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International Journal of Information Engineering and Electronic Business @ijieeb
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 693
E-health Implementation by Private Dental Service Providers in Bulawayo, Zimbabwe
Статья научная
The 2030 Agenda for Sustainable Development Goals recognises the role of Information and Communication Technologies (ICT) in accelerating human development and progress by bridging the digital divide and developing knowledge societies. Internet technologies affect every sphere of human and organisational life. The study was motivated by low e-health adoption in developing countries such as Zimbabwe. A cross-sectional quantitative survey investigated e-health implementation by private dental care service providers in Bulawayo. The research population comprised private dental service providers in Bulawayo. Data was collected using a five-point Likert scale questionnaire that was self-administered. Regression and factor analysis, Kaiser-Meyer-Olkin Measure of Sampling Adequacy, Bartlett’s sphericity test, and the Principal Component Analysis were used for data analysis. The study found that private dental service providers in Bulawayo were already implementing some rudimentary elements of e-health in their day-to-day practice. The findings also revealed that private dental care providers positively perceived the benefits of implementing e-health, such as improved quality of health service delivery, enhanced efficiency, and improved accessibility of services. Participants highlighted ICT costs, communication, lack of experience, and e-readiness as significant barriers to e-health implementation. Most participants were concerned that personal health information may fall into the wrong hands resulting in privacy violations and loss of personal data. The regression coefficient showed that combined variations in the independent variables explain at least 76.4% of the dependent variable (adoption). The study recommends the government enact policies that support private dental service providers in implementing e-health systems to improve service delivery. The study contributes to the literature on e-health adoption in developing countries.
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Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone
Статья научная
Malignant melanoma is the most dangerous kind of skin cancer. It is mostly misidentified as benign lesion. The chance of surviving melanoma disease is high if detected early. In recent years, deep convolutional neural networks have attracted great attention owing to its outstanding performance in recognizing and classifying images. This research work performs a comparative analysis of three different convolutional neural networks (CNN) trained on skin cancerous and non-cancerous images, namely: a custom 3-layer CNN, VGG-16 CNN, and Google Inception V3. Google Inception V3 achieved the best result, with training and test accuracy of 90% and 81% respectively and a sensitivity of 84%. This work contribution is mainly in the development of an android application that uses Google Inception V3 model for early detection of skin cancer.
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Echo Cancellation Research of Channel Estimation based on PN Sequence
Статья научная
For the problem of estimation sequence effect on channel estimation accuracy and echo cancellation effect, this paper, based on the basic principle of echo cancellation, analyses the effect of PN sequence mechanism and the correlation on the channel estimation parameters. Comparing with using the input signal itself as the estimation sequence. With the input signal OFDM, the results of simulation and actual operation show that the method can increase both the accuracy of channel estimation and echo cancellation effect effectively.
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Educational Data Mining: RT and RF Classification Models for Higher Education Professional Courses
Статья научная
Computer applications and business administrations have gained significant importance in higher education. The type of education, students get in these areas depend on the geo-economical and the social demography. The choice of a institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic approach for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. The scientific understanding of these can be had from obtaining patterns or recognizing the attribute behavior from previous academic years. Further, applying data mining tool to the previous data on the attributes identified will throw better light on the behavioral aspects of the identified patterns. In this paper, an attempt has been made to use of some techniques of education data mining on the dataset of MBA and MCA admission for the academic year 2014-15. The paper discusses the result obtained by applying RF and RT techniques. The results are analyzed for the knowledge discovery and are presented.
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Effect of Four Wave Mixing on AP-DCDM-WDM Fiber Optic System with Different Power per Channel
Статья научная
Absolute polar duty cycle division multiplexing over WDM is a multiplexing technique which promises better spectral efficiency. Non linearities in fiber optic communication are major issues, especially the effect of four wave mixing. This paper presents the effect of four wave mixing on 40 Gbps AP-DCDM over WDM fiber optic systems. The system was tested by simulating the AP-DCDM-WDM design in Optisystem software and MATLAB code. From the results the effect of four wave mixing on the system is presented under different configurations such as input power per channel. Simulation results have shown that AP-DCDM-WDM systems have greater tolerance to dispersion and have better receiver sensitivity than other conventional techniques. The effect of FWM on AP-DCDM-WDM system is also less than on other conventional techniques on basis of simulation.
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Effective Networking Model for Efficient Implementation of E-Governance: A Case Study of Nigeria
Статья научная
Nigeria is a nation full of potentials ranging from its human resources advantage to its mineral resources – the list is endless. Ambitious too, it has come to terms with the fact that ICT must be utilized even for the delivery of democracy dividends to actualize its vision of being among the top 20 economies by year 2020. In this paper, we explore the nation's drive towards adopting e-governance by generally itemizing the requirements for e-governance, appraising how far Nigeria has gone in implementing it and then proposing a workable way to achieve it. Our study reveals that while the visions for e-government implementation are well articulated in terms of required components and intended deliverables, there are no clear statements on the processes of implementation. To this end, we propose some networking models adoptable towards realizing the different dimensions of e-government.
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Статья научная
The purpose of this study is basically to explore (know and study) the effectiveness of intelligence which in this case consists of variables of intellectual intelligence (IQ), emotional intelligence (EQ), and spiritual intelligence (SQ) on employee performance in a retail business organization (case study at eramart store in Timbau sub-district, Tenggarong, Indonesia). The sample in this study was 26 employees of eramart store in Timbau sub-district, Tenggarong, Indonesia. Sampling technique by random sampling. The analysis tool used is a multiple regression equation with a hypothesis test used by the F and t tests. The calculation result of the F test obtained F count is 45.252, while the table F value is obtained a value of 2.80 this means that (Fcount 45, 252 > Ft 2.80) with a significant value of < 0.05, so it can be said that the variables of intellectual intelligence, emotional intelligence and spiritual intelligence together / simultaneously are able to show their influence on employee performance at the eramart store in Timbau sub-district, Tenggarong or it can be explained that the regression model that was built can be used to predict the size of employee performance at the eramart store in Timbau sub-district, Tenggarong, so that the first hypothesis in this study was accepted. The three free variables, namely intellectual intelligence, emotional intelligence, and spiritual intelligence, simultaneously have a meaningful (real) effect on employee performance in retail business organizations (case study at eramart store in Timbau sub-district, Tenggarong, Indonesia). The three free variables were able to explain changes in employee performance by 84.2% (Adjusted R square = 0.842) while the remaining 15.2% was influenced by other variables that were not included in this study such as career development, compensation, work stress. From the three results of the t test above, the partial correlation value of the spiritual intelligence variable is the largest compared to the intellectual intelligence and emotional intelligence variables, which is 0.617 or 61.7%, From this description, it can be concluded that spiritual intelligence is the intelligence we use to access our deepest meanings, values, goals, and highest motivations. Spiritual intelligence is our moral intelligence, which gives us an innate ability to distinguish between right and wrong. Spiritual intelligence is the intelligence we use to create goodness, truth, beauty and compassion in our lives. The spiritual intelligence of Eramart Timbau Tenggarong minimarket employees is likely to be largely influenced by the proximity of mosque facilities near the Eramart Timbau Tenggarong minimarket and most of them live close to the mosque/musholla, so that most employees rarely miss prayer time when working when the time comes.
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Статья научная
The study determined the effects of concept mapping and guided discovery instructional strategies on student’s learning achievement in Redox concept in Chemistry in Oyo State, Nigeria. The pretest-posttest control group quasi experimental design with 3x2 factorial matrix was adopted, while six schools with one intact class each; two each for experimental groups and two for control group were used. A total of 176 senior secondary school 2 Chemistry students participated in the study. A validated Chemistry Student Achievement Test (r = 0.77) was used for data collection, while Analysis of covariance and Bonferroni post hoc were used to analyze the data collected at 0.05 level of significance. There was a significant main effect of treatment (F(2, 175) =11.84; p<0.05, partial η2 = 0.13) on student’s achievement. The participants in concept mapping strategy obtained the highest post achievement mean score (12.71), followed by guided discovery instructional strategy (9.24) and conventional strategy (8.60) groups. There was no significant main effect of gender on student’s achievement. There was no significant two- way interaction effect of treatment and gender on student’s achievement in Redox concept of chemistry. Concept mapping and guided discovery instructional strategies enhanced student’s achievement in Redox concept of chemistry. It is therefore recommended that chemistry teachers should adopt these strategies to improve student’s achievement in Chemistry.
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Статья научная
The study sought to investigate the effects of network infrastructure challenges on open ICT infrastructure sharing by Mobile Service Providers in Kenya. Specifically, the study investigated network sharing challenges as the main determinants to open ICT infrastructure sharing by Mobile Service Providers in Kenya. The empirical literature revealed that Open ICT Infrastructure sharing can substantially reduce capital and operational expenditure thereby increasing the speed of network rollouts, improve coverage and help meet the capacity demands of increased data traffic. Other reviews revealed that each sharing environment is different and may have pressures and priorities that change throughout the process of establishing a partnership between two operators with a view to developing a framework for Open ICT infrastructure sharing. Data was collected from employees from Safaricom, Airtel and Orange in order to study the population. A target population of 800 employees from the three Mobile Service Providers in Kenya was considered. Both Stratified and purposive sampling techniques were used to identify the respondents. A sample size of 86 respondents was used in this study using both structured questionnaires and scheduled interviews. Both descriptive and inferential statistics were used to analyse data collected from respondents in this study. Network service control and Coverage, Network growth and Experience and Resources were identified as the main challenges facing Network sharing by Mobile Service Providers. It is hoped that the results obtained from this study will be beneficial to stakeholders in Mobile Service industry formulate policies that promote ICT Infrastructure sharing with a view to promoting universal access and saving on expenditures.
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Статья научная
Electronic passports have known a wide and fast deployment all around the world since the International Civil Aviation Organization the world has adopted standards whereby passports can store biometric identifiers. The use of biometrics for identification has the potential to make the lives easier, and the world people live in a safer place. The purpose of biometric passports is to prevent the illegal entry of traveler into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. The paper analyses the face, fingerprint, palm print and iris biometric e-passport design. The paper also provides a cryptographic security analysis of the e-passport using face fingerprint, palm print and iris biometric that are intended to provide improved security in protecting biometric information of the e-passport bearer.
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Статья научная
In this paper, we have focused on the data mining technique on market data to establish meaningful relationships or patterns to determine the determinate critical factors of commodity price. The data is taken from Ethiopia commodity exchange and 18141 data sets were used. The dataset contains all main information. The hybrid methodology is followed to explore the application of data mining on the market dataset. Data cleaning and data transformation were used for preprocessing the data. WEKA 3.8.1 data mining tool, classification algorithms are applied as a means to address the research problem. The classification task was made using J48 decision tree classification algorithms, and different experimentations were conducted. The experiments have been done using pruning and unpruning for all attributes. The developed models were evaluated using the standard metrics of accuracy, ROC area. The most effective model to determine the determinate critical factors for the commodity has an accuracy of 88.35% and this result is a good experiment result.
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Статья научная
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
Статья научная
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
Статья научная
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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Enhanced Password Based Security System Based on User Behavior using Neural Networks
Статья научная
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
Статья научная
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
Статья научная
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
Статья научная
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
Статья научная
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
Статья научная
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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