulties among financial institutions, which have led to a significant increase in ATM card theft and fraud among clients. Absence of real-time analysis of access points, combined with the intermittent and simultaneous quality of fraudulent dealings, are two major factors that make conventional fraud detection systems fail regularly. Towards reducing ATM fraud, this paper will present a high-performance, intelligent based, AI-based model to integrate three factors of biometric authentication, spending pattern analysis, and password verification into a three-factor model. Results of experiments based on real banking data prove that the proposed solution is superior to traditional models in terms of accuracy, precision, recall, and F1-score. The model uses an optimized Bi -Directional Long Short-Term Memory (BiLSTM) network to analyze historical ATM transaction records and identify behavioral abnormalities that could point to fraud. A Cuttlefish Optimization (MCFA) algorithm that is based on mapping is used to fine-tune the parameters, thus improving the reliability and accuracy of the classification. Biometric verification combined with behavioral modeling using AI stands out as a scalable and dependable framework of minimizing ATM card fraud and instilling confidence within the banking industry.
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Scientific article
This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.
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Enhancing Employee Engagement through Machine Learning: Insights from K-Means Clustering Analysis
Scientific article
This study provides insight into how machine learning methods, in particular k-means clustering algorithm could contribute to greater degree of employee engagement in the businesses. Using Work-Life Balance, Environment Satisfaction and Job Satisfaction found in employee survey data as an illustrative lens of the engagement phenomenon, patterns are identified that differ from traditional perspectives with implications for organizational actions. The study categorizes workers in clusters and identifies the significant gaps of satisfaction among them, using k-means clustering. Logistic regression analysis is used for the prediction of attrition risk, which also helps in determining factors responsible behind employee retention. The findings reveal the importance of understanding such facilitators to generate targeted interventions and strategies that foster a positive work environment and improve organisational performance. This approach ensures less attrition risks, and better job satisfaction leading to greater overall organisation productivity / wellbeing.
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Enhancing Institutional Quality Assessment in Higher Education Using LSTM-NMPSO Hybrid Model
Scientific article
Advancements in educational assessment methodologies, driven by high-speed data networks, have enabled the efficient management and analysis of large datasets, replacing traditional testing methods. Even though they are frequently used, traditional statistical methods have the potential to incorporate biases into assessments of the caliber of universities. To address these limitations, the application of automated technologies is necessary for identifying key factors influencing institutional quality. Developing effective educational programmes in higher education requires quality assurance. Academic performance evaluation using Machine Learning (ML) and Artificial Intelligence (AI) techniques yields more accurate predictive models than traditional methods. This research proposes a hybrid approach that integrates Long Short-Term Memory (LSTM) neural networks with Novel Modified Particle Swarm Optimization (NMPSO) to optimize model architecture, enabling more precise and unbiased assessments of institutional quality. The objective of the proposed methodology is to improve the objectivity and reliability of institutional quality evaluations in higher education.
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Scientific article
The Indonesian Election is one of the most anticipated political contestations among the Indonesian people. Mainly because the results of the Indonesian Election are leaders in Indonesia ranging from governors and legislative members to the president and vice president of Indonesia, who will lead the next five years, considering the importance of the five-year agenda, the dissemination of good information about work programs, the activities of prospective leaders who will elect in the 2024 election and various news stories are starting to spread on Twitter. Based on this, this research aims to analyze public sentiment on Twitter wa The research method used is SMOTE-Tomek Links to overcome imbalanced data. In contrast, sentiment analysis uses Binary Logistic Regression. Evaluation related to this model measures accuracy and ROC Curves. The evaluation results show that the SMOTE-Tomek Links method is less than optimal for the data used in the research, namely the 2024 election data, with an accuracy value of 0.581 for training data and 0.406 for testing data. Undersampling methods such as Tomek Links and Random (undersampling) show higher values when combined with Binary Logistic Regression in analyzing the sentiment produced in this study, namely 0.983 and 0.938 for the Tomek Links method and 0.964 and 0.902 for the Random (undersampling) method, respectively -each for training and testing data.
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Enhancing Student Performance Prediction with ANN-Based Transfer Learning
Scientific article
Predicting student performance in higher education is challenging when data distributions differ across cohorts or programs. This paper proposes an adaptive transfer learning framework to improve prediction accuracy on a student dataset with simulated domain shifts. The dataset contains demographic, academic, and macroeconomic features for university students, with the target outcome indicating whether a student graduated, dropped out, or is still enrolled. We partition the data into distinct domains by academic program to emulate distributional differences. An Artificial Neural Network (ANN) model is first trained on a source domain and then fine-tuned on a target domain with a subset of layer weights frozen. We evaluate model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R^2), comparing the proposed transfer learning approach against a baseline without transfer. The results show that transfer learning significantly improves prediction accuracy: RMSE and MAE are reduced while R^2 increases on the target domain, indicating better generalization. The findings demonstrate that an ANN-based transfer learning method can effectively mitigate domain shift in student performance prediction. This study presents the benefits of transfer learning in an educational context by using attribute-based domain separation, offering a practical approach for academic institutions to predict student outcomes across different programs or semesters.
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Scientific article
The fields of augmented engineering are confronted with formidable obstacles because of the absence of chances for self-paced learning, the wide coverage of undergraduate curricula, uneven academic content standards, and shortages in teacher knowledge. This study suggests a thorough strategy to overcome these drawbacks. In order to enhance current course offerings through bridge or add-on courses, we want to integrate the NPTEL Swayam and NIELIT platforms as additional resources of Self-Paced Learning. This plan will improve students’ knowledge acquisition, give them various learning options, and promote a continuous learning culture reinforced by certification processes. The project intends to solve issues with skill development, student engagement, and standardized academic material by incorporating various online platforms as supplemental or add-on courses which are used for Curriculum Enhancement. To test the efficacy of this strategy, a pilot deployment encompassing course selection, curriculum integration, and student enrollment was carried out. Positive student results in terms of knowledge acquisition and skill enhancement are indicated by preliminary studies. Nonetheless, issues with workload management and technical difficulties were noted.
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Enlightenment on Chinese Evaluation Standard of Educational Game Software from TEEM's
Scientific article
The author takes the evaluation standard of educational game software（EGS） made by TEEM as an example and analyzes its dimensionality, content and effectiveness. Then the author stresses the enlightenment on the evaluation standard of Chinese EGS about evaluation organization, evaluation scientificity and evaluation efficacy. The aim of this paper is to offer guidance to those enterprises producing EGS, schools and families purchasing EGS, teachers and students using EGS and promote Chinese evaluation standard to match international standard.
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