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: 92
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. 3, Jun. 2026
REGULAR PAPERS
AI-driven Psychographic and Behavioral Segmentation of Prospective University Students in Vietnam
The digital transformation of higher education marketing demands more sophisticated approaches to understanding prospective students beyond traditional demographic segmentation. This study develops a machine learning-based psychographic and behavioral segmentation framework for prospective university students in Vietnam, integrating constructs from consumer choice theory and technology adoption literature. We employ established unsupervised and supervised machine learning techniques (k-means clustering, Gaussian Mixture Models, and XGBoost classification) rather than claiming novel artificial intelligence architectures. Analyzing survey data from 1,486 Grade-12 students, our hybrid methodological approach identified three distinct segments: Intrinsically-Motivated Digital Explorers (27.7%), Prestige-Driven Traditionalists (38.9%), and Undecided Ambivalents (33.4%). Supervised learning (XGBoost) achieved 87.2% accuracy in predicting segment membership, with feature importance analysis revealing intrinsic motivation, technology readiness, and risk aversion as the primary discriminators. The findings extend higher education consumer choice theory by integrating technology readiness as an independent discriminative factor and demonstrate the methodological value of combining unsupervised and supervised machine learning for market segmentation.
[...] Read more.A Multi-Modal Transformer Model with Gated-LSTM for Sarcasm Detection in Tweets Using Cross-Attention and Emoji Integration
In the era of social media-driven communication, sarcasm poses a big challenge for the automated sentiment analysis systems, much more on platforms like Twitter, due to the brevity and often contextually ambiguous nature of the text. Misinterpretation of sarcastic content may degrade the reliability of downstream analytics, encompassing opinion mining and content moderation. To address this challenge, we propose, in this paper, a multi-modal transformer-based approach to sarcasm detection, which integrates textual and emoji information through the use of a cross-attention mechanism. The proposed model utilizes RoBERTa for the contextual processing of textual content to generate contextualized text embeddings, whereas emojis are encoded using Emoji-BERT to capture emoji-specific semantic and emotional cuing. A Gated-LSTM layer has been employed to model sequential dependencies among emojis, and a cross-attention mechanism dynamically aligns emoji representations with textual features for enhancing the sarcasm recognition capability. Later, these fused representations are passed to a fully connected classification layer for predicting sarcasm. For the evaluation of the performance of our proposed model against state-of-the-art results, standard metrics of evaluation have been considered. Experimental results demonstrate that the proposed approach outperforms several baseline and state-of-the-art models, with an accuracy of 92.5%, precision of 91.8%, recall of 93.2%, and an F1-score of 92.5%. From these results, we learn that jointly modeling textual and emoji modalities improves the performance of sarcasm detection in social media content. Also, these findings illustrate the potential of the suggested approach in improving sarcasm-aware sentiment analysis in the realm of social media analytics and automated content moderation systems.
[...] Read more.Methodological Approaches to Assessing Enterprise Information Security Using a Process-Oriented Approach
The article proposes a process-oriented methodology for assessing enterprise information security, which serves as an integral indicator of business process security Q based on a multi-level system of mathematical models. The proposed approach combines risk-oriented analysis, stochastic modelling, fuzzy set methods, and optimisation of the distribution of protection resources, ensuring the linkage of security indicators to the enterprise's functional business processes. The simulation model allows the reproduction of the dynamics of cyberattack flows and the assessment of the impact of variable threat intensity on the stability of business processes in near real time. Experimental validation of the methodology on depersonalised incident logs and simulated attack scenarios showed that the integration of the optimisation module provides an increase in the integral security indicator Q by 12-27% depending on the intensity of threats, and also contributes to the rational redistribution of cybersecurity resources in favour of the most critical business processes. A comparative analysis with the Classical Risk Matrix, NIST SP 800-30, and ISO/IEC 27005 confirmed the proposed model's higher accuracy and adaptability in a dynamic cyber environment. Machine learning methods are used as an auxiliary adaptive mechanism to refine model parameters, rather than as the primary risk assessment tool. The results obtained demonstrate the practical applicability of the process-oriented simulation and optimisation model for improving the resilience of enterprise business processes and reducing residual cyber risk.
[...] Read more.Secure Electronic Health Records with Effective Data Management in Blockchain Framework Using FPOS
The widespread adoption of Electronic Health Records (EHRs) has remarkably transformed healthcare delivery through rapid retrieval of patient information and enhancement of internal clinical decision-making. The rapid adoption of digital health infrastructures and remote patient monitoring systems has further highlighted the need for data-driven care. Although digital health infrastructures and remote patient monitoring systems support real-time care and care planning, the majority of EHR systems remain susceptible to data breaches and privacy violations. Major EHR systems also continue to lack cross-institutional interoperability and sufficient protection against data breaches and cyber-attacks. While the majority of EHR systems remain vulnerable, the centralized architectures and cloud-based systems commonly employed to support them also fail to provide the necessary protections and assurances.
This work proposes a tailored management framework for EHR systems based on blockchain technology, incorporating automation through smart contracts. The framework employs a dual storage system in which sensitive clinical information is stored off-chain, while on-chain data pertains to access control logs, integrity proofs, and system control metadata.
The proposed system utilizes blockchain consensus mechanisms to confirm data immutability, smart contracts for role-based and policy-secured access control, and encryption algorithms for secure data storage. Data integrity is maintained through hashing, and the distributed ledgers allow interoperable healthcare network policies to be traced and kept unmodifiable.
Testing indicates that the proposed blockchain-based EHR system demonstrates enhanced data security, reduced access latency due to authorization, and improved resistance to cyber-attacks compared to traditional cloud-based EHR systems. The hybrid storage method effectively minimizes storage overhead while maintaining system efficiency. The system demonstrates a viable approach to integrating smart contract systems and blockchain technologies within EHR management systems.
SOWA Method Framework: New Algorithm for Criteria Weight Balancing with a Hybrid Subjective and Objective Approach
This research proposes the implementation of the subjective and objective weighting approach (SOWA) method as a new approach in determining the criteria weights that combines subjective assessments from experts and data-driven objective calculations. The criteria weights generated from the SOWA method are then used in various multi-criteria decision-making (MCDM) methods, such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of ratio analysis (MOORA), grey relational analysis (GRA), multi-attribute utility theory (MAUT), weighted aggregated sum product assessment (WASPAS), weighted product (WP), simple multi-attribute rating technique (SMART), multi-attributive border approximation area comparison (MABAC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), to evaluate and rank alternatives. The research results show that the SOWA method is capable of producing balanced and representative weights, as well as consistent alternative rankings across MCDM methods. Sensitivity analysis of the ranking results indicates that all methods yield identical ranking results, signifying a high level of stability and reliability of the generated weights. These findings demonstrate that the SOWA method can serve as a solid foundation in decision support systems, particularly in the context of candidate selection or evaluation based on multiple criteria.
[...] Read more.Comparative Performance and Optimization Strategies for Cloud-Native Architectures: A Focus on Scalability, Cost, and Resource Utilization
Cloud-native architectures have become essential for modern application development, offering scalability, flexibility, and cost efficiency through paradigms like microservices, serverless computing, and event-driven systems. However, performance trade-offs, resource underutilization, and operational inefficiencies persist across different architectural models. This study delivers a comparative performance evaluation of four leading cloud-native architectures—Service Mesh, Event-Driven Systems, Serverless Computing, and Polyglot Persistence across AWS and GCP platforms. Using a controlled experimental setup, key performance metrics including response time, throughput, resource utilization, and operational cost (OC) were assessed under varying workloads. Serverless computing demonstrated superior cost-efficiency and dynamic scaling, though hampered by cold-start delays, while event-driven systems struck a balance between responsiveness and cost. Optimization strategies such as cold-start mitigation, adaptive auto-scaling, and hybrid storage improvements yielded significant performance gains across all architectures. The research provides critical insights for developers and system architects, offering data-driven recommendations to guide architectural choices and optimize cloud-native deployments. The study’s significance lies in its empirical approach, bridging theoretical design with real-world implementation to advance best practices in building scalable and sustainable cloud-native applications.
[...] Read more.NeuroFortis: Blockchain-Powered Federated Learning for ADHD Diagnosis via IoMT Data
Attention-Deficit Hyperactivity Disorder (ADHD) represents a challenging neurodevelopmental disorder that consistently displays three major symptoms involving inattention and hyperactivity alongside impulsivity. Traditional approaches for diagnosis use behavioral evaluations that create both wrong conclusions and delayed help timing. This research develops a complete diagnostic solution involving deep learning federated learning and blockchain security to analyze actigraphy signals originating from IoMT devices. This method first uses UMAP as well as PCA and t-SNE to reduce data dimensions before implementing a hybrid CNN-Transformer neural network to achieve improved classification results. A distributed learning method helps medical institutions run model training autonomously while satisfying privacy rules and addressing data centralization challenges. Model updates on blockchain systems gain protection through smart contracts and cryptographic hashing to stop adversarial attacks and sustain data authenticity. Laboratory tests reveal that this approach reaches 99.2% classification precision without significant performance impact, establishing its effectiveness. This presented study provides on-the-next level ADHD diagnosis features with the help of an AIbased system that ensures privacy and guarantees tampering and scalable operations. Such results allow advancing accurate medical works by real-time monitoring of ADHD and offer safe application of medical Artificial Intelligence to distributed healthcare processes. This will provide objective and credible evaluations that will exist on a global scale.
[...] Read more.Scenario-Based Security and Reliability Evaluation of Connected Autonomous Vehicles Using a Hybrid CRITID–Fuzzy BWM–VIKOR Swarm Approach
Connected autonomous vehicles (CAVs) are reshaping mobility but remain vulnerable to technical, organizational, and regulatory risks. This study develops a hybrid multi criteria decision-making framework that integrates CRITID for objective weighting, Fuzzy BWM for expert uncertainty modeling, and VIKOR Swarm for adaptive compromise ranking. To enhance realism, four scenarios were constructed: scalability focused (A1), compliance & reliability focused (A2), resilient high performance ecosystem (A3), and organizational vulnerability focused (A4). Results show that Scenario A3 consistently outperforms others, achieving the lowest group utility shortfall, smallest individual regret, and most favorable compromise measure. Shapley Value sensitivity analysis confirmed cybersecurity and scalability as dominant criteria, while expert AI validation reinforced the robustness of A3’s ranking. Monte Carlo simulations further demonstrated stability underweight perturbations, with A3 retaining its top position in over 80% of runs. The study contributes a transparent, reproducible, and scenario based methodology for vehicular risk assessment, bridging technical and organizational dimensions. Limitations include reliance on static scenario design and expert elicitation, suggesting future work should incorporate dynamic data streams and edge AI for real time risk recalibration.
[...] Read more.AI-Based Metaheuristic Algorithm for Reducing Cost and VM Failure Rate in Task Scheduling within Cloud Computing Environments
Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.
[...] Read more.Knowledge Transfer and Innovation through University - Industry Collaboration
University–industry collaboration (UIC) has become an essential mechanism for fostering innovation and transferring knowledge across institutional boundaries. It is a powerful driver for innovation and sustainable economic development. This study investigates the role of UIC in facilitating knowledge transfer and its impact on innovation outcomes within industries. The study also identifies barriers such as organizational misalignment, lack of trust, and limited funding. A conceptual model is proposed to demonstrate the dynamics of UIC. Recommendations include policy reforms, structured collaboration frameworks, and enhanced R&D investments. Drawing from both qualitative and quantitative methods, the study investigates the nature of UIC, the influencing factors, and its outcomes in terms of innovation capabilities. The findings underscore the importance of trust, absorptive capacity, and aligned goals in enhancing knowledge transfer. The study also identifies critical enablers and barriers, offering strategic insights for optimizing collaborative frameworks.
[...] Read more.IoT-Based Smart Homes: Technologies, Security Risks and Countermeasures
The advent of (Internet of Things) IoT technologies has essentially transformed traditional houses into intelligent, equipped, and networked smart houses that serve to improve the quality in the lives of human beings with respect to security, energy efficiency, and comfort through massive automation, sensing, and remote control. However, with such a shift of paradigm, due to the diversity of devices, the limitation of resources, problems of interoperability, and a growing array of cyberthreats, opens up numerous avenues for security and privacy threats. This review attempts a holistic coverage of IoT-based smart home technologies and then provides a systematic classification of the security vulnerabilities from device, network, cloud, and application layers. The key threats include unauthorized access, data leakage, propagation of malware, denial of service, and exploits targeted against AI, with an analysis of their causes and occurrences in the real world. The paper undertakes a critical assessment of contemporary countermeasures, ranging from lightweight cryptographic protocols, AI-driven intrusion detection systems, blockchain-based authentication, privacy-preserving edge computing, and zero-trust frameworks. A comparative insight into each approach conversed with the views of the established literature draws out trade-offs between security efficacy, scalability, computational overheads, and user adoption. Based on a synthesis of the modern findings, continued gaps are identified, and future directions provided: including quantum-resistant encryption, interoperable standards, and user-centric security design, acting as the working platform or actionable directions for any researchers, developers, or policymakers in building of secure, resilient, and privacy preserving smart home ecosystem.
[...] Read more.A Hybrid Quantum-Classical Transformer Framework for Robust Depression Detection on Social Media: Enhancing Sensitivity via Variational Quantum Circuits
This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.
[...] 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.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.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.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.Analyzing the Impact of Artificial Intelligence on Shaping Consumer Demand in E-Commerce: A Critical Review
The surge in scholarly articles on e-Commerce mirrors its rapid ascent in the market's legitimacy. According to customer product recommendation theory, e-Commerce research may exhibit a bias toward specific customer product recommendations due to its evolving nature. To address this concern, this study examines five of the leading e-Commerce journals. The findings reveal a predominant focus on two main groups: customers and the integration of artificial intelligence (AI) in e-commerce recommendation systems. However, there is a notable lack of attention toward other critical groups, such as suppliers, indirect stakeholders, investors, and regulators. With e-Commerce continuing to mature, it is crucial to explore these neglected themes, sectors, and entities. This paper identifies gaps in current research through targeted keyword searches by aiming to bring these overlooked areas to the forefront. By highlighting persisting challenges in e-Commerce research, this study seeks to raise discourse and innovation in the field by ensuring that emerging topics are not overlooked. The role of AI in e-Commerce, particularly in the development of advanced recommendation systems, is identified as a key area shaping consumer experiences and market dynamics.
[...] 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.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.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.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.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.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.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.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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