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: 93
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, SCImago, Google Scholar, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, 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. 4, Aug. 2026
REGULAR PAPERS
Enabling Data-Driven Governance through Collective Analytics: Challenges and Framework for Indian E-Governance
The demand for data-driven insights in government has highlighted the importance of collective analytics. This study attempts to explore the key challenges of collective analytics in the context of Indian e-governance and the framework for addressing them. The study is based on a literature review, references to two cases, and expert views obtained from professionals involved with analytics solutions in government. In this study, analytics projects are considered as dashboard-based analytics. Based on the content analysis of expert responses, 14 key challenges of collective analytics in e-governance have been identified. The novelty of the present study is the focused exploration of challenges and their framework related to collective analytics in e-governance-a topic that received limited attention in the extant literature. This study brings forth the fact that unless the challenges of collective analytics in e-governance, including those related to data visualization, data quality, capacity building, technological capabilities, and inter-agency communications, are recognized, the implementation of collective analytics can be challenging. This study provides the basic understanding needed for data-driven governance through collective analytics. The output of the study will be helpful to the managers, e-governance experts, academicians, planners, and policymakers to understand the dynamics of collective analytics in government for handling discussed challenges well in advance. This study will also helpful to reduce the cost and time of the collective analytics project for effective decision-making.
[...] Read more.Information and Analytical Support of Tax Control in the Context of Digitalization of the Fiscal System
The research objective is to formalize the cognitive stratified framework of digital fiscal control through the unification of information and analytical tools based on decomposition, topological analysis, and Unified Modelling Language (UML). The study employed the following methods: SWOT analysis of solutions for the digitalization of fiscal tax control systems, decomposition and range analysis of digitalization technologies, topological analysis of digital information and analytical tools, and UML modelling of the framework for the digitalization of fiscal tax control systems. The developed framework is presented as a conceptual and architectural design structure that integrates artificial intelligence (AI)/machine learning (ML) risk stratification, Distributed Ledger Technology (DLT) traceability, autonomous compliance, and P2P interoperability to outline architectural integrity and procedural resilience as intended design properties rather than empirically demonstrated effects. SWOT, decomposition and range analysis, as well as topological analysis supported the identification of a unitary routing logic for risk, compliance, and verification flows that may contribute to fiscal transparency and evasion risk mitigation under subsequent pilot testing. The academic novelty is associated with the systemic identification, decomposition, structured organization, and topological mapping of information-analytical tools of fiscal control, which enabled the formal representation of a cognitively stratified architectural and functional topology of a digital fiscal control framework using UML modelling.
[...] Read more.Enhancing Option Pricing Precision in Financial Markets with a Hybrid Ga-Bp Neural Network Approach
Accurate option pricing is critical for the effective functioning of financial markets, providing traders and investors with the means to hedge risks and capitalize on market movements. Traditional models such as the Black-Scholes, Binomial Tree, Trinomial Tree, Monte Carlo Simulation, and the Garman-Kohlhagen model have long been the standard for option pricing. However, these models often face limitations in capturing market complexities and extreme events. We propose here a hybrid approach that combines Genetic Algorithm (GA) optimization with Backpropagation (BP) neural networks to enhance the precision of option pricing. It uses HS300 index stock data from 2013 to 2022, including stock prices, volumes, and price changes. The hybrid GA-BP model is tested for its ability to make more accurate price predictions. The model helps investors make better decisions by improving pricing strategies and managing risks effectively. The Hybrid GA-BP neural network model leverages the global search capabilities of GA to optimize the initial weights and biases of the BP neural network, thereby avoiding local minima and improving convergence rates. This integrated model is trained and tested on historical market data, with its performance benchmarked against traditional models. Empirical results demonstrate that the Hybrid GA-BP neural network model significantly outperforms traditional models in terms of pricing accuracy. The model shows superior precision when comparing actual market prices with predicted prices, reducing errors and increasing reliability. This enhancement in pricing precision can lead to more informed trading decisions and better risk management strategies. The findings of this research contribute to the growing body of knowledge in financial engineering by showcasing the potential of hybrid machine learning approaches in financial modeling. The Hybrid GA-BP neural network model presents a promising tool for practitioners and researchers aiming to improve option pricing methodologies in increasingly complex financial markets.
[...] Read more.RAFA-BioAuth: Risk-Adaptive and Fairness-Aware Biometric Authentication for Secure Mobile Banking
This research examines RAFA-BioAuth, a risk-adaptive, fairness-aware framework for mobile banking in cases of presentations and facial occlusions. The proposed solution combines aspects of: Identity Similarity, Passive Presentation Attack Detection (PAD), Asymmetric Financial-Risk Estimation and Fairness Regularization at the Identity Level. For evaluation purposes, all benchmark datasets were split into subject disjoint training, validation, and testing sets. The threshold values from the validation set were used. Bootstrap resampling was employed to estimate the variance. Monte Carlo simulations were performed to estimate the risk. The results showed that identity verification (AUC = 0.548) and PAD (AUC ≈ 0.55) were poor. Comparing RAFA to the AND rule resulted in FAR = 0.20, FRR = 0.31, and expected risk = 340. On the other hand, the AND rule had lower FAR of 0.09, but increased FRR to 0.78. Finally, a conservative end-to-end approach produced an FRR of 0.686. Thus, our results indicate trade-offs rather than production-readiness as we did not perform deployment, cross-device, or cross-dataset validations on our solutions. We present the contributions of this research as being an interpretable integration and not a new algorithm.
[...] Read more.Privacy Preserving Federated Learning with Blockchain for Validating and Enhancing Technology Business Incubation Performance Prediction Models
Technology Business Incubation (TBIs) has become a global phenomenon integral to the growth of regional innovation and startup ecosystems. The availability of high-quality infrastructure and facilities lays the foundations of the entire startup ecosystem for providing essential support services that directly impact entrepreneurial success. The incubation capacity of TBIs across different regions can foster competition and collaboration among these regions, provide avenues for enhancing enterprises’ incubation capabilities, and assist entrepreneurs in assessing the strength of regional incubation. However, with their rapid expansion, the performance evaluation also becomes increasingly complex due to the diversity of converging factors such as complex technologies, varying nature of relationships of VCs, and entrepreneurial competencies of the founders incubating startups at the TBIs. Traditional Machine Learning performance evaluation and prediction models struggle to capture these dynamic variables, while also suffering from privacy vulnerabilities, low accuracy, and reliance on centralized third parties. This often leads to single points of failure, performance bottlenecks, and sometimes increased costs. To address these challenges, we employed Privacy-Preserving Federated Learning with Blockchain (PPFL-BC), a novel framework designed for improving the mechanism of performance measurement and prediction for remote TBIs while ensuring that the privacy of entities and the data remains secure. We utilize capabilities of Artificial Neural Network (ANN) and gradient boosting-enabled federated learning to train the model of each TBI locally. In the process, no private and sensitive business data is shared outside the network, significantly reducing the risk of privacy breaches. Besides this, all the locally trained models are aggregated into a unified predictive model at the central aggregation unit, which ultimately improves the overall accuracy of the performance prediction mechanism for the entire population of TBIs. In our model, the decentralized blockchain network is also used to address security concerns related to unauthorized access and data manipulation thereby ensuring transparent and tamper-proof model updates. We evaluate the performance of our proposed PPFL-BC model by utilizing real-world business incubation datasets. The simulation results show that our model outperforms the centralized performance prediction models in terms of accuracy, precision, recall, and F1-score. The results show that the proposed PPFL-BC model outperforms benchmark models with an accuracy of 84% and precision of 0.92, which shows the efficiency and reliability of our model in predicting and validating TBI success rates.
[...] Read more.GenAI-Driven Interview Performance Assessment: Revolutionizing Recruitment with AI Insights
This paper proposes IPAMS (Interview Performance Assessment using Gen AI), which is an AI-driven platform that automates interview evaluations using advanced technologies like Convolutional Neural Networks (CNN) to gain insights on facial emotions and expressions, Large Language Models (LLM) to generate and process interview questions, YOLO (You Only Look Once) for real-time object detection, and APIs for speech-to-text transcription and behavioral analysis. The system captures video responses and analyzes key elements such as sentiment, speech patterns, body posture, and facial expressions, generating a detailed report. This report highlights a candidate’s strengths and areas of improvement and is sent directly to their email with actionable insights. IPAMS modernizes recruitment by providing unbiased assessments, saving time and resources for recruiters. For candidates, it offers a valuable mock interview tool, delivering feedback on technical skills, confidence, stress levels, and nonverbal communication. By combining cutting-edge AI and analytics, IPAMS delivers an efficient, objective, and insightful solution for recruitment and self-assessment, benefiting all stakeholders in the interview process.
[...] Read more.AFSO-JSSP: Artificial Fish Swarm Optimization for Efficient Job Shop Scheduling
Job Shop Scheduling Problem (JSSP) has become one of the key issues in a contemporary manufacturing system in which the task is to optimally schedule jobs to the machines to reduce the time and resources used in production. Good scheduling is critical in enhancing the productivity and competitiveness of manufacturing industries. In this research, Artificial Fish Swarm Optimization (AFSO) algorithm is used to optimize the JSSP in minimizing makespan, total work load and maximum work load in machines. The AFSO strategy models the swarm behaviour of fishes to search and forage the search space in an efficient manner to prevent its early convergence to local optima. The model incorporates a disturbed state in the world to improve the direction in search and the speed of convergence. The effectiveness of the suggested AFSO method is compared and tested with the traditional and sophisticated optimization algorithms like Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) methods. The experimental findings prove that the offered technique provides better results in convergence rate and solution quality. The results prove that AFSO is a useful and promising method of solving complex problems in production system scheduling.
[...] Read more.Integrating Mathematical Programming and Stakeholder Consensus for Warehouse Inventory Compression: A Case Study
The article discusses methods for averting irrational product placement in warehouses. This problem is relevant for many manufacturing enterprises and trade organizations. The most common embodiments of the problem are unoccupied areas, or a lack of free storage space, difficulty in locating a specific product, and challenges in shipping it from the warehouse. All this leads to unnecessary costs for the business entity and hurts its financial and economic activities. The author suggests an integrated approach to warehousing. It integrates both classical optimization models and iterative approval procedures for accounting for the human factor. The key criteria in this case are minimizing costs, the cargo flow in the warehouse, and maximizing the utilization factor of the usable area. The optimal placement of goods is to achieve maximum compression of their residues in the warehouse while minimizing their movement. The presence of two contradictory criteria makes the task a task of consistent optimization. The article discusses the possibilities for solving the optimization problem when conflicting target criteria and differing preferences are present. We are using the example of a storage room with 16 racks for water heaters and similar equipment. As a result of matching optimization procedures, it was possible to reduce the average cost of moving goods by 7.1% and increase the free warehouse area by 15 times. We performed the experiments over the seven days of the warehouse’s operation. The practical value of the research is that, through this approach, we find a compromise in conditions of conflicting opinions and interests.
[...] Read more.Soft Attention Enhanced CNN and LSTM Based Framework for Semantic Description Generation of Remote Sensing Imagery
Remote sensing images are complex, which makes it difficult to interpret and generate semantically appropriate textual description. To get a semantically relevant description, it is important to identify complex objects and understand the contextual relationships between them. In such cases, deriving contextually accurate information while maintaining semantic coherence is challenging. Therefore, a specifically designed model architecture is required to generate semantically relevant descriptions. This paper discusses a deep learning-based approach to generate remote sensing image descriptions using an end-to-end encoder-decoder model with soft attention. The UC Merced (UCM) dataset is used for training, which includes multiple captions per image capturing various scene aspects. To further assess the robustness and generalizability of the proposed approach, its performance is additionally evaluated on more complex datasets such as RSCID and Sydney Captions. This study presents an end-to-end CNN–LSTM encoder–decoder framework enhanced with soft attention for semantic description generation from remote sensing imagery. The framework employs a VGG16 encoder to extract a 4096-dimensional visual feature vector, which is projected into a 256-dimensional representation and processed by a 256-unit LSTM decoder. The soft attention mechanism dynamically computes attention weights using the encoder features and decoder hidden state, enabling the model to emphasize relevant visual information during word generation. Multiple CNN encoders and learning rates are evaluated with LSTM decoders, both with and without attention, on the UCM, RSCID, and Sydney Caption datasets. At a learning rate of 0.0001, VGG16–LSTM with soft attention achieves BLEU-4 (B4) scores of 0.6636, 0.6636, and 0.5864 on the UCM, RSCID, and Sydney Caption datasets, respectively, compared with 0.1643, 0.1647, and 0.1745 for VGG16–LSTM without attention. The results demonstrate that soft attention substantially improves description generation by strengthening visual–linguistic alignment and enabling more contextually relevant and semantically coherent descriptions across datasets with varying scene complexity.
[...] Read more.Formal Verification and Statistical Evaluation of a Strengthened Lightweight AMI Authentication and Key Agreement Protocol for Smart Grid Environments
Advanced Metering Infrastructure (AMI) connects smart meters, data concentrator units, and utility control centers through persistent two-way communication. This architecture improves demand response and distributed-energy management, but it also exposes resource-constrained meters to replay, false-data injection, physical extraction, and long-term key compromise. This article develops a formally verified and statistically evaluated lightweight AMI authentication and key agreement protocol for resource-constrained smart-grid deployments. We first reconstruct the AMI authentication workflow as a four-message lightweight authenticated key exchange and map each entity, message, and key dependency to a smart-grid deployment model guided by NISTIR 7628 and IEC 62351. We then identify replay-within-window exposure, insufficient responder freshness, weak identity-to-key binding, missing key-compromise impersonation protection, and retrospective session-key recovery. To address these weaknesses, we propose AMI-AKE, a transcript-bound protocol using ephemeral Curve25519 contributions, session identifiers, nonce and timestamp binding, binding signatures, and separate key-derivation function (KDF) outputs for encryption and integrity. ProVerif-style verification queries and an extended Canetti-Krawczyk (eCK)-oriented game proof are provided for mutual authentication, secrecy, forward secrecy, and key-compromise impersonation (KCI) resistance. A Contiki-OS and ARM Cortex-M4 benchmark with 1,000 repeated trials reports 18.4 +/- 1.2 ms authentication latency, 542 +/- 9.1 sessions/s throughput, and 99.2 +/- 0.4% false-data-injection detection under controlled prototype conditions. The proposed design replaces subjective security labels with objective metrics, confidence intervals, and a reproducible simulation plan for 1,000-10,000 smart meters.
[...] Read more.Coalition Tolerance to Multiple Failures of Its Members
The paper deals with coalitions whose members are unselfish. Coalition members do their best to complete the arising tasks, and do not expect to receive a reward. A coalition member can be an entity such as a social or governmental organization, a military unit, or a complex technical device such as an autonomous robot, or any other entity that has the capabilities, willingness, and ability to cooperate. The paper considers the non-redundant coalitions, which have only those coalition members without whom they cannot perform the tasks. In the paper, we consider only the situations when substitution of failed coalition members is impossible. A coalition tolerates the failure of its members by using the surplus of coalition capabilities. In our research, coalition capabilities are understood as resources and services (e.g., materials, energy, power etc.). During the execution of tasks, one or more members of the coalition may fail. The paper uses the probability of the event that the coalition tolerates the failures of its members to evaluate the coalition fault tolerance. A method is proposed for determining the coalition fault tolerance in the case of multiple member failures. Complexity of the proposed method and its applicability are assessed.
[...] Read more.Enhanced Educational Recommendations via Feedback-Refined Learning-to-Rank with XGBoost
A system that is personalized and capable of automatically suggesting appropriate courses based on a user's particular questions and interests provides customized recommendations. The system employs an LTR model relying on XGBoost to learn relationships between the queries and the courses. Dynamic ranking feature refinement enhances ranking, and a feedback loop constructs incrementally improving the recommendations by applying relevant courses to update the model. The scraped educational sites are the foundation of the dataset, where there are granular course descriptions as well as the interaction logs. Evaluation results indicate that the system is able to produce high ranking outcomes as evidenced by an NDCG value of 0.85 and high values for MRR. The system is able to produce low query processing latency, making it possible for real-time responsiveness. User feedback analysis following system retraining indicated a 90% increase in user satisfaction. The suggested framework is dynamic and provides personalized recommendations for courses in various learning environments.
[...] 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.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.
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.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.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.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.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.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.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.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.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.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.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.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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