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: 89
IJIEEB is committed to bridge the theory and practice of information engineering and electronic business. From innovative ideas to specific algorithms and full system implementations, IJIEEB publishes original, peer-reviewed, and high quality articles in the areas of information engineering and electronic business. IJIEEB is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information engineering and electronic business applications.
IJIEEB has been abstracted or indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, VINITI, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..
IJIEEB Vol. 17, No. 6, Dec. 2025
REGULAR PAPERS
Income inequality is a persistent issue in both developed and developing economies, influenced by complex socio-economic factors such as education, occupation, and gender. This study addresses a critical gap by applying advanced machine learning techniques to analyze the socio-economic determinants of income in Bangladesh and global contexts. The primary objectives were to identify the most influential factors affecting income and assess the effectiveness of various machine learning models in predicting income levels. Using datasets from Bangladesh and global sources, this study employed Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machines to predict income and assess feature importance. Key findings showed that education, occupation, gender and hours worked per week were the most significant predictors of income. The Bangladeshi dataset highlighted limited access to higher education and pronounced gender disparities, while the global dataset reflected gender pay gaps and more equitable educational access. Random Forest Classifier appeared as the most effective model, achieving 100% accuracy in Bangladesh and 96% accuracy globally. These findings underscore the need for targeted policies to improve educational access, promote vocational training, and address gender inequality to reduce income disparities. Additionally, the study demonstrates the potential of machine learning to uncover non-linear relationships in socio-economic data, providing valuable insights for evidence-based policymaking. This research highlights the importance of integrating advanced data-driven methods to address the socio-economic drivers of income inequality and promote inclusive economic growth.
[...] Read more.Effective information technology governance is essential for improving public service delivery and administrative efficiency at the village government level. This research focuses on Simpang Pasir Village in Palaran District, Samarinda City, East Kalimantan Province, aiming to establish a foundation for an electronic-based government system (e-government). By employing the TOGAF Architecture Development Method (ADM) version 9.2, this study systematically designs an enterprise architecture encompassing business, data, application, and technology domains. The process spans from the preliminary phase to migration planning, with gap analysis conducted to align baseline and target architectures. Key outputs include the development of integrated systems for administrative tasks and digital public services, supported by cloud server technology to ensure scalability and efficiency. Validation of the design using the Enterprise Architecture Scorecard yielded a score of 82.27%, indicating strong alignment with Simpang Pasir Village's objectives and readiness for implementation. This initiative addresses critical challenges, including data integration, transparent governance, and improved public services. The research outcomes provide a comprehensive roadmap for transitioning to e-government, supporting the village's mission to advance IT-based governance while fostering self-reliance and community empowerment. The findings contribute valuable insights for digitally transforming rural governments, positioning Simpang Pasir Village as a model for innovation and modernization.
[...] Read more.The E-commerce platform has provided the user and the organization with a new avenue for the product distribution and selling. The product distribution is greatly hampered by the opinions provided by the end user and if tampering and fake reviews are generated then it affects the product badly. The Natural language processing domain deals with the analysis of this review and provide the user with recommendation for decision making. The NLP domain deals with several issues like fake reviews, tampering with the reviews, and security for transfer of reviews etc. In this paper, a Blockchain based sentimental analysis module framework is proposed that provides the user with a secure and trustful environment for the opinions reviews as well as it provide a hybrid sentimental module that uses the algorithms from machine learning and deep learning for sentiment score generation. The Proposed Model was evaluated on different datasets of the varied domain. The proposed model performs a substantial improvement in providing the accurate results.
[...] Read more.This paper proposes DFI-ADR (Dynamic Fuzzy Information System with Agriculture Decision Retrieval) aimed at improving agricultural decision-making through case-based reasoning and precise information retrieval. This approach uses fuzzy logic and machine learning techniques, such as IndRNN, to compute similarity scores between historical agricultural cases and new queries. This enables dynamic classification of cases as "distinct," "similar," or "highly comparable" based on fuzzy membership values. These values significantly enhance the accuracy of decisions related to agricultural factors like crop yield, soil quality, and irrigation. The methodology outperforms traditional methods in terms of accuracy, recall, and precision, proving highly effective for agricultural analysis and decision-making. In experiments with the Agriculture Dataset Karnataka, DFI-ADR achieved an accuracy of 95%, a precision of 100%, and an F1-score of 94.74%, significantly outperforming traditional methods by a margin of 10-15% across these metrics. These values demonstrate its effectiveness for agricultural analysis and decision-making.
[...] Read more.Machine learning models that lack transparency can lead to biased conclusions and decisions in automated systems in various domains. To address this issue, explainable AI (XAI) frameworks such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) have evolved by offering interpretable insights into machine learning model decisions. A thorough comparison of LIME and SHAP applied to a Random Forest model trained on a loan dataset resulted in an Accuracy of 85%, Precision of 84%, Recall of 97%, and an F1 score of 90%, is presented in this study. This study's primary contributions are as follows: (1) using Shapley values, which represent the contribution of each feature, to show that SHAP provides deeper and more reliable feature attributions than LIME; (2) demonstrating that LIME lacks the sophisticated interpretability of SHAP, despite offering faster and more generalizable explanations across various model types; (3) quantitatively comparing computational efficiency, where LIME displays a faster runtime of 0.1486 seconds using 9.14MB of memory compared to SHAP with a computational time of 0.3784 seconds using memory 1.2 MB. By highlighting the trade-offs between LIME and SHAP in terms of interpretability, computational complexity, and application to various computer systems, this study contributes to the field of XAI. The outcome helps stakeholders better understand and trust AI-driven loan choices, which advances the development of transparent and responsible AI systems in finance.
[...] Read more.This research tackles the fundamental requirement of synthetic data generation to tighten up machine learning model precision and one-shot shot learning to lessen the need to pursue data input. The project aims to develop a service that can generate reasonable synthetic data from a given dataset. It was first designed and developed, and then the project structure was set, and libraries were chosen for predevelopment analysis. This continued development process also included subsequent phases that included dataset collecting, assessment, and iterative research. Different hyperparameters were run over multiple models to select an optimal configuration. To evaluate the model's performance over produced synthetic datasets, about 1.5 and 2 times the original, synthetic data was produced, providing a basis for a robust synthetic data generating process.
[...] Read more.This qualitative study explores human resource managers' perceptions of blockchain technology adoption within the automobile sector in Punjab, India. Based on semi-structured interviews with HR managers from 52 organizations, the research uncovers critical insights into blockchain's potential benefits and challenges. The findings reveal that 80% of participants recognize blockchain’s ability to enhance data security, improve operational transparency, and streamline processes such as recruitment and supply chain management. For instance, blockchain’s ability to automate credential verification is perceived to reduce recruitment time by up to 30%. However, significant barriers impede adoption. Approximately 70% of HR managers cited technical complexity and a lack of in-house expertise as primary challenges, while 60% expressed concerns about high implementation costs and the absence of a clear regulatory framework. Furthermore, 50% highlighted resistance to change among employees as a critical obstacle. The study emphasizes the importance of targeted training programs to address skill gaps, strategic planning to manage high costs, and effective change management strategies to reduce resistance. These findings underscore the transformative potential of blockchain technology to improve HR efficiency and organizational performance while highlighting the need for addressing adoption barriers to unlock its full benefits. This research provides actionable insights for the automobile industry, contributing to academic discourse and offering a roadmap for blockchain integration into HR practices.
[...] Read more.The introduction of cloud technology changes the face of data management by eliminating tedious concerns with regards to proper storage and accessibility as it can done from any location, therefore, it can be said that the emergence of this technology came with a number of challenges related to data confidentiality, integrity, and authentication as well. As a resolution to certain weaknesses presented in this case, the authors in this paper suggest a hybrid security model which integrates both quantum cryptography and blockchain technology, and improves security flaws on cloud and quantum models. There are three characteristics of data that are crucial to its safety and security; confidentiality, integrity, and availability, and cloud technology has been known to be accompanied with plenty of challenges concerning these aspects, however, with the use of Blockchain technology, data becomes immutable, decentralized, and transparent thereby reducing the risk of unauthorized access. The combination of strategies proposed in this paper, helps to eliminate a number of drawbacks like key loss, data loss, and man in the middle attacks that are common in cloud infrastructure. This study shows the structural design, data transmission and processes of the architecture for the hybrid model, looking forward to achieve better data security. The analysis of the model suggests its advantages over conventional encryption model and a purely constructed model of blockchain. Performance benchmarks are also included, demonstrating that the model is resilient to cyber threats during the quantum age. The architecture is seamless with the current cloud stature, takes cloud security a notch higher by solving the considerable challenges and is poised to be deployed on a larger scale The directional works will include improving the system’s computational efficiency and extending the model to multiple cloud infrastructures to achieve higher security in today’s complex cloud systems.
[...] Read more.Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, but it remains vulnerable to privacy risks. This study introduces FL-ODP-DFT, a novel framework that integrates Optimal Differential Privacy (ODP) with Discrete Fourier Transform (DFT) to enhance both model performance and privacy. By transforming local gradients into the frequency domain, the method reduces data size and adds a layer of encryption before transmission. Adaptive Gaussian Clipping (AGC) is employed to dynamically adjust clipping thresholds based on gradient distribution, further improving gradient handling. ODP then calibrates noise addition based on data sensitivity and privacy budgets, ensuring a balance between privacy and accuracy. Extensive experiments demonstrate that FL-ODP-DFT outperforms existing techniques in terms of accuracy, computational efficiency, convergence speed, and privacy protection, making it a robust and scalable solution for privacy-preserving FL.
[...] Read more.Colon cancer remains a significant global health challenge, contributing to high morbidity and mortality rates. Accurate diagnosis through histological analysis is critical for effective treatment and improved patient outcomes. In this study, we present ColoNet, a convolutional neural network (CNN)-based system designed to enhance the early detection and classification of colon adenocarcinoma using LC25000 dataset comprising 10,000 digital histopathology images. Unlike conventional CNN-based models, ColoNet integrates an optimized feature extraction strategy with deeper convolutional layers, and dropout regularization, leading to improved generalization and reduced overfitting. Additionally, the proposed model achieves faster convergence and superior classification performance compared to existing methods. The system addresses the unique challenges in distinguishing benign from malignant conditions, automating the diagnostic process and streamlining colon cancer assessments for pathologists. ColoNet was rigorously evaluated across key performance metrics, including recall, accuracy, precision, and F1-score, achieving a maximum accuracy of 96.66%. This surpasses several state-of-the-art CNN models in colon cancer classification, demonstrating its effectiveness. Its high accuracy and robust classification capabilities make it a reliable tool for identifying different colon cancer stages. By providing an efficient and automated solution for pathologists, ColoNet is expected to significantly enhance colon cancer diagnosis, supporting early detection and staging, ultimately leading to better treatment outcomes and reduced cancer-related mortality. This research underscores the importance of AI-driven systems in transforming the landscape of digital pathology and improving clinical decision-making for colon cancer.
[...] Read more.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.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.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.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.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.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.An Enterprise Resource Planning (ERP) system is a software application that serves as a centralized platform to streamline and automate organizational functions and share real-time data, facilitating efficient communication and collaboration. It provides an all-inclusive approach to managing and optimizing business processes, boosting efficiency, fostering cooperation, and giving an overall picture of how the organization is operating. However, the traditional centralized databases in ERP systems pose security concerns. Blockchain Technology can be an appealing alternative as it comes with immutable and decentralized data as well as enhanced security. This study focuses on two methods of securing data management in ERP systems: Organizing the distributed information using The Ralph Kimball data model and optimizing an individual block using Database Sharding. This study does an extensive examination to determine the effectiveness of both suggested strategies, comprising a detailed evaluation that highlights the benefits and limitations of both techniques. This paper intends to patch the security holes in ERP systems to safeguard sensitive data and mitigate risks.
[...] Read more.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.
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.Systems for registering vehicles are essential for keeping track of ownership changes. However, severe flaws in the current systems permit vehicles that have been stolen or illegally sold to be registered. Inefficient verification techniques, drawn-out administrative processes, and dishonest employees cause these problems. This paper introduces a transparent system to prevent denial, alteration, or unauthorized manipulation. The proposed method employs hybrid blockchain architecture, distinguishing between confidential and non-confidential data. Personal information is stored privately, while vehicle-related data is maintained as public information. The adoption of blockchain technology is driven by its robust security features, transparency, and traceability, as well as its immutability and ability to handle many users effectively.
[...] Read more.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.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.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.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.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.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.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.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.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.Shariah-based banking aims to apply Islamic finance while adhering to Shariah principles. The primary distinction between conventional and Islamic finance is that Sharia law explicitly forbids several of the activities and principles applied in conventional banking. According to Sharia law, “Paying or charging an interest (Riba)” lending with interest payments as an exploitative practice that benefits the lender at the expense of the borrower, “investing in businesses engaged in banned activities" like producing and selling alcohol or pork, “speculation or gambling(Maisir)” That means financial institutions are prohibited from participating in contracts where the ownership of goods depends on an unpredictable future event, and Participation in contracts with a high level of risk or uncertainty is referred to as "uncertainty and risk (Gharar)", which are strictly prohibited. As a result, consumers must be aware of whether they are taking Riba, Gharar, Maisir or if their money is invested in a halal firm. Because of its qualities, blockchain would be useful in this situation. This research tries to determine how blockchain technology can be used to make investments and profit or loss returns more transparent, more secure, Immutable and sharia compliant. Blockchain networks can be utilized in the financial industry, such as banking, to provide safe sharia banking.
[...] Read more.