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International Journal of Information Engineering and Electronic Business @ijieeb
Journal articles - International Journal of Information Engineering and Electronic Business
All articles: 719
Scientific article
The Electronic Fiscal Device (EFD) Machines have been operating in Tanzania since the year 2010 for the purpose of helping the Tanzania Revenue Authority (TRA) to increase revenues from tax collection. Regard-less of years of its existence, there are still reported cases of tax evasion, and this study was conducted to review the current tax collection system and analyze require-ments for the development of Stock Tracking Module (STM) to be embedded in the current tax collection sys-tem. This paper earmarked some problems relating to Electronic Fiscal Device Machine Management System (EFDMS) and EFD machine. Data collection was done in Kilimanjaro and Arusha, the two regions of Tanzania that involved tax officers and Information Technology (IT) personnel from TRA and drug traders. Data collection process involved both qualitative and quantitative methods to gather data for the development of the system Stock Tracking Module (STM) such as interview, questionnaire, role-playing and observation. The major findings of the study: The efficiency of the EFDMS is at average, thus, need some improvements. The major problems encountered by TRA are; under declaration of sales by traders, non-usage of EFD machines, usage of fake EFD, overestimate of expenses, division of business and conducting business in unknown areas. The proposed solution will reduce the existing challenges and increase revenue collections, reduce manual work and human resource, and improve accuracy on tax estimation process.
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Emotion Recognition System Based On Skew Gaussian Mixture Model and MFCC Coefficients
Scientific article
Emotion recognition is an important research area in speech recognition. The features of the emotions will affect the recognition efficiency of the speech recognition systems. Various techniques are used in identifying the emotions. In this paper a novel methodology for identification of emotions generated from speech signals has been addressed. This system is proposed using Skew Gaussian mixture model. The proposed model has been experimented over a gender independent emotion database. In order to extract the features from the speech signals cepstral coefficients are used. The developed model is tested using real-time speech data set and also using the standard and data set of Berlin. This model is evaluated in the presence of noise and without noise the efficiency of the model is evaluated and is presented by using confusion matrix.
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Enable Better and Timelier Decision-Making Using Real-Time Business Intelligence System
Scientific article
Today's businesses need support when making decisions. Business intelligence (BI) helps businesses to make decisions based on good pre-analysis and documented data, and enables information to be presented when and where the decisions need to be made. Real time business intelligence (RTBI) presents numbers in real time, providing the decision makers at the operational and tactical layers with data as fresh as it can be. By having accurate, fresher and a bigger amount of data, businesses will be able to make decisions in a faster pace, and eliminate tedious complexity of the decision-making process. The objective of this research is to show that a real time business intelligence solution would be beneficial for supporting the operational and tactical layers of decision-making within an organization. By implementing an RTBI solution, it would provide the decision-maker with fresh and reliant data to base the decisions on. Visualization of the current decision processes showed that by adding a real time business intelligence solution it would help eliminate the use of intuition, as there would be more data available and the decisions can be made where the work is performed. The aim of this research is to contribute by visualizing how a real time business intelligence solution can shorten a complex decision process by giving the correct information to the right people. Organizations need to address potential challenges as part of a pre-project of a real time business intelligence implementation.
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Scientific article
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.
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Enhanced Educational Recommendations via Feedback-Refined Learning-to-Rank with XGBoost
Scientific article
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.
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Enhanced Password Based Security System Based on User Behavior using Neural Networks
Scientific article
There are multiple numbers of security systems are available to protect your computer/resources. Among them, password based systems are the most commonly used system due to its simplicity, applicability and cost effectiveness But these types of systems have higher sensitivity to cyber-attack. Most of the advanced methods for authentication based on password security encrypt the contents of password before storing or transmitting in the physical domain. But all conventional encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. In this paper an enhanced password based security system has been proposed based on user typing behavior, which will attempt to identify authenticity of any user failing to login in first few attempts by analyzing the basic user behaviors/activities and finally training them through neural network and classifying them as genuine or intruder.
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Enhanced Predictive Modelling of Heart Disease Using Optimized Machine Learning Algorithms
Scientific article
Cardiovascular disease (CVD) remains a leading global cause of mortality, underscoring the importance of its early detection. This research leverages advanced Machine Learning (ML) algorithms to predict Coronary Heart Disease (CHD) risk by analysing critical factors. A comprehensive evaluation of ten ML techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), AdaBoost, Multi-Layer Perceptron Neural Network (MLPNN), and Extremely Randomized Trees (ERT), was conducted. The ERT algorithm demonstrated superior performance, achieving the highest test accuracy of 88.52%, with precision, recall, and F1-scores of 0.89, 0.88, and 0.88, respectively, for class 0 (no CHD), and 0.88, 0.91, and 0.89, respectively, for class 1 (CHD). The model was optimized using hyperparameters such as a bootstrap setting of False, no maximum depth, a minimum sample split of 2, a minimum leaf size of 4, and 300 estimators. This study provides a detailed comparison of these techniques using metrics such as precision, recall, and F1-score, offering critical insights for optimizing predictive models in clinical applications. By advancing early detection methodologies, this work aims to support healthcare practitioners in reducing the global burden of cardiac diseases.
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Enhanced Word Sense Disambiguation Algorithm for Afaan Oromoo
Scientific article
In various circumstances, the same word can mean differently based on the usage of the word in a particular sentence. The aim of word sense disambiguation (WSD) is to precisely understand the meaning of a word in particular usage. WSD utilized in several applications of natural language to interpret an ambiguous word contextually. This paper enhances a statistical algorithm proposed by Abdo [36] that performs a task of WSD for Afaan Oromoo (one of under-resourced language spoken in East Africa by nearly 50% of Ethiopians). The paper evaluates appropriate methods that used to increase the performance of disambiguation for the language with and without morphology consideration. The algorithm evaluated by 249 sentences with four evaluation metrics: recall, precision, F1 and accuracy. The evaluation result has achieved state of the art for Afaan Oromoo. Finally, future direction is highlighted for further research of the task on the language.
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Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection
Scientific article
In recent years, the rapid advancement of machine learning (ML) has surpassed many expectations, and its application in the healthcare sector has emerged as one of the most fascinating areas of exploration. This thesis looks into whether machine learning can increase the precision and efficacy of breast cancer diagnosis. With the help of nine classification algorithms including Random Forest, XGBoost and MLP Classifier the given work intends to propose a reliable automatic solution for malignant and benign classification of breast tumor. The main idea of the project is the development of the Web based tool that would allow doctors and other medical practitioners to make quick decisions The MLP Classifier was found to be the optimal solution after its efficiency was evaluated based on the accuracy rate, and such parameters as precision rate, recall rate, and F1-score. This leads to development of a user friendly app; even those that would not originally consider themselves technical can easily operate the application. Apart from addressing the matter of high accuracy of diagnostics, the system shows the possibility of minimizing the rates of human factors and optimizing clinical decision. Seeking for that day when technology and human opinion will complement each other in the delivery of healthcare, our study neither only contributes to the growing literature on applying artificial intelligence in healthcare but also evolves the blueprint to integrate ML models in everyday practice.
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Enhancing E-commerce Sentiment Analysis with Advanced BERT Techniques
Scientific article
This study introduces an improved BERT-based model for sentiment analysis in several languages, specifically focusing on analyzing e-commerce evaluations written in English and Bengali. Conventional sentiment analysis techniques frequently face difficulties in dealing with the subtle linguistic differences and cultural diversities present in datasets containing multiple languages. The model we propose integrates sophisticated methodologies and utilizes Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy, interpretability, and dependability of sentiment assessments in various language situations. To tackle the challenges of sentiment categorization in a multilingual setting, we enhance the pre-trained BERT architecture by incorporating extra neural network layers. Compared to traditional machine learning and current deep learning methods, the model underwent a thorough evaluation, showcasing its superior capabilities with accuracy, precision, recall, and F1-score of 0.92. Including LIME improves the model’s transparency, allowing for a better understanding of the decision-making process and increasing user confidence. This research highlights the potential of utilizing advanced deep learning models to address the difficulties of sentiment analysis in global e-commerce environments, providing major implications for both academic research and practical applications in industry.
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Enhancing Employee Onboarding through Blockchain-Based Identity Verification in HR Management
Scientific article
This research paper explores Blockchain (BC) technology-based identity verification's role in streamlining and securing the employee onboarding process within Human Resource (HR) management. It addresses this technology's potential benefits, challenges, and limitations in enhancing HR practices. This study is grounded in the theoretical foundation of BC technology and its applications. It examines existing identity verification systems in HR management and delves into the potential implications of adopting BC-based solutions. This research employs a comprehensive design encompassing a discussion of the background, research problem, objectives, and significance. A detailed overview of BC technology and its applications and an analysis of existing identity verification systems are presented. The study employs a well-defined research design, including a sampling strategy, sample size determination, data collection methods, and data analysis techniques. The study's findings reveal that BC-based identity verification has the potential to streamline and secure the employee onboarding process in HR management. However, the investigation also identified scalability, interoperability, and data security challenges. These findings contribute to understanding the feasibility of adopting BC technology in HR practices. The study informs HR managers and BC developers on the potential benefits and hurdles of implementing BC-based identity verification, enabling them to make informed decisions.
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Enhancing Mobile Software Developer Selection through Integrated F-AHP and F-TOPSIS Methods
Scientific article
This study delves into the impact of employee recruiting within the dynamic and fiercely competitive realm of information technology (IT), focusing on the role of mobile software developers in a software development company situated in Bandung, Indonesia. Given that the quality of employees and their alignment with organizational needs are pivotal drivers of productivity and overall performance, the recruitment process assumes paramount importance. However, this process is riddled with complexity and challenges, stemming from the need to define precise criteria and navigate decision-making amidst uncertainty and ambiguity. To confront these challenges, this research advocates for the utilization of the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). The F-AHP method, employing Chang's extent analysis approach, assists in establishing weights for uncertain criteria. Meanwhile, F-TOPSIS is leveraged to evaluate alternatives based on predefined criteria. The focal point of this study is the selection of mobile software developers within a software development company in Bandung, Indonesia. Decision-makers, drawing insights from policy documents and assessment forms, identified pertinent criteria and sub-criteria. Utilizing F-AHP, they determined the weights for criteria and sub-criteria through paired comparisons using fuzzy numbers. Subsequently, F-TOPSIS was applied to rank 10 mobile software developer candidates, culminating in the identification of alternative-7 (CK-7) as the top mobile software developer candidate. In essence, the application of F-AHP and F-TOPSIS methods presents an effective approach to navigate the complexity of Multi-Criteria Decision Making (MCDM) in employee selection, particularly within the competitive landscape of the information technology industry. This study's findings underscore the significance of employing advanced decision-making techniques to enhance the efficiency and effectiveness of employee recruitment processes, thereby bolstering organizational performance and competitiveness.
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Enhancing Nigerian Telecommunication Customer Service Channels Using Self-Service Software Model
Scientific article
Until recently, the most common methods used by Nigerian Telecommunication Operators for providing services to their customers include: customer service centers and online channels. With the rapid increase in the number of customers, the existing channels of responding to customers queries through walk in centers and online customer agent cannot be adequate due to the time required to respond to each customer's queries. Hence the need to provide an alternative channel that will often provide faster, reliable, convenient, less expensive and most affordable customer service. In this paper, a self-service model was developed for Nigerian Telecommunications Operators to improve customer service delivery. Self-Service Software Model (SSSM) allows customers to request for specific services without interacting with customer care representative at their own convenient time and have these services delivered to them within a short period of time. SSSM was designed using Model-View-Controller design pattern and implemented using Hypertext Markup Language, Cascading Style Sheet and MySQL relational database management. The prototype of the SSSM was tested with data collected and analysed from three Telecommunication subscribers in Nigeria. The results of the study showed that the model allows customers to request for specific services at their own convenience in a timely manner and it is faster, reliable, less expensive, and reduces cost of maintaining hardware, software and overhead cost of existing customer service delivery.
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Enhancing Option Pricing Precision in Financial Markets with a Hybrid Ga-Bp Neural Network Approach
Scientific article
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.
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Enhancing Traceability in Agricultural Supply Chain Using Blockchain Technology
Scientific article
The work highlights exploring the usage of blockchain technology for enhancing traceability in agricultural supply chain management. The aim is to develop a secure and transparent system, which improves the easy tracking and tracing of agricultural products from the point of origin until it reaches the end consumer. Currently, Blockchain is a technology, which provides security in various fields of transactions. The work utilizes to improve supply chain efficiency, increase transparency and accountability, and enhance consumer trust in the agricultural products. The system will utilize smart contracts to automate processes and ensure compliance with regulations and standards, which improves supply chain efficiency. Smart contracts enable agreement between two parties present in the supply chain. Further, the financial transactions can be improved with the help of block chain. Additional, the work will also provide recommendations for companies and organizations looking to implement blockchain-based results in their supply chain management. The work implements an application using ganache, solidity and truffle. Ethereum block chain is used as primary infrastructure for the application. Smart contracts generated using solidity is deployed into Ethereum network using truffle. The deployment of the application in agricultural sectors improves the accountability in the field of the supply chain. The deployment in a wider range will avoid manipulation of the data. Agricultural supply chain tracing website involves the use of several tools and technologies, including Ganache, Solidity, and Truffle. The system uses the Ethereum blockchain as the underlying infrastructure to store and manage supply chain data securely and transparently. The smart contracts in the supply chain tracing system are generated using Solidity and deployed to the Ethereum network using Truffle.
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Ensembles of Classification Methods for Data Mining Applications
Scientific article
One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed using classifiers in both homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of data mining applications like intrusion detection, direct marketing and signature verification. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of direct marketing. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual Classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of data mining applications.
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Enterprise Architecture for E-Government Development Using TOGAF ADM 9.2 in Simpang Pasir Village
Scientific article
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.
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Entity Based Distinctive Secure Storage and Control Enhancement in Cloud
Scientific article
Cloud computing acts as rising evolution in Information Technology (IT), boosting the delivery of services and eye-catching returns to its tenants enrolled at low costs of per usage basis. Cloud computing means "everywhere" and provides enormous available resources via internet with ensured quality. With the numerous profits involved, it clears the viewpoint of various businesses to invest in cloud services for accomplishment of their needs in the cloud habitat. Cloud enables computing resources in a service oriented flair instead of burden with lags in traditional setup of unified architecture. With delivery of cloud services occur many obstacles in the cloud to work securely without downfall in its performance. Security has always emerged as a long handed concern with its progression which affects its virtuous implementation. We commence with aspect of security based on parameters named Confidentiality (C), Integrity (I) and Granular Access (GA) and then sent over a secure channel via Secure File Transfer Protocol (SFTP) for secure storage with Elliptic Curve Cryptography (ECC) encryption laid on data. Secure Hash Algorithm (SHA) is used for hash value generation maintaining integrity. The authentication mechanism of secure Graphical One Time Password (GOTPass) provides high end to end security for retrieval process and boost security appliance for data. Data is divided into three security levels as per Secure Quality Index (SQI) generated and storage is isolated to have different security aspects. It provides supplemental controlled security and data protection as associated with the file. User is responsive to pass all security mechanisms to gain access.
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Entrepreneurial Development of "Ojek Sampah" (OJAH) through Android Applications
Scientific article
The purpose of this research is to develop application-based "Ojah" entrepreneurship. This research will be conducted using the type of Research and Development. The entrepreneurship of the "Ojah" has been developed in an application that will become a medium for collecting waste. Data collection that will be used in this research is to use (1) Observation, which will be carried out for the maintenance of the development process and product testing (2) validation sheets, used as instruments in testing, and validation of the developed product (3) The questionnaire given to the public or users of the "Ojah" application to see the characteristics from the application used, a satisfaction questionnaire will also be used to measure the effectiveness of the resulting product (4) Field notes that are carried out simultaneously with the implementation of product trials that contain things that happened during the trial product; (5) Documentation that includes images or photographs during the research development being carried out. The results of the research show evidence that the development of the "Ojah" application. The results showed that the development of the android application-based garbage motorcycle taxi business showed measurable success through assessments conducted by several parties, including media experts, entrepreneurship experts, and users of android app-based "Ojah" services. Validation results by media experts and entrepreneurial experts showed that both businesses and applications developed to support The "Ojah" business are in a decent category. The validity score given by expert validators is 86.6 from entrepreneurial experts. In contrast, media experts provide an assessment of 84.24. Based on limited scale trials, the android-based "Ojah" application has characteristics that deserve to use in terms of practicality, use, service, and completeness, with a feasibility score of 77.35.
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Epidemic Dynamics for the Two-stage Model on Scale-free Networks
Scientific article
In this paper, we will study a two-stage model on complex networks. The dynamic behaviors of the model on a heterogeneous scale-free (SF) network are considered, where the absence of the threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Four immunization strategies, proportional immunization, targeted immunization, acquaintance immunization and active immunization are applied in this model. We show that both targeted and acquaintance immunization strategies compare favorably to a proportional scheme in terms of effectiveness. For active immunization, the threshold is easier to apply practically.
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