International Journal of Information Engineering and Electronic Business (IJIEEB)
ISSN: 2074-9023 (Print)
ISSN: 2074-9031 (Online)
DOI: https://doi.org/10.5815/ijieeb
Website: https://www.mecs-press.org/ijieeb
Published By: MECS Press
Frequency: 6 issues per year
Number(s) Available: 91
IJIEEB is committed to bridge the theory and practice of information engineering and electronic business. From innovative ideas to specific algorithms and full system implementations, IJIEEB publishes original, peer-reviewed, and high quality articles in the areas of information engineering and electronic business. IJIEEB is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information engineering and electronic business applications.
IJIEEB has been abstracted or indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..
IJIEEB Vol. 18, No. 2, Apr. 2026
REGULAR PAPERS
Mathematical Modelling of the Ukrainian IT Industry Potential Assessment in the Context of Global Challenges
In this article, practical issues are considered at the complex level, in the field of realizing mathematical modelling of the assessment of the potential of the Ukrainian IT industry in the conditions of global challenges. The purpose of the study is to analyze modern approaches to evaluating this potential using advanced mathematical methods, such as multifactorial regression, cluster analysis and factor analysis. The work describes in detail the methods that allow you to evaluate the relationships between the main economic, technological and social indicators that influence the globalized development of the modern IT sector in Ukraine. The study emphasizes the strategic importance of assessing the capabilities of the IT sector to solve modern economic and technological tasks. Within mathematical modeling in the work, an improved model of evaluation of the above potential is proposed in which the combination of considered methods with the combination of the principles of Bayesian data analysis is combined. This approach allows you to more accurately take into account the uncertainty and variability inherent in modern economic and technological conditions. Usage of the Bayesian approach makes it possible to get a more flexible and adaptive model that better reflects dynamic processes that affect the development of IT industry in Ukraine in global challenges. The analysis begins with multifactorial regression, which examines the relationship between economic, technological and infrastructure factors that influence the development of the IT sector. The next stage is a cluster analysis that allows you to distinguish regional IT-hubs, comparing their effectiveness and revealing differences in Ukraine. With the help of factor analysis, we reveal the hidden variables that have a significant impact on the development of the industry. We also compare the results of each of the models to identify common trends and differences, which allows not only to identify opportunities for development, but also to indicate problems that need attention. Based on the results obtained, we offer specific recommendations for improving the competitiveness and stability of the Ukrainian IT industry. Our conclusions emphasize the importance of using innovative approaches to achieve more efficient development of this field.
[...] Read more.Improving Agricultural Commodity Trading through Data Imputation Methods for Price Prediction Accuracy
Agricultural price prediction in developing regions faces significant challenges from missing data in Internet of Things (IoT)-based environmental monitoring systems, particularly in tropical fruit cultivation where sensors frequently experience connectivity and operational failures. This study evaluates the impact of missing data imputation methods on agricultural price prediction model performance using environmental and market data from a commercial durian orchard in Chanthaburi Province, Thailand (2023-2024). Three imputation strategies—Linear Interpolation, Prophet, and Kalman Filter—were systematically compared across four machine learning algorithms (Regression Trees, Random Forest, XGBoost, and Artificial Neural Networks) using 10-fold cross-validation. The dataset comprised 182 observations with 28.02% missing environmental data and 68.13% missing price data, representing realistic constraints in developing agricultural economies. Results demonstrated that XGBoost consistently achieved superior performance across all imputation methods, with Kalman Filter combined with XGBoost showing the best testing performance (R² = 0.9767, MSE = 0.0013, MAE = 0.0287, MAPE = 1.49%). However, these results require careful interpretation given the limited sample size, high missingness, and potential temporal data leakage from random train-test splitting. Time series visualization revealed distinct characteristics: Linear Interpolation provided computational efficiency but oversimplified data complexity, Prophet captured seasonal patterns but introduced excessive noise, while Kalman Filter offered balanced performance preserving both smoothness and natural variability. Practical price prediction analysis showed substantial variations up to 35 Thai Baht per kilogram between imputation methods. The findings provide methodological evidence for imputation strategy selection in agricultural IoT systems with missing data, though validation with larger multi-site datasets is essential before operational deployment.
[...] Read more.Understanding the Dynamics of Trust and Engagement in E-Commerce Recommender Systems: Trends and Influences
With increasing developments in artificial intelligence and the need for more personalized digital experiences, user trust and engagement have become relevant factors to be considered for the success of e-commerce recommender systems. This study presents a bibliometric analysis of research trends from 2003 to 2023 by exploring the evolution of trust and engagement in this domain. Using data from the Scopus database, we investigated publication trends, influential works, key contributors, and emerging research themes. Our results reveal a surge in research output between 2020 and 2023, which shows an increasing scholarly appreciation of trust as a critical determinant of user engagement of recommender systems. The leading role of China in global contributions emphasized its reliance on social commerce models, where recommendations are powered by a community-based trust mechanism to drive user engagement. While foundational topics such as collaborative filtering and machine learning remain central, emerging themes (explainability, blockchain integration, and adaptive AI) highlight a shift toward more user-centric and secure systems. These reinforce trust through transparency and security while boosting engagement through active personalization. Thematic evolution from algorithmic development to AI-driven innovations shows how transparency, personalization, and security serve as vital trust-building influencers that drive user engagement in recommender systems. Also, regional disparities in research output, especially in Africa and South America reveal considerable gaps in understanding culturally specific trust factors and engagement patterns. This indicates the need for collaborative studies to develop inclusive recommender systems tailored to local context to bridge these gaps. These findings reflect that trust and engagement are not simply complementary features, but fundamental pillars that are influencing the future of e-commerce recommender systems. As AI advances toward explainable, secure, and adaptive designs, this research calls for urgent globally inclusive frameworks that address both technological sophistication and cultural diversity to ensure that recommender systems emerge as equitable tools for global e-commerce.
[...] Read more.Feature Selection for Fraud Detection: Improving Machine Learning Capabilities on Portable Wallet
The rapid growth of mobile wallet usage has led to a sharp increase in fraudulent transactions, making fraud detection in portable wallets a pressing concern. Accurately detecting fraud is difficult because transaction data is complicated and unbalanced. Conventional rule-based systems are less flexible and frequently provide large false positive rates along with poor accuracy. Effective feature selection is crucial to the performance of Machine Learning (ML) models, notwithstanding their increased detection rates. Redundancy and noise are introduced by high-dimensional data, which lowers model performance and raises computing costs. The advantages of hybrid feature selection are frequently overlooked in current research, particularly when it comes to portable wallet fraud detection. By combining Random Forest Importance, LASSO Regression, Recursive Feature Elimination (RFE), and Mutual Information (MI) with resampling to solve class imbalance, this study fills that gap. Our approach provides a more reliable and effective solution for safe portable wallet fraud detection by removing superfluous features, increasing accuracy, and reducing computing cost. The model becomes faster and more effective when superfluous characteristics are eliminated because this reduces the computational effort. By concentrating just on the most instructive data, it increases accuracy. By addressing class imbalance and combining several selection strategies, the hybrid approach guarantees robustness. All things considered, this leads to a scalable and safe fraud detection system for transactions using mobile wallets. Our results show that a successful feature selection approach improves fraud detection accuracy, which in turn improves operational effectiveness and financial security.
[...] Read more.Model Real-Time Viewer Monitoring Based on SDLC for Student Learning Outcomes
In modern educational environments, particularly within computer laboratory settings in higher education institutions, the lack of effective real-time supervision and streamlined assessment processes presents a persistent challenge. Most current systems still rely on manual monitoring and evaluation, which are not only inefficient and time-intensive but also vulnerable to academic dishonesty, such as copy-paste behaviour during lab work. This study identifies and addresses this critical gap by proposing the development and implementation of an integrated real-time monitoring and assessment system tailored for use in academic computer labs. The proposed solution is a desktop-based application that incorporates four key features: Real-Time Viewer (RTV) for live monitoring of student activities, Block Inappropriate Websites (BIW) to restrict access to non-educational or harmful content, Manage Computer Time (MCT) to regulate system usage duration, and Form Learning Assessment (FLA) for digitalized and efficient performance evaluation. The development process followed the System Development Life Cycle (SDLC) framework, ensuring a structured approach across analysis, design, implementation, testing, and maintenance stages. Empirical testing involved a series of functional test cases simulating real-use conditions. All seven critical scenarios—such as input validation, session management, access control, and data deletion—were executed and passed successfully, indicating the system’s robustness and usability. In a pilot study conducted at Pekanbaru College of Technology, the application was tested among 30 students across multiple laboratory sessions. The results demonstrated a notable improvement in student engagement and learning performance. Quantitatively, students achieved learning assessment scores ranging from 84 to 96, with a calculated mean of 89.6 and a standard deviation of 4.1. These outcomes suggest that the introduction of automated, real-time monitoring significantly enhances not only instructional supervision but also the accuracy and fairness of learning assessments. This research contributes to the field by bridging the gap between digital classroom management and performance assessment in a higher education context. It introduces an innovative and practical approach for educators to maintain instructional quality while managing multiple learners in digital settings. Moreover, the findings provide empirical evidence supporting the integration of real-time supervision tools into educational systems to foster accountability, deter academic misconduct, and support data-driven instructional improvements.
[...] Read more.Predicting Employee Attrition: A Machine Learning Approach in Human Resource Analytics
Employee attrition is an important factor that can affect organizations, both financially and operationally. Human Resource (HR) managers often find it difficult to identify exactly which employees might be planning to leave the organization and what is the root cause for their decision. With the recent advances in computing, Machine Learning (ML) techniques are available for analysing, understanding, and solving complex problems. This study analyses the IBM HR Analytics dataset using ML techniques to predict employee attrition and identify the key factors that influence attrition. Four ML models based on Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting have been used for analysing attrition. It is found that Logistic Regression outperformed all other models in predicting attrition. At the same time, Decision Tree is found to be the weakest among the four techniques. On the analysis of feature importance, it is found that variables related to compensation (Monthly Income), career stage (Total Working Years, Age), and tenure at the organization are among the most significant factors influencing attrition. The insight from this study is expected to help HR managers in developing effective, data-driven strategies to retain their talent in their organization.
[...] Read more.Sustainable and Fair Task Scheduling in Cloud Computing Using Hybrid Bio-Inspired Algorithms for Green Computing
Despite cloud computing's scalability and economy, energy efficiency, security, and equitable scheduling remain significant concerns. The traditional scheduling approach often fails to optimize execution time, energy consumption, and security concerns, resulting in less resource utilization and less secure systems. This paper proposes the Hybrid Bat-Genetic Algorithm (HBA-GA), which combines the Bat Algorithm for fast exploration with the Genetic Algorithm for accurate exploitation. This method reduces energy use while also reducing security risks like unauthorized access and data leaks. It uses Jain's Fairness Index (JFI) in order to ensure that workloads are evenly distributed and VM overload and conflicts are avoided. Based on simulations results, proposed HBA-GA improves energy efficiency while reducing security exposure and risk likelihood at the scheduling level by incorporating security-aware risk scoring into task–VM allocation decisions.
[...] Read more.IoT-Enabled Estrus Detection in Dairy Cattle Using Machine Learning Technique
In the dairy industry, optimizing reproductive management is crucial for sustainable operations and enhancing animal welfare. The traditional manual detection methods usually miss many of the estrus incidences and hence have resulted in a 20-30% decline in conception rates and further massive economic losses.This paper presents an advanced framework integrating machine learning and Internet of Things (IoT) technologies to improve estruses detection in dairy cattle, thereby supporting efficient herd management and productivity. The proposed solution leverages a stacking model of Random Forest and Gradient Boosting Machine (GBM) algorithms to accurately identify estruses events, providing a reliable method for reproductive monitoring. The experimental evaluation yields accuracies of 92.1 % using RF, 92.3 % using GBM, and an improved 93.19 % when the stacking model is applied, along with improvements in precision of 94 and an F1-score of 94 %, reflecting its strength in complex behavioral pattern recognition. Rigorous evaluation across key performance metrics confirms the model’s high accuracy, underscoring its suitability for practical deployment. The system employs IoT-enabled smart collars equipped with temperature sensors, accelerometers, GPS, and RFID to gather real-time data on cattle health and reproductive status. By analyzing this data, the system delivers precise and timely insights into estruses cycles, enabling targeted breeding interventions and enhanced reproductive management. Data collected through the smart collars is securely stored in Google Firebase, facilitating efficient data archiving and rapid access via a user-friendly web application. The proposed integration of IoT, machine learning, and cloud computing presents a holistic, scalable, and economically viable solution for enhancing reproductive efficiency, animal welfare, and sustainable dairy management.
[...] Read more.Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection
In recent years, the rapid advancement of machine learning (ML) has surpassed many expectations, and its application in the healthcare sector has emerged as one of the most fascinating areas of exploration. This thesis looks into whether machine learning can increase the precision and efficacy of breast cancer diagnosis. With the help of nine classification algorithms including Random Forest, XGBoost and MLP Classifier the given work intends to propose a reliable automatic solution for malignant and benign classification of breast tumor. The main idea of the project is the development of the Web based tool that would allow doctors and other medical practitioners to make quick decisions The MLP Classifier was found to be the optimal solution after its efficiency was evaluated based on the accuracy rate, and such parameters as precision rate, recall rate, and F1-score. This leads to development of a user friendly app; even those that would not originally consider themselves technical can easily operate the application. Apart from addressing the matter of high accuracy of diagnostics, the system shows the possibility of minimizing the rates of human factors and optimizing clinical decision. Seeking for that day when technology and human opinion will complement each other in the delivery of healthcare, our study neither only contributes to the growing literature on applying artificial intelligence in healthcare but also evolves the blueprint to integrate ML models in everyday practice.
[...] Read more.From Hype to Hesitation: A Longitudinal Analysis of User Sentiments towards AI‑Enabled Fintech Lending Platforms
The rise of FinTech lending in India has transformed credit access, yet studies examining customer experiences with artificial intelligence (AI)-enabled FinTech lending platforms remain limited. This study investigates the key drivers of user experience and the evolving sentiment toward AI-enabled lending platforms by analysing online reviews from 2017 to 2024 using LDA topic modelling and lexicon-based longitudinal sentiment analysis. Twelve key topics emerged, revealing significant negative sentiment around customer support, eligibility checks, documentation, repayment, and app trustworthiness. In contrast, app usability and interface design maintained strong positivity, while loan approval and disbursement processes saw declining sentiment. Despite these pain points, overall user experience remained positive, indicating that the perceived benefits such as speed, efficiency, and convenience provided by these platforms outweighed concerns like high interest rates, privacy risks, and poor customer service. The findings highlight a nuanced balance between technological advantages and operational shortcomings, offering insights for improving AI-enabled lending platforms.
[...] Read more.IoT for Industrial Automation: A Review on Industry 4.0 Applications
The beginning of the fourth industrial revolution or Industry 4.0 has changed the concept of automation in industries by adopting the Internet of Things (IoT) in manufacturing, logistics, and production processes. The IoT is the digital foundation of Industry 4.0 that allows real-time monitoring, predictive maintenance, data-based decision making,
and autonomous processes with in-between devices and smart sensors. This review explores the uses of the IoT in industrial automation through analyzing the enabling technologies, communication protocols, integration with cloud computing, wireless sensor networks, edge computing, artificial intelligence (AI) and machine learning (ML), as well as presenting important applications, current challenges, and future trends in smart industrial systems. Instead of considering the technologies separately, this paper takes the system-level viewpoint by integrating the way IoT architecture, communication protocol, intelligent analytics, and security controls collectively facilitate Industry 4.0 automation.
Predicting Diabetes Using Machine Learning: Models and Insights
Diabetes mellitus is a chronic metabolic disorder with a rapidly increasing global prevalence, posing a significant public health challenge. Early detection of diabetes can enable timely intervention and preventive measures, thereby reducing the risk of long-term complications. In this study, a machine learning (ML)-based methodology is proposed for the early prediction of diabetes mellitus. The proposed approach enhances existing prediction systems by improving key performance metrics, including precision, recall, and F1-score, and achieves an efficiency improvement of 4%–10% compared to state-of-the-art methods. Experimental results demonstrate that the support vector machine outperforms other ML algorithms for diabetes prediction, achieving 92% accuracy, 95% precision, 92% recall, 93% F1-score, 92% specificity, and an area under the receiver operating characteristic curve of 0.97.
[...] Read more.PARADAJuan: A Web-Based Parking Lot Management System Designed and Developed Using Multi-Paradigm Programming Languages
This study aims to develop a web-based parking lot management system using multi-paradigm programming languages. This application is designed to help parking lot owners in monitoring the ins and outs of the parking spaces including the income they generated from it. The researchers used multi-paradigm programming languages where more than one programming paradigm was employed. This allows them to use the most suitable programming style and associated language constructs to build the system. Specifically, the researchers made use of the following languages in creating the system: HTML5, CSS3, JavaScript, PHP, MySQL, and Flutter. The study utilized developmental research methods in which the product-development process is analyzed and described, and the final product is evaluated. As a result, the creation of the system has been successful.
[...] Read more.Diabetes Prediction: A Deep Learning Approach
Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five and ten-fold cross-validation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for ten-fold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.
[...] Read more.A Review of Applications of Linear Programming to Optimize Agricultural Solutions
Quantitative methods help farmers plan and make decisions. An apt example of these methods is the linear programming (LP) model. These methods acknowledge the importance of economizing on available resources among them being water supply, labor, and fertilizers. It is through this economizing that farmers maximize their profit. The significance of linear programming is to provide a solution to the existing real-world problems through the evaluation of existing resources and the provision of relevant solutions. This research studies various LP applications including feed mix, crop pattern and rotation plan, irrigation water, and product transformation; that have the main role to enhance various facets of the agriculture sector. The paper will be a review that will probe into the applications of the LP model and it will also highlight the various tools that are central to analyzing LP model results. The review will culminate in a discussion on the different approaches that help optimize agricultural solutions.
[...] Read more.Mobile-based Attendance Monitoring System Using Face Tagging Technology
Manual checking of attendance may lead to inconsistency of data inputs and may generate unreliable attendance result. Hence, Radio Frequency Identification (RFID) system has been developed to solve this problem, but it allows only checking student’s attendance as they enter and exit the school premise only. In consequence, teachers in every subject still need to check and monitor students’ attendance manually. Nevertheless, due to a usual large number of students entering and existing the school premise as they are tapping their RFID card, there is always a possibility of proxy attendance. Thus, Mobile-Based Attendance Monitoring System Using Face Tagging Technology (MBAMSUFTT) was developed to provide an attendance monitoring system through biometric authentication such as face recognition. The system serves as a tool for teachers to check and monitor student’s attendance in most reliable and accurate way using their smart phones. The MBAMSUFTT generates attendance report intended for close monitoring and printing of student’s attendance result. But the reliability of the attendance result (output) of the system depends on the quality of picture (input) sent by the user. Camera specification, ambiance lighting condition, and proper position of students while taking photo is exclusively required. The server and the mobile part can only run together if Wireless Fidelity is on, otherwise, monitoring will not be executed.
As a developmental research, this study used the Agile Model based on System Development Life Cycle (SDLC) intended for building a project that can adapt to change requests quickly. The MBAMSUFTT was evaluated based on the ISO/IEC 25010; MBAMSUFTT’s software quality characteristics by the IT experts, and its functionality, performance efficiency, and usability by the teachers. The analysis of the data revealed that the MBAMSUFTT serves its intended purpose in checking and monitoring students’ attendance per subject area with more accurate and reliable attendance results and has also met the ISO software quality standards.
Sentiment Analysis of Review Datasets Using Na?ve Bayes‘ and K-NN Classifier
The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantages, the most important of all being monetization. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The focus of our project is sentiment focussed web crawling framework to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same. We use statistical methods to capture elements of subjective style and the sentence polarity. The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naïve Bayes‘ and compares their overall accuracy, precisions as well as recall values. It was seen that in case of movie reviews Naïve Bayes‘ gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.
[...] Read more.Barriers to E-Commerce Adoption in Egyptian SMEs
E-commerce has been predicted to be a new driver of economic growth for developing countries. The SME sector plays a significant role in its contribution to the national economy in terms of the wealth created and the number of people employed. Small and Medium Enterprises (SMEs) in Egypt represent the greatest share of the productive units of the Egyptian economy and the current national policy directions address ways and means of developing the capacities of SMEs. Many factors could be responsible for the low usage of e-commerce among the SMEs in Egypt. In order to determine the factors that promote the adoption of e-commerce, SMEs adopters and non-adopters of e-commerce were asked to indicate the factors inhibiting the adoption of e-commerce. The results show that technical barriers are the most important barriers followed by legal and regulatory barriers, whereas lack of Internet security is the highest barrier that inhibit the implementation of e-commerce in SMEs in Egypt followed by limited use of Internet banking and web portals by SMEs. Also, findings implied that more efforts are needed to help and encourage SMEs in Egypt to speed up e-commerce adoption, particularly the more advanced applications.
[...] Read more.Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute
Registration of new students’ academic information is essential for every educational institute to continue their education at every semester level and go through their whole student life. And this registration information is used when they do their form fill up of consecutive semesters. Nowadays, almost all educational institutes are using paper based registration and form fill up systems which is prone to many human errors and very time consuming for both students, teachers as well as other related administrative bodies. In this paper, we developed a web based application for academic purposes to control and save student registration and form fill up data that will be helpful for students, teachers and admin authority to make the process easier, less time consuming and error free. There are four main types of users who can use this system: student, department authority, students’ hall authority and administrator. The student can submit their registration and form fill up information by using a web form. Moreover, he/she can download their admit card and registration form after the approval of the concerned authority. The students also can be able to do other module activities. The hall and department authority can use the system to approve the students' registration, semester examination form and to provide the students' attendance data. In addition, the department and hall authority has a choice to see all students’ academic information. Moreover, the system administrator controls the system by managing (add, delete, update) student, hall and department authority, exam or registration date, subjects of a particular semester, notice board of the institute, module and programme data. The administrator can also add and remove the running and passed student data. The students also can pay their semester fees by using an online banking system.
[...] Read more.Development of an Intelligent Tutoring System for English Reading Comprehension: Design Based on Philippine Public School Flexible Learning Experience
This paper presents an intelligent tutoring system as seen to be successful in assisting in the instruction of basic skill, particularly, reading comprehension. The goal of the study is to develop an Intelligent Tutoring System that will greatly help the Grade 7. The system adapted considerable instructional needs of learners from early development to advanced reading comprehension skills. The developed system provided an immediate feedback to learners upon completion of an activity without requiring intervention from a Teacher. To improve the system, learners and teachers filled out survey questionnaires. The result reveals that teachers and students want the system to be user-friendly, have a user log-in, lesson content with text, audio and video as well as various types of questions in quizzes. They also perceived that the developed ITS is useful and the content is valid thus is very acceptable to be utilized by the learners. In addition, result reveals that student’s reading comprehension could be improved and developed by the proposed ITS.
[...] Read more.Two Proposed Models for Securing Data Management for Enterprise Resource Planning Systems Using Blockchain Technology
An Enterprise Resource Planning (ERP) system is a software application that serves as a centralized platform to streamline and automate organizational functions and share real-time data, facilitating efficient communication and collaboration. It provides an all-inclusive approach to managing and optimizing business processes, boosting efficiency, fostering cooperation, and giving an overall picture of how the organization is operating. However, the traditional centralized databases in ERP systems pose security concerns. Blockchain Technology can be an appealing alternative as it comes with immutable and decentralized data as well as enhanced security. This study focuses on two methods of securing data management in ERP systems: Organizing the distributed information using The Ralph Kimball data model and optimizing an individual block using Database Sharding. This study does an extensive examination to determine the effectiveness of both suggested strategies, comprising a detailed evaluation that highlights the benefits and limitations of both techniques. This paper intends to patch the security holes in ERP systems to safeguard sensitive data and mitigate risks.
[...] Read more.Integrating QR Code to e-Class System for Managing Attendance Lecture Services in Higher Education
Currently attendance is still done manually by recapping each lecturer's attendance sheet. The method used is the prototype method and testing using the User Acceptant Test (UAT) method. This takes a long time, even misinterpretations of existing absences sometimes cause problems when giving salary receipts to lecturers, besides that, reporting to campus management also takes time. The lecturer attendance system can help the finance department to calculate lecturer teaching attendance faster and more easily, which can be used to calculate salaries and evaluate lecturer attendance. The system at the implementation stage and during implementation the average teaching attendance of lecturers is easier to control. Some of the features in this attendance system include Check-in, Lecturer, profile, History, Schedule, Help, Tutorial and Chat Group. Apart from that, the main menu also provides information regarding the check-in time limit, waiting time for the next check-in, campus information, and application updates. The system was built with a QR Code and is Android-based to make things easier for lecturers, admin, and the finance department.
[...] Read more.PARADAJuan: A Web-Based Parking Lot Management System Designed and Developed Using Multi-Paradigm Programming Languages
This study aims to develop a web-based parking lot management system using multi-paradigm programming languages. This application is designed to help parking lot owners in monitoring the ins and outs of the parking spaces including the income they generated from it. The researchers used multi-paradigm programming languages where more than one programming paradigm was employed. This allows them to use the most suitable programming style and associated language constructs to build the system. Specifically, the researchers made use of the following languages in creating the system: HTML5, CSS3, JavaScript, PHP, MySQL, and Flutter. The study utilized developmental research methods in which the product-development process is analyzed and described, and the final product is evaluated. As a result, the creation of the system has been successful.
[...] Read more.A Review of Applications of Linear Programming to Optimize Agricultural Solutions
Quantitative methods help farmers plan and make decisions. An apt example of these methods is the linear programming (LP) model. These methods acknowledge the importance of economizing on available resources among them being water supply, labor, and fertilizers. It is through this economizing that farmers maximize their profit. The significance of linear programming is to provide a solution to the existing real-world problems through the evaluation of existing resources and the provision of relevant solutions. This research studies various LP applications including feed mix, crop pattern and rotation plan, irrigation water, and product transformation; that have the main role to enhance various facets of the agriculture sector. The paper will be a review that will probe into the applications of the LP model and it will also highlight the various tools that are central to analyzing LP model results. The review will culminate in a discussion on the different approaches that help optimize agricultural solutions.
[...] Read more.Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute
Registration of new students’ academic information is essential for every educational institute to continue their education at every semester level and go through their whole student life. And this registration information is used when they do their form fill up of consecutive semesters. Nowadays, almost all educational institutes are using paper based registration and form fill up systems which is prone to many human errors and very time consuming for both students, teachers as well as other related administrative bodies. In this paper, we developed a web based application for academic purposes to control and save student registration and form fill up data that will be helpful for students, teachers and admin authority to make the process easier, less time consuming and error free. There are four main types of users who can use this system: student, department authority, students’ hall authority and administrator. The student can submit their registration and form fill up information by using a web form. Moreover, he/she can download their admit card and registration form after the approval of the concerned authority. The students also can be able to do other module activities. The hall and department authority can use the system to approve the students' registration, semester examination form and to provide the students' attendance data. In addition, the department and hall authority has a choice to see all students’ academic information. Moreover, the system administrator controls the system by managing (add, delete, update) student, hall and department authority, exam or registration date, subjects of a particular semester, notice board of the institute, module and programme data. The administrator can also add and remove the running and passed student data. The students also can pay their semester fees by using an online banking system.
[...] Read more.Development of a Decision Support System on Employee Performance Assessment Using Weighted Performance Indicators Method
Employee Performance Assessment is a part of the Decision Support System. One of the decision support system methods that are most used in performance assessment is Simple Additive Weighting (SAW). In the SAW method, each criterion has a weight value to show the interest level. The determination of the criteria on the SAW method is subjective and the final result is on the ranked system and creates many problems. The study utilizes the Weighted Performance Indicators (WPI) method to solve the problems in the SAW method. The criterion is determined based on the respondent's opinion so that it will be more realistic to achieve the target. The population of the study is the employee of Indo Global Mandiri University which reach 30 persons. WPI method consists of 9 steps. The research result is shown that 4 employees has a performance below MSV and 36 employee has above MSV. The general value of the employee performance value = is 0.69. It shows that the performance of the employee at Indo Global Mandiri University is good enough. However, it needs to be increased, so that the target could be achieved. WPI method is easy to implement, it is not just limited to the employee performance assessment only, but it could be implemented for the other performance assessment, for example, human resource performance, finance, company, industry, system, etc.
[...] Read more.Sales Management Application at Widya Collection Store Web-based
Widya Collection Store is a business that provides sports clothing, as well as one of the producers in the Samarinda area. Sales management is still not optimal because it still uses paper notes and is still being written which makes it easy for errors to occur in writing prices, quantities of goods and total prices so that it takes a long time to process transactions, both from payment in full or receivables. In addition, managing stock of goods is also more difficult because it is not recorded in the database. Therefore, a Sales Management application was made at the Web-Based Widya Collection Store to process item data, sales transactions, make complete notes and reports and make the transaction process faster. The long-term goal to be achieved is that the stock management process has been recorded in order to know the stock that must be ordered from the supplier. In addition, to simplify and expedite activities in searching for sales transaction data if one day it is needed. In this study, the method used to build a Sales Management Application at a Web-Based Widya Collection Store is the System Development Life Cycle (SDLC) development stage which consists of needs analysis, system design, and implementation.
[...] Read more.House Price Prediction Modeling Using Machine Learning
Machine Learning is seeing its growth more rapidly in this decade. Many applications and algorithms evolve in Machine Learning day to day. One such application found in journals is house price prediction. House prices are increasing every year which has necessitated the modeling of house price prediction. These models constructed, help the customers to purchase a house suitable for their need. Proposed work makes use of the attributes or features of the houses such as number of bedrooms available in the house, age of the house, travelling facility from the location, school facility available nearby the houses and Shopping malls available nearby the house location. House availability based on desired features of the house and house price prediction are modeled in the proposed work and the model is constructed for a small town in West Godavari district of Andhrapradesh. The work involves decision tree classification, decision tree regression and multiple linear regression and is implemented using Scikit-Learn Machine Learning Tool.
[...] Read more.Developing Smart Conversation Agent ECOM-BOT for Ecommerce Applications using Deep Learning and Pattern Matching
Chatbots are a technological leap in conversational services, generating messages to users either following a set of rules to respond based on recognized patterns or training themselves from previous data or conversations. The primary goal is to enable a device to communicate with a user upon receiving natural language user requests using artificial intelligence and machine learning to generate automated responses. Technology is progressively catering to the questions, both in academic and business contexts, such as situations that require agents to investigate the cause of customer dissatisfaction or to recommend products and services. Significance of this research is to reduce the human dependency and improving customer support by providing close to human natural responses using pattern matching and deep learning on the custom-made data. The main objective of this work is to (a) study the existing literature on cutting-edge technologies in chatbot development in terms of research trends, legacy components, techniques, datasets, and domains specifically in e-commerce and (b) to develop a product that fill some of the gaps/missing functionality identified in current frameworks. We have achieved the following, (a) generated small yet generic dataset, which can be used for all types of products, (b) the intents are identified accurately by the bot using deep learning, whenever a user query.
[...] Read more.Development E-commerce Information System of Agriculture in Samarinda
Samarinda village is a village that is predominantly working as a farmer and has a wide range of agricultural products, in addition to the abundance of agricultural products there is a problem of marketing of agricultural products that do not have access to sell their agricultural products. Authors conducted research in order to increase sales and expand marketing in the Village Samarinda through sales system-based Business to the Business and method development using the Research and Development. The results obtained in the form of a web site that can be accessed to serve online sales transaction so that it can increase sales in the village Samarinda.
[...] Read more.Systematic Analysis of Virtual Reality & Augmented Reality
Nowadays, users are moving from old 2D screens to modern devices such as 3D screens and virtual reality devices to enjoy videos and games like real-world experience, and this demand increased further development. Virtual Reality (VR) is based on the creation of a simulated environment of real-world with computer creation, and Augmented Reality (AR) is based on the addition of simulation components (environment) in the real-world scene. In this paper, systematic analysis of relationships and features both VR and AR varies by outline, arrangement, administrations, and devices for associations and clients. This paper provides a difference between AR and VR, advantages, future, and open research issues.
[...] Read more.A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains
The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology’s weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.
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