International Journal of Education and Management Engineering (IJEME)

ISSN: 2305-3623 (Print)

ISSN: 2305-8463 (Online)

DOI: https://doi.org/10.5815/ijeme

Website: https://www.mecs-press.org/ijeme

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 90

(IJEME) in Google Scholar Citations / h5-index

IJEME is committed to bridge the theory and practice of education and management engineering. From innovative ideas to specific algorithms and full system implementations, IJEME publishes original, peer-reviewed, and high quality articles in the areas of education and management engineering. IJEME is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of education and management engineering applications.

 

IJEME has been abstracted or indexed by several world class databases: Google Scholar, Microsoft Academic Search, Baidu Wenku, Open Access Articles, Scirus, CNKI, CrossRef, JournalTOCs, etc..

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IJEME Vol. 16, No. 3, Jun. 2026

REGULAR PAPERS

ONTOGRAZING: A Semantic Monitoring and Decision-Support Framework for Sustainable Grazing Management

By Ngazia Balama Gazissou Balama Isaac Touza Daouda Hassana Daouda Dayang Paul

DOI: https://doi.org/10.5815/ijeme.2026.03.01, Pub. Date: 8 Jun. 2026

Sustainable grazing management requires balancing livestock productivity with ecosystem preservation, yet existing monitoring systems integrate heterogeneous data from IoT sensors, satellite imagery, and field surveys without a unified semantic layer, limiting holistic decision support. This paper proposes ONTOGRAZING, an ontology-based monitoring architecture for sustainable grazing management. Using the Uschold and King ontology engineering framework, domain knowledge was collected through surveys involving 23 livestock farmers and 4 agro-pastoral institutions in Cameroon, complemented by a systematic literature review. Seven core concepts and fourteen semantic relationships were modeled in OWL using Protégé. A five-module monitoring architecture composed of Query Reformulator, Data Integrator, Source Monitoring, Alert, and Storage modules was designed around the ontology. ONTOGRAZING was evaluated using the HermiT 1.4.3.456 reasoner and SPARQL queries. The ontology contains 47 classes, 14 object properties, and 9 data properties, and passed all consistency checks. Comparative analysis demonstrates that ONTOGRAZING is the first ontology to jointly cover forage management, dietary preferences, pasture composition, ecological–economic trade-offs, and land-use regulations. These results highlight the potential of ontology-based integration to improve interoperability and semantic decision support in agro-pastoral systems, while future work will focus on full prototype implementation and integration with real-world IoT platforms and agricultural databa.

[...] Read more.
Enhancing ATM Card Fraud Detection in Nigeria: A High-Performance Model with AI-Based Spending Pattern Analysis and Biometric Authentication

By Pradeep B. M. Sudeep J Shivashankara S Pavithra D R Ananth G. S.

DOI: https://doi.org/10.5815/ijeme.2026.03.02, Pub. Date: 8 Jun. 2026

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.

[...] Read more.
Enhancing Customer Experience in Real-Time Travel Reservation Systems through AI-Powered Multi-Agent Systems for Dynamic Support Optimization

By Biman Barua M. Shamim Kaiser

DOI: https://doi.org/10.5815/ijeme.2026.03.03, Pub. Date: 8 Jun. 2026

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.

[...] Read more.
An Intelligent, Bilingual Pregnancy Health Monitoring System

By Isah Omeiza Rabiu Bitrus Judah Tanko Nuhu Bello Kontagora

DOI: https://doi.org/10.5815/ijeme.2026.03.04, Pub. Date: 8 Jun. 2026

This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%. 
In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.

[...] Read more.
Smart Diagnosis: An Ensemble Machine Learning Web Application for Early Detection of Alzheimer’s Disease

By Yetunde D. Otun Abosede O. Oguntunde Samson A. Arekete Oluwole B. Olajide Benjamin S. Aribisala

DOI: https://doi.org/10.5815/ijeme.2026.03.05, Pub. Date: 8 Jun. 2026

Alzheimer disease is a chronic neurodegenerative disorder and the primary cause of dementia among the population, which has a huge burden to the patients, their caregivers and the health care system. Timely intervention is necessary to reduce disease progression, facilitate timely intervention and improve the quality of life. But the traditional forms of diagnostic are frequently costly and non-available especially in resource-deficient environments. The research paper proposes an interpretable and cost-efficient machine-learning model that can be used to identify the presence of Alzheimer disease at its early stages based on clinical and demographic metrics based on the Open Access Series of Imaging Studies cross-sectional dataset, which contains 436 participants. The data consists of seven numeric and two categorical variables, whereas the Clinical Dementia Rating was changed into two categories namely demented and non-demented. An extensive preprocessing pipeline was used, which entailed missing value imputation, categorical encoding and elimination of irrelevant variables, as well as class balancing with the Synthetic Minority Oversampling Technique. A number of machine learning models were tested, which comprise Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. The results show that the highest accuracy of 92% was attained using the model implemented by the ensemble and the tree, with the most accuracy being returned by the Random Forest and the ensemble model. Random Forest, too, had a sensitivity of 95%, whereas Gradient Boosting and Extreme Gradient Boosting had the highest area under the receiver operating characteristic curve of 98%. The models were implemented as a lightweight web application on the Flask framework, which can make real-time predictions and color coded. The system illustrates the possibility of combining interpretable machine learning with web technologies to make it possible to conduct easy and effective early screening of Alzheimer disease under resource-limited healthcare conditions.

[...] Read more.
Enhancing Student‟s Skillset by Add-on Certification of NPTEL/SWAYAM NIELIT Courses under ISE Component

By Sachin S. Patil Reva S. Patil Ankita S. Patil

DOI: https://doi.org/10.5815/ijeme.2026.03.06, Pub. Date: 8 Jun. 2026

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.

[...] Read more.
Quantum Key Distribution-Enabled Federated Learning over Blockchain for Privacy-Preserving AI in Large-Scale IoT Networks

By David Shiala Ongoma

DOI: https://doi.org/10.5815/ijeme.2026.03.07, Pub. Date: 8 Jun. 2026

The proliferation of massive IoT networks has created an environment where distributed AI can be achieved. At the same time, it introduces serious privacy and security challenges. Federated learning (FL) allows training local models on IoT devices and aggregating them without sharing data, but still suffers from problems such as gradient inference attack, Byzantine model poisoning attack and the failure in single point of failure centralized aggregation point. In this paper, we propose QFL-BC, a framework combining Quantum Key Distribution (QKD) and a permissioned blockchain to holistically tackle the problem. Using the BB84 protocol with decoy states, QKD generates a One-Time Pad key to encrypt the model update and achieve information-theoretic security with provable security against a quantum attacker. The central aggregator is replaced by the permissioned blockchain with a smart contract, which ensures an immutable audit trail and distributes the orchestration of FL training decent rally, as well as imposes a penalty on malicious participants by automatic reputation score maintenance. The experiments with MNIST and CIFAR-10 on 100 IoT clients under Non-IID conditions show QFL-BC obtains an accuracy of 96.8% against 41.5% for classic FL under 10% poisoning attack (133% relative improvement). We have tested its robustness across adversary percentages of 10%-40% with accuracy above 87.3% and measured scalability up to 500 clients, showing good degradation, communications overhead of 5.84 MB per round, which is only 12.3% higher than the classic FL and analysed latency and energy to evaluate its feasibility on resource-constrained IoT devices.

[...] Read more.
RiceVision: A Cross-Platform System for Real-Time Rice Variety Identification Using Deep Learning

By Al Hossain Abid Mirza Niaz Morshed Md. Ashif-Ul-Haque Md Masudul Islam Md. Shafiqul Islam

DOI: https://doi.org/10.5815/ijeme.2026.03.08, Pub. Date: 8 Jun. 2026

This study presents RiceVision, a cross-platform software system for real-time rice variety identification using deep learning–based image analysis. Unlike prior work that primarily focuses on classification accuracy, RiceVision emphasizes reproducibility, deployment, and usability in real-world agricultural environments. The system integrates a web-based platform and an offline-capable Android application within a unified architecture, ensuring consistent preprocessing and inference across platforms. Deep learning models are deployed using TensorFlow and TensorFlow Lite to support both online and on-device inference. The proposed hybrid framework combines convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures using a stacked ensemble strategy. Experimental evaluation on a 62-class rice variety dataset demonstrated strong classification performance, where the stacked ensemble achieved an average 5-fold validation accuracy of 98.64%, outperforming individual VGG16 (90.64%) and ViT-B/16 (91.28%) models. The system further demonstrated stable convergence behavior and low inter-fold variance, indicating robust generalization capability. A centralized model management mechanism enables version control and seamless updates across deployment platforms. Detailed model configurations, validation results, and explainability analyses are provided in the Supplementary Material. RiceVision highlights the potential of deployable AI systems for practical decision support in digital agriculture.

[...] Read more.
Stressors and Stress-Coping Mechanisms of Academic Scholars in HEIs: A Basis for Stress Management Plan Formulation

By Ruth G. Luciano Mickel John Salvatierra

DOI: https://doi.org/10.5815/ijeme.2022.03.01, Pub. Date: 8 Jun. 2022

This study aims to describe the stress coping mechanism of the academic scholars from the College of Education (COEd) in one of the private higher education institutions in Cabanatuan City, Philippines. This is an action research that focuses on the assessment of the academic scholars’ stressors and their correlates. It involves systematic observations and data collection that enables the researchers to reflect, decide and develop a training plan for stress management. The findings show that monthly family income and economic-related stressors were highly correlated. This further explains that students with high family income are less likely to experience frequent stress. In contrary, students who belong to low-income families are more prone to experience frequent stress. In other words, students who belong to poor families are more vulnerable to stress. Likewise, monthly family income and physiological responses to stress had high interdependence, which means that students with higher socio-economic status are less likely to experience severe anxiety, while students belonging to low-income families tend to experience severe anxiety. The results of this quantitative analysis served as basis in designing or preparing the stress management plan for these students. 

[...] Read more.
Push Management Platform Based on Wechat Small Program and Cloud Development

By Yan Wu Fang Wang Yanying Zou Huaijin Zhang Bingsheng Chen Mengshan Li

DOI: https://doi.org/10.5815/ijeme.2020.01.03, Pub. Date: 8 Feb. 2020

On the Wechat platform, the current article push is mainly completed by the Wechat Public Account, but it is not perfect in the aspects of user information collection, user service, data storage and management. With economic development and progress of the times, people seek development in spiritual and cultural aspects. This program "One Thing One Story" uses Wechat Web Developer Tools as the medium and Wechat Small Program and Cloud Development as the platform. The purpose of push management platform is "use at any time". Small program cloud development has a relatively complete cloud background. It does not need to rebuild the server in the development cycle. Through the relevant interface, small program development can be started and time cost can be reduced. Using JavaScript, CSS style, JSON database and other technologies, we can realize user data collection, article push, push classification management, push data storage, user praise collection and other functions. This program is applied to article pushing, cultural dissemination and other aspects. Through the platform of Wechat applet, the dream of "accessible" can be realized. 

[...] Read more.
A Study on Malware and Malware Detection Techniques

By Rabia Tahir

DOI: https://doi.org/10.5815/ijeme.2018.02.03, Pub. Date: 8 Mar. 2018

The impact of malicious software are getting worse day by day. Malicious software or malwares are programs that are created to harm, interrupt or damage computers, networks and other resources associated with it. Malwares are transferred in computers without the knowledge of its owner. Mostly the medium used to spread malwares are networks and portable devices. Malwares are always been a threat to digital world but with a rapid increase in the use of internet, the impacts of the malwares become severe and cannot be ignored. A lot of malware detectors have been created, the effectiveness of these detectors depend upon the techniques being used. Although researchers are developing latest technologies for the timely detection of malwares but still malware creators always stay one step ahead. In this paper, a detailed review of malwares types are provided, malware analysis and detection techniques are studied and compared. Furthermore, malware obfuscation techniques have also been presented.

[...] Read more.
Classroom Management Strategies and Academic Performance of Junior High School Students

By Maxwell Kontor Owusu Bakari Yusuf Dramanu Mark Owusu Amponsah

DOI: https://doi.org/10.5815/ijeme.2021.06.04, Pub. Date: 8 Dec. 2021

The study examined the influence of classroom management strategies of Junior High School teachers on the academic performance of students in the Ashanti Akim North District. The descriptive survey design was used for the study. One hypothesis and two research questions were developed to guide the study. Multistage sampling technique was used to select 48 teachers and 297 year two students to respond to the Behaviour and Instructional Management Scale (BIMS). Test scores in English Language, Integrated Science, Mathematics and Social Studies were used to measure students’ academic performance. The statistical tools used to analyse the data collected were means, standard deviation, Pearson’s Product Moment Correlation Coefficient (PPMCC) and Multiple Regression. The findings revealed that both students and teachers identified good relationship and reinforcement as the mostly used classroom management strategies. It was found that a significant positive relationship existed between reinforcement and antecedent as classroom management schemes and students’ academic performance. However, good relationship and punishment as classroom management strategies did not have a positive relationship with the academic performance of students. It is recommended that teachers should use reinforcement and antecedent strategies frequently in their classrooms since they play a dual role of managing behaviour and predicting the academic performance of students. Good relationship as a classroom management strategy should be cautiously used because it could potentially be misinterpreted or abused and can lead to low academic performance. Using punishment as a classroom management strategy should be avoided as its use hinders academic performance of students.

[...] Read more.
The Application of Computer Softwares in Chemistry Teaching

By Wei Yu Lifei Chen

DOI: https://doi.org/10.5815/ijeme.2012.12.12, Pub. Date: 29 Dec. 2012

Chemistry is very interesting, but it is often regarded as a difficult subject. Computer software can make chemistry teaching easier, and keeps the students active. This paper seeks to introduce some implications of computer softwares in chemistry classroom teaching. These softwares include Powerpoint, Chemoffice, computer simulation softwares, LabVIEW software, some computational chemistry softwares, and other chemistry softwares, such as ACD/ChemSketch, ChemDB software, Chemical Reagent Calculator, Atom Builder and Atoms, Symbols and Equations. We gave the simple directions of these softwares and presented some applicable examples.

[...] Read more.
Medicine Management System: Its Design and Development

By Ruth G. Luciano Rhoel Anthony G. Torres Edward B. Gomez Hardly Joy D. Nacino Rodmark D. Ramirez

DOI: https://doi.org/10.5815/ijeme.2023.03.02, Pub. Date: 8 Jun. 2023

The researchers conducted this study with the main purpose of helping the residents of the municipality to expedite the process of obtaining free medicine. In the current setup, an individual who needs to avail of free medicine from the barangay or municipal health center personally visits the place to request maintenance medicine. This motivated the researchers to make a research study focusing on converting the manual requisition system to something that people can access quickly and comfortably without necessarily going out of their households, especially during these challenging times – the pandemic. The researchers called it a “Medicine Management System”. The researchers aimed to speed up the requisition of medicine using this online system. The patients or qualified recipients need not consume time lining up to request medicine from the municipal health center. This system can be accessed over the internet anytime and anywhere. Users must register and upload a legit doctor’s prescription. Researchers have created this system using HTML for the system interface, XAMPP for maintaining database records, and PHP for other system functionalities.

[...] Read more.
The Effectiveness of the TaRL Approach on Moroccan Pupils’ Mathematics, Arabic, and French Reading Competencies

By Abdessamad Binaoui Mohammed Moubtassime Latifa Belfakir

DOI: https://doi.org/10.5815/ijeme.2023.03.01, Pub. Date: 8 Jun. 2023

Teaching at the Right Level (henceforth, TaRL) is a new trending remedial educational approach being piloted in many countries. It basically matches pedagogical content to pupils’ educational needs through various adapted activities after segmentation of pupils’ depending on their actual difficulties and needs. In this respect, Morocco has been piloting this relatively new approach during the beginning of the school year 2022-23. Therefore, this study aimed at measuring the effectiveness of the TaRL approach on Moroccan pupils’ mathematics, Arabic, and French reading competencies. An experimental study took place involving 106 pupils from 4th grade to 6th grade during a one-month remedial course (half an hour per day, one subject per day) based on TaRL guidelines. After carefully examining the data through the Wilcoxon Signed Ranks Test by comparing the baseline and endline results in all three subjects. The results showed statistically high improvements with large effect sizes in the levels of the three subjects suggesting that TaRL was effective in raising the levels of numeracy and literacy and may be, safely, further adopted throughout Moroccan primary schools.

[...] Read more.
Machine Learning Applications in Algorithmic Trading: A Comprehensive Systematic Review

By Arash Salehpour Karim Samadzamini

DOI: https://doi.org/10.5815/ijeme.2023.06.05, Pub. Date: 8 Dec. 2023

This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.

[...] Read more.
A Critical Review by Teachers on the Online Teaching-Learning during the COVID-19

By Malik Mubasher Hassan Tabasum Mirza Mirza Waseem Hussain

DOI: https://doi.org/10.5815/ijeme.2020.05.03, Pub. Date: 8 Oct. 2020

The world has witnessed a sudden change in the teaching-learning processes due to the ongoing pandemic of COVID-19. The worldwide compulsive lockdown for ensuring the preventive measures to stop the spread of this infection has equally affected education sector as other business sectors. As all of us know that quality education is the only long-term rescue for all the challenges and therefore, the need to find out the alternative solution to the traditional classroom teaching-learning is the concern of all stakeholders and the only option found is online mode of teaching-learning, which was somehow already available and had attracted an intense attention during this period. The aim of the paper is to study the teacher’s perspective in India about this mode of learning, challenges and issues faced by them in migration to online platform, experience about online tools/platforms used for instructional delivery and their suggestions to improve the process for effective teaching. This study will help in gaining insight towards the possible improvements in the ongoing mode of online teaching and in future situations also. The results obtained based on sample collection through web based questionnaire clearly gives some information, which could be an eye opener for enhancing the implementation of the online teaching-learning among the learners especially teachers, who can further help in implementation of the large. Although, the online mode was already in place and was utilized in blended form to a substantial level in the developed countries, but in developing countries like India, where teachers are not familiar with online platforms/tools, lack of knowledge and skills to handle the online ICT infrastructure in a challenging situation. The results also give an impression about the need of professional development with special focus on digital literacy skills and awareness among the teacher community about the merits of online platforms for the teaching-learning process.

[...] Read more.
The Digital Literacy in Teachers of the Schools of Rajouri (J&K)-India: Teachers Perspective

By M Mubasher Hassan Tabasum Mirza

DOI: https://doi.org/10.5815/ijeme.2021.01.04, Pub. Date: 8 Feb. 2021

The present age is the age of information. The globalization has affected every sphere of the life including education. In spite of availability of ICT infrastructure in schools, their potential is underutilized because of digital incompetence of the teachers.  New digital technologies are acting as a catalyst towards improvement of learning outcome and enhancing quality of education, but only introduction of such technologies in schools for producing change and innovation is not enough, it requires digitally competent teachers to facilitate the use of ICT in education. These teachers will act as facilitators and mentors to students to lead them towards problem solving and innovation to meet the new challenges of globalization. Teachers must be able to create learning environments which are student centric and foster creativity, Meta cognition, meta-literacy, collaboration and communication in learners. Mere superficial use of ICT in teaching will not yield the required learning outcome, but the integration of ICT in pedagogy is important to enhance teaching, learning process. This can be done only when teachers are competent enough to use ICT tools and facilitate ICT integrated education. In this paper, we tried to assess the teacher’s perspective about the ICT and investigate the factors responsible for resistance of teachers in using ICT in schools and suggestive measures for successful integration of ICT in the teaching process by the teachers of Rajouri district (J&K, India). The ICT skills are very important for teachers to support alternative modes of teaching, learning, i.e. e-learning, mobile learning in the present outbreak of pandemic disease caused by Coronavirus-COVID19. 

[...] Read more.
Push Management Platform Based on Wechat Small Program and Cloud Development

By Yan Wu Fang Wang Yanying Zou Huaijin Zhang Bingsheng Chen Mengshan Li

DOI: https://doi.org/10.5815/ijeme.2020.01.03, Pub. Date: 8 Feb. 2020

On the Wechat platform, the current article push is mainly completed by the Wechat Public Account, but it is not perfect in the aspects of user information collection, user service, data storage and management. With economic development and progress of the times, people seek development in spiritual and cultural aspects. This program "One Thing One Story" uses Wechat Web Developer Tools as the medium and Wechat Small Program and Cloud Development as the platform. The purpose of push management platform is "use at any time". Small program cloud development has a relatively complete cloud background. It does not need to rebuild the server in the development cycle. Through the relevant interface, small program development can be started and time cost can be reduced. Using JavaScript, CSS style, JSON database and other technologies, we can realize user data collection, article push, push classification management, push data storage, user praise collection and other functions. This program is applied to article pushing, cultural dissemination and other aspects. Through the platform of Wechat applet, the dream of "accessible" can be realized. 

[...] Read more.
Stressors and Stress-Coping Mechanisms of Academic Scholars in HEIs: A Basis for Stress Management Plan Formulation

By Ruth G. Luciano Mickel John Salvatierra

DOI: https://doi.org/10.5815/ijeme.2022.03.01, Pub. Date: 8 Jun. 2022

This study aims to describe the stress coping mechanism of the academic scholars from the College of Education (COEd) in one of the private higher education institutions in Cabanatuan City, Philippines. This is an action research that focuses on the assessment of the academic scholars’ stressors and their correlates. It involves systematic observations and data collection that enables the researchers to reflect, decide and develop a training plan for stress management. The findings show that monthly family income and economic-related stressors were highly correlated. This further explains that students with high family income are less likely to experience frequent stress. In contrary, students who belong to low-income families are more prone to experience frequent stress. In other words, students who belong to poor families are more vulnerable to stress. Likewise, monthly family income and physiological responses to stress had high interdependence, which means that students with higher socio-economic status are less likely to experience severe anxiety, while students belonging to low-income families tend to experience severe anxiety. The results of this quantitative analysis served as basis in designing or preparing the stress management plan for these students. 

[...] Read more.
Medicine Management System: Its Design and Development

By Ruth G. Luciano Rhoel Anthony G. Torres Edward B. Gomez Hardly Joy D. Nacino Rodmark D. Ramirez

DOI: https://doi.org/10.5815/ijeme.2023.03.02, Pub. Date: 8 Jun. 2023

The researchers conducted this study with the main purpose of helping the residents of the municipality to expedite the process of obtaining free medicine. In the current setup, an individual who needs to avail of free medicine from the barangay or municipal health center personally visits the place to request maintenance medicine. This motivated the researchers to make a research study focusing on converting the manual requisition system to something that people can access quickly and comfortably without necessarily going out of their households, especially during these challenging times – the pandemic. The researchers called it a “Medicine Management System”. The researchers aimed to speed up the requisition of medicine using this online system. The patients or qualified recipients need not consume time lining up to request medicine from the municipal health center. This system can be accessed over the internet anytime and anywhere. Users must register and upload a legit doctor’s prescription. Researchers have created this system using HTML for the system interface, XAMPP for maintaining database records, and PHP for other system functionalities.

[...] Read more.
A Study on Malware and Malware Detection Techniques

By Rabia Tahir

DOI: https://doi.org/10.5815/ijeme.2018.02.03, Pub. Date: 8 Mar. 2018

The impact of malicious software are getting worse day by day. Malicious software or malwares are programs that are created to harm, interrupt or damage computers, networks and other resources associated with it. Malwares are transferred in computers without the knowledge of its owner. Mostly the medium used to spread malwares are networks and portable devices. Malwares are always been a threat to digital world but with a rapid increase in the use of internet, the impacts of the malwares become severe and cannot be ignored. A lot of malware detectors have been created, the effectiveness of these detectors depend upon the techniques being used. Although researchers are developing latest technologies for the timely detection of malwares but still malware creators always stay one step ahead. In this paper, a detailed review of malwares types are provided, malware analysis and detection techniques are studied and compared. Furthermore, malware obfuscation techniques have also been presented.

[...] Read more.
Machine Learning Applications in Algorithmic Trading: A Comprehensive Systematic Review

By Arash Salehpour Karim Samadzamini

DOI: https://doi.org/10.5815/ijeme.2023.06.05, Pub. Date: 8 Dec. 2023

This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.

[...] Read more.
Classroom Management Strategies and Academic Performance of Junior High School Students

By Maxwell Kontor Owusu Bakari Yusuf Dramanu Mark Owusu Amponsah

DOI: https://doi.org/10.5815/ijeme.2021.06.04, Pub. Date: 8 Dec. 2021

The study examined the influence of classroom management strategies of Junior High School teachers on the academic performance of students in the Ashanti Akim North District. The descriptive survey design was used for the study. One hypothesis and two research questions were developed to guide the study. Multistage sampling technique was used to select 48 teachers and 297 year two students to respond to the Behaviour and Instructional Management Scale (BIMS). Test scores in English Language, Integrated Science, Mathematics and Social Studies were used to measure students’ academic performance. The statistical tools used to analyse the data collected were means, standard deviation, Pearson’s Product Moment Correlation Coefficient (PPMCC) and Multiple Regression. The findings revealed that both students and teachers identified good relationship and reinforcement as the mostly used classroom management strategies. It was found that a significant positive relationship existed between reinforcement and antecedent as classroom management schemes and students’ academic performance. However, good relationship and punishment as classroom management strategies did not have a positive relationship with the academic performance of students. It is recommended that teachers should use reinforcement and antecedent strategies frequently in their classrooms since they play a dual role of managing behaviour and predicting the academic performance of students. Good relationship as a classroom management strategy should be cautiously used because it could potentially be misinterpreted or abused and can lead to low academic performance. Using punishment as a classroom management strategy should be avoided as its use hinders academic performance of students.

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The Effectiveness of the TaRL Approach on Moroccan Pupils’ Mathematics, Arabic, and French Reading Competencies

By Abdessamad Binaoui Mohammed Moubtassime Latifa Belfakir

DOI: https://doi.org/10.5815/ijeme.2023.03.01, Pub. Date: 8 Jun. 2023

Teaching at the Right Level (henceforth, TaRL) is a new trending remedial educational approach being piloted in many countries. It basically matches pedagogical content to pupils’ educational needs through various adapted activities after segmentation of pupils’ depending on their actual difficulties and needs. In this respect, Morocco has been piloting this relatively new approach during the beginning of the school year 2022-23. Therefore, this study aimed at measuring the effectiveness of the TaRL approach on Moroccan pupils’ mathematics, Arabic, and French reading competencies. An experimental study took place involving 106 pupils from 4th grade to 6th grade during a one-month remedial course (half an hour per day, one subject per day) based on TaRL guidelines. After carefully examining the data through the Wilcoxon Signed Ranks Test by comparing the baseline and endline results in all three subjects. The results showed statistically high improvements with large effect sizes in the levels of the three subjects suggesting that TaRL was effective in raising the levels of numeracy and literacy and may be, safely, further adopted throughout Moroccan primary schools.

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An Empirical Study on Make-or-buy Decision Making

By Monika Arora Anand Kumar

DOI: https://doi.org/10.5815/ijeme.2022.01.03, Pub. Date: 8 Feb. 2022

Every enterprise will be based on the other enterprise to manufacture, product items/parts, for make or buy. The make-buy decision is based on the assessment whether it should be manufactured or buy it from an outside supplier to produce a component internally or to buy it from the outside. It depends on cost and profitability. The cost for both the alternatives may be calculated and the alternative with less cost is to be chosen. The aim of any enterprise is to improve its performance that is measured in terms of profitability. There is some research that has been carried out to make the decision based on profitability of the enterprise for make or buy decision.  The strategy is based on cost, flexibility and responsiveness of work to be carried out. However, some of the research is required to maintain the relationship between profitability and make or buy decision.

The reports of this paper attempt made on how buying decision influences the performances of an enterprise. The different sectors were chosen for the study such as Manufacturing, Automobile, Food, Textile and Hospitality. The focus of the study was based on three theories such as operational control, performance management and decision. The paper reveals the current trends and make or buy decision of the components and its relationship in taking decisions. It also discusses the two techniques break even analysis and economic analysis for decision making in make or buy decision. The study discusses the advantage of outsourcing and discusses the four theories in the study for make and buys decision

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An Internet of Thing based Agribot (IOT- Agribot) for Precision Agriculture and Farm Monitoring

By Kakelli Anil Kumar Aju. D.

DOI: https://doi.org/10.5815/ijeme.2020.04.04, Pub. Date: 8 Aug. 2020

Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT- Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT- Agribot has performed well for variable weather conditions, soli type, moisture content and crops.

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Essential and New Maintenance KPIs Explained

By Fatima Zohra Berrabah Chahira Belkacemi Leila Zemmouchi-Ghomari

DOI: https://doi.org/10.5815/ijeme.2022.06.02, Pub. Date: 8 Dec. 2022

Maintenance in any manufacturing organization is critical, given its significant role in ensuring business continuity. Maintenance plays a crucial role and has a significant impact on the results of industrial companies. Therefore, it is essential to manage maintenance, observe, understand, and improve actions by adopting well-chosen performance indicators according to the company's needs. These indicators are known as Maintenance KPIs or Key Performance Indicators, which allow for gathering knowledge and exploring the best means to achieve the organization's goals. Maintenance KPIs are critical to keeping track of the function, monitoring performance, and ensuring fulfillment of business expectations. In addition, KPIs drive reliability growth while guiding decisions to improve maintenance efficiency and performance. A helpful maintenance KPIs help to identify the problems causing the maintenance effect and help to select the right strategy to support or correct the actions that produced the results. They also allow to identify the causes of equipment failures (measure the influence of life cycle factors), direct what maintenance does with its time and resources (measure the efficiency and effectiveness of the maintenance group) and identify if maintenance removes failure causes ( measure the improved reliability and operational risk reduction results of maintenance effort) and help drive the business benefits provided by maintenance (measure the contribution to the business value of maintenance).

Essential maintenance KPIs are the most commonly used for maintenance management and are adopted by most industries; among these primary KPIs which are essential for maintenance management, we cite Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Overall Equipment (OEE). Nevertheless, it is crucial to continuously redefine and update KPIs to ensure they are appropriate for the organization's current environment, significantly when the constant market or research methodologies change. Hence, researchers and the industry propose several other maintenance KPIs outside the essential ones used in the industry according to the needs and within the performance improvement framework. These proposed KPIs aim to compensate for the lack of maintenance data, the absence of decision support, and the problems related to specific equipment, also in the context of improving the management strategy, the application of predictive maintenance, and the quality control of a maintenance process or the monitoring of systems reviews. Unfortunately, these indicators are not sufficiently known and are, therefore, not used by the industry. However, we believe that some of them should gain maturity and reach the status of widely used traditional indicators, such as the KPI of obsolescence management in maintenance operations and schedule compliance KPIs that aim to link maintenance planning with production. In addition, although not all proposed KPIs in the literature are generalizable, it has been identified that they can sometimes be specific to problematic situations, equipment categories, and even sectors of industry activity. Therefore, this work aims to inventory the most widely used maintenance KPIs and some of the KPIs proposed by researchers and the industry. In addition, we study the trends and challenges of selecting these KPIs and for what purposes they are used to help their understanding and usability. Indeed, Maintenance managers need to select relevant KPIs aligned with the maintenance strategy and the company objectives. 

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