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: 94
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. 5, Oct. 2026
REGULAR PAPERS
AI Adoption and E-Commerce Development in SMEs: Comparative Evidence from the EU and Ukraine
Artificial intelligence (AI) is increasingly associated with changes in e-commerce development, including demand generation, operational processes, and consumer interaction. The aim of the article is to examine and quantify the relationship between AI adoption and e-commerce development in 2023–2025 in an international context, with an emphasis on Ukraine’s position among European markets. The methodological framework of the study is a quantitative comparative analysis of data for Germany, France, Italy, Spain, Poland, the Netherlands, and Ukraine. An integral E-Commerce Index and an AI Adoption Index were created based on statistical sources, which were normalized in the range from 0 to 1. The hypothesis was tested by using correlation and regression analysis, as well as clustering of countries by a combination of e-commerce and AI maturity levels. Given the correlational design of the study, the findings are interpreted as evidence of statistical association rather than causation. The results show a persistent positive and statistically significant relationship between the AI adoption and the development of e-commerce levels. The leaders in terms of the AI Adoption Index are the Netherlands (0.88) and Germany (0.85), while Ukraine demonstrates the earliest phase of integration (0.25). The findings suggest that AI adoption is a significant correlate of e-commerce development rather than merely an auxiliary technological feature. The practical value of the results is the possibility of their use for building state policy of digital transformation and business strategies in the context of international competition.
[...] Read more.Python–based Hybrid Decline Curve–Machine Learning Model for Forecasting Oil Production and Deriving CO₂ Emission Potential from Estimated Remaining Reserves
This study develops a Python-based hybrid forecasting framework that integrates classical Decline Curve Analysis (DCA) with Support Vector Regression (SVR) to improve the reliability of oil production forecasting and the estimation of Estimated Remaining Reserves (ERR), which are subsequently used to derive cumulative CO₂ emission potential. Historical production data are segmented to represent boundary-dominated flow conditions, enhancing the stability and physical consistency of decline parameters. SVR is then applied to correct localized deviations in the historical response that are not captured by conventional DCA formulations, yielding improved decline representations for forecasting. The hybrid DCA–SVR model achieves a more coherent historical match and reduced average residual errors compared to standalone exponential, hyperbolic, and harmonic decline models. Using the hybrid framework, ERR estimates of 4.92×10⁵, 1.74×10⁶, and 3.62×10⁶ STB were obtained for the exponential, hyperbolic, and harmonic decline scenarios, respectively, corresponding to cumulative CO₂ emission potentials of approximately 213,036, 753,420, and 1,567,460 tons of CO₂. These scenario-based results provide a bounded range of emission outcomes, explicitly demonstrating that uncertainty in decline model selection propagates directly into long-term ERR and CO₂ emission estimates. Overall, the proposed hybrid machine learning–decline curve analysis (ML-DCA) framework offers a transparent and reproducible approach for linking production forecasting with quantitative CO₂ impact assessment in data-driven reservoir management.
[...] Read more.Multi-class Financial Payment Fraud Detection via a CNN-CfC Hybrid Architecture with Explainable Feature Engineering
With the growing digitization of financial services, fraudulent activities in online transactions are becoming increasingly sophisticated and widespread. An automated and intelligent financial transaction fraud detection system is necessary to properly detect and categorize fraudulent incidents in order to address this challenge. The majority of previous research has been on using binary classification to identify fraudulent transactions. Previous efforts were often hindered by class imbalance, inadequate feature selection, suboptimal prediction accuracy, and insufficient hyperparameter tuning. This paper suggests a hybrid deep learning framework for multi-class financial fraud detection that combines Convolutional Neural Network (CNN) and Closed-form Continuous-time (CfC) models in order to handle these problems. By analyzing transactional and behavioral characteristics, the model is intended to categorize fraud situations into distinct groups, including Phishing, Account Takeover (ATO), Card Skimming, and No Fraud. The proposed CfC-CNN model was not only trained but also tested on a large transaction augmented dataset that was enhanced with contextual characteristics. This work uses the IBM financial fraud dataset and considers 5 million transactions. This work investigated four classes such as phishing, ATO, card skimming, and no fraud. Label encoding was used as a preprocessing method for the contextual and category information. The dataset was refined for best performance by feature selection using Chi-Square and the random search method for hyper parameter tweaking. The proposed CfC-CNN model achieved at least .03 percent accuracy gain and .49 percent F1 score gain over the compared previous works.
[...] Read more.The Impact of Digital Platforms on the Transparency of Customs Operations in International Trade
Digital technologies optimize certain aspects of customs operations, but also change the functional purpose of customs, raising its role in ensuring the transparency of international trade. The aim of the study was to assess the impact of factors measured by the Organisation for Economic Co-operation and Development (OECD) trade facilitation indicators on the transparency of customs operations. The study employed correlation, regression analyses, and analysis of variance (ANOVA). The study provides an integrated empirical analysis of the complex impact of technological, operational, and regulatory factors on the transparency of customs operations. The results revealed that the automation of customs procedures with a regression coefficient for the corresponding indicator of 0.3132 had the strongest positive impact on the availability of information (transparency of customs operations). A strong impact of the opportunities and methods for appealing administrative decisions of border authorities (0.2688), as well as simplifying customs control procedures and introducing “single windows” (0.2384) was observed. A less considerable, but statistically significant impact was shown by compliance with the discipline of fees, payments, and fines (0.1438) and simplification and standardization of trade documentation (0.1366). It is concluded that strategies for increasing the transparency of customs operations should focus on the above factors, while increasing the effectiveness of areas that did not show a significant impact on transparency. These are the involvement of the trade community, interaction between internal and external border agencies, the development of Advance rulings, as well as ensuring good governance and impartiality. The effectiveness of these areas should be increased by improving the mechanisms for their provision at the national and international levels. The results of the study are useful for determining priority areas of strategies for increasing the transparency of customs operations, especially regarding the development of digital platforms, for example, accelerating automation, developing single windows, etc.
[...] Read more.Deep Learning Model for Mitigating Keylogging Attack in HCI Using Stochastic Gradient Descent with Adam-based LSTM Optimization
Keylogging has become a crucial attacking strategy adopted by cyber criminals to fetch keystrokes of legitimate users during their interaction with computer systems. The data gathered serves as a powerful weapon for attackers to exploit. Existing techniques for detecting or preventing keylogging operations rely on mundane systems or traditional machine learning algorithms that lack deep intelligence to identify and mitigate activities of keyloggers accurately due to the volume of data that is generated from keystrokes with simple interactions from the users. This study developed a deep learning-based predictive model capable of preventing keylogging attacks in human-computer interaction using hyperband hyperparameter tuning and a hybrid genetic algorithm on stochastic gradient descent with Adam-based LSTM optimization on a UNSW-NB keylogging dataset with nine families of attacks to build the deep learning model called HH-GASA-OLSTM. From the experiments, the proposed predictive model on an 80:20 validation ratio gives the best result on six performance metrics, which are loss function of 0.0187, accuracy of 99.45%, precision of 0.9930, recall of 0.9945, F1-Score of 0.9938, MCC of 0.9889, RMSE of 0.0054, MAE of 0.0054 and MSE of 0.00003. These results are suitable for the accurate detection and prevention of keylogging attacks in HCI. The study therefore recommends further studies; the implementation of other hyperparameter tuning approaches with other parameter optimization techniques using other deep learning architectures, such as radial basis function network and deep belief network, for improved model optimization.
[...] Read more.Application of Artificial Intelligence for Engagement Personalization in B2C Business Models: A Literature Review
In an increasingly competitive world, retail companies are implementing marketing strategies aimed at gaining customer preference by building lasting emotional relationships. This facilitates the conversion of so-called followers into loyal customers and brand ambassadors, thereby increasing return on investment (ROI), facilitating direct feedback to improve the product mix, and reducing customer acquisition costs over time, generating competitive advantages. The purpose of this systematic review is to analyze recent scientific production (2020-2025) on the application of Artificial Intelligence (AI) for the personalization of engagement in B2C business models, in order to analyze the predominant techniques, evaluate their impact on the consumer and determine the critical factors that condition long-term customer loyalty; the methodology used consisted of a systematic review in the main scientific databases in publications of high-impact journals. The main findings suggest that personalization tends to be dominated by machine learning models focused on algorithmic efficiency; however, a critical asymmetry was observed: the literature shows a hyper-focus on quantitative studies and functional efficiency, while studies on trust and ethics are the least relevant. Additionally, knowledge is biased by Asian dominance (53%) and the "novelty effect," limiting the applicability of strategies in markets with high demands for transparency, such as the American market. Furthermore, an emerging trend toward an Ethical-Relational Model has been observed in the literature. It is concluded that moving beyond the functional efficiency-centric approach and integrating Ethical AI as a strategic consideration may be key to fostering sustainable engagement, as evidenced by the reviewed literature.
[...] Read more.Anxiety Classification Using an Information Geometry–Driven Fuzzy Neural Network
Information geometry is a contemporary geometric exploration of the universe of information. Presently, informational theories have primarily been researched using algebraic, logical, analytical, and probabilistic approaches. Geometry, which analyses reciprocal relationships between components such as distance and curvature, could give major advantages to the science of information. In this study, we proposed a novel model for EEG signals classification based on a Riemannian space fuzzy neural network. The sample covariance matrix is utilized as an EEG signal characteristic in this suggested approach, and Riemannian mean and distance are used to categorize the matrix, therefore achieving the usage of manifold learning. The experimental findings indicate that this procedure is more successful and precise than earlier methods for recognizing messages from brain waves.
[...] Read more.Algorithmic Digital Finance Platforms as a Driver of Transformation of Financial Behaviour of Economic Entities
The study was determined by the growing need to quantify behavioural and institutional transformations driven by algorithmic digital finance platforms. This justified developing an integrative analytical framework for behavioural institutional assessment within artificial intelligence (AI)-driven financial ecosystems.
The aim of the study was to develop and empirically substantiate a multidimensional framework for assessing the transformative influence of algorithmic digital finance systems on financial behaviour and responsibility, integrating cognitive, institutional, and ethical dimensions of digital financial governance.
Research methods: Comparative Qualitative Critical Assessment, ITI Modelling of Behavioural and Institutional Transformation, Structural Decomposition of Robo-Advisory Systems, Optimized Logic-Structural Architecture, ITI Verification of the Optimized Framework.
The integrated research validated the systemic transformation capacity of algorithmic digital finance platforms through behavioural and institutional dimensions. Comparative analysis revealed asymmetric configurations of behavioural rationalization and responsibility formalization, emphasizing architecture-specific functional differentiation. ITI modelling quantified these interdependencies, producing average indices of behavioural transformation (0.81) and responsibility institutionalization (0.77). Structural and optimization modelling of Robo-Advisory Systems demonstrated enhanced algorithmic transparency, cognitive traceability, and compliance integrity. Final ITI verification confirmed the optimized Robo-Advisory System as the most effective configuration (ITI* = 0.86; Bₚ = 0.85; Rₚ = 0.90), substantiating its role as a scalable paradigm for ethical and cognitively rational digital financial governance.
The academic novelty of the study lies in the development of an integrated behavioural-institutional assessment framework for evaluating algorithmic digital finance systems. The Integral Transformative Index (ITI*) is not positioned as a new mathematical operator; rather, it operationalizes a theoretically grounded construct that combines behavioural transformation and responsibility institutionalization within a single analytical architecture. The innovation is determined by the original conceptual linkage, indicator taxonomy, and platform-specific interpretation of cognitive rationalization, algorithmic transparency, and institutional accountability. The Optimized Robo-Advisory System (ORAS) model embodied this construct through explainability, bias-correction, traceability, and ethical governance mechanisms.
The next stage of research will focus on the algorithmic improvement and structural enhancement of the ORAS framework developed in this study. Controlled pilot implementation will be conducted to test behavioural adaptability, ethical compliance, and regulatory resilience. Subsequent multi-phase empirical validation will ensure the full verification of cognitive, normative, and operational dimensions of the optimized model.
Cross-Domain Outdoor Scene Understanding Using PC-GAN and TL-SZKSE-DenseNet in Adverse Weather Conditions
In autonomous driving, Outdoor Scene (OS) understanding in unfavorable weather conditions is pivotal for enabling safe navigation. Conventional studies failed to capture the intricate pairwise object relationship within scenes in OS classification. To mitigate these challenges, this research introduces a dual framework bringing together a Panoptic Cycle-Generative Adversarial Network (PC-GAN) with a Transfer Learning-based Squeeze-and-Zero-Knowledge Spatial Excitation DenseNet (TL-SZKSE-DenseNet). The suggested PC-GAN achieves realistic cross-weather image translation with semantically aligned representations in clear and degraded scenes. The TL-SZKSE-DenseNet improves feature learning with the addition of a zero-knowledge spatial excitation mechanism that selectively enhances weather-independent and context-dependent features for enhanced discrimination in crowded scenes. This generative–discriminative hybrid method effectively overcomes structured and unstructured driving domain gaps, as demonstrated on benchmark datasets such as BDD and IndiaScene365. Primarily, the OS images are generalized to adverse conditions using Panoptic Cycle-Generative Adversarial Network (PC-GAN). After that, using saliency mapping, the most significant regions in the generalized output are highlighted. In the meantime, the significant features are extracted utilizing the Orangutan Optimization Algorithm (OOA). In addition, from the detected objects, the Object Frequency and Relationship (OFR) is analyzed. Later, using TL-SZKSE-DenseNet, the OS are classified based on the OFR and the selected features. The performance of our proposed approach was tested on our newly developed IndiaScene365 dataset as well as various benchmark datasets such as BDD100k. Our newly developed dataset, IndiaScene365, includes 3000 images that entail three different weather conditions, which are foggy weather, rainy weather, and poor light conditions along with clear day weather. The proposed approach records 94.87% accuracy, 93.24%, and 94.18% sensitivity on our dataset.
[...] Read more.ProCLAMUS: A Matrix Completion Based Proactive Spectrum Allocation Protocol for Security of IoT based CRNs
Proactive prediction based channel allocation for Cognitive Radio Networks (CRNs) remains a challenging area of research. In Internet of Things (IoT) scenarios, prediction algorithms must balance resource efficiency with high accuracy. The proposed algorithm, ProCLAMUS, a Matrix Completion (MC) Based Proactive Spectrum Allocation Protocol for CRNs, couples nuclear norm matrix completion with short horizon sliding window prediction. By reconstructing sparse spectrum sensing data and down weighting inconsistent reports, ProCLAMUS enables infrequent sensing and decentralized decisions without a fusion center, reducing energy and attack surface. In a network of 40 Secondary Users (SUs), 400 Primary Users (PUs) and 400 channels with malicious data ranging from 5 to 50%, ProCLAMUS sustains the highest channel utilization (avg 89.96%) with the lowest backoff rate (3.81 s^(-1)) and sensing delay (0.58 channels/success). ProCLAMUS achieves the lowest radio energy (16.82×10^(-3) J/s), a 44–53% reduction when compared with recently proposed techniques, while using 33–43% less memory. These gains arise from sparse sensing, fusion (majority voting) and conservative allocation. The results demonstrate superior energy efficiency and robust prediction under malicious data conditions during the Spectrum Sensing Data Falsification Attack.
[...] Read more.Lightweight Cryptographic Protocol for Resource-Constrained IoT Devices in Smart Healthcare
IoT healthcare devices often lack resources, and encrypting patient data flows securely is an active area of research. Current cryptographic solutions are typically too computationally and energy intensive to be used on constrained IoT devices. In this work we introduce HealthCrypt: an authenticated encryption scheme for smart IoT deployments in healthcare on highly-constrained microcontrollers. It consists of a novel compact SP-network paired with native authenticated encryption, removing the need to have a separate MAC pass and minimizing total cycle cost. We implement HealthCrypt on an ARM Cortex-M0 running at 48 MHz with the FELICS benchmark suite. Results show HealthCrypt uses only 90 bytes of RAM, only requires 1,620 gate equivalents (under the proposed IoT limit of 2,000 GE) of hardware area, and only uses 5.93 nJ of energy per block encryption. Measurements of its statistical security show it has an almost perfect avalanche of 0.5026, Shannon entropy only 0.0094 bits/byte away from the ideal, and good key sensitivity of 0.4949. Against competitors like PRESENT-80, SPECK-64/128, and SIMON-64/128, HealthCrypt has been rated to have the best overall IoT Healthcare Suitability Score of 79.23.
[...] Read more.E-Government Development, Corruption Perception, and Tax Burden: A Panel Regression Analysis of Shadow Economy Determinants in the EU Countries
The aim of the study is to assess the impact of digitalisation, institutional transparency, and tax burden on the scale of the shadow economy in European Union (EU) countries in order to formulate strategic guidelines for Ukraine. The study employed correlation analysis, multiple regression analysis, robustness testing, and logical structure analysis. The results revealed that an increase in the Corruption Perceptions Index (CPI) by one point reduces the share of the shadow economy by 0.2604 percentage points (p = 0.0026), highlighting the critical role of institutional transparency and anti-corruption policy in the de-shadowing process. Tax burden demonstrated a positive and statistically significant relationship with the shadow economy (coefficient = 0.1843; p = 0.0125), indicating that excessive fiscal pressure may stimulate informal economic activity. The E-Government Development Index (EGDI) retained a positive but only marginally significant effect (coefficient = 50.3170; p = 0.0667), suggesting that digitalisation becomes effective primarily when embedded in public administration systems. In contrast, the overall level of digital development measured by DESI was not statistically significant after controlling for institutional and fiscal factors (coefficient = −0.2476; p = 0.1037). The constructed multifactor model demonstrated high explanatory power (R² = 0.8503), while robustness analysis confirmed the stability of the core findings across alternative model specifications. The results indicate that digitalisation alone is insufficient to reduce informality and that its effectiveness depends on institutional quality and balanced fiscal policy. The practical value of the study lies in identifying policy directions for Ukraine, including the expansion of e-government tools, digital tax administration, integrated data analytics, and anti-corruption measures aimed at reducing the shadow economy.
[...] 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.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.Sentiment Analysis of Review Datasets Using Na?ve Bayes‘ and K-NN Classifier
The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantages, the most important of all being monetization. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The focus of our project is sentiment focussed web crawling framework to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same. We use statistical methods to capture elements of subjective style and the sentence polarity. The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naïve Bayes‘ and compares their overall accuracy, precisions as well as recall values. It was seen that in case of movie reviews Naïve Bayes‘ gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.
[...] Read more.Barriers to E-Commerce Adoption in Egyptian SMEs
E-commerce has been predicted to be a new driver of economic growth for developing countries. The SME sector plays a significant role in its contribution to the national economy in terms of the wealth created and the number of people employed. Small and Medium Enterprises (SMEs) in Egypt represent the greatest share of the productive units of the Egyptian economy and the current national policy directions address ways and means of developing the capacities of SMEs. Many factors could be responsible for the low usage of e-commerce among the SMEs in Egypt. In order to determine the factors that promote the adoption of e-commerce, SMEs adopters and non-adopters of e-commerce were asked to indicate the factors inhibiting the adoption of e-commerce. The results show that technical barriers are the most important barriers followed by legal and regulatory barriers, whereas lack of Internet security is the highest barrier that inhibit the implementation of e-commerce in SMEs in Egypt followed by limited use of Internet banking and web portals by SMEs. Also, findings implied that more efforts are needed to help and encourage SMEs in Egypt to speed up e-commerce adoption, particularly the more advanced applications.
[...] Read more.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.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.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.
[...] 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.
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